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Int. J. Production Economics 138 (2012) 242–253

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Int. J. Production Economics

0925-52

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n Corr

E-m

journal homepage: www.elsevier.com/locate/ijpe

The effect of lean production on financial performance: The mediating role of inventory leanness

Christian Hofer a,n, Cuneyt Eroglu b, Adriana Rossiter Hofer a

a Department of Supply Chain Management, Sam M. Walton College of Business, University of Arkansas, 475 Business Building, Fayetteville, AR 72701, USA b Information, Operations & Analysis Group, College of Business Administration, Northeastern University, 214 Hayden Hall, 360 Huntington Avenue, Boston, MA 01115, USA

a r t i c l e i n f o

Article history:

Received 28 September 2011

Accepted 23 March 2012 Available online 30 March 2012

Keywords:

Lean production

Inventory management

Financial performance

73/$ - see front matter & 2012 Elsevier B.V. A

x.doi.org/10.1016/j.ijpe.2012.03.025

esponding author. Tel.: þ479 575 6154.

ail address: [email protected] (C. Hofer

a b s t r a c t

The purpose of this paper is to empirically investigate the relationship between lean production

implementation and financial performance. Particular emphasis is placed on the mediating role of

inventory leanness in deriving the financial performance benefits commonly associated with lean

production. Moreover, the interaction among different lean practice bundles in affecting financial and

inventory performance is assessed. Based on an analysis of a combination of survey and secondary data,

the effect of lean production on financial performance is found to be partially mediated by inventory

leanness. In addition, there is strong evidence that the concurrent implementation of internally-focused

and externally-focused lean practices yields greater performance benefits than selective lean produc-

tion implementation. Thus, this study contributes to the theory of lean production by providing insights

into the mediated and moderated effects of lean production on inventory leanness and financial

performance.

& 2012 Elsevier B.V. All rights reserved.

1. Introduction

Lean production is often regarded as the gold standard of modern operations and supply chain management (e.g., Guinipero et al., 2005; Goldsby et al., 2006). Numerous studies have investigated the relationship between lean production and financial performance (e.g., Fullerton et al., 2003; Jayaram et al., 2008). Yet, the exact mechanism(s) through which lean production affects financial performance remain underresearched. Conventional wisdom holds that, as a manufacturing strategy, lean production strives to mini- mize waste and thereby increase efficiency (Womack et al., 1990), and by extension, financial performance.

Given the multiplicity of lean production practices such as kanban, JIT, and TQM, for example, it is apparent that the relationship between lean production and financial performance may be complex and multi-faceted. Indeed, one factor that is often implicitly con- sidered as a mediator of this relationship is inventory efficiency. For example, several studies have examined the effects of lean produc- tion implementation on inventories (e.g., Huson and Nanda, 1995; Balakrishnan et al., 1996). Likewise, analytical research has examined the linkage between production and inventory (e.g., Miyazaki, 1996; Dobos, 2007). In a separate literature stream, prior research has investigated the performance implications of efficient inventory

ll rights reserved.

).

management (e.g., Capkun et al., 2009; Eroglu and Hofer, 2011). Moreover, Fullerton and Wempe (2009) contend that the effects of lean production implementation on financial performance are mediated by various operational performance measures, such as delivery performance, manufacturing cycle times, and labor produc- tivity. However, these authors do not consider inventories as a mediating factor. Yet, inventory costs are of great significance in the context of logistics and supply chain management (Stock and Broadus, 2006).

Thus, the purpose of this study is to add to our understanding of lean production by examining the relationship between lean production and financial performance, with an emphasis on the mediating role of inventories. In addition, and consistent with the notion that lean production is a system of lean practices (Womack et al., 1990), interactions among various facets of lean production and their effects on inventories and performance are investigated.

This research contributes to the existing literature in multiple ways: First, it provides a richer, more nuanced conceptualization of the relationships among lean production, inventory leanness, and financial performance. Specifically, we draw on existing lean production and inventory literature to develop a research model that examines the mediating role of inventory in delivering the commonly expected financial performance benefits of lean produc- tion implementation. This model is tested using a data sample with firm-level observations from a diverse set of US manufacturing industries which is compiled from two distinct sources: survey data and matched secondary financial data. Beyond conventional

Inventory Leanness

Stream 1

Stream 3Stream 2

Financial Performance

Lean Production

Fig. 1. Research streams on lean production and firm performance.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253 243

mediation analysis, we also test for potential reverse causality and, thus, gain a better understanding of the interplay of lean produc- tion, inventory leanness, and financial performance.

