The Impact of Lean Practices on Value to Customer · 2018-12-07 · The Impact of Lean Practices on...

The Impact of Lean Practices on Value to Customer

Michael J. Braunscheidel*, James W. Hamister

Lean practices have been the focus of considerable research. Lean is associated with low cost production, consistent quality and the elimination of non-value added activities. This research evaluates the impact of six (6) lean practices, internally and externally focused, on product satisfaction in a variety of manufacturing settings. In keeping with Shah and Ward (2003), these internal and external lean practices are ‘bundled’ to evaluate the synergistic effects of the implementation of complimentary facets of lean techniques on product satisfaction. Product satisfaction is measured using the ‘value to customer’ scales developed by Tu et al. (2001). In this work, Tu et al. (2001) assess the degree to which customers are satisfi ed with an organization’s products. In addition to the overall model, the effects of fi rm size (sales dollars), inventory strategy (MTO, MTS) and process type (high volume vs. low volume) are investigated. Our fi ndings indicate that both internal and external lean practices are associated with increased product satisfaction, and that these relationships do not vary by contextual variables considered.

Keywords: Lean, Survey, Structural Equation Modeling, Partial Least Squares

INTRODUCTION

Competition in the global manufacturing arena has been fi erce since the 1970’s. Firms have had to change from an industrial environment

Michael J. Braunscheidel, Department of Management and Marketing, Richard J. Wehle School of Business, Canisius College, 2001 Main Street, Buffalo, NY 14208-1098 USA, 716-888-3710, E-mail: [email protected]; James W. Hamister, Department of Information Systems & Operations Management, Raj Soin College of Business, Wright State University, Dayton, OH 45435-0001, USA, 937-775-2748, E-mail: [email protected].

* Corresponding Author

52 THE BRC ACADEMY JOURNAL OF BUSINESS

that emphasizes internal effi ciency to one where customer value is of paramount importance (Tu et al. 2001). In order to remain profi table and competitive, organizations have sought methods by which they could improve and sustain their operations. This paradigm shift from inter-nal effi ciency to adding customer value requires fundamental changes in manufacturing system design (Tu et al. 2001). One of the most popular methods that organizations have embraced is Lean. Lean systems are manufacturing systems that maximize the value added by the company’s activities (Krajewski, Ritzman and Malhotra 2010).

Past research has centered on whether or not Lean techniques effec-tively improved performance. Typically performance measures have included: inventory performance, cycle time performance, delivery per-formance, quality performance, cost performance, and manufacturing fl exibility. Several studies have found strong support for Lean’s positive impact on operational performance. When conducting a meta-analysis of JIT practices, Mackelprang and Nair (2010) found that “each JIT prac-tice results in improved aggregate performance even though the practice may not be positively associated with performance measures when con-sidered individually” (p. 290). This suggests, as recommended by Shah and Ward (2003), that lean practices must be bundled. Lean production is a multi-dimensional construct and the “core thrust of lean production is that these practices can work synergistically to create a streamlined, high quality system” (Shah & Ward, 2003; p. 129).

Deming (2000) indicated that the ultimate goal of an organization is to satisfy their customers. This is accomplished by providing value to its customers. As indicated above, most of the performance measures that the literature focuses on are operational in nature. While these perfor-mance measures defi ne a set of variables that infl uence customer satis-faction indirectly, they do not measure satisfaction directly. Since lean has been a readily adopted paradigm in many organizations, it makes sense to address this gap in the literature. That is, what is the impact of bundles of lean practices on the customer’s degree of satisfaction with the organization’s products? “Firms achieve high levels of satisfaction by providing high value to their customers.” (Zhang, Vonderembse, &

53The Impact of Lean Practices on Value to Customer

Lim, 2003, p. 179). Value to customer “is an external measure that assesses the customers’ degree of satisfaction with the organization’s products. It is the extent to which products provide benefi ts that custom-ers believe are important.” (Tu et al. 2001, p. 204) Thus, in this research we seek to understand the relationships between bundles of lean prac-tices and the performance measure “value to customer.”

This paper is organized as follows. In the next section we perform a brief review of the lean production and JIT literature and develop the hypotheses to be tested. In the methods section, we discuss instrument development, sampling, the measurement model and the structural model. After that we conclude with a Discussion section and Conclusions.

LITERATURE REVIEW

There has been substantial interest in lean manufacturing among both researchers and practitioners since the 1980’s. The popular interest in this topic has been motivated in part by the success of Japanese manu-facturing companies, particularly Toyota, at exploiting export markets and relentless competitive pressures on incumbent automotive manu-factures. Japanese manufacturers were perceived to have advantages in both quality and cost level, and continued to improve those advantages. Lean manufacturing was thought to drive this high-level operational per-formance and thus understanding this phenomenon was considered as highly important for both business and public policy reasons.

