Pharmaceutical Lean Practices

Lean Practices in Pharmaceutical Manufacturing - An empirical investigation

DISSERTATION

of the University of St.Gallen,

School of Management,

Economics, Law, Social Sciences

and International Affairs

to obtain the title of

Doctor of Philosophy in Management

submitted by

Saskia Penelope Gütter

from

Germany

Approved on the application of

Prof. Dr. Thomas Friedli

and

Prof. Dr. Oliver Gassmann

Dissertation No. 4276

Difo-Druck GmbH, Bamberg 2014

The University of St.Gallen, School of Management, Economics, Law, Social

Science and International Affaires hereby consents to the printing of the present

dissertation, without hereby expressing any opinion on the views herein expressed.

St.Gallen, May 19, 2014

The President:

Prof. Dr. Thomas Bieger

Vorwort

Die vorliegende Dissertation entstand während meiner Tätigkeit als

wissenschaftliche Mitarbeiterin am Lehrstuhl für Produktionsmanagement des

Instituts für Technologiemanagement der Universität St.Gallen. Von 2009 bis 2012

hatte ich die Gelegenheit, im Rahmen zahlreicher Beratungs- und

Forschungsprojekte Einblick in das Management produzierender Unternehmen

verschiedenster Industrien zu nehmen und so mein Wissen und meinen Horizont zu

erweitern.

Mein besonderer Dank gilt meinem Mann, Rudolf Gütter, und seiner unermüdlichen

Geduld. Seine Motivation und Unterstützung haben mir den nötigen Rückhalt für

die Beendigung dieser Arbeit gegeben.

Bedanken möchte ich mich auch bei meiner Familie, insbesondere meiner Mutter,

die mich während meiner gesamten Studienzeit unterstützt und ermutigt hat.

Mein weiterer Dank gebührt meinem Doktorvater Prof. Dr. Thomas Friedli, der

mich mit seinen Ideen und seinem Fachwissen unterstützt und mir ein freies und

eigenständiges Arbeiten in vielen interessanten Projekten ermöglicht hat. Herrn

Prof. Dr. Oliver Gassmann danke ich für die Übernahme des Korreferates.

Franziska Ebert, Maria Fischl, Andreas Hinz, Andreas Mundt, Maike Scherrer,

Simone Thomas, Stefan Thomas und Caroline Ubieto sind neben Arbeitskollegen

und Diskussionspartnern gute Freunde geworden und haben die Jahre in St.Gallen

zu einer unvergesslichen Zeit gemacht. Dies gilt ebenso Roman Frick, Weini Zhang

und Renate Policzer. Meinen Kollegen am Lehrstuhl möchte ich für inspirierende

Diskussionen und ein einzigartiges Arbeitsumfeld danken.

Saskia Gütter

Januar 2014

Abstract

In recent years pharmaceutical companies faced a lot of changes in their business

environment. Especially the newly arisen cost pressure forces them to focus more

on manufacturing than they were used to. They are in the good position that other

industries already had the same experience. Adequate tools have been developed

which they can adapt to their own environment. A lot of studies exist which explain

and analyse these tools mainly under the term lean production. Lean production is

composed of different lean practices and their implementation supports a plant’s

success in manufacturing. Even if a lot of research has been done, researchers do

not have a common understanding of the topic and different models and approaches

exist. Especially the relations between the single lean practices are not analysed in

detail. Further, their interaction with different production strategies is unclear.

Therefore, identifying production strategies in pharmaceutical manufacturing and

based thereon analysing the level of lean implementation and the relations between

single lean practices is the subject of this research.

Based on literature a map of relations between lean practices is drawn. It serves as a

foundation for identifying the influence of single lean practices on each other. A

quantitative analysis with a sample of 208 sites identifies 17 lean practices used in

pharmaceutical manufacturing. Further, four strategic groups are developed each

focusing on a different set of the competitive priorities delivery, flexibility, costs,

and quality. These strategic groups are empirically analysed to understand how they

differ concerning the 17 lean practices. The general level of implementation is

investigated per group. Within the groups the practices are tested for differences in

implementation level to find those practices that are most relevant. In a last step, the

correlations between the practices in a group are calculated and filled into the map

of relations.

The results show that the implementation of lean practices depends on the strategic

goals of a production site. Independent from the strategic group, lean practices are

highly related and therefore should not be implemented separately. This research

contributes to theory by linking production strategy to the process of lean

implementation in pharmaceutical manufacturing. It provides a comprehensive

overview of relations between lean practices and offers an approach on how to

decide which practices to implement. These findings provide guidance for managers

facing lean implementation.

Zusammenfassung

Das Umfeld pharmazeutischer Unternehmen hat sich stark verändert. Vor allem der

entstandene Kostendruck zwingt sie, sich mehr als bisher auf den

Produktionsbereich zu fokussieren. Sie können dabei auf Erfahrungen anderer

Branchen zurückgreifen und müssen dort entwickelte Tools nur anpassen. Viele

Studien erklären und analysieren diese Tools unter dem Begriff Lean Production. Er

vereint verschiedene Lean Praktiken, deren Umsetzung den Produktionserfolg

fördert. Trotz intensiver Forschung auf dem Gebiet der Lean Production gibt es kein

einheitliches Verständnis welche Lean Praktiken zu unterscheiden sind,

verschiedene Modelle und Ansätze existieren. Die Beziehungen zwischen den

einzelnen Praktiken sind nicht im Detail analysiert und das Zusammenspiel mit

verschiedenen Produktionsstrategien ist unklar. Daher ist es das Ziel dieser Arbeit

Produktionsstrategien in der pharmazeutischen Produktion zu identifizieren und

darauf aufbauend den Grad an Lean Implementierung sowie die Beziehungen

zwischen den einzelnen Lean Praktiken zu analysieren.

Literaturbasiert wird ein Beziehungsnetz zwischen Lean Praktiken gezeichnet. Es

dient als Grundlage für die Identifizierung des Einflusses der einzelnen Praktiken

aufeinander. Eine quantitative Analyse mit einer Stichprobe von 208 Standorten

identifiziert 17 Lean Praktiken, die in der pharmazeutischen Produktion eingesetzt

werden. Zudem werden vier strategische Gruppen entwickelt, die sich jeweils auf

ein anderes Set der Wettbewerbsvorteile Lieferung, Flexibilität, Kosten und Qualität

fokussieren. Die strategischen Gruppen werden empirisch auf Unterschiede

bezüglich der 17 Lean Praktiken untersucht. Es wird das allgemeine

Implementierungsniveau pro Gruppe untersucht sowie Unterschiede im

Implementierungsniveau der Praktiken innerhalb der Gruppen. In einem letzten

Schritt werden innerhalb der Gruppen die Korrelationen zwischen den Praktiken

berechnet und in das Beziehungsnetz eingetragen.

Die Ergebnisse zeigen, dass die Lean Implementierung von den strategischen Zielen

eines Produktionsstandortes abhängt. Unabhängig von der strategischen Gruppe

lässt sich feststellen, dass einzelne Lean Praktiken stark mit einander verbunden

sind und daher gemeinsam implementiert werden sollten. Die Verknüpfung der

Produktionsstrategie mit dem Prozess der Lean Implementierung ermöglicht einen

umfassenden Überblick über die Beziehungen zwischen Lean Praktiken und bietet

einen Entscheidungsansatz für Managern, welche Praktiken zu implementieren sind.

Table of Contents I

Table of Contents

List of Abbreviations ............................................................................................... V

List of Figures ........................................................................................................ VII

List of Tables ........................................................................................................... IX

1 Introduction ...................................................................................................... 1

1.1 Research motivation .................................................................................. 1

1.1.1 Research interest .......................................................................... 1

1.1.2 Practical relevance ....................................................................... 2

1.1.3 Theoretical gaps ........................................................................... 3

1.2 Research objective ..................................................................................... 4

1.3 Research design ......................................................................................... 5

1.3.1 Research process .......................................................................... 5

1.3.2 Research methodology ................................................................. 6

1.3.3 Research theory ............................................................................ 7

1.4 Layout of the dissertation .......................................................................... 8

2 Theoretical framework .................................................................................. 10

2.1 Lean manufacturing ................................................................................. 10

2.1.1 Evolution of lean manufacturing ................................................ 10

2.1.2 Lean manufacturing practices .................................................... 11

2.2 Production strategy .................................................................................. 14

2.2.1 Competitive priorities and improvement actions ....................... 14

2.2.2 Configurations of manufacturing strategy ................................. 16

II Table of Contents

2.3 Summary and framework development .................................................. 18

3 Mapping of relations between lean practices .............................................. 19

3.1 Detailed literature analysis ...................................................................... 19

3.2 Causal loop diagram of relations ............................................................ 21

3.2.1 Linkages between single lean practices ..................................... 22

3.2.2 Linkages between lean practices and goals of lean bundles ...... 24

3.2.3 Feedback loops ........................................................................... 24

3.3 Summary mapping of relations ............................................................... 25

4 Empirical analysis .......................................................................................... 26

4.1 Data gathering and data set ..................................................................... 26

4.2 Factor analysis: Identification of lean practices ...................................... 28

4.2.1 Identification of variables .......................................................... 29

4.2.2 Factor extraction method ........................................................... 30

4.2.3 Number of factors, rotation and interpretation .......................... 31

4.2.3.1 Total productive maintenance (TPM) ......................... 32

4.2.3.2 Total quality management (TQM) .............................. 34

4.2.3.3 Just-in time (JIT) ......................................................... 36

4.2.3.4 Effective management system (EMS) ........................ 40

4.2.4 Summary factor analysis ............................................................ 42

4.3 Adaption of the map of relations ............................................................. 44

4.4 Cluster analysis: Development of strategic groups ................................. 47

4.4.1 Identification of clustering variables ......................................... 48

4.4.2 Outlier analysis .......................................................................... 49

4.4.3 Hierarchical clustering ............................................................... 49

Table of Contents III

4.4.4 Non-hierarchical clustering ........................................................ 51

4.4.5 Validation of the groups ............................................................. 51

4.4.5.1 Cluster 1: Do all .......................................................... 53

4.4.5.2 Cluster 2: Flexible deliverers ...................................... 53

4.4.5.3 Cluster 3: Flexible starters .......................................... 53

4.4.5.4 Cluster 4: Efficient conformers ................................... 54

4.5 Comparison of strategic groups ............................................................... 54

4.5.1 Analysis of variance and multiple comparisons ........................ 54

4.5.1.1 Do all ........................................................................... 59

4.5.1.2 Flexible deliverers ....................................................... 59

4.5.1.3 Flexible starters ........................................................... 60

4.5.1.4 Efficient conformers ................................................... 61

4.5.1.5 Summary ..................................................................... 61

4.5.2 Within-cluster paired-sample t-tests .......................................... 62

4.5.2.1 Do all ........................................................................... 63




4.5.2.5 Summary ..................................................................... 69

4.5.3 Correlations between lean practices ........................................... 70

4.5.3.1 Do all ........................................................................... 71




4.5.3.5 General view ............................................................... 85

IV Table of Contents

4.5.3.6 Summary ..................................................................... 87

4.6 Summary empirical analysis ................................................................... 88

5 Conclusion ...................................................................................................... 92

5.1 Contributions to theory ........................................................................... 92

5.2 Contributions to practice ......................................................................... 92

5.3 Limitation and future research ................................................................ 93

References ................................................................................................................ 95

Appendix A: Overview lean practices and bundles ........................................... 106

Appendix B: OPEX questionnaire (excerpt of questions) ................................ 115

Appendix C: Details cluster analysis .................................................................. 122

Curriculum Vitae .................................................................................................. 125

List of Abbreviations V

List of Abbreviations

ANOVA Analysis of variance

Bn Billion

CFA Confirmatory Factor Analysis

CITC Corrected Item to Total Correlation

CLD Causal Loop Diagram

CP Competitive Priority

DOH (Inventory) Days on Hand

ed. Edition

Ed(s). Editor(s)

EFA Exploratory Factor Analysis

e.g. Exempli gratia (for example)

EI Employee Involvement

EMS Effective Management Systems

et al. Et alii (and others)

etc. Et cetera

FTE Full Time Equivalent

JIT Just-in Time

KMO Kaiser-Meyer-Olkin- criterion

MSA Measure of Sampling Adequacy

No. Number

n.s. Not significant

OEE Overall Equipment Effectiveness

OPEX Operational Excellence (research project and survey)

PCA Principle Component Analysis

VI List of Abbreviations

p. / pp. Page / Pages

SCM Supply Chain Management

SD System Dynamics

SE Standard Error

SPC Statistical Process Control

TPM Total Productive Maintenance

TPS Toyota Production System

TQM Total Quality Management

List of Figures VII

List of Figures

Figure 1: Research process ......................................................................................... 5

Figure 2: Research approach....................................................................................... 6

Figure 3: Example Causal Loop Diagram .................................................................. 7

Figure 4: Research structure ....................................................................................... 9

Figure 5: Research framework .................................................................................. 18

Figure 6: CLD of lean practices ................................................................................ 22

Figure 7: Procedure used for factor analysis ............................................................ 28

Figure 8: Lean practices identified in pharmaceutical manufacturing ..................... 43

Figure 9: Adapted map of relations between lean practices ..................................... 46

Figure 10: Procedure used for cluster analysis ......................................................... 47

Figure 11: Implementation levels for do all-cluster ................................................. 59

Figure 12: Implementation levels for flexible deliverers-cluster ............................. 60

Figure 13: Implementation levels for flexible starters-cluster .................................. 60

Figure 14: Implementation levels for efficient conformers-cluster .......................... 61

Figure 15: Correlations for do all-cluster ................................................................. 71

Figure 16: Correlations for flexible deliverers-cluster ............................................. 74

Figure 17: Correlations for flexible starters-cluster.................................................. 78

Figure 18: Correlations for efficient conformers-cluster .......................................... 82

Figure 19: Influence - importance do all-cluster ...................................................... 88

Figure 20: Influence - importance flexible deliverers-cluster .................................. 89

Figure 21: Influence - importance flexible starters-cluster ...................................... 90

Figure 22: Influence - importance efficient conformers-cluster ............................... 90

Figure B- 1: General information and competitive priorities ................................. 115

Figure B- 2: Four categories of lean practices ........................................................ 120

Figure B- 3: Key performance indicators for the goals of lean bundles................. 121

Figure C- 1: Dendrogram – outlier analysis ........................................................... 122

VIII List of Figures

Figure C- 2: Dendrogram – hierarchical clustering ................................................ 123

Figure C- 3: Number of clusters based on agglomeration coefficients .................. 124

List of Tables IX

List of Tables

Table 1: Taxonomies in production strategy ............................................................ 17

Table 2: Attribution of lean practices to lean bundles .............................................. 21

Table 3: Direction of relations between lean practices ............................................. 23

Table 4: Lean practices according to their influence ................................................ 23

Table 5: Size of pharmaceutical production sites ..................................................... 27

Table 6: Total productive maintenance – Initial items ............................................. 33

Table 7: Total productive maintenance – Scale reliability scores ............................ 33

Table 8: Total productive maintenance – EFA category level ................................. 34

Table 9: Total quality management – Initial items ................................................... 34

Table 10: Total quality management – Scale reliability scores ................................ 35

Table 11: Total quality management – EFA category level ..................................... 36

Table 12: Just-in time – Initial items ........................................................................ 37

Table 13: Just-in time – Scale reliability scores ....................................................... 38

Table 14: Just-in time – EFA category level ............................................................ 39

Table 15: Effective management system – Initial items ........................................... 40

Table 16: Effective management system – Scale reliability scores .......................... 41

Table 17: Effective management system – EFA category level ............................... 42

Table 18: Implementation of lean practices .............................................................. 44

Table 19: Assignement of the lean practices ............................................................ 45

Table 20: Measures for the goals of lean bundles .................................................... 46

Table 21: Competitive priorities used ....................................................................... 48

Table 22: Competitive priorities – EFA ................................................................... 49

Table 23: Analysis of agglomeration coefficient - Ward's method .......................... 50

Table 24: Final cluster results - K-means method .................................................... 51

Table 25: Competitive priorities emphasised by strategic groups ............................ 52

Table 26: Implementation of lean practices by competitive priority clusters .......... 58

X List of Tables

Table 27: Pairwise t-test for do all-cluster................................................................ 64

Table 28: Pairwise t-test for flexible deliverers-cluster ........................................... 66

Table 29: Pairwise t-test for flexible starters-cluster ................................................ 68

Table 30: Pairwise t-test for efficient conformers-cluster ........................................ 69

Table 31: Interpretation of the correlation coefficient ............................................. 70

Table 32: Lean practices and their influence for do all-cluster ................................ 73

Table 33: Lean practices and their influence for flexible deliverers-cluster ............ 77

Table 34: Lean practices and their influence for flexible starters-cluster ................ 80

Table 35: Lean practices and their influence for efficient conformers-cluster ........ 84

Table 36: Observable correlations in all four clusters .............................................. 85

Introduction 1

1 Introduction

Research motivation 1.1

1.1.1 Research interest

Over the last years markets became more competitive and global. The changing

environment forces companies to be more flexible (Dreyer & Grønhaug, 2004) in

order to face this challenge. The importance of aligning production to customer

needs while still being able to efficiently manufacture good quality is rising. The

perception of manufacturing’s strategic role is increasing (Ward et al., 2007) and

companies start to improve their production system in terms of efficiency and

effectiveness to develop competitive advantages (Grichnik et al., 2008; Voss, 2005).

A popular approach to reach this aim is the concept of lean production which allows

a company to on the one hand improve productivity of processes and assets and on

the other hand to boost flexibility. It can be understood as "(...) an integrated

manufacturing system that is intended to maximize the capacity utilization and

minimize the buffer inventories of a given operation through minimizing system

variability (related to arrival rates, processing times, and process conformance to

specifications)" (de Treville & Antonakis, 2006, p. 102). Over the last decades lean

has become an often used term in operations management and several studies have

shown that the implementation and use of lean practices leads to superior

performance compared to competitors that do not implement such practices (Cua et

al., 2001; de Menezes et al., 2010). Consequently, the adoption of lean production is

a central challenge for manufacturing firms. Although there is quite a number of

comprehensive literature on lean and its elements (see Appendix A) every company

has its own idea of how to get started. When discussing with representatives from

the industry it becomes obvious that not all managers have a holistic view of lean

and that they rely on single elements without seeing the whole. Therefore, they are

not able to use the full potential lean is able to provide (Scherrer-Rathje et al.,

2009). Another point is the often missing consistency of the implemented practices

with a plant’s business strategy (Flynn & Flynn, 2004).

A lot of companies already implemented elements of lean years ago whereas others

are still on their way to implement them. Particular industries, like the

pharmaceutical industry and process industries in general, are lacking behind in

2 Introduction

adopting lean (Melton, 2005). On the other hand, having started to introduce lean

later than other industries they now have the chance to learn from others and the

problems they were confronted with on their journey to lean.

To be able to fully capture these learning possibilities it is necessary to deeper

analyse the lean concept. This analysis includes on the one hand the relations that

exist between single lean practices and on the other hand the manufacturing strategy

a company is pursuing that might have an influence on the success or failure of lean

implementation.

The influence of the manufacturing strategy pursued on the implementation level of

some lean practices as well as the relations between lean practices on a higher level

are identified in theory. In contrast, the influences that exist on a lower level are not

fully captured yet. But it might be that exactly these influences are the reason for

the failure of companies to fully and successfully implement lean and therewith to

achieve perfection as proclaimed by Womack and Jones (1996). Therefore, this

research should help to promote companies in being more successful while

implementing lean.

1.1.2 Practical relevance

For many years pharmaceutical companies were in the favourable position of

having a stable environment with excellent profit opportunities (Kickuth, 2005)

further secured by patents. Meanwhile, the situation changed and they have to deal

with more dynamics and intense competition (Gronauer & Friedli, 2010).

Additionally, a lot of patents are expiring resulting in a reduced brand spending of

US$127Bn through 2016 (IMS Health, 2012). The focus had to shift from

innovation as the key to success to other areas, like manufacturing, that have been

neglected in the past (FDA, 2004). The importance of this shift becomes obvious

when looking at the manufacturing costs that, depending on the company type,

account for up to 50% of the overall costs (OPEX Benchmarking, 2011). Most

pharmaceutical companies reacted to the changed environment by starting to

implement efficiency initiatives (Friedli et al., 2010) based on lean production.

Their content and structure often follow the methods and tools used in other

industries especially the automotive sector. The Pharmaceutical Executive stated in

2009:"Just as Toyota revolutionized automaking with its Lean production system,

pharma executives are aiming to secure their industry's brave new future by

adopting the Lean philosophy and tools." Considering the first experiences of

Introduction 3

pharmaceutical production sites, results are until now not as good as they might be

e.g. the average Overall Equipment Effectiveness (OEE) in pharma is 30%, good

companies reach 74% but in other industries the average is at 92% (Benson, 2004).

This shows that pharmaceutical companies are still struggling with the

implementation of lean maybe because of simply copying the standardised tools of

other industries or companies. But to be successful, the initiatives need to be aligned

to the particularities of the industry as well as the business strategy of the single

manufacturing plants (Dean & Snell, 1996). In literature and practice; there is a

variety of programs available under the term lean production. Analysing the

relations between the single elements of these programs can help to better

understand the impact of implementing specific lean practices and to choose the

right ones according to the own strategy. Therefore it is vital for pharmaceutical

manufacturing sites to envision their production strategy and the competitive

priorities they pursue.

1.1.3 Theoretical gaps

When looking at the existing literature, as done in chapter 2, various theoretical

gaps can be identified. These will be discussed in the following section.

There is no common way of naming and structuring lean practices.

As shown in Appendix A a lot of different approaches to name and structure lean

practices exist. The same name can stand for different practices and the

differentiation between practice, principle or technique is not always clear (Sousa &

Voss, 2002). Therefore a first step in this research will be to structure and define the

practices used in order to generate a common understanding.

Recent publications are mainly focusing on single lean bundles and the associated practices. The complex interrelations that exist between single practices are not considered.

Only focusing on some lean practices can lead to results that lack a holistic view.

Studies show that not the implementation of single practices leads to superior

performance but the aligned use of different practices (Cua et al., 2001). Until now

it is not possible to say in detail which configuration of which practices enables the

best results. Therefore the interrelations between the practices have to be examined.

4 Introduction

There is no approach that allows a company to decide depending on the production strategy followed how to start the lean implementation.

Different studies integrated production strategy into their investigation on lean

implementation (Christiansen et al., 2003). Results were that the production strategy

can have a significant influence on the choice which lean practices are

implemented. As proclaimed by the contingency theory, there has to be "fit"

between the structure of the company and the environment (Drazin & Van de Ven,

1985). The aim should be to have an implementation plan for lean practices

depending on the respective production strategy of the plant.