Second, this study explores interactions among lean practices. Existing literature suggests that when lean practices are imple- mented concurrently, the total performance effect will exceed the sum of performance effects of individual lean practices (Shah and Ward, 2003). While some recent studies (e.g., Furlan et al., 2011a) have empirically tested the complementarity (synergy) among lean practice bundles, these analyses were restricted to specific aspects of lean production and focused on plant-level perfor- mance. In this study, we conceptualize lean production as two lean practice bundles (internal and external) that collectively encompass all lean practices, and we test the synergy between these lean practice bundles at the firm-level instead of at the plant level. In addition, this research addresses concerns of potential common methods bias (Podsakoff et al., 2003) that may arise when lean production and performance data are provided by the same survey respondent by using secondary inventory and financial performance metrics along with primary survey data on lean production implementation.

Third, the two main constructs of this study, i.e., lean production and inventory leanness, are operationalized using measures pro- posed in recent research. More specifically, lean production is assessed using a survey instrument developed by Shah and Ward (2007) which consists of a set of 10 distinct lean practices: supplier feedback, supplier JIT, supplier development, customer involvement, pull manufacturing, continuous flow manufacturing, setup time reduction, statistical process control, employee involvement, and total productive maintenance. Inventory leanness, in turn, is mea- sured using the Empirical Leanness Indicator (ELI) developed by Eroglu and Hofer (2011). The ELI measures a firm’s inventory leanness as the deviation of a firm’s inventory levels from size- adjusted within-industry average inventory levels. As such, the study extends operations management literature by providing independent empirical evidence for the validity of these instruments.

The remainder of this paper is structured as follows: The relevant literature is reviewed and hypotheses are proposed in Section 2. Data and measurement issues are discussed in Section 3. In Section 4, mediation and interaction hypotheses are tested and empirical estimation results are presented. Section 5 presents a discussion of the findings, research and managerial implications, limitations, and future research opportunities.

2. Literature review and hypothesis development

Lean production is a strategy or philosophy that promotes the use of practices, such as kanban, total quality management (TQM) and just-in-time (JIT), to minimize waste and enhance firm performance (Womack et al., 1990). Thus, the implementation of lean production practices is expected to result in improved operational outcomes, such as lower inventories, higher quality, and shorter throughput times, which, in turn, should improve financial performance. This description of lean production clearly indicates a number of mediating factors between lean production and financial performance. This notion is consistent with the ‘‘new inventory paradigm’’ (Chikán, 2011, 2009) which empha- sizes the connectedness to other processes and functions within firms and to firm profitability.

In this study, we focus on inventory leanness as the mediator of interest and suggest that inventory leanness mediates the effect of lean production implementation on financial perfor- mance. Inventory leanness is defined by comparing a firm’s inventory levels to the size-adjusted average inventory level within the firm’s industry (Eroglu and Hofer, 2011). It is expected

that lean production implementation not only carries direct financial benefits, but also results in greater inventory leanness which, in turn, contributes to improved financial performance.

In accordance with the proposed research model, relevant literature is grouped in three distinct streams as shown in Fig. 1 adapted from Fullerton and Wempe (2009). The first stream consists of studies exploring the relationship between lean produc- tion and financial performance. The second stream examines the effect of lean production on inventory leanness and other opera- tional outcomes as potential mediators. The third stream focuses on the analysis of the relationship between inventory leanness and financial performance. All three streams are reviewed below, followed by the presentation of research hypotheses on the mediating role of inventory leanness in the lean production- financial performance relationship and the interaction effects among lean production practice bundles.