Lean techniques are composed of a variety of separate and distinct practices. Shah and Ward (2007) explain that due to the multidimension-ality and complicated nature of lean production, many managers focused on “a single visible aspect of the process while missing the invisible, highly inter-dependent links of the system as a whole” (p. 786). This implies that while lean production may consist of many parts, the value of lean production is its impact as a system on performance. Several authors have recommended the use of bundles of practices when mul-tidimensional constructs are employed (Ketokivi & Schroeder, 2004; MacDuffi e, 1995; Shah & Ward, 2003, 2007). Jayaram, Vickery and


Droge (2008) indicated that lean strategy requires the integration of business processes, not individual functions, to create value. The core objective of a lean strategy is to systematically integrate all activi-ties, both internally and externally, that provide value to the customer (Jayaram et al. 2008). Shah and Ward (2003) maintain that lean pro-duction systems are integrative in nature and require the implementa-tion of a diverse set of practices. It is this simultaneous implementation that results in higher operational performance. In keeping with Shah and Ward (2003), we envision that lean practices are bundled. However, we see the lean bundles being in two distinct bundles: internal bundles and external bundles. The difference arises from the fact that one is contained within the boundaries of the organization and whereas the other entails working with suppliers and customers. While Shah and Ward (2007) did not specifi cally divide the ten (10) lean practices that were identifi ed, it is clear that four (4) of the lean practices had an external focus and the remaining six (6) had an internal focus. There is evidence however in categorizing lean practices into internally and externally oriented prac-tices (Shah, 2002).

Internal Lean PracticesWe consider the following four (4) internal lean practices: Pull produc-tion, processing fl ow, set-up time reduction, and quality efforts. Each of these techniques is individually important to the adoption and imple-mentation of a lean production system. These internal practices have been identifi ed in the literature are being critical components of the lean paradigm (Shah, 2002; Shah & Ward, 2007). However we will test and understand the synergistic or ‘bundled’ impact of these four (4) internal practices of lean. In order to complete this we create a second order con-struct, Internal Lean Practices, in an attempt to understand the impact of these bundled practices on value to the customer. Thus we intend to test the following hypothesis.

H1: Firms with high levels of internal lean practices will have high levels of customer satisfaction with the fi rm’s products.


External Lean PracticesIn some ways, lean production systems were a precursor to integration efforts with customers and suppliers. The Japanese keiretsu required close integration between an organization and its customers and suppliers (Dyer, 1996; Liker & Choi, 2004). One of the elements of a lean opera-tion is close supplier relationships (Krajewski, Ritzman, & Malhotra, 2010; Stevenson, 2007). This relationship and integration with suppliers

FIGURE 1. Research Model.


includes just-in-time delivery of high quality goods. Relationships with suppliers are expected to be long term in nature. Jayaram et al. (2008) found that relationship building (integration with customers and suppli-ers) was signifi cantly related to the implementation of lean strategies (lean design and lean manufacturing). Regarding external lean practices, we employ two constructs: Integration with Key Suppliers and Integra-tion with Key Customers that are used to describe the External Lean practice bundle.

H2: Firms with high levels of external lean practices will have high levels of customer satisfaction with the fi rm’s products.

Value to CustomerThe idea of lean manufacturing systems suggests an overall approach that requires the integration of business processes that create value for the fi rm’s customers (Jayaram, Kannan, & Tan, 2004; Jayaram, et al., 2008; Stevens, 1989; Tan, Kannan, & Handfi eld, 1998; Vickery, Jayaram, Droge, & Calantone, 2003). Jayaram et al. (2004) found that internal and external integration enabled value creation across the supply chain.

We measure value to customer using scales from prior research. Value to customer is defi ned as “an external measure that assesses the cus-tomers’ degree of satisfaction with the organization’s products. It is the extent to which products provide benefi ts that customers believe are important. VC measures perceptions of the value of product variety, cus-tomer satisfaction with product quality and features, and customer loy-alty and referrals (Tu et al. 2001, p. 204).”

Contextual VariablesIn this research, in addition to investigating the effects of internally and externally oriented lean practices on value to the customer, we also seek to understand if there are certain contexts where the effects of these lean bundles are stronger or weaker than in the overall model. To this end we investigate the impact of fi rm size (sales dollars), production process employed (Low volume [job shop & batch] and high volume [repetitive


assembly & continuous fl ow]) and inventory strategy (make-to-order & make-to-stock). These contextual variables are consistent with other research (Sousa & Voss, 2008).

Firm SizeIn this research, we categorized the sampled fi rms into two categories: Small and Large. Small fi rms had annual sales up to and including $100 million. Large fi rms were those that indicated sales greater than $100 million. It has been reported that larger fi rms, due to the availability of more resources, may be more prone to adopt new methods such as lean production (Harrington, Khanna, & Deltas, 2008; Jarrod & Chester, 2008; Soares-Agular & Palma-Dos-Reis, 2008).

H3a: Larger fi rms will have higher levels of internal lean practices.H3b: Larger fi rms will have higher levels of external lean

practices.H3c: Internal lean practices will have a higher relationship with

customer satisfaction with the fi rm’s products for larger fi rms.

H3d: External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for larger fi rms.

TABLE 1. Contextual Variables.

