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1. An empirical analysis of warehouse measurement systems in the context of supply chain implementation 1

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An empirical analysis of warehouse measurement systems in the context of supply chain

implementation

Author Kiefer, Allen W; Novack, Robert A

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Abstract

Supply chain management (SCM) is one of the most popular management concepts to impact

business and the logistics concept in the 1990s. Problems facing the concept of SCM include: 1. the lack of

research on what it means to practice SCM, 2. how to implement a SCM program, and 3. how to measure the

performance of a supply chain. A major contributing factor to these problems is defining which processes are

managed in a supply chain and which firms, or intermediaries, are included in a supply chain. Two types of firms

used for analysis are: those implementing a supply chain orientation and those that are not. An empirical

analysis offers a comparison between common warehouse performance measurements for SCM-oriented firms

and non-SCM-oriented firms and provides insight into the relationship between managers' perceptions of

warehouse measurement effectiveness and the degree of SCM sophistication.

Full text Supply Chain Management (SCM) is one of the most popular management concepts to impact

business and the logistics concept in the 1990s. Problems facing the concept of SCM include (1) the lack of

research on what it means to practice SCM, (2) how to implement a SCM program, and (3) how to measure the

performance of a supply chain. A major contributing factor to these problems is defining which processes are

managed in a supply chain and which firms, or intermediaries, are included in a supply chain.

This research will focus on the warehousing component of the supply chain process and, in particular, on how

firms measure the performance of their warehouse (intermediary) operations. Two types of firms will be used for

the analysis: those implementing a supply chain orientation and those that are not. The empirical analysis will

offer a comparison between common warehouse performance measurements for SCM-oriented firms and non-

SCMoriented firms and provide insight into the relationship between managers' perceptions of warehouse

measurement effectiveness and the degree of SCM sophistication.

BACKGROUND

Measuring Supply Chain Performance

Before presenting a discussion on supply chain performance measurement, it is necessary to offer how this

research defines a supply chain. Many definitions for the supply chain have been offered in the literature.'

These definitions are too limited in their scope because they imply that the supply chain focuses on just

manufacturing or logistics processes. Because this research examines the supply chain as an enterprise-to-

enterprise model, the following definition for the supply chain is used:

An integrated collection of organizations that manage information, product, and cash flows from a point of origin

to a point of consumption with the goals of maximizing consumption satisfaction while minimizing the total costs

of the organizations involved.

A supply chain is truly an enterprise model, linking logistics processes, marketing/sales processes, financial

processes, and information processes among multiple firms. Its planning and implementation begin with

executive management. A good example of a supply chain can be seen in the most recent report on Efficient

Consumer Response.2 This report followed the initial identification of the supply chain concept in the grocery

industry.3

Organizations have found it difficult to effectively measure their own logistics processes because of their cross-functional and boundary-spanning characteristics. Measuring supply chain performance increases the

complexity of this task. A different set of metrics that capture all aspects of the supply chain must be developed

for this purpose. Caplice and Sheffi stated that measures used to capture the performance of a transformational

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process fall into one of three primary dimensions: utilization, productivity, and effectiveness.' These authors also

stated that for any measure to be effective, it must be assessed across eight separate criteria: validity,

robustness, usefulness, integration, economy, compatibility, level of detail, and behavioral soundness.5 A

consortium of companies and academic institutions, under the guidance of Pittiglio, Rabin, Todd, and McGrath

(PRTM), developed a comprehensive set of agreed-upon supply chain metrics that can be used as standards

and can pass assessment using the eight criteria stated above.6 These measures fall into one of four

categories: customer satisfaction/quality, time, costs, and assets. The warehouse measures used in this

research represent all four of these categories.

A customer focus is of paramount importance when developing performance measures. For the purposes of this

research, these customers are industrial buyers and receivers, not consumers. Many organizations focus solely

on what can be called internal, or conformance, measures such as order fill and inventory turns. While these are

extremely important because of their downstream impact on the customer, they must not be measured alone.

Other metrics focusing on customer reaction to service and cost levels must be incorporated into a

comprehensive performance measurement system. Understanding how order fill and inventory turns influence

the customer's reaction is far more important than the internal measure alone. Developing the relationships

between internal measurement performance and external customer reactions allows a firm to estimate the

relationships between service and revenue, thus allowing logistics to be a competitive advantage in the

management of the supply chain.