2.1. Stream 1: Relationship between lean production and financial

performance

The first stream of research explores the direct effects of lean production practices on financial performance (Table 1). Most of these studies employ a survey methodology to assess the degree of implementation of lean production practices and to measure financial performance. The measures of lean production are typically narrowly focused on JIT (e.g., Inman and Mehra, 1993; Fullerton and McWatters, 2001) which is part of but not synon- ymous with lean production. Other studies identify companies that have adopted JIT practices via a search of news articles and company reports (Biggart, 1997; Kinney and Wempe, 2002). Firm financial performance, in turn, is estimated using metrics such as ROS, ROA, and ROI in most studies.

The studies’ findings are largely consistent: Evidence of positive effects of lean production implementation on at least some financial performance indicators is presented by Inman and Mehra (1993), Callen et al. (2000), Fullerton and McWatters (2001), Germain et al. (1996), Kinney and Wempe (2002), Fullerton et al. (2003), Fullerton and Wempe (2009), and Yang et al. (2011). Only Biggart (1997) and Jayaram et al. (2008) find no statistically significant relationships between lean production practices and firm profitability.

2.2. Stream 2: Relationship between lean production and inventory

leanness

The second stream of research explores the effects of lean production implementation on inventory leanness and other operational performance measures (Table 2). Most of the studies in this literature stream are based on surveys of manufacturing executives (e.g., White, 1993; Norris et al., 1994; Droge and Germain, 1998; Shah and Ward, 2003). These studies typically employ multi-item scales to measure the degree of implementa- tion of lean production. Inventory and operational performance,

Table 1 Studies on the lean production–financial performance relationship (Stream 1).

Author (s) Sample Dependent variable(s)

Independent variable(s)

Empirical methodology

Findings

Inman and

Mehra (1993)

US manufacturing firms

adopting JIT (N¼114)

ROI, total cost,

service

JIT adoption Regression Firm performance improves as a result of JIT adoption.

Biggart (1997) US manufacturing firms

adopting JIT (N¼106)

ROA JIT adoption Regression No evidence of a significant effect of lean production

adoption on ROA is found.

Claycomb et al.

(1999)

US manufacturing

managers (N¼200)

ROS, ROI, profit,

profit growth

JIT adoption Regression JIT use with customers results in better financial

performance.

Claycomb et al.

(1999)

US manufacturing

managers (N¼200)

ROS, ROI, profit JIT adoption Regression The greater the share of JIT transactions, the greater ROI,

ROS, and firm profitability.

Callen et al.

(2000)

Canadian manufacturing

plants (N¼100)

Profitability, total

costs

JIT adoption Regression JIT adoption results in lower costs and higher profits.

Fullerton and

McWatters

(2001)

US manufacturing firms

adopting JIT (N¼95)

Profitability

improvement

JIT adoption ANOVA Greater JIT implementation results in greater profitability

improvement.

Germain et al.

(1996)

US manufacturing

managers (N¼200)

ROS, ROI, profit JIT adoption Regression JIT results in greater financial performance relative to

industry peers.

Kinney and

Wempe (2002)

US manufacturing firms

adopting JIT (N¼201�2)

Profitability, ROA JIT adoption Regression Profitability and return on assets improve after JIT adoption.

Fullerton et al.

(2003)

Manufacturing firms

(N¼253)

Profitability, cash

flow margin, ROA

Lean production

implementation

Regression Three lean production practice bundles are associated with

greater firm performance.

Matsui (2007) Japanese manufacturing

plants (N¼46)

Manufacturing cost JIT adoption Canonical

correlations

JIT production systems contribute to competitive

performance outcomes such as lower manufacturing costs.

Jayaram et al.

(2008)

Auto parts manufacturers

(N¼57)

Profitability, ROA Lean production

implementation

SEM Firm performance is not significantly affected by lean

production.

Fullerton and

Wempe (2009)

Manufacturing executives

(N¼121)

ROS Lean production

implementation

SEM Lean practices have a direct and mediated positive effect on

financial performance.

Yang et al.

(2011)

IMSS survey data (N¼309) ROS, ROA Lean

manufacturing

SEM Lean manufacturing has a significant positive impact on

financial performance.

Table 2 Studies on the lean production–inventory leanness relationship (Stream 2).

Author (s) Sample Dependent variable(s) Independent variable(s)

Empirical methodology

Findings

White (1993) Manufacturing and

service firms (N¼1035)

Throughput time JIT adoption Percentage

breakdown

About 50% of the respondents report decreased throughput

times after JIT implementation.