Firm Size n

Large fi rms (Sales ≥ $101 M) 116

Small fi rms (Sales ≤ $100 M) 97

Production Process n

High Volume (Repetitive assembly & Continuous) 116

Low Volume (Job Shop & Batch) 97

Inventory Strategy n

Make-to-Order 107

Make-to-Stock 57


Production ProcessLean manufacturing had its roots in repetitive assembly and continuous operations. Many of the fi rst applications of lean were within these types of production processes (Womack, Jones, & Roos, 1990). One of the main goals of lean is to eliminate waste (Womack & Jones, 1996). Cer-tainly the elimination of waste is applicable to all production processes and elements of lean are appropriate to achieve this goal. One of the primary goals of a lean production system is the smooth rapid fl ow of goods through the manufacturing process (Shah & Ward, 2007). Regard-ing the product-process matrix, both repetitive assembly and continu-ous operations rely on rapid fl ow to attain a low unit cost (Safi zadeh, Ritzman, Sharma, & Wood, 1996).

H4a: High volume operations will have the highest levels of internal lean practices.

H4b: High volume operations will have the highest levels of external lean practices.

H4c: Internal lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for high volume operations.

H4d: External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for high vol-ume operations.

Inventory StrategyLean manufacturing functions best when supply, processing, and demand variability is reduced (Shah & Ward, 2007). Through the minimization of inventory, responsiveness to customer demand, and the use of pull production techniques, lean production systems attempt to match pro-duction (supply) with demand. While high volume operations typically rely on a make-to-stock inventory strategy and low volume operations rely on a make-to-order inventory, a make-to-order inventory strategy is more responsive to the ‘pull’ of customer demand. Thus a conundrum exists between inventory strategy and production process. In order to investigate this dilemma, we investigate the following hypotheses.


H5a: Make-to-order operations will have the highest levels of internal lean practices.

H5b: Make-to-order operations will have the highest levels of external lean practices.

H5c: Internal lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for make-to-order operations.

H5d: External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for make-to-order operations.

METHODS

SampleA database of 4,000 names of supply chain management professionals was obtained from the Institute of Supply Management (ISM). A wide variety of manufacturing organizations were sampled so that generaliz-able results could be attained. The SIC codes sampled, along with the job titles of the survey respondents are shown in Table 2. We requested job titles of senior-level supply chain professionals from ISM and recruited them for participation because of the experience and exposure to the practices of the fi rm their advanced positions provide.

Following the recommendations of Dillman (2000), a web based sur-vey, hosted on a university web site, was employed for several reasons: the effective and effi cient contact of a large sample, added legitimacy to the survey request and alleviation fears of accessing a website that may pose potential harm such as computer viruses.

The overall response rate was 11.95%. While the response rate was not as high as we would have liked, it is believed that is was due to the focus on high level managers and measures of fi rm level constructs. However, a suffi cient sample size was received in order to perform the required data analysis (Devaraj, Krajewski, & Wei, 2007; Deveraj, Fan, & Kohli, 2002; Koufteros, Vonderembse, & Doll, 2002; Pfl ugheoft, Ramamurthy, Soofi , Yasai-Ardekani, & Zahedi, 2003). Thus the sample size obtained is suitable for our analysis. Additionally the absolute sample size is more


No. of Firms %

SIC Code 20 Food and Kindred Products 11 5.0

23 Apparel and other textile products 1 0.5

25 Furniture and Fixtures 7 3.2

28 Chemicals and Allied Products 19 8.7

30 Rubber / Misc. Plastic Products 12 5.5

34 Fabricated Metal Products 19 8.7

35 Industrial & Comm. Machinery & Eqpt. 15 6.9

36 Electrical Equipment & Components 48 22.0

37 Transportation Equipment 11 5.0

38 Measurement & Instrumentation 10 4.6

39 Miscellaneous Mfg. Industries 57 26.2

Missing Data 8 3.7

Title VP/Director Purchasing 36 16.5

Purchasing Manager 109 50.0

VP/Director Manufacturing 10 4.6

Plant Manager 2 0.9

VP/Director Logistics 3 1.4

Logistics Manager 8 3.7

Other 50 22.9

TABLE 2. Profi le of Survey Respondents.

important than the proportion of the population sampled (Black, 1999; Devaraj, Krajewski, & Wei, 2007; Fowler, 1993). Table 3 provides a list of the original sample along with the calculation of the response rate.

In order to determine if there were any differences between early and late responders to the survey, a chi-squared test was performed. Four demographic variables, Title: χ2 = 2.344, p = 0.886, Annual Sales: χ2 = 1.978, p = 0.992, Product Line: χ2 = 6.165, p = 0.104, and Manufactur-ing Process: χ2 = 1.346, p = 0.718, were used. No signifi cant differences were found.


Likewise, a Chi-square test was performed to check for differences between respondents and non-respondents. No allowances were made for those who opted out of the survey (e.g. who were out of the offi ce). These potential respondents were coded as being non-responders. The SIC code was used to determine if there were differences between those who responded and those who did not. In order to support generaliz-ibility of the study, it was desired to understand if there was a response

Early vs. Late Responders χ2 p

Title 2.344 0.886

Annual Sales 1.978 0.992

Inventory Strategy 6.165 0.104

Production Process 1.346 0.718

Responders vs. Non-responders

SIC code 14.87 0.189

TABLE 4. Response Bias.