Research objective 1.2

The objectives of this research are fourfold. First, this research seeks to identify and

structure the practices associated with lean production in general. Identifying and

defining these practices is crucial for the second step which is devoted to the

examination of interrelations between the single practices. These interrelations are

structured in a map which will be adapted to pharmaceutical manufacturing in a

third step. Forth, this research aims to proposing an approach which allows a

pharmaceutical production site the implementation of lean according to the specific

strategic approach followed.

The following central research question can be derived:

How are single lean practices interconnected and how does their interaction

support the implementation of lean in pharmaceutical manufacturing against

the background of different manufacturing strategies?

To answer this research question the following sub-questions need to be discussed

in detail:

(1) Which lean practices exist and how can they be structured?

(2) Which direct and indirect influences exist between the single lean practices?

(3) Which different manufacturing strategies exist in pharmaceutical

manufacturing and how are single lean practices related to them?

(4) In which sequence has a plant to adopt and foster single lean practices to

achieve positive impact with regard to the manufacturing strategy followed?

Introduction 5

Research design 1.3

1.3.1 Research process

This research is based on the understanding of business administration as an applied

social science (Hill & Ulrich, 1979) which faces the problems of designing,

controlling, and developing purpose-oriented social systems (Ulrich, 1984).

Companies are recognized as complex social systems and a full controllability is

dismissed (Ulrich, 1984).

The starting point of the research process connects actual problems in the

pharmaceutical industry with relevant questions from the area of lean

manufacturing. The motivation of this research stems from problems faced by

practitioners and, like proclaimed by Ulrich (1991), has the aim to contribute to the

knowledge base by generating practical solutions for manufacturing companies.

These are namely solutions to support managers in the systematic implementation

of lean practices in their production sites. The concretisation and reprocessing of the

research topic puts forward the connections between practitioners’ problems and

new areas of research. It shows that the current reality is only a starting point for

analysing possible future realities in the research process (Ulrich, 1984).

The research process is therefore understood as an iterative learning process which

enriches empirically gained results with insights from practice to gain theoretical

conclusions.

The basis is Kubicek’s (1977) iterative heuristic as shown in Figure 1.

Figure 1: Research process (Kubicek, 1977; Tomczak, 1992; Gassmann, 1999)

To gain an initial understanding of a specific problem and possible solutions,

Questions addressed to reality

(Preliminary)theoretical knowledge

Critical reflectionData collection

Differentiation, abstraction Literature review

First findings from practice

Practical problems

Practical phenomenaResearch as an

iterative learning process

Field workTheory

6 Introduction

literature is reviewed and relevant theories are identified. Further, this specific

problem is reflected in practice and questions are raised. To answer these questions

empirical data is collected and critically reflected. This reflection leads to

differentiation, abstraction and changes in perspective of the original problem and

new questions are raised. Thus, theoretical knowledge is incrementally generated

and added to the existing knowledge base.

1.3.2 Research methodology

The research methodology used is a combination of quantitative and qualitative

research with a focus on the quantitative part. As suggested by Weick (1989) three

systematic processes are involved: literature review, use of data, and use of intuition

and assumptions. This three step approach is illustrated in Figure 2.

Figure 2: Research approach

In a first step, a broad review of lean management and general operations

management literature is conducted to extract and define the lean practices relevant

for the research question. They are enriched by insights from discussions with

industry representatives on their experience with lean. A map of relations is

developed based on these practices and interconnections identified in previous

research (see chapter 3). Therefore the methodology of System Dynamics (SD),

namely Causal Loop Diagrams is used. System dynamics is an approach to analyse,

understand and structure complex problems by showing dependencies between

single items that are part of the problem. It is a combination of the modelling,

simulation and control of complex dynamic systems, originating from Jay W.

Forrester (1961, 1969, and 1989). The approach leads to a continuous improvement

of model quality and insights into the domain or issue modelled. However in most

cases where system dynamics is used mathematical models and simulation are

Introduction 7

omitted. Anyway, this approach called Causal Loop Diagram (CLD) gives good

insights and learning possibilities as shown by Senge (1990). A CLD consists of the

crucial variables of a system which are in relationship to each other. This

relationship is displayed by using arrows, each having a positive or negative causal

link. A positive causal link implies that the variables are changing in the same

direction; accordingly a negative causal link means that the variables are changing

in opposite directions, when one increases the other decreases. When all linkages

are displayed feedback loops can be identified. Two kinds of feedback loops exist:

loops with a positive polarity - reinforcing loops (R) - and loops with a negative

polarity - balancing loops (B) (Meadows, 2008). An example is illustrated in Figure

3 below.

Figure 3: Example Causal Loop Diagram

The next step focuses on the statistical examination of this map based on data from

205 pharmaceutical production plants gathered in an on-going questionnaire based

survey on operational excellence. The map of relations is analysed for different

strategic groups that were formed from the 205 production plants as previous

research shows that the strategy plays an important role in the selection of lean

practices (e.g. Christiansen et al., 2003). To test the map of relations factor analysis,

cluster analysis, analysis of variance, and correlation analysis are used.

1.3.3 Research theory

Contingency theory has become popular in operations management research over

recent years and is especially useful in areas where operations management theory

is not yet fully developed (Sousa & Voss, 2008). Basically, contingency theory

states that context and structure have to be synchronised to allow an organization to

perform well (Drazin & Van de Ven, 1985; Donaldson, 2001). This "fit" is reflected

externally by the adaptation of internal structures to the environment and internally

by aligning structures and processes in the organization, but there is not one

universally efficient organizational structure (Friedli, 2006).

Lean production can be seen as a highly interrelated system. From a contingency

perspective lean practices are interrelated response variables. Response variables

Birth rate Population Death rate+

+

+

-BR

8 Introduction

are actions an organization can take in response to contextual or contingency factors

(Sousa & Voss, 2008). These variables have to be aligned not only with the

contextual factors but also among each other. Internal fit can consequently be seen

as an alignment between the single lean practices while external fit reflects the

alignment of lean practices and environmental factors like plant size.

Another theoretical view on lean is the configurational perspective. Configurations

are defined as "any multidimensional constellation of conceptually distinct

characteristics that commonly occur together" (Meyer at al., 1993 cited from Shah

& Ward, 2007) and are used when the representation with contingency relationships

is not possible. Shah and Ward (2007) see lean production as a configuration of

practices that needs to be explained as a whole and not focussing on single

elements. Here they see the contribution to superior performance as well as the

difficulty of imitation by competitors.

Contingency theory has also been criticised, mainly in three points. Kieser (2002)

notes, that only one specific form of structure is seen to properly support the

organization for any given context. The consequence would be that there is no

variety in the design of organizational structures in a specific situational context.

Furthermore, the situational context has to be seen as given by the organization and

therefore cannot be influenced (Kieser, 2002). In addition, it is felt that contingency

theory only provides limited explanatory power as the independence of the

examined contexts is not proven (Kreikebaum, 1998).

Layout of the dissertation 1.4

The thesis is organised into five chapters. The organization is outlined in Figure 4.

• The first chapter provides a general overview of the research motivation, the

research question and research design as well as the methods used to answer

the research question.

• Chapter 2 presents a literature review of management principles and

operations management research that is relevant to this study. Implications

for the current work are derived and based on them a framework is

developed.

• Chapter 3 develops a map of relations between lean practices based on

findings from literature. This map serves as the basis for the following

empirical investigation.

Introduction 9

• Chapter 4 presents the results of an empirical investigation of pharmaceutical

production sites. Based on a cluster analysis, strategic groups are identified

and the relations between lean practices implemented in these groups are

analysed. The analysis shows which lean practices should be implemented

first according to the strategic group.

• Finally, Chapter 5 concludes the thesis by summarising and discussing the

implications of this study and its contributions to theory and practice.

Limitations and possible directions for future research are highlighted.

Figure 4: Research structure

Chapter 1

Introduction

Research motivation Research objective Research design

Layout of the dissertation

Chapter 2

Theoretical framework

Lean manufacturing Production strategySummary and

framework development

Chapter 3

Mapping of relations between lean practices

Detailed literature analysis

CLD of relationsSummary mapping of

relations

Chapter 4

Empirical analysis

Adaption of the map of relations

Data gathering and data set

Identification of lean practices

Comparison of strategic groups

Development of strategic groups

Summary empirical analysis

Chapter 5

Conclusion

Contribution to theory Contribution to practiceLimitations and future research

10 Theoretical framework


The research at hand is based on operations management literature, especially from

the areas production management methods and production strategy. In the following

chapter first the three related research streams, which form the basis for the actual

discussion on lean, are reviewed. Further, the single elements normally included in

lean studies are identified. Second, a short review of content-related manufacturing

and production strategy literature is conducted and the relationship between lean

manufacturing and production strategy is suggested.

Lean manufacturing 2.1

2.1.1 Evolution of lean manufacturing

When analysing the evolution of lean manufacturing three related research streams

have to be considered. Starting point is the Toyota Production System (TPS)

described by Ohno in the late 1970ies in Japan. Ohno, who was responsible for the

development of the Toyota Production System (TPS) since the 50ies first published

his book "Toyota Production System: Beyond Large Scale Production" in English in

1988 (publication in Japan 1978). He defines TPS and describes its main underlying

components as elimination of waste, zero defects and continuous improvement.

Almost simultaneously, in 1984, Hayes and Wheelwright start studies under the

term “World Class Manufacturing” aiming to analyse which factors are able to

explain the extraordinary success of some manufacturing companies. They found

out two central dimensions: the effectiveness of the production system and the

efficiency of the applied practices used in production.

Hereby effectiveness stands for the role of manufacturing in a company and its

ability to support the company's strategy and to develop a unique position. This

unique position or competitive advantage is reached via the so called competitive

priorities, those factors manufacturing has to aim for (for details see 2.2).

Efficiency is measured by a unique combination of practices coming from the six

dimensions workforce skills and capabilities, management technical competence,

competing trough quality, workforce participation, rebuilding manufacturing

engineering, and incremental improvement approaches (Hayes & Wheelwright,

1984). These dimensions which should lead to a superior operational performance

Theoretical framework 11

have been discussed and expanded by others (e.g. Hall, 1987; Schonberger, 1986).

At the beginning of the 90ies the International Motor Vehicle Program started a

detailed study to examine new Japanese techniques in production (mainly the

Toyota Production System) which they named "lean production". This term was

first used by Krafcik in 1988.

Some of the conclusions drawn are published in "The Machine that Changed the

World" by Womack et al. (1990). The book was the first attempt to gather all

practices discussed under lean manufacturing in one holistic consideration

(Karlsson & Ahlström, 1996). It does not only focus on analysing production but

also on product development, procurement and distribution. In their second book

"Lean Thinking" (1996) Womack and Jones define the central practices which lead

to lean production as follows: specify value, identify the value stream, create flow,

establish pull and seek for perfection. The book can be understood as a guide on

how to practically apply lean in any organization and achieve the lean production

system they described in 1990 (Garnett et al., 1998).

In 1995, Voss stated an already high level of research into lean which even was

increasing in the following years. Nevertheless, the three related research streams

have in common that they all propose certain practices which should be

implemented to reach a better performance. These practices will be discussed in

detail in the following section.

2.1.2 Lean manufacturing practices

Since these first academic publications about lean there has been a big boom

concerning this topic and a lot of theoretic papers and studies were published. In

contrast to the early publications about lean these works see lean from a practical

perspective, focussing more on practices, tools, and techniques that are directly

observable (Hines et al., 2004; Liker & Meier, 2006; Pettersen, 2009; Shah & Ward,

2007). As there is no general agreement under researchers on how to define the

dimensions of lean a lot of different approaches and understandings of lean exist;

concepts are changing over time, the same item is used to display different concepts

or, the other way round, different items are used to display the same concept (Shah

& Ward, 2007). Furthermore Sousa & Voss (2002) stress the fact that studies on

lean are using different levels (principle, practices or techniques) of the single

concepts without showing clearly which level they are addressing. Even more

problematic from their point of view is the use of the terms themselves; "practices",


"factors", and "implementation constructs" are standing for the same level of a

concept.

In this research lean practices are understood as "... the observable facet (...), and it

is through them that managers work to realize organizational improvements."

(Sousa & Voss, 2002, p. 92).

Generally, papers dealing with lean practices build bundles grouping different

aspects of the lean concept (e.g. Cua et al., 2001; Kickuth, 2005; McKone et al.,

2001; Shah & Ward, 2003). Comparing various authors shows that no common

understanding exists, which practices belong to which bundles. An illustrative

example is the paper of Dow et al. (1999) which provides insight into the different

number of dimensions of quality management practices perceived by different

authors. In the different models displaying lean the bundles are mostly

differentiated between Total Productive Maintenance (TPM), Total Quality

Management (TQM), Just-in Time (JIT), and Effective Management System

(EMS). Some papers also include Supply Chain Management (SCM) and Employee

Involvement (EI).

Unlike the work of Womack et al. (1990) the studies mainly focus on single aspects

of lean and their influence on (manufacturing) performance figures. Kannan and

Tan (2005) find that commitment to quality and understanding of supply chain

dynamics have the greatest effect on manufacturing performance. McKone et al.

(2001) investigate the effect of Total Productive Maintenance (TPM) on

manufacturing performance by also considering Total Quality Management (TQM)

and Just-in Time (JIT) practices. They find that multiple manufacturing practices in

a plant are mutually supportive and cannot be seen as independent. Higher levels of

TPM implementation are associated with higher levels of JIT and TQM

implementation.

On the other hand there are also some studies that focus explicitly on lean

manufacturing as an integrated system. Cua et al. (2001), as one of the first, show

that the joint implementation of lean manufacturing practices has an influence on

the manufacturing performance. Depending on the strategic importance of single

performance dimensions different configurations of the practices are useful. De

Menezes et al. (2010) investigate in their paper if early implementers of lean

practices really have an advantage with regard to productivity. The outcome is that

integration, early adoption and continuous improvement may be linked to


organizational performance. Others go even further and also integrate context

variables in their study, e.g. Shah and Ward (2003) examine how plant size, plant

age and unionization status can potentially influence the implementation of 22 lean

manufacturing practices. Many researchers propose that the combined use of

different practices leads to a better result in performance because the practices are

complementary and inter-related. This linkage has also been examined by Kickuth

(2005) who supposes that the best way to implement manufacturing practices is to

first focus on TPM for stable equipment, then on TQM for stable processes and

finally on JIT to reduce inventories and fixed assets. Simultaneously, all these more

technically related practices are supported by EMS practices. De Treville and

Antonakis (2006) lay the focus in their paper more on these people oriented

practices and show that a certain level of lean implementation can increase workers'

motivation. Concerning the implementation process there is no agreement on which

sequence to follow. Hayes et al. (1988) suggest parallel implementation as they

consider that lean practices cannot be isolated. Ferdows and de Mayer (1990)

defend a sequential implementation as there is a natural sequence and efforts and

resources may be limited. Also Womack and Jones (1996) opt for different phases

of implementation. Zayko et al. (1997) describe a sequential process of lean

implementation which is empirically based.

In an attempt to further structure and clarify the field of lean research Shah and

Ward (2007) identify 48 practices/ tools that were previously associated with lean

production by other researchers. Based on a factor analysis they propose that ten

highly inter-related factors can represent lean production including both internal and

external dimensions. Later research works do not use these factors but continue to

form their own lean bundles with a variety of practices included. Appendix A shows

the different lean bundles and practices used by different authors in a chronological

order. The terminology of the respective publication is used.

It is obvious that a lot of research has been done in the field of lean and its

underlying practices. Different papers analyse different lean bundles and their

interrelations but there is no approach which systematically displays the

interrelations between the single practices associated with lean.


Production strategy 2.2

As one of the first, Skinner (1978) stresses the importance of production for the

overall success of a company and therewith its function as a competitive weapon.

Since then, in academia as well as in practice the interest in this topic is raising

(Kathuria, 2000). Essentially, there are two literature streams in production strategy.

The content-related literature deals with competitive priorities and decision

categories or improvement actions (Christiansen et al., 2003). The focus of process-

related literature is on the influence of environment and business strategy. For the

research at hand the content-related literature is considered. In addition to

displaying the actual status of literature on competitive priorities and improvement

actions, research dealing with typologies or taxonomies of production strategy is

analysed.

2.2.1 Competitive priorities and improvement actions

Competitive priorities are crucial dimension of the production strategy. Skinner

(1969, 1974) defined these manufacturing objectives as costs, quality, delivery and

flexibility. There is consensus in literature that the three factors costs, quality, and

delivery a fundamental. They can be found in most of the studies (Ward et al.,

1996). A later literature analysis by Dangayach and Deshmukh (2001) of 260 papers

showed that costs, quality, delivery dependability, delivery speed, flexibility and

innovation are mostly used in content-related literature to name competitive

priorities. Other studies (Christiansen et al., 2003; Frohlich & Dixon, 2001) also

included service as a seventh factor. Nevertheless, research mostly sees production

strategy as a combination of the four basic competitive priorities (Boyer & Lewis,

Implications for the research proposal

I 1: Implementing lean manufacturing practices as an integrated system augments

the positive impact on manufacturing performance.

I 2: Various lean practices and interrelations can be derived from the existing

literature. Most publications focus on single bundles of practices.

I 3: Researchers are not in agreement of how to structure the single practices.


2002) as defined by Skinner (1969, 1974).

In this research the focus will also be on costs, quality, delivery, and flexibility. The

factor innovation will be excluded as it is mainly seen as relevant for research and

development and not for production. There are trends in pharmaceutical

manufacturing to integrate research and development into production, but yet the

factor can be neglected. In the pharmaceutical industry also service can be

excluded.

There are different opinions about the way to use competitive priorities. Under the

term trade-off a discussion on how many competitive priorities a company can

focus is held. Skinner (1992) pointed out that it is necessary to focus on one or two

of the factors only in order to be able to assign resources. The decision is depending

on the business strategy as well as the technical feasibility. The simultaneous focus

on several competitive priorities is not possible as improving one factor leads to the

decline of another factor. This opinion is also shared by others (Boyer & Lewis,

2002). But there is also criticism of this assumption, Schonberger (1986) even

completely neglected the occurrence of trade-offs. But also the complete neglection

of trade-offs is rejected. Other authors (Ferdows et al., 1986; Mapes et al., 1997;

Noble, 1995; Noble, 1997; Roth & Miller, 1992) showed that it is possible to focus

on more than one or two competitive priorities and that the sequence in which they

are adopted is relevant to avoid trade-offs. This sequence is reflected in the sand

cone model (De Meyer & Ferdows, 1990). It states that the sequence of adoption

should be quality, dependability, speed, and cost efficiency.

Often, competitive priorities are used to build typologies or taxonomies of

production strategy (see Table 1). These typologies or taxonomies help to reduce

complexity and to identify similarities (see chapter 2.2.2). Further, they show that

focusing on more than one competitive priority is possible.

As already stated by Hayes and Wheelwright (1984) a unique combination of

practices is vital for success. These practices are included in the production strategy

literature under the term improvement actions (e.g. Total Quality Management).

They should be directly linked to the competitive priorities, allowing managers to

choose those practices that support their aims best (Kim & Arnold, 1996). Even

though there is a lot of research dealing with the linkage of competitive priorities

and improvement actions, the analyses are often just focussing on single practices

(Ketokivi & Schroeder, 2004). This picture is similar to the one drawn for the


analysis of the implementation of lean manufacturing practices (see chapter 2.1.2).

2.2.2 Configurations of production strategy

Configurations can be divided into typologies and taxonomies. They describe the

production strategy and can be built based on competitive priorities. Typologies are

ideal types each representing a unique combination of criteria whereas taxonomies

are classifications of real organizations which form representative and mutually

exclusive groups (Bozarth & McDermott, 1998). Often, the results are analysed

using cluster or factor analysis. Table 1 provides a short overview of competitive

priority based taxonomies in production strategy literature.

The single studies use different dimensions and the database is varying by size,

country, and industry. Nevertheless, the strategic types proposed resemble each

other. These strategic types will serve as references for interpreting and naming the

taxonomies that will be developed for pharmaceutical production sites in this

research.

Implications for the research proposal

I 4: The production strategy is defined by using competitive priorities.

I 5: Competitive priorities are the basis for developing taxonomies of strategic

types which help to identify similarities between plants.

I 6: Competitive priorities and improvement actions like lean production are

linked to each other.


Table 1: Taxonomies in production strategy (Bozarth & McDermott, 1998;

Deflorin, 2007; Martín-Peña & Díaz-Garrido, 2008; own analysis)

Author Strategic types

• high-performance products group • cost • flexibility

• manufacturing innovators • delivery • quality

• marketing-oriented group • delivery network • after-sales service

• caretakers • low price • design flexibility • advertising

• marketeers • dependability • conformance • broad distribution

• innovators • speed • performance • broad line

• volume flexibility

• efficiency • after-sales service

• delivery deadlines • low cost-quality manufacturers • flexibility

• manufacturers focused on delivery • quality

• variant producers • cost • quality

• innovators • in-time delivery • product performance

• mass producers • quick delivery

• mass customizers

• starters • cost

• efficient conformers • delivery

• speedy conformers • flexibility

• do all • quality

• designers • low price • performance quality

• specialists • delivery dependability • after-sales service

• caretakers • delivery speed • broad product line

• idlers • volume flexibility

• servers • design flexibility

• mass customizers • conformance quality

• low pricers • price • customization • time to market

• quality deliverers • delivery reliability • design/ innovation

• speedy deliverers • delivery speed • product features

• aesthetic designers • quality conformance • product variety

• all-rounders • cost • quality

• efficient innovators • delivery

• differentiators • flexibility

• quality customizers • cost • after-sales service

• low emphasizers • delivery

• mass servers • flexibility

• specialized contractors • quality

• experts • price • conformance quality • product design/ -quality

• logisticians • dependable deliveries • customer service

• classics • faster deliveries • product range

• service provider • order size flexibility

• speedy conformers • cost

• starters • delivery

• efficient conformers • flexibility

• agile • quality

Miller & Roth(1994)

Avella et al.(1996, 1999)

Classification variables

de Meyer(1992)

• after sales service

Kathuria et al.(2010)

Deflorin(2007)

• flexible manufacturers focused on the market

• new products more frequently

• changes in product design

• more innovative products

Zhao et al.(2006)

Frohlich & Dixon(2001)

Christiansen et al.(2003)

Sum et al.(2004)

Sweeney & Szwejczewlski(1996, 2000)

Kathuria(2000)


Summary and framework development 2.3

The aim of the research at hand is to show, how single lean practices are

interconnected and how their interaction supports the implementation of lean

against the background of different production strategies. To answer these questions

a research framework is developed based on the implications from literature. It

allows showing which aspects will be examined and how they are positioned to

each other. It is the basis for the following analyses.