Norris et al.

(1994)

Plant managers (JIT

users) (N¼48)

Inventory performance,

process/quality control

items

JIT adoption Percentage

breakdown

Respondents report lower inventories and greater inventory

visibility and accuracy following JIT implementation.

Huson and

Nanda

(1995)

US manufacturing firms

(N¼55)

Inventory turnover JIT adoption Simultaneous

equations

Inventory turnover increases following lean production

implementation.

Balakrishnan

et al.

(1996)

US manufacturing firms

(N¼46�2)

Inventory turnover JIT adoption t-test Firms that adopt JIT achieve higher inventory turnover than

firms that do not use lean production.

Droge and

Germain

(1998)

US manufacturing

managers (N¼200)

Inventory JIT adoption Correlation

analysis

A negative correlation between JIT adoption and inventory

levels is found.

Irvine (2003) Aggregate US inventory

and sales data (34 years)

Inventory-to-sales ratio JIT adoption Descriptive

analysis

Descriptive evidence suggests that JIT adoption has resulted in

large inventory reductions in the durable goods manufacturing

sector.

Biggart and

Gargeya

(2002)

US manufacturing firms

(N¼74)

Inventory-to-sales ratio JIT adoption t-test A decrease in total and raw materials inventories is observed

after JIT implementation.

Shah and

Ward

(2003)

Manufacturing plants

(N¼1575)

Operational performance

construct

Lean

production

implementation

Regression Lean production practice bundles have a positive effect on

operational performance.

Demeter and

Matyusz

(2011)

International

Manufacturing Strategy

Survey (N¼711)

Inventory turnover Lean

production

implementation

Cluster

analysis,

F-tests

Firms that implement lean practices have higher inventory

turnover.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253244

in turn, is measured in terms of inventory levels (e.g., Droge and Germain, 1998), cycle time (White, 1993) or as a multi-dimen- sional construct including elements such as inventory visibility and accuracy as well as quality and process control (Norris et al., 1994; Shah and Ward, 2003). Even though there are some differences in variable measurement and survey populations, all studies present evidence that greater implementation of lean production is associated with improvements in at least some aspects of inventory and operational performance. These findings

are further corroborated by a set of studies that identify the adoption of JIT practices, a subset of lean production practices, via a literature search and rely on secondary data only to estimate the effect of JIT adoption on inventory performance (Huson and Nanda, 1995; Balakrishnan et al., 1996; Biggart and Gargeya, 2002). Specifically, these studies find that JIT adoption leads to increased inventory turnover (Huson and Nanda, 1995; Balakrishnan et al., 1996) and lower raw materials inventories (Biggart and Gargeya, 2002). It is noteworthy, however, that only

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253 245

one study has explored the effect of lean production practices other than JIT adoption on inventory leanness (Demeter and Matyusz, 2011). Based on a series of univariate tests, these authors conclude that raw materials, work-in-process and finished goods materials inventories are lower when the company has implemented lean practices. Consistent with these firm-level studies, aggregate analyses of inventory and sales data for also indicate that the implementation of JIT has resulted in lower inventory holdings (Irvine, 2003; Obermaier, 2012).

2.3. Stream 3: Relationship between inventory leanness and

financial performance

The third stream of research analyzes the link between inventory leanness and financial performance (Table 3). There is, of course, a large body of conceptual and analytical literature that discusses the profit implications of inventories (e.g., Relph and Barrar, 2003; Chikán, 2011). Empirical studies in this stream of research use large cross-sectional time series data sets compiled from secondary databases to explore the link between inventory metrics and financial outcomes. Chen et al. (2005, 2007) find that firms with inventory levels below industry average tend to have greater stock returns. Similarly, Eroglu and Hofer (2011) find that firms whose inventory levels are (slightly) below size-adjusted industry averages tend to exhibit greater financial performance. Swamidass (2007), Koumanakos (2008) and Capkun et al. (2009) also present evidence of a negative relationship between inven- tory levels and profitability. Cannon (2008), however, finds no significant relationship between inventory turnover and financial performance.