TABLE 3. Response Rate.

Original Sample 4000

Invalid email addresses 511

Inappropriate titles 1130

Out of offi ce 354

Requested removal from survey 161

Undeliverable/Rejected as Spam 19

Adjusted Sample Size 1825

Total responses received 303

Unusable due to missing/incomplete data, multiple responses 85

Usable responses 218

Response rate (usable responses/adjusted sample size) 11.95%


bias or if this was due to the sampling frame. For the SIC code, no signifi cant difference was found between responders and non-responders (χ2 = 14.87, p = 0.189). Thus it is believed that no bias existed between respondents and non-respondents. Results of these chi-squared tests are found in table 4.

Survey InstrumentsPrevious research has demonstrated that the survey instruments employed in this research are both valid and reliable (Braunscheidel & Suresh, 2009). The appendices contain all of the scales employed in this research. All con-structs were measured on a 7-point Likert scale. Survey items are listed in appendix A, with item averages and standard deviations in table 5.

RESULTS

In order to analyze the model considered in this research, we employed a structural modeling technique known as Partial Least Squares (Ringle, Wende, & Will, 2005). This approach has been utilized in a variety of academic disciplines (Sosik, Kahai, & Piovoso, 2009) including stra-tegic management (Birkinshaw, Morrison, & Hulland, 1995; Hulland, 1999; Mezner & Nigh, 1995), and management information systems (Chin & Gopal, 1995; Chin & Newsted, 1995; Majchrzak, Beath, Lim, & Chin, 2005).

PLS is a structural equation modeling technique that relies on ordi-nary least squares methods (components based approach) as opposed to a covariance based methods as employed by other SEM techniques such a LISREL and AMOS (Chin, Marcolin, & Newsted, 2003; Gefen, Straub, & Boudreau, 2000). PLS is suited for explaining complex rela-tionships (Fornell & Bookstein, 1982; Fornell, Lorange, & Roos, 1990) and for application and prediction (Anderson & Gerbing, 1988). PLS does not assume normality of data distributions, observation indepen-dence or variable metric uniformity (Faulk & Miller, 1992; Sosik, et al., 2009). Another advantage of the components-based approach to SEM is its ability to provide satisfactory results when sample sizes are small


Construct Item Avg. St. Dev.

External Lean Bundle IC IC01 5.97 1.21

IC02 5.11 1.42

IC03 4.91 1.37

IC04 4.24 1.44

IC05 3.89 1.54

IS IS01 4.81 1.53

IS02 6.05 1.13

IS03 6.11 1.04

IS04 4.73 1.76

IS05 5.32 1.51

Internal Lean Bundle LP LP01 4.30 1.71

LP02 4.20 1.76

LP03 3.86 1.89

LP07 5.07 1.59

LF LP04 4.81 1.59

LP05 5.05 1.51

LP06 4.82 1.69

LSU LP08 5.21 1.34

LP09 4.45 1.44

LSP LP10 4.23 1.83

LP11 4.36 1.83

LP12 4.62 1.79

LP13 4.08 1.84

LP14 4.60 1.88

Dependent Variable VC CS01 5.40 1.02

CS02 5.61 0.84

CS03 5.27 1.19

CS04 4.69 1.10

CS05 5.52 0.90

TABLE 5. Survey Response Items.


(Chin & Newsted, 1995; Gefen, et al., 2000). Sample size requirements for PLS are ten (10) times the larger of the following (a) the block with the largest number of formative indicators or (b) the dependent latent variable with the largest number of independent latent variables impact-ing it (Chin, 1998b).

The structural and measurement models under PLS consist of three (3) sets of relations: (a) the inner (structural) model which specifi es the rela-tionships between latent variables, (b) the outer (measurement) model which specifi es the relationships between the latent variables and their associated observed variables and (c) the weight relations upon which the case values for the latent variables can be estimated (Chin, 1998b).

While PLS does not use fi t indices, good model fi t is established with signifi cant path coeffi cients, acceptably high R2 and internal consistency (construct reliability) being above .70 for each construct (Gefen, et al., 2000). The overall fi t of structural equation models using PLS are evalu-ated by examining the R2 for each of the constructs. The larger the value of R2, the better is the fi t. In addition, the structural paths are measured by path weights and t-statistics similar to the way that beta weights are interpreted in linear regression (Chin, 1998a; Gefen, et al., 2000).