Figure 5: Research framework

fit

Strategic type

costs

qualitydelivery

Interrelated lean practices

fithousekeeping

setup time reduction

supplier quality management

pull system

management support and commitment

TQM

EMS

JIT

JIT

TPM

Mapping of relations between lean practices 19

3 Mapping of relations between lean practices

The literature review in chapter 2 showed that there has been a lot of research on

lean practices in the last years. All of them investigated different relations in detail

and came to conclusions which practices influence each other. In this chapter the

single lean practices and their relations identified in previous research are gathered

and mapped. As described in chapter 1.3.2 a CLD is used to display the relations.

The map developed will serve as a basis for the identification of relations between

lean practices in pharmaceutical manufacturing. This chapter is mainly based on a

paper by Gütter (2010).

Detailed literature analysis 3.1

A combination of keyword search in databases and the “snowball” method is used

for literature analysis. Keyword search is used for the identification of relevant

articles. As described in chapter 2.1.2 different authors used different names for the

single lean practices, therefore using only keyword search in databases could be

misleading. For the “snowball” method a relevant article in the topic which was

identified by keyword search is chosen as a seed and the papers cited in this article

as well as the papers that cite the article are found by e.g. using the Web of

Knowledge1. From the articles found the ones relevant for the actual research need

to be extracted.

Literature on lean which is dealing with different lean bundles was identified as

relevant for the actual research. Papers from all kinds of industries were included.

From these papers, the lean practices described and their relations were gathered.

Lean practices rarely named or without stated interrelations to other practices were

not included in the analysis. Building on prior research the single lean practices

identified are grouped to the lean bundles TPM, TQM, JIT and EMS as done by

Cua et al. (2001) in their integrating framework. A similar framework was

developed by Kickuth (2005) for pharmaceutical manufacturing. As far as possible

these two frameworks were used.

1 webofknowledge.com


In the following, the single bundles are explained. Table 2 depicts the classification

made by Cua et al. (2001), Kickuth (2005), and the classification derived from the

literature review.

The practices in the TPM bundle are focusing on the maximisation of equipment

effectiveness (Nakajima, 1988) which is defined as stable running machines with a

high availability rate. Three lean practices are assigned to the TPM bundle:

preventive maintenance, according to Cua et al. (2001), technology assessment and

usage following Kickuth (2005) which includes Cua’s technology emphasis and

proprietary equipment, and housekeeping as stated by McKone et al. (2001) and

Kickuth (2005).

The TQM bundle reflects a holistic quality management approach (Powell, 1995)

that involves supplier, workforce, customers, and management into the continuous

improvement of quality. The lean practices included in the bundle are almost the

same as in the frameworks of Cua et al. (2001) and Kickuth (2005). To stress the

importance of variance reduction in processes the practice statistical process control

(SPC) is included as in Shah and Ward (2007).

The reduction and finally the elimination of waste (Ohno, 1988) is the goal of the

JIT practice bundle. Compared to the framework of Cua et al. (2001), which

includes five lean practices, most of the practices stayed the same, only with

different names. In addition, three practices have been added following Shah and

Ward (2003). These practices are: lot size reduction, cycle time reduction, and

continuous product flow.

The lean bundles defined so far are all more technically focused. They are

supported by management and strategy focused lean practices gathered in the

bundle EMS. According to Shah and Ward (2003) lower level lean practices can be

grouped into two main factors. One factor is named flexible, cross-functional work

force and consists of job rotation, job design, and formal, cross-functional training.

The second factor, self-directed work teams, includes organization in work teams

and employee involvement in problem solving groups. As a third practice

management commitment and support was included to not only stress the role of

employees but also the role of management.


Table 2: Attribution of lean practices to lean bundles

As a next step the lean practices identified in the literature review are structured

using a CLD.

Causal loop diagram of relations 3.2

A CLD helps to structure the relation between the single lean practices and

therewith shows their dependencies. It also gives a first idea of which practices are

very connected to others and therewith strongly influence the implementation of

lean in a plant.

In addition to the lean practices identified in the literature review also the goals of

the technically oriented lean bundles are included into the CLD; they are marked in

bold. The goals are improved equipment performance for TPM, quality

improvement and stable processes for TQM, and elimination of excess inventories

for JIT. Including the goals of the bundles helps to see if the lean practices assigned

to one lean bundle have more causal relations to the goal of this specific bundle than

to those of other bundles.

The relations displayed in the CLD were primarily taken from de Menezes et al.

(2010), Shah and Ward (2007, 2003), Kannan and Tan (2005), Ahmad et al. (2003),

Cua et al. (2001), McKone et al. (2001) and Dow et al. (1999). But also inputs from

other papers were considered.

Cua et al. 2001 Kickuth 2005 Literature review

Autonomous & planned maintenance Preventive maintenance Preventive maintenanceTechnology emphasis Effective technology usage HousekeepingProprietary equipment development Housekeeping Technology assessment and usage

Cross-functional product design Cross-functional product design Cross-functional product designProcess management Process management Process mgmt. and variance reductionSupplier quality management Supplier quality management Supplier quality managementCustomer involvement Customer integration Customer involvement

Statistical process control (SPC)

Setup time reduction Setup time reduction Setup time reductionPull system production Pull system Pull systemJIT delivery by suppliers Planning adherence JIT delivery by suppliersEquipment layout Layout optimization Equipment layout optimizationDaily schedule adherence Planning adherence

Cycle time reductionLot size reductionContinuous product flow

Committed leadership Direction setting Mgmt. support and commitmentStrategic planning Mgmt. commitment & company culture Self-directed working teamsCross-functional training Flexible, cross-functional workforceEmployee involvementInformation and feedback Functional integration& qualification

TQM basic techniques/ TQM

JIT basic techniques/ JIT

Human - and strategic-oriented practices/ Effective management system

TPM basic techniques/ TPM

Employee involvement & continuousimprovement


Figure 6: CLD of lean practices

The map of relations will be discussed in the following regarding three aspects: the

linkages between the single lean practices, the linkages between the lean practices

and the goals of the lean bundles, and the reinforced feedback loops.

3.2.1 Linkages between single lean practices

A high number of linkages can be observed between the single lean practices

themselves and the goals of the bundles TPM, TQM, and JIT. It is obvious that

some practices show more direct as well as indirect relations than others. Besides

the number of relations also the direction of the relations are interesting, as the

implementation of a practice that has a positive influence on another practice

supports the overall implementation of lean in a plant. There are, on the one hand,

lean practices that are only or mainly influencing others and on the other hand there

are lean practices that are only or mainly being influenced. Table 3 structures the

lean practices following this approach.

pull system

continuousproduct flow

setup timereduction

quality improvementand stable processes

elimination ofexcess inventories

lot size reduction

process managementand variance reduction

self-directedworking teams

supplier qualitymanagement

preventivemaintenance

++

JIT delivery bysuppliers

+ +

SPC

+

equipment layoutoptimization

++

+

flexible, cross-functionalworkforce

+

+

+

customerinvolvement

+

+

+management support

and commitment +

+

+

++

technologyassesment and usage

++

housekeeping

improved equipmentperformance

cycle timereduction

+

+

+

+

++

++ +

+

+

+

+

+

+

planning adherence

+

cross-functionalproduct design

+

+

+

+ ++

+

+

+


Table 3: Direction of relations between lean practices

To have the highest impact on the overall implementation of lean in a plant, the

early adoption of lean practices that are only or mainly influencing others seems

most promising. Table 4 sorts the lean practices regarding their influence on other

practices. It distinguishes between the overall number of direct and indirect

influences and the number of direct influences. Considering also the indirect

linkages shows that some practices have more influence than it seems in the first

place. An example is the practice housekeeping with only one direct linkage to

improved equipment performance (following McKone et al. 2001) but six indirect

linkages.

Table 4: Lean practices according to their influence

Following the assumption that lean practices that influence several other lean

practices have the highest influence on the overall success of lean implementation,

Only influencing Mainly influencing Balanced Mainly being influenced Only being influenced

• Housekeeping • JIT delivery by suppliers • Preventive maintenance • Planning adherence• Technology assessment and usage • Lot size reduction • Cycle time reduction• Cross-functional product design • Continuous product flow• Supplier quality management • Self-directed working teams• Customer involvement • Pull system• Statistical process control (SPC)• Setup time reduction• Equipment layout optimization• Mgmt. support and commitment

• Process mgmt. and variance reduction

• Flexible, cross-functional workforce

PracticeNumber of influences

thereof direct

Mgmt. support and commitment 11 4

Flexible, cross-functional workforce 10 4Setup time reduction 10 3

Lot size reduction 9 5Equipment layout optimization 9 2

Supplier quality management 8 3

Continuous product flow 7 4Self-directed working teams 7 3Pull system 7 2Customer involvement 7 2JIT delivery by suppliers 7 2Preventive maintenance 7 2Process mgmt. and variance reduction 7 2Housekeeping 7 1Technology assessment and usage 7 1Cross-functional product design 7 1Statistical process control (SPC) 7 1

Cycle time reduction 5 1

Planning adherence 0 0


eight lean practices seem worth to focus on. These eight lean practices have a lot of

direct and/ or indirect relations with others: management support and commitment

(EMS), flexible, cross-functional workforce (EMS), setup time reduction (JIT), lot

size reduction (JIT), equipment layout optimization (JIT), supplier quality

management (TQM), continuous product flow (JIT), and self-directed working

teams (EMS). Interestingly, all practices coming from the bundle Effective

Management System (EMS) are among these practices with a lot of relations. It

shows how important these people and culture oriented lean practices are for the

overall implementation of the more technical practices of TPM, TQM, and JIT.

3.2.2 Linkages between lean practices and goals of lean bundles

When looking at the goals of the single lean bundles it becomes obvious that the

lean practices stemming from the relevant bundle have the most influences.

Nevertheless, they are also influenced by lean practices from other bundles. The

goals are also influencing each other; the JIT goal is influenced by the TPM as well

as the TQM goal. Further, the TQM goal is also influenced by the JIT goal. The

assumption of Kickuth (2005), that practices assigned to the JIT bundle are the last

to implement because they are facilitated by the other bundles, is supported. Also

the finding of Shah and Ward (2007) can be confirmed, stating that practices

associated with the TPM bundle have least direct relations to other lean practices.

3.2.3 Feedback loops

As explained in chapter 1.3.2 two kinds of feedback loops exist. For the relations

between lean practices five reinforced feedback loops were identified. They are

marked with bold arrows in Figure 6. All of the feedback loops include the goals of

the bundles TPM, TQM, and JIT. Hence, the conclusion of e.g. McKone et al.

(2001), that multiple manufacturing practices in a plant are mutually supportive and

therewith not independent, is supported.

An example for a direct feedback loop can be found between the TQM goal quality

improvement and stable processes and the JIT goal elimination of excess

inventories. Ahmad et al. (2003) stated that lower inventories, which lead to a

higher number of turns, ease the detection of quality problems. This helps to

improve the internal quality. A higher level of quality enables a plant to have a

lower level of inventory as the reliability of the process output is better.


Summary mapping of relations 3.3

From literature 19 lean practices could be extracted and analysed regarding their

direct as well as indirect linkages to each other. A map was developed using a

Causal Loop Diagram. It shows that some lean practices are more connected than

others. Eight practices were identified that seem to have a high influence on a

successful lean implementation as their implementation positively influences the

implementation of other practices. This is a first hint for manufacturing managers

which lean practices to focus on.

So far, relations between lean practices were analysed based on literature and

therewith for manufacturing in general. The next chapter investigates which of the

lean practices identified in literature can also be found in pharmaceutical

manufacturing. The investigation is based on data from a survey in the

pharmaceutical industry. It will test the linkages empirically.

26 Empirical analysis


This chapter is divided into 6 parts. First, the data set is described. Then, the lean

practices used in pharmaceutical manufacturing are identified and the map derived

from literature is adapted accordingly. After that, strategic groups are developed and

characterized. Subsequently, the relationships between the strategic groups and their

use of lean practices are analysed based on the adapted map from chapter 3. Finally,

the sequence of adopting lean practices to positively impact manufacturing

performance is discussed.

Data gathering and data set 4.1

This research uses data collected in the project Operational Excellence in the

Pharmaceutical Industry (OPEX). The OPEX project started in 2004 at the Institute

of Technology Management at the University of St.Gallen. The study focuses on the

implementation of different lean practices and associated key performance

indicators in pharmaceutical production. Further, details on the production structure

and the production strategy are questioned. The OPEX database consists of data

from 208 pharmaceutical production sites.

The OPEX project is questionnaire based. For the development of the questionnaire

three steps were taken. First, based on a thorough literature review a model to

display lean management was adapted from existing models especially taking into

account the model of the Toyota Production System. The model was discussed with

experts from the automotive and the pharmaceutical industry and some adaptations

have been made. Second, a prototype questionnaire was developed based on the

model. The questionnaire consists of approximately 370 variables that can be

classified into different types of data. Approximately 200 variables are directly

measureable information, like key performance indicators. The other variables are

measured using a five‐point Likert scale, ranging from 1 = ʺstrongly disagreeʺ to 5

=ʺstrongly agreeʺ and including an option ʺdon’t knowʺ. According to Bortz &

Schuster (2010) the Likert scale can be regarded as an interval scale, which will be

done in this research. Constructs from preceding research were used whenever

possible to obtain high construct validity. Where it was necessary to develop new

scales they were built with close proximity to elaborated constructs. Third, the

prototype questionnaire was pre‐tested to ensure that the questions are interpreted

Empirical analysis 27

correctly. This test included on the one hand a discussion with seven experts and on

the other hand nine production plants filling in the questionnaire (Kickuth, 2005). In

case of unclear questions these were adapted. The measurement items used in this

work can be found in Appendix B.

The questionnaire was sent to pharmaceutical production sites all over the world.

Nevertheless, the location of St.Gallen leaded to an emphasis of the data on Europe

due to accessibility of production sites on other continents. The sample of target

firms is partly taken from the institute’s industry data base. In addition, existing

personal contacts and publicly available information (websites, industry

associations etc.) complemented the sample to avoid convenience sampling. In total

about 1050 production sites were identified and contacted by telephone or e‐mail

from which 208 responses returned. This makes a return rate of approximately 20%.

The respondents are managers from the area quality, production, operational

excellence, or site leaders.

From the 208 questionnaires returned, data from three sites was not used due to

missing values. Thus, the final sample consists of 205 pharmaceutical production

sites. Most of the participating plants are from European countries (89%), but some

questionnaires were also returned by plants located outside of Europe. Thereof, 7%

are from American countries and 4% from Asian countries. The participating

pharmaceutical production sites are of different size, measured with the number of

FTEs (full time equivalents).

Table 5: Size of pharmaceutical production sites

More than half of the sites have over 250 FTEs, nevertheless most sites employ

between 100 to 499 FTEs.

Number of FTEs Sample

1 to 49 3%

50 to 99 12%

100 to 249 31%

250 to 499 35%

500 to 999 14%1000 or more 5%


Factor analysis: Identification of lean practices 4.2

To check if the relations between items and corresponding lean practices that were

derived from theory can be empirically confirmed for pharmaceutical production

sites an exploratory factor analysis was conducted. This approach also opened the

possibility to find other constellations than assumed. Nevertheless, the practices

derived from theory were used as a basis for interpreting the resulting factors. This

is in line with Hair et al. (2006), who state that the analysis is most efficient when

the factors extracted correspond to the previously conceptually defined dimensions.

Factor analysis helps to define the underlying structure of variables (R factor

analysis). It is based on the assumption that there are latent variables which cannot

be observed directly. These so called factors influence the values of the observable

variables and are the reason for correlations between these values. Consequently,

variables can only be used for factor analysis if a correlation value can be calculated

among all variables. This is always possible for metric variables. Aim of the factor

analysis is to empirically determine the number and quality of theses latent

variables and therewith condense the information provided. The basic objective is

the grouping of highly intercorrelated variables (Hair et al., 2006).

There are two main types of factor analysis: the Exploratory Factor Analysis (EFA)

and the Confirmatory Factor Analysis (CFA). As the CFA demands an a priori

specification of the number of factors and other parameters, normally an EFA is

conducted as a start. Based on the results a CFA can be performed if necessary.

Figure 7: Procedure used for factor analysis

Identification of variables

Factor extraction method

Number of factors

Rotation of factors

Interpretation


Figure 7 shows the procedure used for the factor analysis in this research. Before

starting with the actual factor extraction in steps two to four, the underlying

variables have to be defined and checked for their usability in the analysis. After

extracting the factors the results need to be interpreted. The following sections

describe each of the steps in greater detail.

4.2.1 Identification of variables

The items related to the four categories of lean practices, as asked in the

questionnaire discussed in chapter 4.1, were the basis for the factor analysis. Some

tests had to be performed to ensure their usability for a factor analysis. Descriptive

statistics and missing item analysis were conducted for each of those 105 items. The

missing item analysis did not show any problems. Nevertheless, two items from the

category EMS were excluded from further analysis as they were only provided in 31

respectively 76 of the cases. In the following, the terms “item” and “variable” will

be used interchangeably.

With these 103 items the suggested number of at least five observations per variable

(Hair et al., 2006) cannot be reached for an analysis across multiple categories.

Therefore the factor analysis is conducted separately per category of lean practices.

Generally, there have to be more observations than variables with a minimum

number of 50 observations in total.

A first overview of the variables in the single categories can be obtained by

calculating the correlation matrix. It contains the bivariate correlations between the

single variables but does not yet give evidence if connected variables can be

explained by a common factor. This correlation matrix is the basis for performing

the factor analysis. Therefore, it has to be tested if the correlation matrix is suitable

for such an analysis.

One common criterion for testing the correlation matrix is the Measure of Sampling

Adequacy (MSA) proposed by Kaiser, Meyer and Olkin. It ranges on a scale from 0

to 1 and shows how well each variable is predicted by the other variables without

error. Aim is a value of above 0.80 but a value of 0.50 is still acceptable for a factor

analysis (Backhaus, 2006). The anti-image correlation matrix shows these values

per variable and indicates if variables should be excluded from further analysis. If

there are unacceptable values the variable with the lowest MSA should be deleted

first and then the correlation matrix should be recalculated. This procedure should

be performed until no unacceptable values are left.


The Bartlett test of sphericity is another possibility to test for correlations among the

variables. However, a higher number of observations leads to a higher chance of

detecting correlations among the variables.

As well, scale reliability can be ensured by calculating the Corrected Item to Total

Correlation (CITC) and Cronbach’s alpha if item deleted. According to Kerlinger

(1978) an item should not be used if the item’s correlation with its corrected item

total is less than 0.30. If the item is regarded to be essential for the category a

slightly lower CITC can be accepted. Cronbach’s alpha is a measure for internal

consistency that varies between 0 and 1. A value of above 0.70 is regarded as

essential for existing scales (Nunnally, 1978).

Using these criteria in total 16 (2 TPM, 6 TQM, 4 JIT, 4 EMS) items were deleted

thereof six (1 TPM, 1 TQM, 1 JIT, 3 EMS) were revers-coded. Previous research

has also indicated lower item reliability with reverse-coded items (Flynn et al.,

1990). For the following factor extraction 87 items can be used. Further details

concerning the items can be found in chapter 4.2.3.1 to chapter 4.2.3.4.

4.2.2 Factor extraction method

As the variables are specified, the next step is the selection of the factor extraction

method. There are two basic methods available, the Common Factor Analysis and

the (Principle) Component Analysis. The difference is the variance that is

considered in each method. The variance of a variable is composed of three parts.

First, there is the common variance that is shared with all other variables. It is

estimated by the so called communality. The second form of variance is the specific

or unique variance that cannot be explained by other variables and is only

associated to one specific variable. The third form of variance is the error variance

that is caused during data gathering or based on measurement errors or random

components (Hair et al., 2006). The Component Analysis considers the total

variance and is best used when having the primary goal of reducing data. The

interpretation focuses on finding a collective term for the variables assigned to one

factor. The Common Factor Analysis considers only the common variance and has

the primary goal of identifying the latent dimensions. The interpretation focuses on

finding a name for the reason for which the variables load on a factor. As the aim of

this research is to reduce data for further analysis the Principle Component Analysis

(PCA) is chosen.


4.2.3 Number of factors, rotation and interpretation

After identifying the variables and choosing the factor extraction method the actual

factor analysis can be performed. Aim of the factor analysis is to find as little

factors as possible that represent the data as good as possible in terms of explaining

more variance than another factor structure would do. Preferably, these factors

should be independent. There is no formal mathematical rule to decide on the

number of factors that should be extracted. However, some stopping criteria exist

(Hair et al., 2006):

• Latent Root criterion/ Kaiser criterion: Only factors which explain more

variance than a single variable should be extracted. For component analysis

the variance explained by one variable is 1, therefore all factors with an

eigenvalue >1 are regarded as significant. The results for this method are best

when the number of variables ranges between 20 and 50. Otherwise too few

(<20) or too many (>50) factors might be extracted.

• A Priori criterion: The exact number of factors is known before starting the

analysis.

• Percentage of Variance criterion: A specific amount of variance should be

explained by the factors extracted. In social science normally 60% of the

variance should be explained by the factor solution.

• Scree Test criterion: The latent roots are plotted against the number of

factors in the order of their extraction. This graph is used to determine the

appropriate number of factors by searching the point at which the curve first

starts to straighten. Generally, the Scree Test results in suggesting more

factors than the Latent Root criterion.

In practice, several criteria are used to extract factors and the results are compared

to find the best solution. Also the conceptual foundation is considered. This

approach is also chosen to extract the right number of lean practices.

After deciding on the number of factors to extract it has to be defined which

variables load on which factor. To ease the assignment a simple factor pattern is the

aim, meaning that each variable only has significant loadings on one factor. A

loading is regarded as significant if it is bigger than 0.50. From practical

considerations also smaller loadings can be added to the interpretation (Hair et al.,

2006). If variables load on more than one factor they are cross-loading and the

interpretation is more difficult. Rotation methods are used to eliminate these cross-


loadings. Two different types of factor rotation exist: orthogonal factor rotation and

oblique factor rotation. With orthogonal rotation the factors remain uncorrelated

whereas oblique rotation leads to correlated factors. Orthogonal rotation methods

are preferred when data reduction and the use in further statistical analysis is the

research goal. One commonly used orthogonal rotation method is VARIMAX

rotation which is also used in this research.

Following these two decisions the factors are extracted for the four categories of

lean. To assess the appropriate number of factors to retain multiple factor analyses

were run, specifying three to six factors for each category. After a VARIMAX

rotation the item loading tables were compared and the one with the cleanest factor

structure was chosen (Costello & Osborne, 2005). Also managerial interpretation

was considered in the selection process. Results suggested that for the category

TPM a 3-factor solution, for the category TQM a 4-factor solution, and for the

category JIT and the category EMS a 5-factor solution was best. All items had

loadings higher than 0.50 on their respective factor or could be confirmed by using

the Fürntratt criteria (Fürntratt, 1969). Eigen values lay above 2, and the percentage

of variance explained ranged from 58% to 65% (TPM 2.4/58.2%, TQM 2.6/ 60.5%,

JIT 2.3/ 65.2%, EMS 2.2/ 63.1%). The factor structure shows similarities to past

research. The internal consistency of each factor was examined using Cronbach’s

alpha. As all scales are above or approaching 0.70, internal consistency is indicated.