Research has indicated that companies that use a supply chain strategy might use different types of

performance metrics than firms that do not utilize the concept of the supply chain.7 No research was found

indicating whether firms believe their measures for evaluating performance are effective, regardless of SCM

implementation. Therefore, this research will attempt to empirically determine the differences, if any, between

performance measurement systems used by firms that use a SCM strategy and those that do not, and to

assess the relationship between performance measure effectiveness and level of SCM implementation.

Research PurposeThis research is exploratory. It attempts to evaluate the extent of SCM implementation among firms and the

nature and effectiveness of their performance metrics. Supply chains include many types of firms, from

manufacturers to transportation carriers to retailers. Because of this complexity, this research will focus on only

one type of firm-warehousesin order to simplify data collection and analysis. This choice was made for two

reasons: First, warehouses play a critical intermediate role between supply chain members, affecting both

supply chain costs and service. Second, the performance metrics shown in Table 1 are very applicable to the

operations in a warehouse and capture its cost and service impacts on the supply chain.

RESEARCH QUESTIONS AND HYPOTHESES

A critical aspect of this research was the requirement for segregating respondent firms into those that haveimplemented a supply chain strategy and those that have not. Research conducted by Mercer Management

Consultants identified four constructs necessary for the presence of supply chain management: (1) strategy; (2)

integrated processes; (3) technology and information; and (4) structure, people, and culture.8 The Mercer

research identified several questions to be used for each construct to identify both its importance and status of

implementation. This research utilized only one question per construct because it was intended to segregate the

respondents only by status of implementation. Appendix A contains a definition of each construct and the

question used to classify firms on each construct. The survey instrument asked the respondents to indicate on a

scale of zero to seven the level of implementation of supply chain management for each construct within their

firms. A zero response meant implementation was not planned; responses of one through seven indicated that

implementation of supply chain management was planned, in progress, or fully implemented, respectively. The

respondent base was then divided into those that were not planning to implement supply chain management (a

response of zero) and those that were (responses one through seven). This resulted in two distinct groups of



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comments that were made about the content and clarity of the survey. Nine of the pre-tests were returned, none

with significant changes to the instrument. As such, all nine pre-test responses were included in the overall data

analysis.

A nonresponse bias test was performed on the two mailing groups to establish internal selection validity. A chi-

square test was performed on one categorical variable, four demographic variables, and five response variables

from the survey. None of the results were significant at the .10 level; thus nonresponse bias will not be assumed

to be a significant factor in this analysis.

The survey instrument was constructed in order to best identify the warehouse measures used by firms. The

survey consists of common warehouse measures obtained from previous research by Mentzer and Conrad,'2

Capplice and Sheffi,'3 Ackerman,l4 Jenkins,'5 and PRTM.16 The measures are divided into five categories:

order fulfillment, storage, receiving, customer satisfaction, and cost and earnings. Order fulfillment is further

broken into five subcategories: labor and equipment productivity; overall productivity; labor, equipment, and

overall utilization; labor and equipment performance; and overall performance. Receiving is divided into two

subcategories: labor, equipment, and overall productivity; and utilization and performance. Overall, the

respondents were given seventy-seven different measures and were asked which ones they used. Each section

supplied an "other" category in case a specific measure was not identified. Each section also asked the

respondents to identify how effective they perceived the measures in that section to be.

The respondents were also asked to identify which primary unit of measurement they would be using when

responding to the measurement questions. This was done in an attempt to make the survey instrument less

complex and shorter. The options were dollar value, cartons, units/pieces, pallets, weight, lines, invoices,

orders, and other.

The next section asked the respondents to identify their perceived level of implementation on four items that are

used to define the supply chain management concept. These items were taken from the Mercer research, as

mentioned previously. Four items were used because one item alone could not define the complexity of the

supply chain concept, resulting in face/content validity of the supply chain construct.Finally, various types of demographic data were collected to help describe the respondent base as well as serve

as a basis for analysis.

An initial mailing of the survey and cover letter were sent to 982 warehouse and logistics executives. The

effective sample size was reduced to 980 because either the intended respondent left the firm or the firm's

business was not relevant to this research. The initial mailing resulted in 169 usable responses and a second

mailing produced an additional 127 responses for a total of 296 responses, or a 30 percent response rate.