Measurement ModelA two-step process (Anderson & Gerbing, 1992) was employed, where the measurement model is analyzed prior to assessing the structural model. Items are checked for reliability followed by various levels of statistical validity analysis for construct measurement. Reliability is the ability of a scale to consistently yield the same response (Nunnally, 1978). Three different methods were used to measure reliability. First Cronbach’s alpha, a popular measure of internal consistency, was cal-culated. A minimum value of 0.70 is considered acceptable for existing scales (Nunnally, 1978). Two other measures of reliability have been recommended by Chin (1998a). They are composite reliability (CR) with a lower bound of 0.70 (Hair Jr., Anderson, Tatham, & Black, 1995) and average variance extracted (AVE) which has a suggested lower value equal to 0.50 (Chin, 1998b; Fornell & Larker, 1981). Table 6


provides details with respect to reliability and validity. Measurement items exhibited acceptable reliability, with the exception of Lean-Setup which is low in Cronbach’s alpha. Chin (2010b) points out that alpha is a lower-bound estimate for reliability since all indicators are equally weighted. Since the other two measures of reliability, CR & AVE, exhib-ited values above the recommendations, this scale was deemed reliable in the context of the structural model.

Reliability Scale

Cronbach’s Alpha

(≥ 0.70)

CR ( ≥ 0.70) AVE (≥ 0.50)

Internal Lean PracticesLean-Pull (LP) .82 .89 .74

Lean-Flow (LF) .77 .87 .68

Lean-Setup (LSU) .47 .79 .65

Lean-SPC (LSP) .88 .92 .80

External Lean PracticesIntegration with Key Customers (IC) .72 .84 .65

Integration with Key Suppliers (IS) .74 .85 .66

Value to Customer (VC) .85 .90 .68

TABLE 6. Reliability & Convergent Validity.

Convergent ValidityScale

Factor Loading (Range)

Internal Lean Practices (INTERNAL)Lean-Pull (LP) .77−.92

Lean-Flow (LF) .78−.87

Lean-Setup (LSU) .74−.87

Lean-SPC (LSP) .87−.92

External Lean Practices (EXTERNAL)Integration with Key Customers (IC) .78−.83

Integration with Key Suppliers (IC) .77−.85

Value to Customer (VC) .77−85


Convergent validity is tested by determining whether items in a scale converge or load together in a single construct (Garver & Mentzer, 1999). The individual loadings for each block of indicators should share more variance with the component score than with the error variance. The stan-dardized loadings should be greater than 0.707 (Chin, 1998b). Hulland (1999) suggests a minimum value of 0.50. Several items exhibited poor convergent validity and were removed from the measurement model.

Discriminant validity is the extent to which items from one con-struct discriminate from items that represent another construct (Garver & Mentzer, 1999). Discriminant validity was assessed by comparing the correlations among the latent variables to the Average Variance Extracted (AVE) of the latent variable. The square root of the AVE should be larger than the correlations between construct (Chin, 1998a; Fornell & Larcker, 1981; Koufteros, 1999; Koufteros, Vonderembse, & Doll, 2001). Requirements for discriminant validity are met.

Structural ModelThe structural model was analyzed using partial least squares (PLS) estimation (Chin & Newsted, 1999). PLS is suggested in research with small sample sizes, and in theory building phase in general, and in particular for this research the ability to analyze formative constructs.

Construct Correlation (Square Root of AVE on diagonal) LP LF LSU LSP IC IS VC

Lean-Pull (LP) .86

Lean-Flow (LF) .38 .82

Lean-Setup (LSU) .41 .61 .81

Lean-SPC (LSP) .38 .41 .39 .90

Integration with Key Customers (IC) .23 .26 .30 .30 .80

Integration with Key Suppliers (IS) .32 .53 .45 .45 .49 .75

Value to Customer (VC) .13 .26 .28 .28 .28 .25 .83

TABLE 7. Discriminant Validity.

67The Impact of Lean Practices on Value to CustomerFI

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The software employed was SmartPLS (Ringle, et al., 2005). Variance values for structural parameters were estimated by bootstrapping (Chin, 2010a). Structural parameters are tested with one-sided t tests, since hypotheses are directional, with signifi cance levels established at p- values less than 0.05. Consistent with the literature review, we treat lean as two separate bundles of attributes labeled EXTERNAL and INTERNAL, as shown in fi gure 2. Overall the model explains 14.2% of the variation in Value to Customer (VC). Structural parameters for the practice dimensions that make up both INTERNAL and EXTERNAL bundles were all statistically signifi cant (p < .001).

H1 posits that Internal Lean Practices are positively associated with Value to Customer. H1 is accepted with the structural parameter β equal to 0.14 (t = 1.9, p < .05). This value is statistically signifi cant and in the correct direction, yet this represents a rather modest effect size which suggests that the practical signifi cance may not be high. H2 posits that External Lean Practices are positively related to VC. H2 is also accepted with β equal to 0.28 (t = 3.8, p < .001). Since this effect size is larger than the effect of Internal Lean Practices, we compared the two struc-tural parameters using Hotelling’s t-test (Rosenthal & Rosnow, 1991, p. 506) to compare two nonindependent correlations. We found that the difference is statistically signifi cant (tH = 2.2, p < .05), with external lean bundles explaining more of the variation in value to customer than does internal lean bundles.