The detailed results for each of the four categories and their interpretation are

presented in the following sections (4.2.3.1 through 4.2.3.4) by using three tables.

The tables are: 1) items used for the construct, 2) scale reliability scores, and 3)

construct level EFA results and Cronbach’s alpha. Definitions for the theoretically

expected lean practices can be found in Kickuth (2005); only changes in the

definition are commented.

4.2.3.1 Total productive maintenance (TPM)

Originally, 17 items grouped to three practices were meant to establish the category

total productive maintenance (TPM). The three practices are: preventive

maintenance, technology assessment and usage, and housekeeping. The practices

and the items theoretically related to each of the practices are shown in Table 6.


Table 6: Total productive maintenance – Initial items

Table 7 presents the results of the reliability analysis for each of the items in the

category total productive maintenance. CITC scores were above the 0.30 cut-off

except for two items. The CITC score for TPM8 was 0.165 and for TPM12 0.050,

therefore they were dropped. Also the scores for Cronbach’s alpha if item deleted

indicated the removal of those two items. A second reliability analysis was

conducted and each of the remaining items reached a CITC score of above 0.30.

Table 7: Total productive maintenance – Scale reliability scores

The Exploratory Factor Analysis (EFA) was run as explained above and its results

are displayed in Table 8. Three factors corresponding to the theoretically expected

practices were extracted on the category level. They account for 58.2% of the

variance explained. All items had loadings higher than 0.50 on their respective

factor. The eigenvalues for the factors and their Cronbach’s alpha were suitable.

Practice Item name Item label

TPM1TPM2TPM3TPM4TPM5TPM6TPM7TPM8

TPM9TPM10TPM11TPM12TPM13TPM14

TPM15TPM16TPM17

House-keeping

Our employees strive to keep our plant neat and clean. Our plant procedures emphasize putting all tools and fixtures in their place.We have a housekeeping checklist to continuously monitor the condition and cleanness of our machines and equipment.

Technology assesment and usage

Our plant is situated at the leading edge of technology in our industry.We are constantly screening the market for new production technology and assess new technology concerning its technical and financial benefit. We are using new technology very effectively.We rely on vendors for all of our equipment.Part of our equipment is protected by the firm`s patents.Proprietary process technology and equipment help us gain a competitive advantage.

Preventive maintenance

We have a formal program for maintaining our machines and equipment.Maintenance plans and checklists are posted closely to our machines and maintenance jobs are documented.We emphasize good maintenance as a strategy for increasing quality and planning for compliance. All potential bottleneck machines are identified and supplied with additional spare parts. We continuously optimize our maintenance program based on a dedicated failure analysis.Our maintenance department focuses on assisting machine operators perform their own preventive maintenance. Our machine operators are actively involved into the decision making process when we decide to buy new machines. Our machines are mainly maintained internally. We try to avoid external maintenance service as far as possible.

Item name Initial CITCCronbach's alpha if item

deleted

Initial Cronbach's

alpha (scale)Final CITC

TPM1 .336 .853 .855 .386TPM2 .455 .849 .493TPM3 .576 .844 .592TPM4 .561 .844 .544TPM5 .616 .841 .602TPM6 .670 .838 .566TPM7 .710 .834 .486TPM8 .165 .861 Item droppedTPM9 .553 .843 .569TPM10 .565 .843 .557TPM11 .497 .846 .609TPM12 .050 .866 Item droppedTPM13 .414 .850 .371TPM14 .390 .854 .380TPM15 .568 .844 .522TPM16 .523 .845 .487TPM17 .386 .852 .429


Table 8: Total productive maintenance – EFA category level

4.2.3.2 Total quality management (TQM)

Originally, 26 items grouped to four practices were meant to establish the category

total quality management (TQM). The four practices are: process management,

cross functional product development, customer involvement, and supplier quality

management. The practices and the items theoretically related to each of the

practices are shown in Table 9.

Table 9: Total quality management – Initial items


category total quality management. CITC scores were above the 0.30 cut-off except

Practice Item nameFactor loading

Eigen valueCronbach's

alphaTPM1 .702 3.561 .819

TPM2 .777

TPM3 .648

TPM4 .636

TPM5 .636

TPM6 .586

TPM7 .595

TPM9 .760 2.810 .790

TPM10 .734

TPM11 .715

TPM13 .625

TPM14 .734

TPM15 .801 2.352 .783

TPM16 .832

TPM17 .771


Technology assesment and usage

House-keeping


TQM1TQM2TQM3TQM4TQM5TQM6TQM7TQM8

TQM9TQM10TQM11TQM12TQM13

TQM14TQM15TQM16TQM17TQM18TQM19

TQM20TQM21TQM22TQM23TQM24TQM25TQM26

Supplier quality

management

Quality is our number one criterion in selecting suppliers.We rank our suppliers, therefore we conduct supplier qualification and audits.We use mostly suppliers that we have validated.For a large percentage of suppliers we do not perform any inspections of the incoming parts/ materials.Inspections of incoming materials are usually performed in proportion to the past quality performance or type of supplier.Basically, we inspect 100% of our incoming shipments. We jointly have improvement programs with our suppliers to increase our performance.

Customer involvement

We are frequently in close contact with our customers.Our customers frequently give us feedback on quality and delivery performance.We regularly survey our customer`s requirements.We regularly conduct customer satisfaction surveys. On time delivery is our philosophy.We jointly have improvement programs with our customers to increase our performance.

Cross functional product

development

Manufacturing engineers are involved to a great extent in the development of a new drug formulation and the development of the necessary production processes. In our company product and process development are closely linked to each other.Due to close collaboration between the R&D and the manufacturing department, we could significantly shorten our time for product launches in our plant.For the last couple of years we have not had any delays in product launches at our plant. For product and process transfers between different units or sites standardized procedures exist, which ensure a fast, stable and complied knowledge transfer.

Process management

In our company direct and indirect processes are well documented.We continuously measure the quality of our processes by using process measures (e.g. On-time-in-full delivery rate).Our process measures are directly linked to our plant objectives.In our company there are dedicated process owners who are responsible for planning, management and improvement of their processes. A large percentage of equipment on the shop floor is currently under statistical process control (SPC).We make use of statistical process control to reduce variances in processes. For root cause analysis we have standardized tools to get a deeper understanding of the influencing factors (e.g. DMAIC).We operate with a high level of PAT implementation for real time process monitoring and controlling.


for seven items. The CITC scores for TQM17, TQM19, TQM20, TQM23, and

TQM25 as well as the scores for Cronbach’s alpha if item deleted indicated their

removal. After a second reliability analysis also TQM24 was dropped. A third

reliability analysis was conducted and each of the remaining items reached a CITC

score of above 0.30. TQM21 and TQM22 only reached a score close to 0.30. As

their scores for Cronbach’s alpha if item deleted were suitable and as they were

essential for the practice they were assigned to, those two items were kept.

Table 10: Total quality management – Scale reliability scores


are displayed in Table 11. Four factors were extracted on the category level. They

account for 60.5% of the variance explained. Three items had loadings lower than

0.50 on their respective factor but could be assigned using the Fürntratt criteria. The

eigenvalues for the single factors as well as their Cronbach’s alpha were suitable.


deleted

Initial Cronbach's

alpha (scale)

Final CITC

(2nd

loop)

TQM1 .432 .841 .847 .473TQM2 .623 .835 .627TQM3 .493 .839 .527TQM4 .623 .834 .659TQM5 .488 .838 .527TQM6 .391 .841 .466TQM7 .514 .836 .537TQM8 .322 .844 .353TQM9 .657 .831 .690TQM10 .608 .833 .628TQM11 .655 .832 .700TQM12 .388 .841 .418TQM13 .587 .834 .613TQM14 .407 .841 .374TQM15 .498 .838 .472TQM16 .446 .839 .376TQM17 .136 .853 Item droppedTQM18 .369 .842 .319TQM19 .205 .848 Item droppedTQM20 .083 .850 Item droppedTQM21 .305 .844 .288TQM22 .228 .846 .271TQM23 .075 .852 Item droppedTQM24 .251 .847 Item droppedTQM25 .085 .853 Item droppedTQM26 .411 .841 .356


Table 11: Total quality management – EFA category level

The four factors extracted are not corresponding to the theoretically expected four

practices. Items loading on other practices than expected were assigned to this new

practice if it was also comprehensible from a content perspective.

In contrast to the theoretically expected practice process management the factor

analysis revealed a more detailed understanding of processes. Sites that emphasize a

cross-functional process development also seem to be more focused on process

control, especially on the use of statistical process control. This is included in the

factor cross-functional process development and process control. Process

measurement and reliability is a second factor aiming at processes. It focusses more

on the on-time aspect of processes than on their exact development over time. In

addition to the theoretical understanding of supplier quality management, the aspect

of supplier development and management in general is stressed by the factor

supplier management and development. Some items related to customer

involvement could not be assigned to the respective factor. Therefore the

theoretically assumed factor is renamed as customer focus and satisfaction.

4.2.3.3 Just-in time (JIT)


just-in time (JIT). The four practices are: set-up time reduction, pull production,



alphaTQM5 .766 3.658 .824

TQM6 .769

TQM8 .648

TQM9 .566

TQM10 .652

TQM11 .518

TQM2 .494 3.070 .758

TQM3 .448

TQM7 .685

TQM12 .768

TQM13 .585

TQM18 .527

TQM1 .545 2.703 .711

TQM4 .620

TQM21 .762

TQM22 .591

TQM26 .725

TQM14 .779 2.664 .654

TQM15 .840

TQM16 .490

Process measurement and reliability

Cross-functional process

development and process

control

Supplier management

and development

Customer focus and

satisfaction


layout optimization, and planning adherence. The practices and the items

theoretically related to each of the practices are shown in Table 12.

Table 12: Just-in time – Initial items


category just-in time. CITC scores were above the 0.30 cut-off except for four

items. The CITC score for JIT4 was 0.096, for JIT9 0.194, for JIT11 0.108 and for

JIT12 0.164, therefore they were dropped. Also the scores for Cronbach’s alpha if

item deleted indicated the removal of those four items. A second reliability analysis

was conducted and each of the remaining items reached a CITC score of above

0.30.


JIT1JIT2JIT3JIT4JIT5JIT6

JIT7JIT8JIT9JIT10JIT11JIT12JIT13JIT14

JIT15JIT16JIT17JIT18JIT19JIT20JIT21JIT22JIT23JIT24

JIT25JIT26JIT27JIT28JIT29JIT30

Set-up time reduction

We are continuously working to lower set-up and cleaning times in our plant.We have low set-up times for equipment in our plant.Our crews practice set-ups regularly to reduce the time required.To increase the flexibility, we put high priority on reducing batch sizes in our plant. We have managed to schedule a big portion of our set-ups so that the regular up-time of our machines is usually not effected.

Optimized set-up and cleaning procedures are documented as best-practice process and rolled-out throughout the whole plant.

Currently our manufacturing processes from raw material to finished goods involve almost no interruptions and can be described as a full continuous flow.At the moment we are strongly working to reach the status of a full continuous flow with no interruption between raw material to finished goods.

Pull production

Our production schedule is designed to allow for catching up, due to production stoppings because of problems (e.g. quality problems).We use a pull system (kanban squares, containers or signals) for production control.We mainly produce according to forecasts.Suppliers are integrated and vendors fill our kanban containers, rather than filling our purchasing orders. We value long-term associations with suppliers more than frequent changes in suppliers.We depend on on-time delivery from our suppliers.We deliver to our customers in a demand-oriented JIT way instead of a stock-oriented approach.

We mainly produce one unit when the customer orders one. We normally do not produce to stock.

We use "Value Stream Mapping" as a methodology to visualize and optimize processes.

We have laid out the shop floor so that processes and machines are in close proximity to each other

Planning adherence

We usually meet our production plans every day.We know the root causes of variance in our production schedule and are continuously trying to eliminate them.To increase our planning adherence we share data with customers and suppliers based on a rolling production plan.We have smoothly leveled our production capacity throughout the whole production process. Our plant has flexible working shift models so that we can easily adjust our production capacity according to current demand changes.

A smoothly leveled production schedule is preferred to a high level of capacity utilization.

Layout optimization

Our processes are located close together so that material handling and part storage are minimized.Products are classified into groups with similar processing requirements to reduce set-up times.Products are classified into groups with similar routing requirements to reduce transportation time.The layout of the shop floor facilitates low inventories and fast throughput.As we have classified our products based on their specific requirements our shop floor lay-out can be characterized as separated into "mini-plants". Currently our manufacturing processes are highly synchronized over all steps by one tact.


Table 13: Just-in time – Scale reliability scores


are displayed in Table 14. Five factors were extracted on the category level. They

account for 65.2% of the variance explained. Three items had loadings lower than



Item name

Initial CITC

Cronbach's alpha if item

deleted

Initial Cronbach's

alpha (scale)Final CITC

JIT1 .447 .923 .924 .916JIT2 .583 .921 .916JIT3 .838 .917 .913JIT4 .096 .928 Item droppedJIT5 .682 .920 .916JIT6 .578 .921 .918JIT7 .492 .922 .918JIT8 .686 .919 .916JIT9 .194 .926 Item droppedJIT10 .581 .922 .918JIT11 .108 .926 Item droppedJIT12 .164 .926 Item droppedJIT13 .397 .924 .919JIT14 .567 .921 .920JIT15 .564 .922 .918JIT16 .372 .923 .919JIT17 .777 .917 .915JIT18 .787 .917 .913JIT19 .486 .922 .919JIT20 .737 .918 .914JIT21 .782 .918 .913JIT22 .412 .924 .920JIT23 .677 .919 .919JIT24 .529 .922 .919JIT25 .560 .921 .919JIT26 .617 .921 .915JIT27 .342 .924 .920JIT28 .707 .920 .915JIT29 .388 .923 .920JIT30 .431 .923 .918


Table 14: Just-in time – EFA category level

The five factors extracted are not corresponding to the theoretically expected four



Apparently, sites that are focussing on set-up time reduction are also optimizing

their layout. Therefore these two practices are not independent and result in one

factor named optimization of set-up times and layout. The initial practice planning

adherence was too focused and is now included in optimized production planning

and control. A stronger emphasis on the process aspect is indicated by the EFA

results; it is reflected in the factor process driven organization. From the

theoretically expected factor pull production three items were dropped during the

reliability analysis, this results in a slight shift of contents. An additional factor

continuous flow production was extracted, based on items that were formerly

included in the practice layout optimization.


Eigen value

Cronbach's alpha

JIT1 .757 4.009 .762

JIT3 .540

JIT5 .643

JIT10 .555

JIT17 .590

JIT23 .763

JIT2 .488 3.825 .783

JIT6 .503

JIT8 .533

JIT19 .591

JIT25 .652

JIT26 .649

JIT27 .857

JIT15 .712 3.628 .813

JIT16 .732

JIT18 .675

JIT24 .714

JIT29 .581

JIT7 .721 3.155 .720

JIT13 .496

JIT14 .748

JIT28 .562

JIT30 .611

JIT20 .427 2.330 .758

JIT21 .610

JIT22 .861

Optimized production

planning and controll

Optimization of set-up times

and layout

Process driven organisation

Pull production

Continuous flow

production


4.2.3.4 Effective management system (EMS)


effective management system (EMS). The four practices are: direction setting,

management commitment and company culture, employee involvement and

continuous improvement, and functional integration and qualification. The practices

and the items theoretically related to each of the practices are shown in Table 15.

Table 15: Effective management system – Initial items


category effective management system. As mentioned in chapter 4.2.1 the items

EMS12 and EMS16 were excluded from further analysis. CITC scores were above

the 0.30 cut-off except for three items. The CITC score for EMS10 was 0.259, for

EMS13 0.263, and for EMS1411 0.229. As the score for Cronbach’s alpha if item

deleted did not indicate the removal of EMS13, only EMS10 and EMS14 were

dropped. After a second reliability analysis also EMS13 and EMS9 were dropped.

A third reliability analysis was conducted and each of the remaining items reached a

CITC score of above 0.30.


EMS1

EMS2

EMS3

EMS4

EMS5

EMS6

EMS7

EMS8

EMS9

EMS10

EMS11

EMS12

EMS13

EMS14

EMS15

EMS16

EMS17

EMS18

EMS19

EMS20

EMS21

EMS22

EMS23

EMS24

EMS25

EMS26

EMS27

EMS28

EMS29

EMS30

Supervisors include their employees in solving problems.

Our plant forms cross-functional project teams to solve problems.

The company takes care of the employees.

Functional integration

and qualification

Each of our employees within our work teams is cross-trained so that they can fill-in for others when necessary.

At our plant we have implemented a formal program to increase the flexibility of our production workers. Employees rotate to maintain their qualification.

In our company there are monthly open feedback meetings.

The information of these official feedback meetings is used systematically in further training.

We continuously invest in training and qualification of our workers. We have a dedicated development and qualification program for our production workers.

Our employees continuously strive to reduce any kind of waste in every process (e.g. waste of time, waste of production space etc.).

Command and control is seen as the most effective leadership style rather than open culture.

Employee involvement

and continuous

improvement

We have implemented tools and methods to deploy a continuous improvement process.

Our employees are involved in writing policies and procedures (concerning Site Vision down to Standard Operating Procedures)

Shop-floor employees actively drive suggestion programs.

Our work teams cannot take significant actions without supervisors or middle managers approval.

Our employees have the authority to correct problems when they occur.

Occurring problems should be solved by supervisors.

Management commitment

and company culture

Plant management empowers employees to continuously improve the processes and to reduce failure and scrap rates.

Plant management is personally involved in improvement projects.

There is too much competition and too little cooperation between the departments.

The communication is made via official channels.

The company has an open communication culture. There is a good flow of information between the departments and the different management levels.

About innovations we are informed early enough.

Problems (e.g. reclamations etc.) are always traced back to their origin to identify root causes and to prevent doing the same mistakes twice.

The achievement of high quality standards is primarily the task of our QA/ QC departments.

Direction setting

Our production site has an exposed site vision and strategy that is closely related to our corporate mission statement.

Our vision, mission and strategy is broadly communicated and lived by our employees.

Goals and objectives of the manufacturing unit are closely linked and consistent with corporate objectives. The production site has a clear focus.

The overall objectives of the production site are closely linked to the team or personal objectives of our shop-floor teams and employees.

Our manufacturing managers (Head of manufacturing, Site-leader etc.) have a good understanding of how the corporate/ divisional strategy is formed.

Our manufacturing managers know exactly what the most important criteria for manufacturing jobs are (i.e. low costs, delivery, quality etc.).


Table 16: Effective management system – Scale reliability scores


are displayed Table 17. Five factors were extracted on the category level. They

account for 63.1% of the variance explained. Two items had loadings lower than




deleted

Initial Cronbach's

alpha (scale)

Final CITC

(2nd

loop)

EMS1 .565 .901 .906 .562

EMS2 .703 .898 .690

EMS3 .670 .900 .670

EMS4 .655 .899 .654

EMS5 .506 .903 .499

EMS6 .549 .902 .548

EMS7 .568 .902 .539

EMS8 .645 .901 .634

EMS9 .309 .906 Item dropped


EMS11 .685 .900 .656



EMS15 .634 .900 .908

EMS17 .487 .903 .911

EMS18 .406 .904 .912

EMS19 .423 .904 .912

EMS20 .373 .905 .914

EMS21 .573 .901 .909

EMS22 .533 .902 .911

EMS23 .492 .903 .911

EMS24 .568 .902 .910

EMS25 .524 .903 .911

EMS26 .430 .904 .912

EMS27 .355 .905 .914

EMS28 .529 .902 .911

EMS29 .574 .901 .909

EMS30 .474 .903 .911


Table 17: Effective management system – EFA category level

The five factors extracted are not corresponding to the theoretically expected four



Shared vision and culture is not only focussed on direction setting as derived from

theory but also includes cultural aspects. As the cultural aspect is assigned to the

first factor, the second factor is now named management commitment. The practice

functional integration and qualification could be confirmed and was enlarged by

one item. Also the practice employee involvement and continuous improvement was

confirmed, but in addition a fifth practice named employee empowerment could be

separated.

4.2.4 Summary factor analysis

The factor analysis identified 17 lean practices assigned to four categories that are

actually used in the pharmaceutical industry. These practices are for TPM:



alphaEMS1 .847 4.222 .866

EMS2 .842

EMS3 .715

EMS5 .618

EMS11 .540

EMS25 .513

EMS4 .534 3.267 .818

EMS6 .719

EMS7 .551

EMS8 .597

EMS23 .721

EMS18 .438 3.011 .789

EMS26 .654

EMS27 .778

EMS28 .643

EMS29 .664

EMS30 .567

EMS15 .491 2.407 .731

EMS17 .512

EMS19 .719

EMS24 .607

EMS20 .834 2.230 .762

EMS21 .720

EMS22 .668

Shared vision and culture


Functional integration and qualification


and continuous improvement

Employee empowerment


preventive maintenance, technology assessment and usage, and housekeeping; for

TQM: cross-functional process development and process control, process

measurement and reliability, supplier management and development, and customer

focus and satisfaction; for JIT: optimization of set-up times and layout, optimized

production planning and control, process driven organization, pull production, and

continuous flow production; and for EMS: shared vision and culture, management

commitment, functional integration and qualification, employee involvement and

continuous improvement, and employee empowerment. They are again displayed in

Figure 8.

Figure 8: Lean practices identified in pharmaceutical manufacturing

The following table shows the level of implementation of the single lean practices

in pharmaceutical manufacturing. It also includes the standard error of the average

value and a ranking of the practices, the lower the number the higher the

implementation. This table gives a first overview of the importance of the single

lean practices based on their level of implementation.

TPM TQM

JIT EMS

Cross-functional process development and process control


Supplier management and development

Customer focus and satisfaction

Optimization of set-up times and layout


Housekeeping

Technology assessment and usage

Optimized production planning and control


Pull production

Continuous flow production




Employee involvement and continuous improvement



Table 18: Implementation of lean practices

After identifying the lean practices used in pharmaceutical manufacturing the map

developed in chapter 3 has to be adapted.

Adaption of the map of relations 4.3

The map developed in chapter 3 is based on lean adoption in general and not only

for pharmaceutical production sites, therefore some adaptations need to be done

before using it for further analysis.

As an outcome of the factor analysis in chapter 4.2.4 17 lean practices could be

identified for pharmaceutical manufacturing that need to be matched with the

practices in the CLD. This leads on the one hand to the combination of some

practices and on the other hand to the deletion of practices that do not seem to be

applicable to pharmaceutical manufacturing according to the survey data. Table 19

shows the name of the single lean practices in the original mapping and the name of

the corresponding lean practices in pharmaceutical manufacturing.