2.4. The mediating effect of inventory leanness

As outlined above, the existing literature documents the effect of select aspects of lean production on financial performance as well as inventories and other measures of operational perfor- mance. The underlying logic is that operational benefits typically associated with lean production, such as lower inventories, higher quality, and less waste ultimately lead to improved financial performance. Mistry (2005) and Fullerton and Wempe (2009) explicitly study the interplay between lean production, opera- tional outcomes, and financial performance. Based on a case study of an electronics manufacturer that had previously implemented select JIT processes, Mistry (2005) identified several processes through which JIT practices impacted various operational outcomes and, ultimately, increased profitability. In addition, Mistry (2005) found anecdotal evidence that the implementation

Table 3 Studies on the inventory leanness–financial performance relationship (Stream 3).

Author (s) Sample Dependent variable(s) Independent variable(s)

Chen et al.

(2005)

US manufacturers

1981–2000 (N¼7,433)

Stock returns, Tobin’s q,

Market-to-book ratio

Abnormal

inventory, time

Chen et al.

(2007)

Retailers, wholesalers

1981–2004 (N¼1,662)

Stock returns Abnormal

inventory, time

Swamidass

(2007)

US manufacturers

1981–1998 (N¼14,400)

Inventory-to-sales ratio Z-score (firm

performance), tim

Cannon

(2008)

US manufacturers

1991–2000 (N¼2,440)

Market value added,

ROA, ROI, Tobin’s q

Inventory turnov

capital intensity

Koumanakos

(2008)

Greek manufacturers

2000–2002 (N¼1,358)

Gross margin, net

operating margin

Inventory days

Capkun et al.

(2009)

US manufacturers

1980–2005 (N¼52,254)

Gross profit, EBIT Inventory scaled

sales

Eroglu and

Hofer

(2011)

US manufacturers

2003–2008 (N¼7,804)

ROS, ROA Empirical leannes

indicator (ELI)

of assemble-to-order production systems contributes to cost savings through reduced work-in-process and raw materials inventory requirements. Fullerton and Wempe (2009), in turn, conducted a survey study and found evidence that non-financial manufacturing performance measures, such as on-time deliveries and labor productivity, mediate the relationship between lean production implementation and ROS. While neither study empiri- cally assessed the mediating role of inventories, both studies support the contention that operational outcomes, such as inven- tory leanness, act as mediating variables in the relationship between lean production implementation and financial perfor- mance. Thus, it is hypothesized that:

H1. The effect of lean production on financial performance is mediated by inventory leanness.

2.5. Interaction among lean practice bundles

Although lean production has traditionally been conceptua- lized as a collection of lean practices (e.g., Shah and Ward, 2007), it is implied that these distinct practices should work together as a system (Womack et al., 1990). Several years after the publica- tion of their seminal book on lean production, Womack and Jones (1996, p. 140) observed many ‘‘unlinked islands of lean operating techniques’’ and commented that ‘‘[a]lthough many managers had grasped the power of individual lean techniques – quality function deployment for product development, simple pull sys- tems to replace complex computer systems for scheduling, and the creation of work cells for operations ranging from credit checking and order entry in the office to parts fabrication in the plant – they had stumbled when it came to putting them all together into a coherent business system. That is, they could hit individual notes (and loved how they sounded) but still could not play a tune.’’ Several conceptual studies have endorsed the view that lean production is a system of inter-related practices (Huang, 1991; Roth and Miller, 1992; Imai, 1998).

A number of recent studies have empirically tested the synergy among different lean practice bundles. Konecny and Thun (2011) find that while the adoption of TQM and TPM bundles individually improves plant performance, their conjoint implementation does not provide additional performance bene- fits. Furlan et al. (2011b) detect synergy between JIT and TQM bundles for firms that also implement human resources related lean practices. In another study, Furlan et al. (2011a) show that simultaneously implementing upstream and downstream JIT practices improves plant performance to a greater degree than implementing these practices separately. Although these studies

Empirical methodology

Findings

Linear mixed

models

Firms with below average inventories have better stock

returns than firms with more or less inventory.

Linear mixed

models

High inventory levels are associated with poor long-term

stock returns.

e

Regression Top performers have decreasing inventory-to-sales ratio

over time, while low performers have increasing ratios.

er, Hierarchical

linear models

Inventory turnover has little or no effect on financial

performance.