Contextual VariablesWe next examine model differences based on context factors: size, man-ufacturing process type, and inventory management type. First we com-pare implementation mean levels of lean across the contextual variables, comparing mean differences in factor scores. Then we test for model differences based on contextual variables. A parametric approach is used to test for model differences between groupings for each contextual vari-able (Qureshi & Compeau, 2009). A t-test is constructed to test for dif-ferences in the path parameter between each of the two hypothesized structural paths, using pooled standard deviation calculated as follows:


1 2

2 22 21 2

( 1) ( 1) 1 12 2 2 2

−=⎡ ⎤ ⎡ ⎤− −+ +⎢ ⎥ ⎢ ⎥+ − + −⎢ ⎥ ⎣ ⎦⎣ ⎦

β βtm nSE SE

m n m n

(1)

The variables m and n are the respective sample sizes and SE is the parameter standard error estimate. Degrees of freedom for this test are m + n – 2. To conduct these tests, the sample is divided into two groups for each variable. Average levels on the lean bundles constructs and structural parameters are estimated for each group.

Sales were divided into respondents of less than or equal to $100 million in sales (n = 97) and those greater than $100 million in sales (m = 116). Internal lean practice factor scores were on average 0.64σ higher for large fi rms than for small fi rms (t = 4.6, p < .001). External lean practice scores were also higher at 0.26σ (t = 1.9, p < .05). Thus H3a and H3b are accepted. However the path parameters of the structural model do not differ between small and large fi rms for internal practices (t = 0.5, p > .05) or for external lean practices (t = 0.5, p > .05). H3c and H3d are therefore rejected.

To analyze production processes, we grouped job shop and batch pro-cesses as “low volume” (n = 97) and repetitive assembly and continuous fl ow as “high volume” (m = 116). High volume fi rms averaged higher on internal lean processes than did low volume fi rms (ES = 0.58σ, p < .001) supporting H4a. H4b is also supported since high volume operations aver-aged higher on external lean practices than did low volume operations (ES = 0.41σ, p < .01). The model parameters between the groups did not differ for internal lean practices (t = 0.6, p > .05), or for external lean practices (t = 0.13, p > .05). Thus neither H4c nor H4d are supported.

Inventory management types were make-to-order (MTO, n = 117) and make-to-stock (MTS, m = 57). There were too few respondents who evaluated their inventory as engineer to order (n = 18) or assemble to order (n = 20) to include in this analysis. MTO operations averaged higher than MTS operations on internal lean practices (ES = 0.10σ, p > .05), but the difference was not statistically signifi cant. Hypothesis

�1��2


External Lean Practices Sample Parameter t p Small Firms 97 .28 0.55 >.05

Large Firms 116 .31

Low Volume 97 .26 .13 >.05

High Volume 116 .28

MTO 117 .22 1.01 >.05

MTS 57 .39

Internal Lean Practices Sample Parameter t p Small Firms 97 .09 .55 >.05

Large Firms 116 .18

Low Volume 97 .15 .06 >.05

High Volume 116 .14

MTO 117 .26 1.23 >.05

MTS 57 .03

TABLE 9. Contextual Variables: Structural Parameter Comparisons.

TABLE 8. Group Comparisons.

External Practices Factor Score

Effect Size

t p

Firm Size Large Firms 5.49 0.26 σ 1.9 0.027

Small Firms 5.28

Production Process High Volume 5.53 0.41 σ 3.0 0.002

Low Volume 5.20

Inventory Strategy MTO 5.55 0.35 σ 2.7 0.004

MTS 5.26

Internal Practices

Firm Size Large Firms 4.78 0.64 σ 4.6 <.001

Small Firms 4.08

Production Process High Volume 4.77 0.58 σ 4.2 <.001

Low Volume 4.13

Inventory Strategy MTO 4.51 0.10 σ 0.7 >.05

MTS 4.40


H5a is rejected. MTO operations averaged higher than MTS operations on external lean practices (ES = 0.35σ, p < .01), which supports hypoth-eses H5b. Group comparisons are reported in Table 8 below. Structural model parameters do not differ based on inventory management type for internal lean practices (t = 1.2, p < .05) or for external lean practices (t = 1.0, p > .05). Therefore H5c and H5d are not supported. A summary of all research hypotheses tests is reported in Table 10.

H1 Firms with high levels of internal lean practices will have high levels of customer satisfaction with the fi rm’s products.

Accept

H2 Firms with high levels of external lean practices will have high levels of customer satisfaction with the fi rm’s products.

Accept

H3a Larger fi rms will have higher levels of internal lean practices. Accept

H3b Larger fi rms will have higher levels of external lean practices. Accept

H3c Internal lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for larger fi rms.

Reject

H3d External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for larger fi rms.

Reject

H4a High volume operations will have the highest levels of internal lean practices.

Accept

H4b High volume operations will have the highest levels of external lean practices.

Accept

H4c Internal lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for high volume operations.

Reject

H4d External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for high volume operations.

Reject

H5a Make-to-order operations will have the highest levels of internal lean practices.

Reject

H5b Make-to-order operations will have the highest levels of external lean practices.

Accept

H5c Internal lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for make-to-order operations.