Practice Mean SEPreventive maintenance 3.59 0.05 7Technology assessment and usage 2.91 0.06 14Housekeeping 3.98 0.06 3Cross-functional process development and process control 2.94 0.06 13Process measurement and reliability 3.69 0.05 5Supplier management and development 3.68 0.05 6Customer focus and satisfaction 3.76 0.06 4Optimization of set-up times and layout 2.71 0.05 16Optimized production planning and control 3.11 0.05 12Process driven organisation 3.40 0.06 9Pull production 3.13 0.05 11Continuous flow production 2.48 0.12 17Shared vision and culture 3.99 0.05 2Management commitment 4.13 0.04 1Functional integration and qualification 3.29 0.06 10Employee involvement and continuous improvement 3.50 0.06 8Employee empowerment 2.86 0.09 15

Rank


Table 19: Assignement of the lean practices

Supplier quality management and JIT delivery by suppliers were derived from

literature as two different lean practices, apparently this is not the case for

pharmaceutical production sites, and therefore the two are merged into supplier

management and development. The same holds true for SPC and process

management and variance reduction which are therefore also merged. The practice

management support and commitment derived from literature includes aspects of

both shared vision and culture and management commitment resulting from the

factor analysis, therefore these two practices are combined into one practice for

further analyses. The practice self-directed working teams is a combination of

employee empowerment and employee involvement and continuous improvement.

The factor analysis revealed that both practices can be regarded separately in the

pharmaceutical context. Based on literature, the practice self-directed working

teams was merged from different concepts, these are now split up again and the

influences in the map are adapted according to literature (dotted lines). Apparently

cycle time reduction and lot size reduction cannot be seen as independent lean

practices in pharmaceutical manufacturing; they are eliminated and the influences

are adapted (dotted lines).

The map including all changes in wording and influences is displayed in Figure 9. It

will be used in the following to display the correlations between lean practices in

strength and in direction.

Practices derived from literature Practices derived from EFA

preventive maintenance preventive maintenance

technology assessment and usage technology assessment and usage

housekeeping housekeeping

cross-functional product design cross-functional process development and process control

process management and variance reduction / SPC process measurement and reliability

supplier quality management / JIT delivery by suppliers supplier management and development

customer involvement customer focus and satisfaction

setup time reduction optimization of set-up times and layout

planning adherence optimized production planning and control

equipment layout optimization process driven organisation

pull system pull production

continuous product flow continuous flow production

management support and commitment shared vision and culture / management commitment

flexible, cross-functional workforce functional integration and qualification

employee involvement and continuous improvement

employee empowerment self-directed working teams


Figure 9: Adapted map of relations between lean practices

Also the measurement of the goals of the technically oriented lean bundles needs to

be defined for pharmaceutical manufacturing. The following table shows the key

performance indicators that will be used; a definition can be found in Appendix B.

Table 20: Measures for the goals of lean bundles

The lean practices used in pharmaceutical manufacturing and their level of

implementation were identified in this chapter. Further, their interconnections in

general were shown based on the adapted map from literature. As the aim is to

analyse the implementation and interconnection of lean practices in relation to

different competitive priorities, strategic groups need to be formed in a next step.

pull production

continuous flowproduction

optimization of set-uptimes and layout

quality improvementand stable processes

elimination ofexcess inventories

processmmeasurement and

reliability

employeeempowerment

supplier managementand development

preventivemaintenance

++

process drivenorganization

++

+

functional integrationand qualification

+

+

customer focus andsatisfaction

management commitment& shared vision and culture +

+

+

++

technologyassesment and usage

++

housekeeping

improved equipmentperformance+

+

+

++

++ +

+

++

+

+

optimized productionplanning and control

cross-functionalprocess

developmentand process

control +

+

+

++ +

+

employee involvement andcontinuous improvement

++

+

++

+

TPM:improved equipment performance

TQM:quality improvement and stable processes

JIT:elimination of excess inventories

OEE availability Complaint rate supplier Inventory days on handLoading Rejected batches Service level - deliveryMaintenance cost Complaint rate customer Production schedule accuracyUnplanned maintenance Cost of quality Production flexibility upsideAverage TPM level Average TQM level Set-up times

Material turnsAverage JIT level


Cluster analysis: Development of strategic groups 4.4

To develop strategic groups for pharmaceutical production sites cluster analysis was

used. It is a statistical method that categorizes objects or variables into homogenous

groups. The aim is to maximise the homogeneity of objects in the same cluster

while simultaneously maximizing the heterogeneity between the single clusters

(Hair et al., 2006). Two decisions are vital for the result of a cluster analysis: 1) the

decision which clustering algorithm to use and 2) the definition of the appropriate

number of clusters. The definition of the appropriate number of clusters does not

follow a clear process but is depending on the choice of the researcher (Janssen &

Laatz, 2010).

The two groups of clustering algorithms most frequently used are hierarchical and

non-hierarchical methods. Hierarchical methods have in common that they form

clusters by making a single pass through the data (Shah, 2002); objects that were

clustered once will not be rearranged later on (Backhaus, 2008). In contrast, non-

hierarchical methods or K-means methods allow a re-grouping of objects until

optima is reached, but they demand an initial partition and a fixed number of

clusters. A combination of both types of methods is recommended. A hierarchical

method is used to define the number of clusters and their centroids, and based on

this input a non-hierarchical method is run to refine the clusters (Ketchen & Shook,

1996; Shah, 2002).

Figure 10: Procedure used for cluster analysis

Identification of clustering variables

Outlier analysis

Hierarchical clustering: Ward’s method

Non-hierarchical clustering: K-means

Validation


Figure 10 shows the procedure used for the cluster analysis in this research. Before

starting with clustering, the underlying variables have to be identified and outliers

have to be excluded. The exclusion of outliers ensures that there is no distortion of

results. The next two steps include the actual clustering. First, the appropriate

number of clusters is defined using Ward’s method and second, the results are used

to define the final clusters. Finally, the results are validated. The following sections

describe each of the steps in greater detail.

4.4.1 Identification of clustering variables

A set of competitive priorities should be the basis for clustering of sites into

strategic groups. In the OPEX questionnaire it was asked for competitive priorities

the respondents planned to pursue (see Appendix B). The competitive priorities

used and the single variables, assigned to them in the OPEX questionnaire, are

shown in Table 21. Their content has been discussed in chapter 2.2.

Table 21: Competitive priorities used

Highly correlated variables may lead to a distortion of the results if they are not

weighted differently. As it is not possible to determine in advance how the weights

are distributed, a factor analysis is a good solution to create independent factors

(Backhaus, 2008). The 13 variables from Table 21 were tested to see if they are

determined by underlying dimensions. The KMO and Bartlett’s test with a MSA

value of 0.813 indicated that a factor analysis can be performed. A principal

component analysis with VARIMAX rotation was used analogue chapter 4.2 and

resulted in four factors (Table 22).

Competitive priority

Item name Item label

CP1

CP2

CP3

CP4

CP5

CP6

CP7

CP8

CP9

CP10

CP11

CP12

CP13 Increase capital investment productivity

Costs

Reduction of lead time

Quality

Service level

Reduction of cycle time

Reduction of set-up and cleaning time

Increase of flexibility of machines and labour

Acceleration of new product introductions

Increase of supplier quality performance

Reduction of process variance through statistical process control

Reduction of scrap rate

Flexibility

Increase on time delivery rate

Reduction of stock

Increase asset utilisation

Increase employee productivity


Table 22: Competitive priorities – EFA

The four factors extracted did not completely correspond to the theoretically

expected four competitive priorities but the changes were logical from a content

perspective. For quality all items could be assigned as expected, also for costs all

items except one could be confirmed. The content of the expected factor service

level was broadened by including three items; therefore the factor was renamed

delivery. Flexibility now only consists of two items. The scores for each factor were

calculated by adding up the individual scores for each of the corresponding

variables and dividing the result by the number of variables (Shah, 2002). The

variables are then standardised because some measures of similarity are influenced

by differences in variance. As suggested by Field (2000) the conversion to Z-scores

is used.

4.4.2 Outlier analysis

A dendrogram from a hierarchical cluster analysis based on the method single

linkage was used to identify outliers (Backhaus, 2008). The elimination of outliers

leads to better results of the following analysis. Five cases were excluded from

further analysis, resulting in a final sample of 200 cases (see Appendix C).

4.4.3 Hierarchical clustering

A two-stage approach was used to classify pharmaceutical production sites based on

their strategic orientation. In a first step Ward’s method with the squared Euclidean

distance measure was applied to determine the appropriate number of clusters. It

was chosen as it is able to create groups with low within cluster differences and

high between cluster differences (Backhaus et al., 2008). As suggested by Ketchen

& Shook (1996), the number of clusters was selected using multiple techniques.


Item nameFactor loading


alphaCP1 .742 2.726 .819

CP2 .716

CP8 .727

CP9 .596

CP10 .635

CP11 .843 2.008 .790

CP12 .662

CP13 .728

CP3 .809 1.587 .790

CP4 .857

CP5 .860 1.490 .783

CP6 .589

CP7 .431

Quality

Delivery

Costs

Flexibility


Based on Lehmann (1979) the number of clusters should be limited to between n/30

and n/60, where n is the sample size. With 200 cases the final number of clusters

should be between three and seven.

Visual inspection of the dendrogram indicated a four cluster solution (see Appendix

C).

The agglomeration coefficient was used to create an elbow-diagram. An “elbow” in

the graph suggests that dissimilar clusters were combined and therewith indicates

the appropriate number of clusters. The interpretation of the graph may be difficult

as there can be no real elbow or several elbows (Ketchen & Shook, 1996). The data

slightly pointed to a four cluster solution (see Appendix C).

Finally, the incremental and percentage change in the agglomeration coefficient

were calculated. A large increase indicates the appropriate number of clusters as it

implies that dissimilar clusters were merged. It might be the case that no jumps or

several jumps can be observed. Table 23 shows the results for a seven to a one

cluster solution.

Table 23: Analysis of agglomeration coefficient - Ward's method

The largest increase in incremental change was from a two to a one cluster solution,

the second largest increase from a three to a two cluster solution and in percentage

change from a four to a three cluster solution. As a two cluster solution is not

corresponding to Lehmann’s (1979) guideline either a three or a four cluster

solution should be considered. Taking all techniques into account a four cluster

solution is chosen.

Cluster membership from Ward’s method was saved and cluster means were

computed.

Number of clusters

Agglomeration coefficient

Incremental change in coefficient

Percentage change in coefficient

7 263.64 19.29 7.3%6 282.92 21.84 7.7%5 304.76 33.14 10.9%4 337.90 83.83 24.8%3 421.73 85.03 20.2%2 506.76 205.77 40.6%1 712.53 - -


4.4.4 Non-hierarchical clustering

To adjust and optimize the results from Ward’s method K-means clustering

algorithm was used. The cluster centres obtained during hierarchical clustering were

used as initials for non-hierarchical clustering (Jensen, 2008 (in Herman); Shah,

2002). The final results are displayed in Table 24 showing the number of sites per

group and the cluster means of the clustering variables for each of the groups. A

positive value indicates a higher emphasis on the clustering variable than the

average; a negative value indicates a lower emphasis on the respective clustering

variable.

Table 24: Final cluster results - K-means method

200 sites were classified into four groups of varying size. Forty-nine sites formed

group one, seventy sites group two, forty-eight sites group three, and thirty-three

sites were part of group four. Group one had high positive loadings on all four

clustering variables whereas group three had (high) negative loadings on them. The

other two groups showed loadings in between.

The interpretation of the groups is based on the most important competitive priority

within a group and significant differences in emphasis among groups.

4.4.5 Validation of the groups

Often, the significance of differences of the cluster centres of variables across

groups is examined by conducting a univariate analysis of variance (ANOVA). It is

a parametric test which requires at least interval-scaled data and a normal curve of

distribution (Janssen & Laatz, 2010). Although the assumptions are met, non-

parametric tests will be used in this case as they are always an alternative (Brosius,

2011). Non-parametric tests do not use the values of the variables but e.g. their

frequency which leads to a lower discriminant power of the tests. Nevertheless, the

results are clear enough to allow a validation of the cluster analysis’ results.

The four groups which were identified in the previous chapter are independent

samples. To analyse if they show a different tendency in distribution the Kruskal-

1 49.000

1 2 3 4 2 70.000

Delivery .92048 .22568 -.80923 -.29485 3 48.000

Flexibility .79559 .32749 -.12338 -1.59837 4 33.000

Costs 1.01978 .00914 -.89295 .08479 200.000

Quality 1.00309 -.02818 -.99145 .24227 .000Missing

Final Cluster Centers

Cluster

Number of Cases in each Cluster

Cluster

Valid


Wallis H-test is used. It is a non-parametric version of a univariate ANOVA and

tests the hypothesis “there is no difference between the groups concerning the

evaluation of the factors”. Results show that the hypothesis can be rejected for all

four factors at a significance level of α=0.000. This means the groups differ

concerning the evaluation of the factors. Details are displayed in Table 25.

The Kruskal-Wallis H-test only shows if the groups differ, therefore a second test is

performed. The post-hoc Mann-Whitney U-test analyses the difference of each

factor of one group to another by testing the hypothesis “variables do not differ in

the two samples of strategic groups”. For this reason, it is an alternative to a

parametric t-test. Results show that the hypothesis has to be accepted for two cases

(see Table 25). The variables costs and quality do not differ in the two samples of

strategic groups, namely group 2 (flexible deliverers) and group 4 (efficient

conformers).

Table 25: Competitive priorities emphasised by strategic groups

The results from cluster analysis, Kruskal-Wallis H-test, and post-hoc Mann-

Whitney U-test are summarised in Table 25. Also the names of the groups, derived

later in this research, are included. For better interpretability the average values per

group are calculated instead of the standardised values. The highest possible value

1 2 3 4

Do All Flexible deliverers Flexible starters Efficient conformers F = Chi square

n = 49 n = 70 n = 48 n = 33 p = Symp. sig.

DeliveryCluster mean 4.69 (2, 3, 4) 4.19 (1, 3, 4) 3.46 (1, 2, 4) 3.82 (1, 2, 3) F = 103.047SE 0.05 0.06 0.07 0.10 p = 0.000Rank 2 2 3 3

FlexibilityCluster mean 4.65 (2, 3, 4) 4.21 (1, 3, 4) 3.79 (1, 2, 4) 2.41 (1, 2, 3) F = 109.093SE 0.06 0.06 0.10 0.07 p = 0.000Rank 3 1 1 4

CostsCluster mean 4.74 (2, 3, 4) 4.09 (1, 3) 3.51 (1, 2, 4) 4.14 (1, 3) F = 99.789SE 0.05 0.06 0.07 0.08 p = 0.000Rank 1 3 2 1

QualityCluster mean 4.43 (2, 3, 4) 3.71 (1, 3) 3.03 (1, 2, 4) 3.90 (1, 3) F = 105.972SE 0.06 0.05 0.07 0.08 p = 0.000Rank 4 4 4 2



would be 5.0, the lowest 1.0. Further, the standard error of the average value per

group and the rank are included. The rank shows the relevance of a factor in a

group. The numbers in parentheses mark those factors which differ between groups

at a significance level of α=0.05.

Building on these results the single groups can be described and named.

4.4.5.1 Cluster 1: Do all

The first group of 49 production sites has the highest emphasis on all competitive

priorities compared to the other three groups. The group’s emphasis on all four

priorities is simultaneously high and exceeds 4.4, which leads to the name do all

analogue to Kathuria (2000). Further, the emphasis on all competitive priorities is

significantly different from that in the other three groups and therewith separates

this group from the rest. This group represents about 25% of all cases in the four

clusters.

4.4.5.2 Cluster 2: Flexible deliverers

The second group, being with 70 production sites the largest, has the highest

emphasis on both delivery and flexibility. Compared to the other groups it has the

second highest emphasis and values are significantly different. This group is similar

to Deflorin’s (2007) logisticians that have a focus on fast and punctual delivery as

well as high volume flexibility combined with a broad range of products. Some

similarities also exist with Christiansen et al.’s speedy deliverers and Kathuria’s

speedy conformers. This group, reflecting 35% of all cases in the four clusters, is

therefore named flexible deliverers. Concerning costs and quality there is no

significant difference in emphasis compared to group four.

4.4.5.3 Cluster 3: Flexible starters

The third group of 48 production sites has a significantly lower emphasis on

delivery, costs and quality compared to the other three groups. Only flexibility is

ranked significantly higher than by group four members, nevertheless is it still

below the average emphasis as can be seen on the standardised values in Table 24.

The group is similar to Miller and Roth’s (1994) caretakers or Kathuria’s (2000)

starters, which also have a low emphasis on the development of competitive

priorities. The emphasis is below 3.80 on all four priorities, with the highest (3.79)

on flexibility. The sites in this group appear to be emphasising flexibility in order to


be able to introduce new products quickly and to make fast changes in

manufacturing. This is in contrast to Miller and Roth’s or Kathuria’s groups which

have a focus on price or quality. The group is therefore named flexible starters. It

accounts for 24% of all cases.

4.4.5.4 Cluster 4: Efficient conformers

The final group of 33 production sites has a high emphasis on costs and also on

quality. It is the only group for which quality does not have the lowest emphasis.

Nevertheless, the values are not significantly different from the second group. It

resembles almost completely the efficient conformers from Kathuria (2000) and is

therefore equally named. It consists of 16% of all cases.

Pharmaceutical production sites can be divided into the four strategic groups do all,

flexible deliverers, flexible starters, and efficient conformers, which are focusing on

different sets of competitive priorities. The strategic groups could be verified by

existing literature; this strengthens their validity.

In the next chapter the relation between the strategic groups and the implementation

of the 17 lean practices identified in chapter 4.2.4 and structured in chapter 4.3 are

investigated. This should show if different strategic groups demand a different

implementation of lean practices.

Comparison of strategic groups 4.5

The four strategic groups identified and described in chapter 4.4 are examined

concerning their implementation of the 17 lean practices from chapter 4.2.4. This

examination is considering the general level of implementation by displaying means

and ranks as well as differences in implementation among groups by using analysis

of variance. Further, the map from chapter 4.3 is filled with data and the relations

between lean practices are tested for the different strategic groups using

correlations.

4.5.1 Analysis of variance and multiple comparisons

Analysis of variance (ANOVA) is a statistical method to compare the means of

more than two groups based on the F-value. To ensure validity of the ANOVA’s

results the following assumptions have to be met (Hair et al., 2006; Brosius, 2011;

Janssen & Laatz, 2010):


• The dependant variable is measured at least at interval level.

• The population of the dependant variable is normally distributed. This can be

tested using Q-Q plots, histograms, and tests of normality like Kolmogorov-

Smirnov (n≥50) or Shapiro-Wilk (n<50).

• Variances in the different groups are equal for the variables examined. To

test for homogeneity of variances the Levene test is used.

• Cases are selected on random.

• In case of a one-way ANOVA the samples have to be independent.

Independent means that the composition of one sample is not depending on

the composition of the other sample.

Nevertheless, according to Herrmann & Seilheimer (2000) the F-test is relatively

robust concerning the violation of the assumption of normal distribution and

homogeneity of variance. This is especially the case for large samples and groups of

equal size. If the assumptions for an ANOVA are not met, non-parametric tests can

be performed. As the assumptions of non-parametric test are weaker than those of

parametric test their results are consequently not as sharp and clear as those of

parametric tests. For more than two independent samples the Kruskal-Wallis test

(see 4.4.5) should be performed.

For the analysis in this chapter the independent variable is the strategic group, the

dependent variables are the single lean practices. Although multiple dependent

variables are considered, a univariate approach is chosen. Following Bortz &

Schuster (2010) a univariate approach should be used if the aim is to explore mutual

relationships of the dependent variables and their importance for group differences.

For testing hypotheses and identifying variables which contribute most to

differences in samples, a multivariate approach would be chosen.

First, a one-way ANOVA is used to test the null hypothesis that all groups are

random samples of the same population and therewith have equal means. If this null

hypothesis can be rejected multiple comparisons are conducted as a second step to

see in detail which groups differ in their means. There are various possibilities of

tests to conduct multiple comparisons, mainly distinguishing between equal

variance assumed and equal variance not assumed. They will be explained in detail

later on.

Before the ANOVAs can be calculated the assumptions have to be checked:

• The dependant variable for each ANOVA is one of the lean practices. They


are measured on a five-point Likert scale which can be regarded as interval

level.

• The 17 dependant variables are tested for normal distribution using Q-Q

plots, histograms, and Kolmogorov-Smirnov and Shapiro-Wilk tests. For 14

lean practices a normal distribution could be confirmed. The practices

housekeeping, continuous flow production, and management commitment are

not normally distributed. For these three practices a non-parametric Kruskal-

Wallis test is performed in addition to the parametric tests. As the number of

observations is higher than 30 also the central limit theorem applies (Bortz &

Schuster, 2010), allowing the use of parametric tests.

• The Levene test for homogeneity of variance reveals that only cross-

functional process development and process control does not have equal

variances in the different groups. In addition to the tests for equal variance

assumed also tests for equal variance not assumed will be conducted.

• The selection of cases on random is given and the samples are independent.

As the assumptions are met the ANOVAs are calculated to test the hypothesis

“there is no difference between the groups concerning the implementation of the

lean practices”. Results show that the hypothesis can be rejected for ten of the 17

lean practices at a significance level of α=0.05. The non-parametric Kruskal-Wallis

test produces the same results. One or more groups are different from each other in

ten lean practices from the categories TQM, JIT, and EMS: cross-functional process

development and process control, process measurement and reliability, supplier

management and development, customer focus and satisfaction, optimization of set-

up times and layout, optimized production planning and control, continuous flow

production, shared vision and culture, management commitment, and employee

involvement and continuous improvement. Lean practices from the category TPM as

well as four other practices do not seem to differ significantly in implementation

between groups. Details are displayed in Table 26.

The ten factors, the groups show significant differences for, are investigated in

greater detail by calculating multiple comparisons as post-hoc tests. As two of the

ten factors are not normally distributed, parametric as well as non-parametric tests

are applied. Further, one of ten factors is not homogeneous in variance which also

requires a different test. All tests are conducted with a significance level of α=0.05.

For homogeneous variances the tests Bonferroni, Scheffé, Tukey, and Waller-

Duncan are applied. For the one factor that is not homogeneous in variance the tests


Tamhane-T2 and Games-Howell are chosen. The single tests are shortly explained

in the next paragraph. In addition, the factors with non-normal distribution are

analysed by applying the Mann-Whitney U-test (see 4.4.5).