To determine trait validity, two tests were conducted.17 First, a factor analysis was conducted on the four

supply chain items and on the ten effectiveness items. These will comprise constructs to be used later in the

analysis. The four supply chain items loaded on a single factor as well as did the ten effectiveness items,indicating that each construct is unidimensional. Cronbach's coefficient alphas were also generated for the

above constructs.18 The alpha for the supply chain construct was .942 and for the effectiveness construct was

.932. The results of these analyses appear to satisfy the requirements of construct validity.'

RESULTS

As previously reported, a total of 296 respondents are included in the analysis. To answer Research Question

1, Table 1 shows that a large majority (79.1 percent) of the respondents indicated that they either were

implementing or have implemented the concept of supply chain management as defined in this research. This

significant difference between these two groups is somewhat surprising. A demographic analysis between these

two groups showed that both are composed primarily of manufacturing firms and both showed a very similar

distribution across the various ranges of firm revenues. However, the single largest revenue category for Level

0 firms was $1 million to $50 million (30.6 percent) and for Level 4 firms was over $1 billion (36.1 percent). This

characteristic offers some explanation for the differences between these groups. Many firms have found that



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implementing a supply chain concept can require significant investment. Smaller firms might not possess the

investment capital to pursue this strategy. Table I also gives other pertinent demographic statistics of the

respondent group. The typical respondent to the survey was a manager at a manufacturing firm, with annual

revenues exceeding $1 billion, that operates its warehouses as cost centers. This is probably representative of

the profile of the WERC membership. mentation for Level 4 firms across the four supply chain constructs. As

can be seen, the average implementation levels are very close for all four constructs and have no statistical

difference. One conclusion about this result is that all four constructs of SCM need to be implemented in concert

with one another. Although the results of Table 2 show that Strategy is the furthest along in implementation,

Process, Information, and Culture are also far along in implementation. This result can be seen in practice with

firms participating in Cooperative Planning, Forecasting, and Replenishment (CPFR) initiatives or with firms

implementing Enterprise Resource Planning (ERP) systems.

Hypotheses 1 generated the results seen in Table 3. The percentages under each column add to more than 100

percent because the survey instrument allowed each participant to select more than one primary unit of

measure. Only one statistically different primary unit of measure existed between the two groups: dollar value.

As such, Hypothesis 1 is supported by the data, and the null hypothesis is accepted. The previous definition of

SCM included the management of product, information, and cash flows. Table 3 shows primary units of

measurement for product (cartons, units/pieces, pallets, weight, and lines), information (invoices and orders),

and cash (dollar value). The results of this analysis imply that the measurement of the impact of warehousing on

cash flow is one element that distinguishes between firms that practice SCM and those that do not.

Hypothesis 2 generated the results shown in Tables 4 through 8. The acronym "UOM" shown on these tables

represents the unit of measure the respondent chose when responding to this section of the survey. In total,



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respondents were presented with seventy-seven different measures to evaluate. Each table (4 through 8)

represents, in the researchers' judgment, the most commonly used measures by both Level 0 and Level 4 firms.

Table 4 shows that thirteen of thirty-five order fulfillment measures were used most frequently by both types of

firms. Of these thirteen, eleven were statistically different at the .10 level. In other words, more Level 4 firms

used the eleven most popular measures than did Level 0 firms. Most of the differences appear to be a result of

Level 4 firms' ability to measure or quantify an entire process rather than just a part of it. For example, under

Labor and Equipment Productivity, Level 0 and Level 4 firms both measured total UOM picked/total labor hours

picking. However, more Level 4 firms were measuring total UOM picked/total labor hours. The difference is in

the latter part of each measure: total labor hours picking versus total labor hours. Total labor hours is more

inclusive of all labor activity, while picking hours is only a part of total labor hours. The differences under Overall

Performance can be attributed to the fact that Level 4 firms are more likely to use measures of total process

time, satisfaction/quality, and asset productivity than are Level 0 firms. These represent three of the four

categories of supply chain metrics, as previously introduced.

Table 5 shows that respondents heavily used 25 percent of the Storage Measures suggested in the survey

instrument, with all three being statistically different. All three measures are representative of asset productivity.

More Level 4 firms are concerned with measuring asset productivity than are Level 0 firms. This might be a

result of being able to implement Integrated Processes and Technology and Information (defined in Appendix

A), which allow firms to better track and manage inventories at a facility level as well as at customer and

supplier levels.