Reject

H5d External lean practices will have a higher relationship with customer satisfaction with the fi rm’s products for make-to-order operations.

Reject

TABLE 10. Hypothesis Summary.


DISCUSSION

The primary purpose of this research is to investigate the impacts of Lean bundles on value to customer. Our structural model supports the fi nding that both internal and external lean practices contribute to the “value to customer” dimension of performance. Prior Lean research has established the value of lean at improving operational performance dimensions such as cost and inventory reduction. We can therefore extend prior Lean fi ndings for customer-focused measures, with associ-ated marketing and strategy implications. The modest amount of varia-tion explained suggests that managers should approach cost-benefi t analysis carefully when contemplating Lean implementations that are strictly focused on value to customer measures of performance improve-ments. A prior Lean study found that implementation explained 23% of the variation in operational performance (Shah & Ward, 2003), which is higher than the 14% of performance variation explained in our study. We also found that external bundles explain a larger share of value to customer than do internal bundles. Prior work has established the value of integration in a supply chain context (Droge, Jayaram, & Vickery, 2004; Flynn, Huo, & Zhao, 2010), yet the relative importance of internal versus external lean practices has not been published previously to the best of our knowledge.

Lean BundlesOur research project involved an empirical investigation into the com-position of lean and its performance impact on product value to cus-tomer. Our research confi rms that lean can be conceptualized as six separate practices, and that these practices are distinct as shown in the analysis of the measurement model. Our research also confi rms that the practices can be “bundled” into external and internal practices. Internal lean practices include setup reduction, pull systems, fl ow, and statisti-cal process control. Pull and statistical process control practices were most closely related to the overall internal practices construct, and thus merit close consideration by management for implementation priorities.


Setup reduction and fl ow had a lesser impact on internal practices, which is somewhat surprising. Practitioner literature such as “The Machine That Changed The World” (Womack, et al., 1990) focus heavily on fl ow and pull as priority elements in a lean implementation. There are sev-eral potential explanations for this lower importance level found in our study. It’s possible that mangers are further along in implementing fl ow and pull due to the high attention that these concepts have received in practice. Average response levels were higher for items measured for these two constructs than they were for LP and LSP (please see table 5), lending some support for this reasoning. Another possibility is that there is a precedence relationship between these various constructs, where fl ow and setup reduction improvements support improvement in fl ow, and potentially also with SPC.

We also found that two factors can be bundled as External Lean Practices, integration with customers and integration with suppliers, both contributing approximately equally to External Lean Factors. Prior research on integration has focused on fi rm-level measures such as Flynn et al. (2010) and Frohlich & Westbrook (2001). It would be relevant to delve more deeply into organizational design to understand where within functions these different capabilities are located. The principle of “bun-dling” as discussed in the literature review suggests that it is important to emphasize both practices to achieve the full performance potential, which may be impacted by organizational design. Organizational culture has been found to infl uence the adoption of internal and external integra-tion practices (Braunscheidel, Suresh, & Boisnier, 2010).

Contextual variablesOur second research question involved examining the particular fi rm context for lean implications. We gathered information on fi rm size, pro-duction process, and inventory strategy. We estimated model structural parameters across these different contexts and found no difference: the model generalizes across the contexts studied. This supports the conten-tion that lean may be considered a generally-applicable improvement strategy across a broad variety of circumstances.


We also found that implementation levels differed by context. Large fi rms have implemented lean to a greater extent than have small fi rms. That fi nding was expected since larger fi rms tend to have greater resources and more managerial sophistication for tackling improvement activities such as lean. In addition, the cost/benefi t calculations may favor large fi rms given a certain fi xed investment necessary for lean implementa-tions. Larger fi rms can leverage the modest improvement levels seen in this study across greater volume. Smaller fi rms should carefully analyze cost/benefi ts of lean, with benefi ts including operational performance improvements identifi ed in previous study.

Regarding the implementation level of lean by production process and inventory strategy we found that both high volume and MTO opera-tions had implemented external lean practices to a greater extent than had either low volume or MTS fi rms. A partial explanation for this dif-ferential diffusion of practices may be due to the historic development of lean in automotive manufacturing, with high-volume assembly plants fed by dedicated suppliers building products based on pull signals (made to order). The success of Japanese automotive manufacturers at imple-menting lean may lead other fi rms with perceived similar problems to implement similar solutions1. We believe that the reason for this in a MTO environment is that fi rms must work closely with their custom-ers to understand their exact needs and in turn fi rms must work closely with their suppliers so that the inputs to their processes are suffi cient to meet the requirements of the end customer. It is interesting to note that there was no statistically signifi cant implementation level for internal lean practices between MTO and MTS fi rms.

CONCLUSION

In this study we investigate the linkages between lean bundles and customer product satisfaction. These lean practices were grouped

1 See Braunscheidel et al. (2011) for a discussion of mimetic processes in the context of institutional theory.


into external and internal bundles. Research hypotheses are evaluated using SEM and other statistical techniques on a sample of 218 manu-facturing fi rms.