Bonferroni is based on the single t-test between group means but it corrects the

error that occurs from multiple testing. The results are also exact for peer groups of

different size. The Scheffé test is based on the F-distribution and is relatively

conservative as it only reports bigger differences in means as significant. The results

are also exact for peer groups of different size. Besides pairwise comparisons it also

offers homogeneous subsets. Turkey’s test is also conducting pairwise comparisons

based on the Student distribution. It is the most common and robust method as it is

not strongly influenced by violations of its assumptions.

The Waller-Duncan test is comparing means based on the t-statistic by using a

Bayesian approach; output is a set of homogeneous subgroups. The speciality of this

test is the possibility to control the Type II Error.

Tamhane-T2 test is based on the t-statistic and offers rather conservative estimates.

For equal variances the results are identical to those of Bonferroni. Games-Howell

is used for pairwise comparisons if variances are not equal. It can also be used in

case of non-normality of the variables.

The results from the one-way ANOVA, Kruskal-Wallis test, and the post-hoc tests

are summarised in Table 26. The average values of the implementation level of the

single lean practices per group are calculated. Further, the standard error of the

average value per group and the rank are included. The rank shows the relevance of

a lean practice in a group measured by its level of implementation. The numbers in

parentheses mark those factors which differ between groups at a significance level

of α=0.05.


Table 26: Implementation of lean practices by competitive priority clusters

2 3 4

Lean practice Flexible deliverers Flexible starters Efficient conformers F = valuen = 70 n = 48 n = 33 p = propability

Preventive maintenanceCluster mean 3.77 3.53 3.44 3.75 F = 2.629SE 0.08 0.09 0.11 0.11 p = 0.051Rank 7 6 6 4

Technology assessment and usageCluster mean 3.13 2.93 2.80 2.70 F = 1.968SE 0.12 0.10 0.11 0.16 p = 0.120Rank 14 13 12 14

HousekeepingCluster mean 4.18 3.95 3.85 3.95 F =1.316SE 0.12 0.10 0.12 0.16 p = 0.270Rank 3 2 2 3

Cross-functional process development and process controlCluster mean 3.25 (3, 4) 3.00 2.76 (1) 2.63 (1) F = 4.319SE 0.12 0.10 0.11 0.19 p = 0.006Rank 12 12 13 15

Process measurement and reliabilityCluster mean 4.05 (2, 3, 4) 3.67 (1) 3.54 (1) 3.39 (1) F = 6.436SE 0.11 0.09 0.10 0.14 p = 0.000Rank 4 5 4 7

Supplier management and developmentCluster mean 3.99 (3, 4) 3.69 3.45 (1) 3.55 (1) F = 5.433SE 0.09 0.08 0.11 0.13 p = 0.001Rank 5 4 5 5

Customer focus and satisfactionCluster mean 4.05 (3, 4) 3.81 3.54 (1) 3.53 (1) F = 4.133SE 0.11 0.11 0.12 0.11 p = 0.007Rank 4 3 4 6

Optimization of set-up times and layoutCluster mean 3.05 (3, 4) 2.71 2.57 (1) 2.48 (1) F = 5.636SE 0.09 0.09 0.09 0.15 p = 0.001Rank 15 16 15 16

Optimized production planning and controlCluster mean 3.34 (4) 3.08 3.00 2.98 (1) F = 3.027SE 0.09 0.08 0.08 0.13 p = 0.031Rank 10 11 11 12

Process driven organisationCluster mean 3.57 3.43 3.42 3.11 F = 2.201SE 0.12 0.09 0.10 0.16 p = 0.089Rank 8 8 7 10

Pull productionCluster mean 3.30 3.12 3.08 2.99 F = 1.298SE 0.11 0.09 0.10 0.13 p = 0.277Rank 11 10 10 11

Continuous flow productionCluster mean 2.81 2.85 (3, 4) 2.12 (2) 2.23 (2) F = 2.963SE 0.30 0.21 0.21 0.20 p = 0.037Rank 16 15 16 17

Shared vision and cultureCluster mean 4.29 (2, 3) 3.95 (1) 3.78 (1) 3.99 F = 5.520SE 0.08 0.07 0.10 0.12 p = 0.001Rank 2 2 3 2

Management commitmentCluster mean 4.52 (2, 3, 4) 4.08 (1) 3.93 (1) 4.01 (1) F = 10.870SE 0.07 0.07 0.08 0.11 p = 0.000Rank 1 1 1 1

Functional integration and qualificationCluster mean 3.54 3.19 3.20 3.20 F = 2.153SE 0.12 0.09 0.13 0.15 p = 0.095Rank 9 9 9 9

Employee involvement and continuous improvementCluster mean 3.87 (3, 4) 3.52 3.29 (1) 3.33 (1) F = 5.594SE 0.11 0.09 0.11 0.11 p = 0.001Rank 6 7 8 8

Employee empowermentCluster mean 3.19 2.91 2.65 2.86 F = 1.129SE 0.24 0.18 0.16 0.19 p = 0.343Rank 13 14 14 13

1

Do Alln = 49


The plants belonging to the do all group have for all except one lean practice the

highest level of implementation. Only the flexible deliverers put higher emphasize

on continuous flow production. In nine of ten cases where lean practices are

significantly different from each other in one or more groups, the do all group is

involved.

4.5.1.1 Do all

Implementation levels for the do all group range on a scale of one to five between

4.52 for management commitment and 2.81 for continuous flow production. For the

do all group the five (due to equal values six) highest implemented lean practices

are: management commitment (4.52), shared vision and culture (4.29),

housekeeping (4.18), process measurement and reliability and customer focus and

satisfaction (both 4.05), and supplier management and development (3.99).

Figure 11: Implementation levels for do all-cluster

4.5.1.2 Flexible deliverers

The group flexible deliverers puts its focus on the implementation of the following

lean practices: management commitment (4.08), housekeeping and shared vision

and culture (both 3.95), customer focus and satisfaction (3.81), supplier

management and development (3.69), and process measurement and reliability

(3.67).

3,19


3,87


3,54

Continuous flow production 2,81


3,30


3,57

Pull production

3,34

Optimization of set-up times and layout 3,05


3,99

3,25


4,18

3,13



3,77

Housekeeping

Customer focus and satisfaction 4,05

Process measurement and reliability 4,05

Shared vision and culture 4,29

Management commitment 4,52



Figure 12: Implementation levels for flexible deliverers-cluster

4.5.1.3 Flexible starters

The flexible starters have the highest implementation levels for: management

commitment (3.93), housekeeping (3.85), shared vision and culture (3.78), process

measurement and reliability and customer focus and satisfaction (both 3.54), and

supplier management and development (3.45).

Figure 13: Implementation levels for flexible starters-cluster

2,93


3,53

Pull production

3,19



3,08


3,43


3,12



3,67

3,00


3,95

2,91



3,52


Supplier management and development 3,69


Housekeeping 3,95



2,76


3,44

Pull production

3,20



3,00


3,29


3,08



3,45

2,80


3,78

2,65



3,42



Process measurement and reliability 3,54

Housekeeping 3,85




4.5.1.4 Efficient conformers

For the group efficient conformers the implementation of the following five lean

practices is rated highest: management commitment (4.01), shared vision and

culture (3.99), housekeeping (3.95), preventive maintenance (3.75), and supplier

management and development (3.55).

Figure 14: Implementation levels for efficient conformers-cluster

4.5.1.5 Summary

For all four groups the implementation of the lean practice management

commitment is the highest. The do all group significantly differs in the

implementation from the other three groups, which have similar implementation

levels. Except for the flexible starters all groups have the second highest

implementation level for shared vision and culture another lean practice coming

from the category EMS. The group do all also significantly differs from the flexible

deliverers and the flexible starters in the implementation of this lean practice.

Housekeeping is implemented third respectively second highest with no significant

difference between groups. Supplier management and development is also among

the highest implemented practices for all groups with significant differences

between the groups do all, flexible starters, and efficient conformers. All groups

except the efficient conformers have high implementation levels for the practices

process measurement and reliability and customer focus and satisfaction. For the

first practice the do all group significantly differs from all other groups, for the

2,70


3,39

Pull production

3,11



2,98


3,20


2,99



3,53

2,86


3,95

2,63



3,33

Housekeeping

Supplier management and development 3,55

Preventive maintenance 3,75

Shared vision and culture 3,99




second practice only from the flexible starters and the efficient conformers. The

efficient conformers are the only group that has preventive maintenance among the

five highest implemented lean practices.

A similar picture can be seen for the five lean practices that are implemented lowest

in the single groups. The lean practice continuous flow production has the lowest

implementation except for the flexible deliverers which significantly differ from the

flexible starters and the efficient conformers. It is the only lean practice where the

implementation level of the do all group is not higher than that of the other three

groups. The second lowest implementation level, respectively the lowest for flexible

deliverers, can be found with the lean practice optimization of set-up times and

layout. The do all group significantly differs from the flexible starters and the

efficient conformers. There are also low implementation levels for the lean practices

employee empowerment, technology assessment and usage, as well as cross-

functional process development and process control. For the last practice the values

from the groups do all, flexible starters, and efficient conformers significantly

differ.

The remaining lean practices show significant differences between groups in two

cases. For optimized production planning and control the do all and the efficient

conformers groups differ; for employee involvement and continuous improvement

the groups do all, flexible starters, and efficient conformers show significant

differences.

This shows that independent from the strategic group the same lean practices are

regarded as important and are therefore implemented but partly with varying levels

between groups. The same holds true for those lean practices regarded as least

important and therewith lowest implemented.

To better understand the importance of single lean practices for the competitive

priority groups, their implementation levels in a specific group are compared in

detail. For this comparison within-cluster paired-sample t-tests are conducted.

4.5.2 Within-cluster paired-sample t-tests

Within-cluster paired-sample t-tests are a good possibility to enlighten the

relationship between the lean practices implemented in the strategic groups, as it

tests if there is a difference in the implementation level. A paired-sample t-test is

used as the variables are not independent. All values are filled in by the same plants


and it is assumed that the implementation of the single lean practices is connected

(Brosius, 2011). An assumption for the use of a paired-sample t-test is the normal

distribution of the difference values. For samples larger than 30 pairs of

observations the central limit theorem is applicable and it can be assumed that they

are normally distributed (Bortz & Schuster, 2010). Again, it is tested whether there

is a difference between the means of single lean practices. In line with the results

for the paired-sample tests also paired sample correlations are provided. If the

correlation of the list of measurements is not positive, but instead negative, the

paired-sample t-test loses power. This means that the probability that existing

differences are identified as significant is declining. In this case the non-parametric

Wilcoxon test can be used. Only if the difference value is very high the reduced test

power has no influence.

For the paired samples examined 29 out of 544 have a negative correlation from

which two are significant at the 0.05 level or less and two on the 0.10 level or less.

Those being significant on the 0.05 level or less show a difference value of 0.224

and 0.097and those at the 0.10 level or less of 0.881 and 0.667. The last two values

are high enough to assume that even with a lower power the results are right, the

first two values indicate, that a Wilcoxon test should be calculated. The Wilcoxon

test leads to the same results as the paired-sample t-test; therefor in the following

the results from the t-test will be reported. All combinations shown in Table 27 to

Table 30 are significantly different at the 0.05 level or less, except those marked "*"

which are significantly different at the 0.10 level or less. The results for the single

strategic groups are described in detail always considering the level of

implementation measured for each lean practice (see Table 26).

4.5.2.1 Do all

The do all group shows a significantly better implementation of management

commitment compared to all other lean practices. The same holds true for shared

vision and culture which is only implemented lower than management commitment

and equal to housekeeping. Housekeeping is implemented significantly higher than

twelve other lean practices. It shows a lower level of implementation than

management commitment and the same implementation level as shared vision and

culture, process measurement and reliability, and customer focus and satisfaction.

Process measurement and reliability as well as customer focus and satisfaction are

implemented significantly higher than ten other lean practices but they show lower


levels of implementation than management commitment and shared vision and

culture. Their implementation levels are equal to those of housekeeping, customer

focus and satisfaction (process measurement and reliability, respectively), supplier

management and development, and employee involvement and continuous

improvement. Supplier management and development as the last one of the highest

implemented lean practices has a significantly higher implementation level than ten

other practices as well but different to process measurement and reliability and

customer focus and satisfaction it has a significantly lower implementation level

than housekeeping. The analysis shows that also employee involvement and

continuous improvement should be considered as one of the top implemented

practices as it does not significantly differ from those ranked fourth and fifth. Also

preventive maintenance seems to be part of the higher implemented practices as it is

not significantly different from employee involvement and continuous improvement.

All practices implemented lower than preventive maintenance significantly differ

from the practices named so far.

Table 27: Pairwise t-test for do all-cluster

Concerning the rank of lean practices this shows that especially management

commitment is significantly outstanding for the do all group. The second highest

priority is already equal to the third highest which does not significantly differ from

Lean Practice

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 + - + - - - + + +* + + - - +*

2 - - - - -* - - - - -

3 + +* + + + + + - + + +

4 - - - - - - - -

5 + + + + + - - + +

6 + + + + + - - + +

7 + + + + + - - +

8 - - - - - - -

9 - - - - -

10 + - - -

11 + - - -* -

12 - - -* -

13 - + + +

14 + + +

15 -

16

17

1 Preventive maintenance, 2 Technology assessment and usage, 3 Housekeeping, 4 Cross-functional process development and process control,5 Process measurement and reliability, 6 Supplier management and development, 7 Customer focus and satisfaction, 8 Optimization of set-up times and layout, 9 Optimized production planning and control, 10 Process driven organisation, 11 Pull production, 12 Continuous flow production, 13 Shared vision and culture, 14 Management commitment, 15 Functional integration and qualification, 16 Employee involvement and continuous improvement,17 Employee empowerment

+ (-) practice on the right side is significantly higher (lower) implemented than practice on top * practices significantly differ at α = 0.10


the two practices coming after. This picture is continued until the lean practice of

preventive maintenance. It can be regarded as a splitting point for the most

important lean practices in the do all group. The do all group has the strongest focus

on management commitment from all groups. This strong focus may be necessary as

they concentrate on a lot of different competitive priorities and therefore need a

committed management even more than others.


The flexible deliverers group shows a significantly better implementation of

management commitment compared to all other lean practices except for

housekeeping and shared vision and culture. The same holds true for housekeeping

which is only implemented equal to management commitment, shared vision and

culture, and customer focus and satisfaction. Shared vision and culture is

implemented significantly higher than thirteen other lean practices. It shows the

same implementation level as housekeeping, management commitment, and

customer focus and satisfaction. Customer focus and satisfaction is implemented

significantly higher than eleven other lean practices but it shows a lower level of

implementation than management commitment. Its implementation level is equal to

those of housekeeping, shared vision and culture, process measurement and

reliability, and supplier management and development. Supplier management and

development has a significantly higher implementation level than ten other practices

but different to the practices mentioned before it is significantly lower implemented

than management commitment, shared vision and culture, and housekeeping. Its

implementation level is equal to customer focus and satisfaction, process

measurement and reliability, and preventive maintenance. Process measurement

and reliability as the last of the highest implemented lean practices has a

significantly higher implementation level than nine other practices. Equally to

supplier management and development it is implemented significantly lower than

three other lean practices but the implementation level is equal to customer focus

and satisfaction, supplier management and development, preventive maintenance,

and employee involvement and continuous improvement. The analysis shows that

also preventive maintenance should be considered as one of the top implemented

practices as it does not significantly differ from those ranked fourth and fifth. It is

also implemented equally to employee involvement and continuous improvement as

well as process driven organization. Also employee involvement and continuous

improvement seems to be part of the higher implemented practices as it is not


significantly different from process measurement and reliability, ranked fifth. Its

implementation is also equal to preventive maintenance and process driven

organization. All practices implemented lower than process driven organization

significantly differ from the practices named so far.

In contrast to the do all group the flexible deliverers do not have a significantly

higher implementation of one outstanding practice. Instead management

commitment, housekeeping, and shared vision and culture form a triad. Following

their strategic focus on fast and punctual delivery the flexible deliverers have

implemented customer focus and satisfaction on an equal level to housekeeping and

shared vision and culture. Like for the do all group there can be found a splitting

point for the most important lean practices. In this case process driven organization

is the last practice to be included, which corresponds to the strategic focus of the

group.

Table 28: Pairwise t-test for flexible deliverers-cluster


The flexible starters group shows a significantly better implementation of


housekeeping. The same holds true for housekeeping which is only implemented

Lean Practice

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 + - + - + + + + - - + +

2 - - - - + -* - - - - -

3 + + + + + + + + + + +

4 - - - + - + - - -* -

5 + + + + + - - + +* +

6 + + + + + - - + +

7 + + + + + - + + +

8 - - - - - - -

9 - + - - - +

10 + + - - +* +

11 + - - -

12 - - - -

13 -* + + +

14 + + +

15 - +

16 +

17




equal to management commitment and shared vision and culture. Shared vision and

culture is implemented significantly higher than fourteen other lean practices. It

shows the same implementation level as housekeeping but a significantly lower

implementation than management commitment. In contrast to the two clusters

analysed before the following practices are significantly different to those three lean

practices. As the name flexible starters indicates this group has a low emphasis on

the development of competitive priorities and therefore apparently has not

implemented as much lean practices as the other clusters. In their implementation

process they seem to focus on some practices only. Customer focus and satisfaction

as well as process measurement and reliability are implemented significantly higher

than nine other lean practices but show a lower level of implementation than

management commitment, housekeeping, and vision and culture. Their

implementation level is equal to those of supplier management and development,

preventive maintenance, and process driven organization. Supplier management

and development has a significantly higher implementation level than seven other

practices and is implemented significantly lower than the top three practices. Its

implementation level is equal to customer focus and satisfaction, process

measurement and reliability, preventive maintenance, process driven organization,

employee involvement and continuous improvement, and functional integration and

qualification.

This large number of equally implemented practices on an intermediate level

reflects again the starting position of the flexible starters. All practices following are

implemented equally to at least one other practice without the splitting point that

could be found in the other two clusters.


Table 29: Pairwise t-test for flexible starters-cluster


The efficient conformers group shows a significantly better implementation of


housekeeping and shared vision and culture. The same holds true for shared vision

and culture which is only implemented equal to management commitment and

housekeeping. Housekeeping is implemented significantly higher than thirteen other

lean practices. It shows the same implementation level as management commitment,

shared vision and culture and preventive maintenance. Preventive maintenance is

implemented significantly higher than eleven other lean practices but it shows a

lower level of implementation than management commitment and shared vision and

culture. Its implementation level is equal to those of housekeeping, supplier

management and development, and customer focus and satisfaction. Supplier

management and development has a significantly higher implementation level than

ten other practices but it is significantly lower implemented than management

commitment, shared vision and culture, and housekeeping. Its implementation level

is equal to preventive maintenance, customer focus and satisfaction, and process

measurement and reliability. Like for the group flexible starters there are several

Lean Practice

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 + - + + + + + - - +

2 - - - - +* - -* + - - - -

3 + + + +* + + + + + + + +

4 - - - +* - - - + - - - -

5 + + + + - - + +* +

6 + + + + - - +

7 + + + + -* - + +* +

8 - - - + - - - -

9 - + - - - +

10 + + - - +

11 + - -

12 - - - - -*

13 -* + + +

14 + + +

15 +

16 +

17




lean practices following which have equal implementation levels and no splitting

point can be found. Only the lowest implemented lean practice continuous flow

production can be separated from the others.

In contrast to all other groups examined before the efficient conformers do neither

have outstanding practices nor do they have a splitting point which separates higher

implemented practices from those that are lower implemented. The strategic focus

on costs and quality is not clearly reflected in the implementation of the different

lean practises.

Table 30: Pairwise t-test for efficient conformers-cluster

4.5.2.5 Summary

This analysis shows that even if the strategic groups focus more or less on the same

lean practices, there are differences in the emphasis on the practices within the

groups. Especially the do all group has a focus on one practice, management

commitment, so that its implementation level is clearly above other practices.

Further, a splitting point, separating the most important lean practices from the

others, could be identified. A contrast is the efficient conformers-cluster where no

clear separation of the practices could be observed.

Lean Practice

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 + + + + + + + + -* -* + + +

2 - - - - -* -* - - - - -

3 + + + + + + + + + + + +

4 - - - -* - -* - - - -

5 + + + + + - - +

6 + + + + + - - + +* +

7 + + + + + - - + +

8 - - - +* - - - - -

9 + - - -* -

10 + - -

11 + - - -

12 - - - - -

13 + + +

14 + + +

15 +*

16 +

17




Besides allowing an evaluation of the different levels of implementation of the

single lean practices in the groups, the paired-sample t-test also provides insights

into the correlations between the lean practices. These will be detailed in the next

chapter.

4.5.3 Correlations between lean practices

For each of the four clusters the correlations between the single lean practices

derived from the paired-sample t-test (see page 62) are analysed. Further, the

correlations between the lean practices and the goals of the single lean bundles are

calculated. They are then filled into the map developed in chapter 3 and adapted in

chapter 4.3. If there is no significant correlation the arrows are deleted, otherwise

the level of correlation is added to the arrow. In case of management commitment &

shared vision and culture the arrow is not deleted but instead n.s. (not significant) is

added if only one correlation could be confirmed. The use of the map helps to not

only display the strength of correlations but also their direction. As the correlation

coefficient itself does not contain any direction of influence, the direction is

obtained by the literature analysis the map is based on. All correlations shown are

linear and significant at the 0.05 level or less. They are based on Pearson’s

correlation coefficient which demands interval-scaled data. As mentioned before the

five-point Likert scale can be treated as interval level (Bortz & Schuster, 2010)

The different clusters show different correlations between the lean practices and

between lean practices and the goals of the single lean bundles. Also the strength of

the single correlations is different. Table 31 shows a guideline for interpretation

according to Brosius (2011).

Table 31: Interpretation of the correlation coefficient

The values are no strict limits and the interpretation should always consider the

context of the question to be answered.

In the following part the map is displayed for each of the strategic groups and

interpreted. Finally, the results are compared and evaluated.

Value of the correlation coefficient Possible interpretation

0 no correlation0 - 0,2 poor correlation

0,2 - 0,4 weak correlation0,4 - 0,6 moderate correlation0,6 - 0,8 strong correlation

0,8 - under 1 very strong correlation1 perfect correlation


4.5.3.1 Do all

Figure 15: Correlations for do all-cluster

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92(s

uppl

ier

com

plai

nt r

ate)

-0.3

99 (sup

plie

rco

mpl

aint

rat

e)

0.44

2 / 0

.439

/ 0.

373

(TP

M-l

evel

/ m

aint

enan

ce c

ost /

unpl

anne

d m

aint

enan

ce

-0.3

72 /

0.35

1(f

lexi

bili

ty /

setu

p ti

mes

)

0.35

1 / 0

.520

(del

iver

y / s

etup

tim

es)

(unp

lann

edm

aint

enan

ce)


For the do all-cluster there are some correlations in all categories but also four

practices that do not show any of the correlations presumed by literature. Especially

in the EMS category less correlations are observable than presumed.