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firms, but both groups have similar perceptions of the effectiveness of this measure.

Finally, Hypothesis 4 generated the results seen in Table 10. The multiple regression model tested is stated:

Measure Effectiveness = f(Strategy, Process, Information, Culture)

The four independent variables are the items in the supply chain implementation construct described earlier.

The relationship implied in this model states that as a firm continues to develop a supply chain strategy, its

perception of its measure effectiveness improves. This is a logical continuation of Hypothesis 3. The results

from this hypothesis, shown in Table 9, imply that firms that are closer to full supply chain implementation

perceive their measures to be more effective than do firms that have not adopted a supply chain philosophy.

Several regression models were run on the data. Table 10 shows that the first model, including all respondents,

is significant at the .0043 level. This implies that there is a relationship between level of supply chainimplementation and perceived measure effectiveness. The R-square statistics were low for the models. These

statistics are a measure of the linear relationship between the dependent and independent variables and

indicate the strength of the relationship. A low R-square also implies that there are other variables, not included

in the model, that account for variation in the dependent variable. This makes intuitive sense since

measurement effectiveness could also be influenced by such variables as data integrity, complexity of the

supply chain, or measurement construction. However, the independent variables included in the models in this

research were significant, indicating that they do have a positive relationship with the dependent variable. As

such, Hypothesis 4 is rejected by the data. Several demographic variables were also captured by the survey.

Regression models were run on these variables to determine if certain sub-groups of respondents wereresponsible for the significance of the relationship. Table 10 also shows these results. The categories under

each demographic variable are different from those shown in Table 1. Several categories had to be collapsed

into others to provide enough degrees of freedom to allow the regression results to be unbiased. As the data

show, manufacturing firms having revenues between $50 million and $499 million, and measuring their

operations as cost centers, show a significant relationship between supply chain implementation and perceived

measure effectiveness. Disappointing, however, are the relatively low R-square statistics for each model. This

implies that even though there is a relationship between the dependent and independent variables in the model,

there are other factors that account for perceived measure effectiveness. One possible explanation for this is

that a supply chain orientation might capture only the "integration" criterion in Capplice and Sheffi's

categorization of measure effectiveness.



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CONCLUSIONS AND RECOMMENDATIONS

The intent of the empirical portion of this research was to examine the nature of warehouse measurements in a

supply chain environment. Three of the four stated hypotheses were accepted. There appears to be a

significant difference between the nature of warehouse measures as well as between perceived measure

effectiveness for firms following different paths concerning the implementation of a supply chain philosophy.

Firms implementing a supply chain orientation are more likely to use measures that reflect an entire process,

rather than just a portion of it. These firms are also more inclined to use measures that reflect the impact of

warehousing on a firm's financial position. Finally, firms in a supply chain mode have a higher interest in

measuring the satisfaction of their customers concerning warehouse process outputs.

Firms implementing a supply chain strategy also expressed a higher perceived effectiveness of their current

measures, although no measure received an effectiveness score higher than a 5.4 on a 7-point Likert type

scale. However, of the ten regression models generated to test the relationships between measure

effectiveness and level of supply chain integration, only four were significant. Although this does not diminish

the fact that certain types of firms find their perceived measure effectiveness increases as they move toward full

supply chain implementation, more significant models were expected.

The results of this research concluded that warehouse measurement systems differ between those firms

implementing a supply chain strategy and those that are not. Although there is a relationship between the

effectiveness of these systems and level of supply chain implementation, it is a relatively weak one. This is not

an unexpected result. The effectiveness of a measurement system is influenced by more than the type of

strategy a firm implements. However, this research showed that firms implementing a supply chain strategy

expressed a higher perceived effectiveness of their measures than did firms that are not implementing this

strategy.

Future research is needed to identify why firms implementing a supply chain strategy perceive their measures to

be more effective. Is it because many of the measures used by these firms are process oriented? Or is it

because these firms are more likely to employ measures that reflect the customer's perception of their performance? Future research is needed to identify what additional factors, other than level of supply chain

implementation, are influencing executives' perceptions of the effectiveness of their warehouse measures.

The saying goes, "You can only manage what you can measure." However, it is entirely possible that some

firms manage only those activities or processes that are easy to measure. The correct method is to identify what

is important to manage, then develop measures for these activities or processes. This research has concluded

that managing a supply chain strategy and warehouse measures and their effectiveness are related and

important. This conclusion could be the result of first identifying the process to be managed and then developing

the measures. Regardless of the cause, this research has shown that firms embarking on a supply chain

implementation strategy need to remember that the effectiveness of their measures and this strategy are

positively related.