We found that lean can be conceptualized as “bundles” of internal and external practices. The impact of lean implementation is enhanced when practices are implemented in whole rather implemented separately. We contribute to the literature by extending prior research on lean by con-sidering the impact of lean bundles on value to customer. Prior work has focused on lean’s impact on operational performance (Hallgren & Olhager, 2009), but ignored the contribution lean makes to generating value for customers. This is particularly important due to the paradigm shift that is occurring in today’s hypercompetitive global environment.

Our research extends the understanding of lean in two ways. First, lean’s impact on performance is understood to be multidimensional, with prior work focusing on operational performance (Shah & Ward, 2003). We extend the understanding of performance dimensions by evaluating the impact of lean bundles on value to customer, which measures satis-faction with products and services. Second, we fi nd that the structural relationship between lean bundles and value to customers does not vary for different contexts in which the fi rm operates: fi rm size, product pro-cess, and inventory strategy. We do fi nd, however, that context matters when considering the implementation level of lean. Large fi rms have implemented lean to a greater extent than have small fi rms, and repet-itive assembly and continuous fl ow fi rms are ahead of job shops and batch processes. Our fi ndings are mixed with respect to inventory strat-egy. MTO fi rms have implemented external bundles to a greater degree than have MTS fi rms, but there was no difference in implementation of internal bundles. These fi ndings suggest that small fi rms and fi rms fol-lowing a MTS strategy can potentially gain customer advantage through greater application of lean, particularly when implemented holistically in lean bundles.

There are several limitations in this research project that should be considered in interpreting our fi ndings. Our research design involved using a single respondent to measure constructs of interest at both the


organizational level (internal bundles) and inter-organizational level (external bundles). A limitation of this design is that common method variance may affect fi ndings through reduced reliability in construct measures and infl ated correlations due to common method bias. Reduced measurement reliability tends to attenuate correlation, thus construct cor-relations may be either infl ated or reduced depending upon which effect dominates (Conway & Lance, 2010). Despite this limitation we believe that our design is reasonable given research budget limitations, and consistency with prior empirical research (Martinez-Costa & Jimenez- Jimenez, 2008; Tu, et al., 2001; Wu, Melnyk, & Flynn, 2010). In addi-tion, our dependent variable, value to customer, was rated by respondents in the selling fi rm, rather than by the customer. It was impractical in our research design to obtain customer evaluations directly, yet future research designs incorporating multiple measurement techniques of this construct would improve measurement precision.

There are several additional projects that could extend this work. Our sample consists of North American manufacturing fi rms. Additional work could extend the external validity of this model by studying lean programs in service or public-sector settings, as well as in different coun-tries. In addition, we could gain more precision in our understanding of lean with a more objective measure of value to customer, such as includ-ing matched-pair samples of manufactures and their customers. Finally, more work is necessary to tease out the exact relationship between implementing internal and external lean bundles in practice. Internal versus external integration research (Frohlich & Westbrook, 2001) can be extended to better explicate the role of lean in this context.


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APPENDIX A

MEASUREMENT ITEMS EVALUATED ON A 7-POINT LIKERT SCALE

Integration with Key Customers (IC)

IC01 Our customers give us feedback on quality and delivery performance

IC02 Customers are actively involved in our new product development process

IC03 Customers frequently share demand information with our fi rm

IC04 Our production plans are shared with our customers

IC05 Our inventory levels are shared with our customers

Integration with Key Suppliers (IS)

IS01 Our inventory levels are shared with our suppliers

IS02 We give our suppliers feedback on quality and delivery performance

IS03 We strive to establish long term relationships with our suppliers

IS04 Our key suppliers deliver to our plant in a JIT basis

IS05 We have high corporate level communication on important issues with key suppliers

Lean-Pull (LP)

LP01 Production is ‘pulled’ by the shipment of fi nished goods………………

LP02 We use a ‘pull’ production system………………………………………

LP03 We use Kanban, squares or containers of signals for production control.

Lean- Flow (LF)

LP04 Products are classifi ed into groups with similar processing requirements

LP05 Equipment is grouped to produce a continuous fl ow of families of products.

LP06 Families of products determine our factory layout……………………...

LP07 Pace of production is directly linked with the rate of customer demand.

(continued on next page)


Lean-Setup (LSU)LP08 We are working to lower setup time in our plant……………………….

LP09 We have low set up times of equipment in our plant……………………

Lean-SPC (LSP)LP10 Large numbers of equipment/processes on the shop fl oor are currently

under SPC

LP11 Extensive use of statistical techniques are used to reduce process variance

LP12 Charts showing defect rates are used as tools on the shop fl oor………...

LP13 We use fi shbone type diagrams to identify causes of quality problems...

LP14 We conduct process capability studies before product launch……….…

Value to Customer (VC)CS01 Our customers are satisfi ed with the quality of our products……………

CS02 Our customers are satisfi ed with the features that our products provide..

CS03 Our customers are loyal to our products……………………………...…

CS04 Our customers refer new customers to purchase our products……….…

CS05 Our customers feel that we offer products with high value…………..…

APPENDIX A (continued )

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