The strongest correlation can be seen for continuous flow production and

elimination of excess inventories (number of turns) which explains almost 80% of

the variance. Further continuous flow production also has a strong positive

correlation with pull production. Both correlations are intuitive as a smoothly

flowing production enables a higher number of turns and therewith a lower

inventory. Furthermore it fosters the implementation of a pull system as a tact is

established in production. Pull production has a strong positive correlation with

optimized production planning and control and explains 28.30% of the variance.

The latest two correlations can be observed in each cluster (see below). There are

also strong positive correlations between management commitment and functional

integration and qualification respectively employee empowerment.

Surprisingly, the correlations between supplier management and development and

quality improvement and stable processes (supplier complaint rate) respectively

elimination of excess inventories (flexibility) are negative with values of -0.392 and

-0.372. The implementation of supplier management and development does not

seem to lower the level of supplier complaints but instead they increase. As the

implementation level of this lean practice is rather high with 3.99 this cannot be

seen as teething troubles. Maybe the expectations towards suppliers are higher when

they are integrated closer into a plant’s production and therewith more complaints

are reported due to another level of tolerance. This also seems to be the case for

process measurement and reliability where a higher implementation leads to a

higher level of supplier complaints. The production flexibility upside is also lower

with a higher implementation of supplier management and development. This might

be due to the fact that a stronger collaboration with suppliers including on time

delivery is preventing short hand changes in the production schedule as materials

for a change are not available. This dilemma can be solved by having a shorter tact

of delivery, which until now does not seem to be common in pharmaceutical

manufacturing.

In total 22 correlations can be observed in the do all-cluster. Looking at the amount

of direct linkages between the single practices it can be assumed that the order of

the mostly direct influencing lean practices is supplier management and

development (3), continuous flow production (2), optimization of set-up times and


layout (2), management commitment & shared vision and culture (2), functional

integration and qualification (2), housekeeping (1), pull production (1), process

measurement and reliability (1), employee involvement and continuous

improvement (1), and preventive maintenance(1). Only influenced are optimized

production planning and control and employee empowerment. The following table

contrasts the level of implementation of the single lean practices with their direct

and indirect influence on other lean practices.

Table 32: Lean practices and their influence for do all-cluster

It is obvious that for the do all-cluster those two practices that have the highest level

of implementation also have the highest number of positive direct and indirect

influences on other practices. The plants can benefit from the high implementation

of management commitment and shared vision and culture as they therewith enable

the implementation of other practices and finally influence the goals of the single

lean bundles. Therefore when starting to implement lean with these specific

competitive priorities the first practices to implement should be those two. This is

also consistent with the relations derived from literature. Further, it is intuitive that a

plant which has a management that is committed to lean and promotes lean in the

vision and culture eases the implementation of further lean practices.

Another very potent practice is functional integration and qualification with five

influences in total. Thus far, the level of implementation of this practice is not very

high with rank number nine. To achieve better results in the overall manufacturing

performance more effort should be put in implementing this practice. The same

holds true for continuous flow production and optimization of set-up times and

layout that have influence on four respectively three other practices but show a very

low level of implementation.

Lean practice Level of implementation Direct influences Direct and indirect influences

Management commitment 4.52 (1) 2 7

Shared vision and culture 4.29 (2) 2 6

Housekeeping 4.18 (3) 1 2

Process measurement and reliability 4.05 (4) 1 2

Supplier management and development 3.99 (5) 3 4

Employee involvement and continuous improvement 3.87 (6) 1 3

Preventive maintenance 3.77 (7) 1 3

Functional integration and qualification 3.54 (9) 2 5

Optimized production planning and control 3.34 (10) 0 0

Pull production 3.30 (11) 1 1

Employee empowerment 3.19 (13) 0 0

Optimization of set-up times and layout 3.05 (15) 2 3

Continuous flow production 2.81 (16) 2 4



Figure 16: Correlations for flexible deliverers-cluster

pull

prod

uctio

n

cont

inuo

us fl

owpr

oduc

tion

optim

izat

ion

of s

et-u

ptim

es a

nd la

yout

qual

ity

impr

ovem

ent

and

stab

le p

roce

sses

elim

inat

ion

ofex

cess

inve

ntor

ies

proc

ess

mea

sure

men

tan

d re

liabi

lity

empl

oyee

empo

wer

men

t

supp

lier

man

agem

ent

and

deve

lopm

ent

prev

entiv

em

aint

enan

ce

proc

ess

driv

enor

gani

zatio

n

++

+

func

tiona

l int

egra

tion

and

qual

ifica

tion

+

+

cust

omer

focu

s an

dsa

tisfa

ctio

n

man

agem

ent c

omm

itmen

t&

sha

red

visi

on a

nd c

ultu

re+

+

tech

nolo

gyas

sesm

ent a

nd u

sage

++

hous

ekee

ping

impr

oved

equ

ipm

ent

perf

orm

ance

+

+

++

+

+

+

optim

ized

pro

duct

ion

plan

ning

and

con

trol

cros

s-fu

nctio

nal

proc

ess

deve

lopm

ent a

ndpr

oces

s co

ntro

l

+

+

++

+

empl

oyee

invo

lvem

ent a

ndco

ntin

uous

impr

ovem

ent

+

+

+

3.52

2.93

3.95

3.53

2.85

3.08

3.19

4.08

/ 3.

95

2.91

3.43

3.00

3.67

3.81

3.69

3.12

2.71

0.72

5 / 0

.687

/ 0.

621

(TP

M-l

evel

/ m

aint

enan

ce c

ost /

unpl

anne

d m

aint

enan

ce)

0.51

3

0.56

7 0.61

5

0.35

9 / 0

.305

0.46

9

0.42

6

0.36

3 / 0

.419

n.s.

/ 0.

464

0.30

4 / 0

.295

0.48

70.

686

0.27

0

0.55

4

0.74

6

0.63

2

0.50

7

0.49

0

0.38

0-0

.363

0.61

20.

317

0.66

0

(TP

M-l

evel

/ O

EE

ava

ilab

ilit

y)

(JIT

-lev

el)

(qua

lity

cos

t)

(qua

lity

cos

t)


For the flexible deliverers-cluster almost all correlations between the lean practices

in the JIT category could be confirmed. Additionally, there are a lot of correlations

between lean practices from the EMS category. On the other hand almost no

correlations are observable in the categories TPM and TQM where in total five

practices are not linked. As the competitive aim of the flexible deliverers is a fast

and punctual delivery with high volume flexibility it seems to be favourable that

there are stronger relations between the JIT practices which promise to reach this

aim.

The flexible deliverers are the only strategic group in which reinforced feedback

loops between the practices can be observed. All feedback loops include the JIT

practice continuous flow production which also shows a number of strong

correlations. One reinforced feedback loop exists between continuous flow

production and improved equipment performance. A higher performance of the

equipment leads to a better flow in production which again supports the TPM goal.

Possibly, due to a constant and balanced utilisation of the equipment the

maintenance costs and the share of unplanned maintenance are lower.

A second reinforced feedback loop exists between continuous flow production,

improved equipment performance, elimination of excess inventories, and quality

improvement and stable processes. Like stated above, a continuous flow in

production helps to reach the goal of TPM. The improved equipment performance

itself has a positive influence on the elimination of excess inventories. With reliably

running machines it is not necessary to produce more stock than needed because of

the lower risk of machine failures. This leads to an improved quality and stable

processes. With an improved quality and processes that are stable it is again easier

to establish and maintain a continuous flow production.

The third reinforced feedback loop can be found for continuous flow production,

pull production, elimination of excess inventories, and quality improvement and

stable processes. To have a continuous flow in production eases the implementation

of pull production which again supports the elimination of excess inventories.

Again, the same elements as in the second reinforced feedback loop follow.

The strongest correlation can be seen for preventive maintenance and continuous

flow production with 0.746. There is also a strong correlation between continuous

flow production and improved equipment performance, namely the TPM-level in

general as well as maintenance cost and unplanned maintenance (see first


reinforced feedback loop). These strong correlations with TPM-related practices

and performance measures show that stable equipment and machines are a

perquisite for a continuous production flow. Preventive maintenance and therewith

low levels of unplanned maintenance and low maintenance cost help to promote a

continuous product flow. Further, continuous flow production has a strong influence

on optimized production planning and control with 44% of the variance explained.

A continuous production flow makes it easier to establish a stable and reliable

production schedule and therewith meet the production plans. This is one

requirement for a fast and especially punctual delivery. Another strong correlation

can be found between optimization of set-up times and layout and continuous flow

production. An optimized layout and lower set-up times boost flexibility and hence

support the strategic goal of the flexible deliverers. Also quality improvement and

stable processes (quality cost) has a strong influence on continuous flow production

(see second reinforced feedback loop). Other strong influences can be observed

with employee empowerment, it is strongly influenced by employee involvement and

continuous improvement and process driven organization. One negative correlation

can be observed which is between process measurement and reliability and quality

improvement and stable processes (quality cost). An explanation might be that a

more rigid measurement of processes and their stability leads to higher quality

costs.

In total 28 correlations can be observed in the flexible deliverers-cluster. Looking at

the amount of direct linkages between the single practices it can be assumed that the

order of the mostly direct influencing lean practices is functional integration and

qualification (4), shared vision and culture (3), continuous flow production (3),

management commitment (2), preventive maintenance(2), employee involvement

and continuous improvement (2), process driven organization (2), pull production

(2), , optimization of set-up times and layout (2), process measurement and

reliability (1), and employee empowerment (1). Only influenced is optimized

production planning and control. The following table contrasts the level of

implementation of the single lean practices with their direct and indirect influence

on other lean practices.


Table 33: Lean practices and their influence for flexible deliverers-cluster

It is obvious that for the flexible deliverers-cluster the practices that have the highest

level of implementation also have the highest number of direct and indirect

influences on other practices. Like in the do all-cluster the plants can benefit from

the high implementation of management commitment and shared vision and culture

as they therewith enable the implementation of other practices and finally influence

the goals of the single lean bundles. Therefore when starting to implement lean as a

plant with competitive priorities on a fast and punctual delivery with a high volume

flexibility the first practices to implement should be those two.

Other very potent practices are employee involvement and continuous improvement

and functional integration and qualification with eight influences in total. Thus far

the level of implementation of these practices is not very high with rank number

seven and nine. To achieve better results in the overall manufacturing performance

more effort should be put in implementing these practices. This is especially crucial

for functional integration and qualification as this practice has four direct

influences which is the highest number for this cluster. The same holds true for

process driven organization and optimization of set-up times and layout that have

influence on seven other practices but show a low respectively very low level of

implementation with rank eight and rank 16. Compared to the do all-cluster in total

more direct and indirect influences could be observed. Especially those lean

practices coming from the category EMS have a lot of direct as well as indirect

influences. This shows that in this cluster an effective management system can even

better accelerate a lean implementation than in the do all-cluster.







Process driven organisation 3.43 (8) 2 7









Figure 17: Correlations for flexible starters-cluster

For the flexible starters-cluster not as much correlations are observable as for the

other clusters. There are some correlations between practices in the JIT category

pull

prod

uctio

n

cont

inuo

us fl

owpr

oduc

tion

optim

izat

ion

of s

et-u

ptim

es a

nd la

yout

qual

ity

impr

ovem

ent

and

stab

le p

roce

sses

elim

inat

ion

ofex

cess

inve

ntor

ies

proc

ess

mea

sure

men

tan

d re

liabi

lity

empl

oyee

empo

wer

men

t

supp

lier

man

agem

ent

and

deve

lopm

ent

prev

entiv

em

aint

enan

ce

proc

ess

driv

enor

gani

zatio

n

++

func

tiona

l int

egra

tion

and

qual

ifica

tion

cust

omer

focu

s an

dsa

tisfa

ctio

n

man

agem

ent c

omm

itmen

t&

sha

red

visi

on a

nd c

ultu

re

+

tech

nolo

gyas

sesm

ent a

nd u

sage

+

hous

ekee

ping

impr

oved

equ

ipm

ent

perf

orm

ance

++

+

+

+

+

optim

ized

pro

duct

ion

plan

ning

and

con

trol

cros

s-fu

nctio

nal

proc

ess

deve

lopm

ent a

ndpr

oces

s co

ntro

l

+

+

++

+

+

empl

oyee

invo

lvem

ent a

ndco

ntin

uous

impr

ovem

ent

+

3.29

2.80

3.85

3.44

2.12

3.00

3.20

3.93

/ 3.

78

2.65

3.42

2.76

3.54

3.54

3.45

3.08

2.57

0.49

5

0.54

5

0.65

6

0.37

1 / 0

.529

n.s.

/ -0

.328

0.38

9 / 0

.340

0.51

2

0.59

70.

556

0.41

1

-0.4

19

0.38

3

-0.4

89-0

.372

/ -0

.448

/-0

.409

0.64

2

(sup

plie

rco

mpl

aint

rat

e)

(sup

plie

rco

mpl

aint

rat

e)

(set

up ti

mes

)

(acc

urac

y)(T

QM

-lev

el /

cust

omer

com

plai

nt r

ate

/ qua

lity

cos

t)


with starting links to TPM and TQM practices. EMS practices are almost not

involved. In total four practices are not linked to others. The flexible starters

generally have a low emphasis on competitive priorities with the highest value for

flexibility. This demand for flexibility is explaining why there are mostly

correlations found in practices related to process optimization.

The strongest correlation can be seen for optimization of set-up times and layout and

continuous flow production. Low set-up times and an optimized layout allow for

fast changes in products and therewith augment flexibility. Nevertheless, this

correlation is with 43% of variance explained much weaker than the strongest

correlation in the two clusters analysed thus far. Another strong correlation can be

found for continuous flow production and optimized production planning and

control with 0.642. The other correlations are only moderate at maximum.

Besides the positive correlations also negative ones can be observed. There is a

moderate negative correlation between pull production and elimination of excess

inventories (production schedule accuracy in the freezing period). It seems that a

higher implementation level of the lean practice pull production is leading to a

lower production schedule accuracy in the freezing period for plants in the flexible

starters-cluster. Theoretically the opposite should be the case. A possible

explanation might be that the pull system is not yet fully implemented (3.08 of 5.00)

and that in this status of implementation the positive aspects could not be observed

yet. There are also negative correlations between customer focus and satisfaction

and quality improvement and stable processes (TQM-level, customer complaint

rate, quality cost) as well as between process measurement and reliability and

quality improvement and stable processes (supplier complaint rate). Apparently, the

focus on customers is hindering a good performance in the TQM-area. The

customer complaint rate is higher which might be due to the reason that more

complaints are regarded as justified to better satisfy the single customer. Further, a

more severe control of customer complaints and requirements leads to a higher

quality cost. The same can be assumed for the correlation between process

measurement and reliability and the supplier complaint rate. A more rigorous

control of one’s own and the suppliers processes leads to a higher supplier

complaint rate. As the plants in this cluster have rather low levels of lean

implementation it can be stated that they are only starting to focus on this topic.

Therefore they might still struggle with the implementation process and problems

occurring from the changes.


In total 19 correlations can be observed in the flexible starters-cluster. Looking at

the amount of direct linkages between the single practices it can be assumed that the

order of the mostly direct influencing lean practices is shared vision and culture (3),

management commitment (2), pull production (2), optimization of set-up times and

layout (2), continuous flow production (2), process measurement and reliability (1),

customer focus and satisfaction (1), supplier management and development (1),

preventive maintenance (1), process driven organization (1), and employee

empowerment (1). Only influenced are functional integration and qualification and

optimized production planning and control. The following table contrasts the level

of implementation of the single lean practices with their direct and indirect

influence on other lean practices.

Table 34: Lean practices and their influence for flexible starters-cluster

Also for the flexible starters-cluster the practices that have the highest level of

implementation have the highest number of direct and indirect influences on other

practices. Like in the other clusters the plants can benefit from the high

implementation of management commitment and shared vision and culture as they

therewith enable the implementation of other practices and finally influence the

goals of the single lean bundles. Therefore, being at the beginning of implementing

lean as a plant with competitive priorities on flexibility of volume and products as

well as fast changes, the first practices to implement should be the aforementioned.

Almost as influencing, especially regarding the direct influences, is the practice

optimization of set-up times and layout but its implementation level is with 2.57 and

therewith rank 15 rather low. Other potent practices are preventive maintenance and

process driven organization each having five direct and indirect influences from

which only one is direct. Their implementation is with rank 6 and 7 on an average





Customer focus and satisfaction 3.54 (4) 1 2











level in comparison to the implementation of other practices in this cluster. A higher

implementation of these practices would positively influence other practices and

therewith the overall implementation level of lean.

Compared to the other two clusters there are less direct and indirect influences

observable. This might also be due to the fact that there is generally a lower number

of correlations as the flexible starters, as the name says, are just starting to

implement lean. Besides two lean practices coming from the category EMS also

practices that are process oriented have a lot of direct as well as indirect influences.

Apparently, in this cluster an effective management system and the ability to react

flexible are core of the lean implementation efforts.


For the efficient conformers-cluster most correlations between practices could be

confirmed, there is only one practice with no correlation at all. Especially the EMS

practices have a lot of linkages between each other. Also the goals of the single lean

bundles are correlated to a lot of practices with a surprisingly high number of

negative correlations for the TQM bundle.

The strongest correlation can be seen for the practices optimization of set-up times

and layout and continuous flow production with a value of 0.816. A higher

implementation of the first practice helps to establish a continuous flow in

pharmaceutical productions.

Further, continuous flow production has strong correlations with the lean practices

process measurement and reliability as well as process driven organization. Both

lean practices support the implementation of the first named as they enable smooth

and reliable processes.

Also optimization of set-up times and layout has other strong correlations. It

strongly correlates with employee empowerment and process measurement and

reliability and therewith influences their level of implementation. As employee

empowerment mainly focuses on autonomous problem solving an optimised

environment seems to be favourable to promote the employees in doing so. To

create reliable processes and measure them clear set-up times and an optimized

layout are a necessary basis as they help to ensure dependable and reproducible

data.


Figure 18: Correlations for efficient conformers-cluster

pull

prod

uctio

n

cont

inuo

us fl

owpr

oduc

tion

optim

izat

ion

of s

et-u

ptim

es a

nd la

yout

qual

ity

impr

ovem

ent

and

stab

le p

roce

sses

elim

inat

ion

ofex

cess

inve

ntor

ies

proc

ess

mea

sure

men

tan

d re

liabi

lity

empl

oyee

empo

wer

men

t

supp

lier

man

agem

ent

and

deve

lopm

ent

prev

entiv

em

aint

enan

ce

++

proc

ess

driv

enor

gani

zatio

n

+

+

func

tiona

l int

egra

tion

and

qual

ifica

tion

+

+

cust

omer

focu

s an

dsa

tisfa

ctio

n

man

agem

ent c

omm

itmen

t&

sha

red

visi

on a

nd c

ultu

re+

+

+

tech

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gyas

sesm

ent a

nd u

sage

+

hous

ekee

pingim

prov

ed e

quip

men

tpe

rfor

man

ce+

++

+

++

+

+

+

+

+

+

optim

ized

pro

duct

ion

plan

ning

and

con

trol

cros

s-fu

nctio

nal p

roce

ssde

velo

pmen

t and

pro

cess

cont

rol

+

+

+

++

+

empl

oyee

invo

lvem

ent a

ndco

ntin

uous

impr

ovem

ent

++

+

+

3.33

2.70

3.95

3.75

2.23

2.98

3.20

4.01

/ 3.

99

2.86

3.11

2.63

3.39

3.53

3.55

2.99

2.48

0.41

9

0.61

8

0.48

7

0.59

4

2*0.

816

0.42

8 / 0

.588

/0.

396

0.35

3 / 0

.414

0.40

8

0.39

1

0.43

4

0.56

3 / 0

.435

n.s.

/-0.

481

n.s

./-0.

515

n.s.

/-0.

506 0.

583

/ 0.5

69

n.s.

/ 0.

406 0.

763

0.53

5

0.70

2

0.56

1

0.48

4 0.61

10.

645

0.54

2

0.46

3 / -

0.42

8

0.47

2 / -

0.50

03*

0.62

8

-0.4

61

0.51

5 / -

0.37

5

-0.4

02 /

-0.4

99 /

-0.5

80

0.47

6

(TP

M le

vel /

load

ing

/O

EE

ava

ilab

ilit

y)

(TP

M le

vel /

OE

E a

vail

abil

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(loa

ding

)

(rej

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d ba

tche

s /

supp

lier

com

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te)

(TQ

M le

vel /

sup

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rate

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(rej

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1*

0.53

3 JI

T le

vel

0.61

5 D

OH

0.61

8 sc

hedu

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ccur

acy

0.69

0 se

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1* 2* 0.64

8 JI

T le

vel

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8 sc

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0.72

4 se

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8 nu

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2 JI

T le

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0 sc

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0.71

0 se

tup

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527

num

ber

of t

urns


Also for functional integration and qualification high correlations can be observed

with employee empowerment on the one hand and continuous flow production on

the other hand. Both practices are positively influenced and 58% respectively 49%

of their variance is explained.

All other strong correlations can be observed in relation to elimination of excess

inventories, the goal of the JIT bundle. The implementation of a pull system has a

strong positive influence on the inventory days on hand (DOH), the production

schedule accuracy in the freezing period, and set-up times. Further, implementing a

continuous product flow positively influences the overall JIT level, the production

schedule accuracy in the freezing period, set-up times, and the number of turns. In

addition, shorter set-up times and an optimized layout positively correlate with the

JIT level, the production schedule accuracy in the freezing period, and set-up times.

In this cluster, the practices belonging to the JIT bundle clearly show a strong

influence on the goal of this bundle.

Also in this cluster some weak to moderate negative correlations can be found.

They are especially associated with quality improvement and stable processes, the

goal of the TQM bundle. The supplier complaint rate is negatively correlated to all

five lean practices that have an influence on quality improvement and stable

processes. Also TQM level and quality cost are negatively correlated with two lean

practices. As the competitive aims of this cluster are costs and quality it is

surprising to see that two relatively high implemented lean practices negatively

influence the quality cost as well as the overall TQM level.

Another weak correlation can be found between supplier management and

development and production flexibility upside, one of the goals of the JIT bundle.

In total 53 correlations can be observed in the efficient conformers-cluster. Besides

being the highest number of correlations that can be observed in the four clusters,

these correlations involve the goals of the single lean bundles to a greater extent

than in all other clusters. Looking at the amount of direct linkages between the

single practices it can be assumed that the order of the mostly direct influencing

lean practices is shared vision and culture (4), optimization of set-up times and

layout (4), employee involvement and continuous improvement (3), functional

integration and qualification (3), employee empowerment (3), continuous flow

production (3), management commitment (2), supplier management and

development (2), process measurement and reliability (2), process driven


organization (2), pull production (2), housekeeping (1), preventive maintenance (1),

technology assessment and usage (1), and cross-functional process development

and process control (1). Only influenced is optimized production planning and

control. The following table contrasts the level of implementation of the single lean

practices with their direct and indirect influence on other lean practices.