Structure, People, and Culture - A clearly formulated and communicated vision of supply chain management to

each player; removing disincentives to teaming; protection of innovation from short-term profit pressures;



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" Shelby 0. Hunt, Richard D. Sparkman Jr., and James B. Wilcox, "The Pretest in Survey Research: Issues and

Preliminary Findings," Journal of Marketing Research, 19 (May 1982), pp. 269-273.

Footnote

" Donald S. Tull and Del I. Hawkins, Marketing Research Meaning, Measurement, and Method (New York;

Macmillan, 1976).

12 John T. Mentzer and Brenda Ponsford Konrad, "An Efficiency/Effectiveness Approach Logistics Performance

Analysis," Journal of Business Logistics, Vol. 12, No. 1 (1991 ), 33-62.

3 Caplice and Sheffi, op.cit. '4 Kenneth B. Ackerman, Practical Handbook of Warehousing, 31d Edition (New

York: Van Norstrand Reinhold, 1990).

Footnote

15 Creed Jenkins, Complete Guide to Modern Warehouse Management (Englewood Cliffs, NJ: Prentice-Hall,

1990). 6 Pittiglio, et.al., op.cit.

" Convergent validity, a type of trait validity, was tested here. Since several different measures were used for

each construct, convergent validity was tested using factor analysis to determine if all of the measures

converged on a common statistical factor.

" Chronbach's Alphas, also a measure of trait validity, compares how well each question correlates with the

combination of all the other questions measuring a construct.

19 Achieving construct validity means that the theoretical phenomena identified in the research have been

correctly defined and measured.

AuthorAffiliation

Mr. Kiefer is support operation manager, $1' Maintenance Department, U.S. Army, and is headquartered in

Germany. Mr. Novack, CTL, is associate professor of business logistics, The Pennsylvania State University,

University Park, Pennsylvania 16802.

The authors would like to thank the Penn State Center for Logistics Research for funding this research.

Appendix

Appendix A. Survey Questions Used to Determine Level of Supply Chain Implementation

Appendix

Strategy -- Aligning supply chain strategy with business goals; senior management commitment to supply chain

management; a total system approach; customer service strategy to meet different customer requirements;

establishing strategy alliances with key suppliers; and outsourcing non-core, non-strategic supply chain

activities.

Appendix

Integrated Processes - Use of cross-functional teams for process design and improvement; use of process

owners; use of life-cycle management into supply chain processes; integrating manufacturing, customers, andsuppliers into the design process; utilizing total corporate leverage in procurement; shifting functions to the most

efficient provider; monitoring supplier performance; integrate and balance supply, demand, and financial plans

and objectives; delivery systems with tailored service; jointly manage inbound and outbound transportation; and

regular monitoring customer satisfaction levels with feedback to all supply chain processes.

Appendix

Technology and Information -- Aligned with key business processes; minimal data redundancy visible to all;

availability of a data warehouse that is widely available; enterprise-wide planning systems; institutionalized

sharing of technology assets across divisions; use of EDI between company, customers and suppliers; system

for demand forecasting, distribution planning, production planning, and material planning that are highly

integrated; and manufacturing execution systems to track material flow and production costs as well as

providing order status information to customer service.



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Subject

Supply chains; Warehouse management systems; Measurement; Statistical analysis; Studies;

Location US

Classification 9190: US; 5160: Transportation management; 5240: Software & systems; 5330: Inventory

management; 9130: Experimental/theoretical treatment

Publication title Transportation Journal

Volume

38

Issue 3

Pages 18-27

Number of pages 10

Publication year 1999

Publication date Spring 1999

Publisher Pennsylvania State University Press

Place of publication Lock Haven

Country of publication United States

Publication subject Transportation

ISSN 00411612

CODEN

TRNJAE

Source type Scholarly Journals

Language of publication English

Document type PERIODICAL

Accession number 01820444

ProQuest document ID 204590562

Document URL

http://search.proquest.com/docview/204590562?accountid=39226

Copyright Copyright American Society of Transportation and Logistics Spring 1999

Last updated 2012-04-04

Database ABI/INFORM Complete

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