Regression In most industries, greater inventory levels are associated

with lower net operating margins.

by Regression Greater inventory performance (raw materials, in particular)

positively affects firm performance.

s Regression The relationship between inv. leanness and financial

performance is concave in most industries.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253246

do not directly analyze financial performance and inventories, they suggest that synergies exist among lean practice bundles. Hence, it is hypothesized that:

H2. Lean practices interact positively to affect financial performance.

H3. Lean practices interact positively to affect inventory leanness.

3. Data collection and measurement

To test the hypotheses set forth in this study, a data set composed of primary survey data and secondary financial data was utilized. Primary survey data were collected to measure the degree of a firm’s implementation of lean production, whereas secondary financial data were obtained to measure a firm’s inventory leanness and financial performance. The combination of both primary and secondary data sources is intended to address common methods bias which is associated with most survey research (Podsakoff et al., 2003).

3.1. Survey data

Firm-level lean production was measured by administering a survey instrument developed by Shah and Ward (2007). This formative scale comprises 41 questions which capture 10 lean production practices: supplier feedback, supplier JIT, supplier development, customer involvement, pull manufacturing, contin- uous flow manufacturing, setup time reduction, statistical process control, employee involvement and total productive maintenance. All questions were answered on a five-point Likert scale ranging from (1) ‘‘no implementation’’ to (5) ‘‘complete implementation.’’ The survey items are listed in Table 11 in Appendix A.

The survey instrument was administered by the Association for Operations Management (APICS) whose professional membership base was deemed a particularly suitable sampling frame because the majority of its members are operations, production, supply chain, logistics, and purchasing managers and executives. Email solicitations were sent by APICS to a mailing list comprising a random sample of 4288 APICS members. Excluding 38% of APICS members that were affiliated with non-manufacturing organiza- tions, the sampling frame consisted of about 2662 APICS members. After an initial invitation email and a subsequent reminder email, 788 email recipients had opened the email and 325 individuals clicked on the survey link contained in the email message. A total of 229 responses were obtained, corresponding to a lower-bound response rate of 8.6% (229/2662) relative to the number of APICS members in the manufacturing sector. However, some of the 2662 email recipients in the manufacturing sector may not be qualified to take the survey due to limited exposure to lean production related activities within their respective organizations. Assuming that all ineligible individuals excluded themselves and only eligible participants opened the email message, we obtain an upper-bound response rate of 29.1% (229/788). While the true response rate may be difficult to pinpoint accurately, in all likelihood, it lies some- where between 8.6% and 29.1%. Our survey effort, thus, compares

Table 4 Sample demographics.

Title n % Department

CEO/COO/CFO 2 1 Manufacturing/operations

VP/EVP/SVP 9 4 Logistics/SCM/procurement

Director 34 15 Other

Manager 89 39

Othera 95 41

a Other titles include, most notably, supervisor, (production) planner, scheduler, an

well – both in terms of response rate as well as in terms of absolute sample size – to other large scale survey studies in operations management (e.g., Braunscheidel and Suresh, 2009; Hult et al., 2007; Bardhan et al., 2007).

It is conceivable that email recipients declined to take the survey due to limited or negative experiences with lean produc- tion. As such, the possibility of non-response bias needs to be considered. To do so, we first follow the procedure suggested by Armstrong and Overton (1977) and Lambert and Harrington (1990) and compare demographic characteristics between early and late respondents. No significant differences were found. Specifically, the survey data of early respondents (first quartile of respondents, nq1¼57) and late respondents (fourth quartile of respondents, nq4¼57), were compared using Hotelling’s T-squared test. This test yielded an F statistic of 1.22 (p¼0.24). Hence, the null hypotheses of equal vectors of means across early and late respondents could not be rejected. Second, we identified publicly traded firms among the respondents and compared their distribu- tion across NAICS codes with the distribution of all publicly traded firms in the COMPUSTAT database. A test of independence failed to detect any significant differences between the publicly traded firms in the survey sample and those in the population of COMPUSTAT (asymptotic chi-square test p¼0.4174; Fisher’s exact test p¼0.2092). These two findings ease potential concerns of non-response bias.