Table 35: Lean practices and their influence for efficient conformers-cluster

In the efficient conformers-cluster the two practices that have the highest level of

implementation also have the highest number of direct and indirect influences on

other practices. Like in the other clusters the plants in the efficient conformers-

cluster can benefit from the high implementation of management commitment and

shared vision and culture as they therewith enable the implementation of other

practices and finally influence the goals of the single lean bundles. This shows that

also plants with the competitive priorities of low costs and a high quality should

focus on those two management related practices first.

Other very potent practices are employee involvement and continuous improvement,

functional integration and qualification, process driven organization, and

optimization of set-up times and layout with seven influences in total. Until now,

these practices are not on a high implementation level with rank number eight, nine,

ten, and sixteen. As these four lean practices could positively influence so many

others there should be put more effort into their implementation. Also the number of

direct influences is high for all four practices, especially optimization of set-up times

and layout with four direct influences has a high relevance. The same holds true for




Housekeeping 3.95 (3) 1 3










Technology assessment and usage 2.70 (14) 1 3


2.63 (15) 1 2




continuous flow production and employee empowerment that have direct influence

on three and direct and indirect influences on four respectively six other practices.

Nevertheless they show a very low level of implementation with rank thirteen and

rank seventeen. Plants should include these practices to a bigger extend into their

implementation efforts.

Compared to the other clusters there are more direct and indirect influences

observable which involve almost all lean practices. Like mentioned before also the

goals of the single lean bundles are integrated to a higher extend. Apparently, for

plants aiming for low costs and a high quality the interaction of all lean practices is

vital. Especially management and process related practices play an important role.

4.5.3.5 General view

Despite all the differences there are some correlations that can be observed in each

of the four clusters. They are listed in the following table including the strength of

the correlation. The first variable of a pair is always the one that is influencing the

other one according to literature.

Table 36: Observable correlations in all four clusters

The correlation between pull production and optimized production planning and

control is positive moderate for all four clusters. This means that a higher

implementation of the lean practice pull production fosters a higher implementation

of optimized production planning and control independent from the competitive

priorities aimed at. In the single clusters 23.72% to 28.30% of the variance is

explained meaning that 71.70% up to 76.28% are explained by other influences.

1 2 3 4

Do all Flexible deliverers Flexible starters Efficient conformers

n = 49 n = 70 n = 48 n = 33

Pull production - Optimized production planning and control

0.532 0.513 0.495 0.487

Continuous flow production - Pull production

0.624 0.567 0.545 0.594

Optimization of set-up times and layout - Process measurement and reliability

0.491 0.380 0.411 0.628

-0.399 -0.363 -0.419 -0.461(supplier complaint rate) (quality cost) (supplier complaint rate) (supplier complaint rate)

Management commitment & Shared vision and culture - Functional integration and qualification

0.630 / 0.380 0.363 / 0.419 0.371 / 0.529 0.563 / 0.435

Management commitment & Shared vision and culture - Preventive maintenance

n.s. / 0.388 0.304 / 0.295 0.389 / 0.340 n.s. / 0.406

Correlation

Process measurement and reliability - Quality improvement and stable processes


For continuous flow production and pull production higher correlation coefficients

are observed that already can be interpreted as positive strong correlation, especially

the first cluster shows with 0.624 a high value. Between 29.70% and 38.94% of the

variance is explained.

For optimization of set-up times and layout and process measurement and reliability

the single clusters show very different levels of positive correlation. With 0.628 the

correlation coefficient in the efficient conformers-cluster is rather high, 0.491for the

do all-cluster is still moderate whereas the values for the flexible deliverers and the

flexible starters with around 0.4 are already weak. This means that in the efficient

conformers-cluster 39.44% of the variance is explained and in the flexible

deliverers-cluster with the lowest value only 14.44% of the variance is explained.

Obviously, the efficient conformers with the aim of high quality and low costs can

benefit more from the positive impact of the implementation of optimization of set-

up times and layout than plants from the other clusters.

For process measurement and reliability and quality improvement and stable

processes, the goal of the lean bundle TQM, only negative weak to moderate

correlations can be observed. In three cases there is a negative correlation with

supplier complaint rate and for the cluster flexible deliverers with the quality cost.

It seems that a high implementation of process measurement and reliability is not

favourable for a low complaint rate towards a plant’s suppliers. There are two

possible explanations; one might be that the standardized and continuous

measurement of processes helps the plants to identify more incorrect deliveries than

the methods used by those plants that do not employ process measurement tools. A

second explanation might be that with process measurement the level of tolerance is

lower and therewith more complaints occur. Also the cost of quality is not lower

with a higher implementation of process measurement and reliability. A reason

might be that statistical process measurement and its tools are rather new to

pharmaceutical manufacturing (FDA, 2004) and therefore extra quality tests are

made which lead to higher costs. In general, it can be seen that in contrast to

literature where process measurement and reliability is positively associated with

the goal of the lean bundle TQM, a negative correlation exists in pharmaceutical

manufacturing which explains 13.18% to 21.25% of the variance.

For management commitment & shared vision and culture and functional

integration and qualification the correlation coefficients also vary but are all

positive. Especially management commitment has varying influence ranging from


13.18% to 39.69% of variance explained. Shared vision and culture is a little more

stable throughout the clusters and explains between 14.44% to 27.98% of the

variance in functional integration and qualification. In general it can be stated that

an engaged management and a common culture foster the integration and

qualification of employees in a plant independent from the competitive aims.

For management commitment & shared vision and culture and preventive

maintenance the values of the correlation coefficients are rather low with a variance

explained of 8.70% to 16.48%. In some cases the correlation is even not significant

(n.s) for management commitment. Nevertheless, a weak correlation exists between

a committed management and the implementation of preventive maintenance in a

plant for the clusters flexible deliverers and flexible starters. For all clusters a weak

correlation exists between shared vision and culture and preventive maintenance.

Obviously, the implementation of a preventive approach for maintenance is at least

lightly influenced by the commitment and mind-set of management and employees

in a plant.

4.5.3.6 Summary

Within the strategic groups different numbers of correlations in the theoretical map

could be confirmed. In total, the efficient conformers have 53 correlations and

therewith almost twice as much as the flexible deliverers with 28 correlations. Even

less correlations are observable in the do all-cluster with 22 and the flexible

starters-cluster with 19 correlations. Besides the number of correlations also their

strength varies in the single groups. The do all-cluster has one very strong

correlation and two strong correlations, being 14% of the correlations observed. The

flexible deliverers-cluster has nine strong correlations (32%), the flexible starters-

cluster has two strong correlations (11%), and the efficient conformers-cluster has

one very strong correlation and 16 strong correlations (32%). With more and higher

correlations it is easier for plants to positively influence the overall lean

implementation. Therefore, especially plants from the clusters efficient conformers

and flexible deliverers have the possibility to use the positive effects between the

practices. This effect is particularly interesting for practices with an already high

level of implementation.


Summary empirical analysis 4.6

In this chapter, the four strategic groups identified for pharmaceutical

manufacturing were analysed using different methods. The results of the different

empirical analyses for investigating the interconnection and interaction between

lean practices have been provided. The results show, that lean practices are not

independent from each other and that they therefore should be implemented in

common. Which practices to implement in which order is depending on the strategic

goals a plant focuses on. A final evaluation is made by looking at the

implementation level and the thereof determined importance of the lean practice in

contrast to the influence it has according to the direct and indirect number of

correlations observed.

A plant that wishes to start a lean implementation should focus on those practices

which have a lot of correlations and a high importance within the relevant strategic

group. In a second step, the practices with a high number of correlations and a lower

importance should be implemented. For the different strategic groups the following

sets of lean practices to focus on were identified.

Figure 19: Influence - importance do all-cluster

For plants with the same strategic priorities as the do all-cluster the practices

management commitment, shared vision and culture, functional integration and

0

1

2

3

4

5

6

7

8

9

10

4,5 5,04,03,53,02,52,0

Infl

uenc

e

Importance




Pull production






Process measurement and reliability Housekeeping




qualification, and supplier management and development should be the first focus.

In a second step, the practices continuous flow production, employee involvement

and continuous improvement, preventive maintenance, and optimization of set-up

times and layout should be implemented (Figure 19).

Figure 20: Influence - importance flexible deliverers-cluster

For plants with the same strategic priorities as the flexible deliverers-cluster the

practices management commitment, shared vision and culture, employee

involvement and continuous improvement, and process driven organization should

be the first focus. In a second step, the practices functional integration and

qualification, optimization of set-up times and layout, process measurement and

reliability, preventive maintenance, and employee empowerment should be

implemented (Figure 20).

For plants with the same strategic priorities as the flexible starters-cluster the

practices management commitment, shared vision and culture, process driven

organization, and preventive maintenance should be the first focus. In a second

step, the practices optimization of set-up times and layout, continuous flow

production, and employee empowerment should be implemented (Figure 21).

0

1

2

3

4

5

6

7

8

9

10

3,52,0 5,02,5 4,53,0 4,0

Importance

Infl

uenc

e








Pull production






Figure 21: Influence - importance flexible starters-cluster

Figure 22: Influence - importance efficient conformers-cluster

For plants with the same strategic priorities as the efficient conformer-cluster the

practices shared vision and culture, management commitment, employee

involvement and continuous improvement, and functional integration and

0

1

2

3

4

5

6

7

8

9

10

3,0 3,5 4,0 4,5 5,02,0 2,5

Importance






Infl

uenc

e






Optimized production planning and controlFunctional integration and qualification

0

1

2

3

4

5

6

7

8

9

10

4,54,0 5,02,52,0 3,53,0

HousekeepingCross-functional process development and process control



Importance

Infl

uenc

e













qualification should be the first focus. In a second step, the practices process driven

organization, optimization of set-up times and layout, employee empowerment, and

process measurement and reliability should be implemented (Figure 22).

Overall, it is obvious that independent from the strategic group a focus should be on

the practices management commitment and shared vision and culture. This has also

been stated by Harrison and Storey (1996) that stress the importance of a change of

the company culture. Hines et al. (2008) also point out that a positive organizational

culture is the basis for strong leadership which leads to an effective strategy.

Further, Zayko et al. (1997) find that the first obstacle and main issue when

implementing lean is the lack of management conviction in the benefits it provides.

In the next chapter the overall contributions of this research will be stated.

92 Conclusion

5 Conclusion

This chapter summarises the contributions of the research at hand to theory and

practice. Further, it discusses limitations and directions for future research.

Contributions to theory 5.1

This research contributes to literature on operations management, especially from

the areas production management methods and production strategy. Its focus is on

the relation between lean practices and between lean practices and competitive

priorities in pharmaceutical manufacturing. Literature discusses both relations but

often focuses on some practices only or the subordinated bundles; therefore no

comprehensive theory has been presented yet. The positive impact of the

simultaneous implementation of lean practices is known, but the exact network of

relations remains unclear leading to a lack of knowledge which practices to

implement in which sequence. This is especially the case for the pharmaceutical

industry, as its lean implementation started relatively late.

Based on a comprehensive literature review at the first stage of this research a

general map on the linkages between single lean practices was developed based on

prior research findings. First insights into the influence of single lean practices were

provided. The general map based on literature was adapted to the reality of

pharmaceutical manufacturing and further analysed by using empirical data.

Different strategic types based on competitive priorities were examined concerning

the implementation and interrelation of lean practices. It was found that depending

on the strategic type different levels of lean implementation were observable.

Further, the number of relations between the lean practices varies according to the

strategic group. The most important practices for the single strategic groups were

identified and a sequence of implementation was sketched.

In summary, this research provides a comprehensive view on the relations between

lean practices in the pharmaceutical industry. The importance of a holistic

implementation approach including the production strategy is shown.

Contributions to practice 5.2

This research is derived from problems observed in reality and should therefore

Conclusion 93

propose solutions for these problems. Especially managers should be guided

through the field of lean practices and their successful and holistic implementation.

First of all, this research shows practitioners that lean practices are highly

interconnected and that the sole focus on one single practice cannot lead to success.

Instead, an integrated approach is necessary for lean implementation. Managers in

pharmaceutical manufacturing are provided with a guideline which practices to

focus on when they start a lean implementation at their plant. This guideline is

differentiated depending on the strategic priorities a plant focuses on as different

goals demand for different approaches. The research shows that independent from

the strategic priorities an initial effort should be put into the management related

lean practices. Especially a committed management and a shared vision and culture

are key factors for a successful lean implementation.

Besides, managers get an overview of the lean practices actually used in

pharmaceutical manufacturing and their implementation levels with respect to

different production strategies. Therewith they can compare their own plant to

similar plants.

Limitation and future research 5.3

The limitations of this research allow the outline of directions for future research:

• In the literature analysis only positive influences between lean practices were

identified. Therefore no negative relations that might hinder the

implementation of certain practices could be displayed in the map elaborated

in this research. Case-based research can be an option to analyse obstacles

that occur when implementing or using different lean practices. It can also

help to better understand the decisions of single plants while implementing

lean and to therewith refine the proposed sequence of lean implementation.

• The strategic groups identified for pharmaceutical manufacturing are not

detailed concerning contextual factors like company culture, product type,

country or size. It would be interesting to analyse if and how these factors

influence the lean implementation process or if they provide an opportunity

to further differentiate it.

• Based on other studies, it is supposed that the implementation of lean has a

positive influence on the manufacturing performance. This positive influence

was also shown based on the benchmarking data used in this research but not

94 Conclusion

explicitly included. Future research should empirically demonstrate this

influence.

• The fundament of this research is mainly based on causal linkages. Although

a careful literature analysis was performed and knowledge from interviews

was integrated other authors might find additional linkages to those

presented here. This might lead to different outcomes.

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106 Appendix A: Overview lean practices and bundles

Appendix A: Overview lean practices and bundles Author Category Practice

Sakakibara et al.

(1993)

Management of people

and schedules in a JIT

system

Set-up time reduction

Small-group problem solving

Training

Daily schedule adherence


Simplified physical flow Equipment layout

Small-lot sizes

Product design simplicity

Kanban

Pull system support

Supplier management JIT delivery from suppliers

Supplier quality level

Flynn et al.

(1995a)

TQM Customer focus

Product design

Statistical process control

JIT Kanban

Lot size reduction

Setup time reduction

JIT scheduling

Common infrastructure

practices

Information feedback

Management support

Plant environment

Workforce management

Supplier relationship

Flynn et al.

(1995b)

Core quality management

practices

Process flow management

Product design process

Statistical control/ feedback

Quality management

infrastructure practices

Customer relationship


Work attitudes


Top management support

Powell

(1995)

TQM Committed leadership

Adoption and communication of TQM

Appendix A: Overview lean practices and bundles 107

Closer customer relationships

Closer supplier relationships

Benchmarking

Increased training

Open organisation


Zero-defects mentality

Flexible manufacturing

Process improvement

Measurement

Sakakibara et al.

(1997)

Quality Management Process control

Feedback

Rewards for Quality

Top management quality leadership

Supplier quality involvement

JIT Setup time reduction

Schedule flexibility

Maintenance

Equipment layout

Kanban

JIT supplier relationship

Product Design New product quality

Design characteristics

Interfunctional design efforts

Workforce Management Supervisory leadership

Incentives for group performance

Labor flexibility

Small group problem solving

Recruiting and selection

Supervisors as team leaders

Koufteros et al.

(1998)

TBC Shop-floor employee involvement in problem solving

Reengineering set-ups

Cellular manufacturing

Quality improvement efforts


Dependable suppliers

Pull production


Koufteros &

Vonderembse

(1998)

JIT Re-engineering setup



Pull production

Quality assurance

Dow et al.

(1999)

TQM Workforce commitment

Shared vision

Customer focus

Use of teams

Personnel training

Co-operative supplier relations

Use of benchmarking

Advanced manufacturing systems

Use of just-in-time principles

Flynn et al.

(1999)

WCM practices Employee development

Management technical competence

Design for customer needs

Worker participation

Proprietary equipment

Continuous improvement

Core quality practices Process control

Feedback of information

Core JIT practices Pull system

JIT supplier relations

McKone et al.

(1999)

TPM Housekeeping

Cross-training

Teams

Operator involvement

Disciplined planning

Information tracking

Schedule compliance

TQM Customer involvement

Rewards for quality

Supplier quality management

Top management leadership for quality

JIT JIT delivery by suppliers

JIT link with customers


Pull system support

Repetitive nature of master schedule

Setup reduction

EI Centralization of authority

Cua et al.

(2001)

TPM Autonomous & planned maintenance

Technology emphasis

Proprietary equipment development

TQM Cross-functional product design

Process management




Pull system production

JIT delivery by suppliers

Equipment layout


Common Practices Committed leadership

Strategic planning

Cross-functional training


Information and feedback

Sila &

Ebrahimpour

(2002)

TQM Top management commitment

Social responsibility

Strategic planning


Quality information and performance measurement

Benchmarking

Human resource management

Training



Employee satisfaction

Teamwork

Employee appraisal, rewards, and recognition

Process management

Process control

Product and service design


Supplier management

Continuous improvement and innovation

Quality assurance

Zero defects

Quality culture

Communication

Quality systems

Just in time

Flexibility

Ahmad et al.

(2003)

Quality Management Customer focus

Feedback

Process control

Supplier involvement

JIT Daily schedule adherence

Equipment layout

The kanban system




Product Technology Simplicity in product design

Customer focus in product design

Work Integration System Interaction facilitation

Coordination of decision making

Job rotation

Management presence on the shop floor

HRM Training

Compensation for breadth of skill

Multifunctional employees

Recruiting and selection

Shah & Ward

(2003)

TPM Predictive or preventive maintenance

Maintenance optimization

Safety improvement programs

Planning and scheduling strategies

New process equipment or technologies

TQM Competitive benchmarking

Quality management programs

Total quality management


Process capability measurement

Formal continuous improvement program

JIT Lot size reduction

JIT/ continuous flow production

Pull system


Cycle time reduction

Focused factory production systems

Agile manufacturing strategies

Quick changeover techniques

Bottleneck/ constraint removal

Reengineered production processes

HRM Self-directed work teams

Flexible, cross-functional workforce

Kannan & Tan

(2005)

TQM Product design

Strategic commitment to quality

Supplier capability

JIT Material flow

Commitment to JIT

Supply management

SCM Supply chain integration

Supply chain coordination

Supply chain development

Information sharing

Kickuth

(2005)

TPM Preventive maintenance

Housekeeping

Effective technology usage

TQM Process management

Customer integration

Cross-functional product development


JIT Set-up time reduction

Pull system

Planning adherence

Layout optimization

EMS Direction setting

Management commitment and company culture


Employee involvement and continuous

i Functional integration and qualification

Shah & Ward

(2007)

Supplier related Supplier feedback


Supplier development

Customer related Customer involvement

Internally related Pull

Continuous flow

Set up time reduction

Total productive/preventive maintenance



Sila

(2007)

TQM Leadership

Strategic planning

Customer focus

Information and analysis

Human resource management

Process management

Supplier management

Zu et al.

(2008)

QM Top management support

Customer relationship



Quality information

Product/ service design

Process management

Six Sigma Six Sigma role structure

Six Sigma structured improvement procedure

Six Sigma focus on metrics

Mackelprang &

Nair

(2010)

Matsui (2007)


Small lot sizes

JIT delivery from suppliers



Equipment layout

Kanban



Pull system

Repetitive nature of master schedule

Menezes et al.

(2010)

HRM Empowerment

Learning culture

Team-based work

OM Integrated computer-based technology

Just-in-time production

Supply-chain partnering

Total quality management

Rahman et al.

(2010)

JIT Reduction of inventory


Cycle time reduction

Use of new process technology

Use of quick change-over techniques

Reducing set-up time

Waste minimization Eliminate waste

Use of error proofing techniques (Pokeyoke)

Using pull-based production system (Kanban)

Removing bottlenecks

Flow management Reducing production lot size

Focusing on single supplier

Continuous/ one piece flow

Angelis et al.

(2011)

Lean Set-up reduction

Inventory and waste reduction

Kanban pull signals

Supplier partnerships

Continuous improvement programs

mixed-model production

TQM

Foolproof or design for assembly systems

TPM

SOPs

Hofer et al.

(2011)

Internal-technical Pull

Flow



Total productive maintenance


Internal-relational Employee involvement

Supply chain Supplier JIT

Supplier feedback

Supplier development


Appendix B: OPEX questionnaire (excerpt of questions) 115

Appendix B: OPEX questionnaire (excerpt of questions)

Figure B- 1: General information and competitive priorities

116 Appendix B: OPEX questionnaire (excerpt of questions)





Figure B- 2: Four categories of lean practices


Figure B- 3: Key performance indicators for the goals of lean bundles

122 Appendix C: Details cluster analysis

Appendix C: Details cluster analysis

Figure C- 1: Dendrogram – outlier analysis

Appendix C: Details cluster analysis 123

Figure C- 2: Dendrogram – hierarchical clustering

124 Appendix C: Details cluster analysis

Figure C- 3: Number of clusters based on agglomeration coefficients

.000

100.000

200.000

300.000

400.000

500.000

600.000

700.000

800.000

0 5 10 15 20 25

Err

or s

um

s of

sq

uar

es

Number of Clusters

Scatter plot elbow

.000

50.000

100.000

150.000

200.000

250.000

0 5 10 15 20 25

Gra

dien

t of

err

or s

ums

of s

qu

ares

Number of Clusters

Scatter plot gradient

Curriculum Vitae 125

Curriculum Vitae

Name: Saskia Gütter

Date of birth: December 8th, 1983

Place of birth: Usingen/ Germany

Practical Experience: 2009 – 2012 University of St.Gallen, St.Gallen/ Switzerland

Institute of Technology Management Research Associate

2008 University of St.Gallen, St.Gallen/ Switzerland Institute of Technology Management Internship and Diploma thesis

2007 NETZSCH do Brasil Ind. e Com. Ltda, Pomerode/ Brazil Internship: Value stream mapping

2005 & 2003 Horiba Europe GmbH, Oberursel/ Germany Technical internship

Education 2009 – 2014 University of St.Gallen, St.Gallen/ Switzerland

Institute of Technology Management Doctoral Studies in Business Innovation

2007 Univ. Federal de Santa Catarina, Florianopolis/ Brazil Departamento de Engenharia de Produção e Sistemas Exchange student

2003 – 2009 Technische Universität Ilmenau, Ilmenau/ Germany Mechanical Engineering & Business Administration

(Dipl. Wirtsch. Ing.)

1994 – 2003 Christian-Wirth-Schule, Usingen/ Germany Abitur (German A-Level Equivalent)

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