Sample demographics are provided in Table 4. A majority of respondents held managerial and supervisory positions in man- ufacturing, operations, logistics, procurement and supply chain management. Likewise, 59% of all respondents had been with their respective firms for more than five years, while another 33% had been with their employer for between one and five years. Based on these demographics the respondents were deemed qualified to complete this survey. A confirmatory factor analysis of the survey items was conducted and acceptable model fit statistics were obtained for all 10 lean production constructs (Table 11 in Appendix A).

3.2. Secondary data

In addition to survey data, firm-level financial and inventory data were obtained from Standard & Poor’s COMPUSTAT database for 2009 (which is also the year in which the survey data were collected). As with the survey data collection, the query was limited to US domestic manufacturing firms that were active and had positive sales and inventory figures. The resulting data set included 1421 firms in 24 four-digit NAICS manufacturing industries.

Inventory leanness was measured using the Empirical Lean- ness Indicator (ELI) developed by Eroglu and Hofer (2011). This measure overcomes some of the shortcomings of measures used in previous studies. Inventory turnover and its variants, while widely used in the literature, are imperfect measures of inventory leanness for two reasons. Since such measures are scaled by firm size, they yield artificially inflated estimates when used with firm performance measures that are also scaled by firm size or some

n % Tenure n %

78 34 Less than 1 year 18 8

133 58 1 to 5 years 76 33

18 8 More than 5 years 135 59

alyst, and (senior) buyer.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253 247

highly correlated variable (Wiseman, 2009). In addition, such measures ignore economies of scale in inventory management (Evers, 1995). More detailed information on the ELI and its computation is provided in Appendix B. We note that inventory leanness is not reflective of lean production implementation. Rather, it is a theoretically and empirically distinct variable that is expected to be impacted by lean production implementation and, at the same time, impact financial performance.

Firm financial performance was measured with return on sales (ROS), a metric commonly used in prior research (Kinney and Wempe, 2002; Cannon, 2008; Koumanakos, 2008). It is noted, however, that the results are largely insensitive to small changes in the measurement of financial performance. Specifically, the empirical results remain essentially the same when return on assets (ROA) is used as a financial performance metric. These results are not reported in this paper due to space constraints.

In line with prior research, sales and sales growth data are included as control variables in the empirical model. Total sales are a measure of firm size, which has been shown to be associated with larger profits (e.g., Barber and Lyon, 1996). Likewise, prior research has established the higher sales growth is correlated with greater profitability (e.g., Fairfield and Yohn, 2001).

Table 6 Lean practice bundles (rotated factor pattern).

Lean practice Factor 1 external lean practices

Factor 2 internal lean practices

Supplier feedback 0.78 0.18 Supplier JIT 0.70 0.37 Supplier development 0.70 0.17 Customer involvement 0.63 0.16 Pull system 0.06 0.71 Continuous flow 0.19 0.70

3.3. Combined data set

The survey responses were matched with corresponding secondary data using company names (for those companies that were identified by name in the survey response and for which secondary financial data were available). Descriptive statistics for secondary financial data, ELI, and 10 distinct lean practices are presented in Table 5.

Empirical researchers have identified a large number of prac- tices commonly associated with lean production, which are typically grouped into lean practice bundles. For example, White and Ruch (1990) identify 10 lean practices and subse- quently, White et al. (2010) aggregate them into four ‘‘practice bundles’’ (conformance quality, delivery reliability, volume flex- ibility, low cost). Similarly, Shah and Ward (2003) categorize 22 lean practices into four ‘‘lean bundles’’ (just-in-time, total pro- ductive maintenance, total quality management, human resource management). It can be argued that combining lean practices into bundles provides parsimony and enhances clarity of exposition.

Table 5 Descriptive statistics.

Variable Mean Standard deviation

Net sales 12,266 20,003

Sales growth �0.12 0.14

ROS 0.02 0.19

Total inventory 1,585 2,390

ELI �0.02 0.77

Supplier feedback 4.32 0.67

Supplier JIT 3.51 0.84

Supplier development 3.04 0.57

Customer involvement 3.80 0.59

Pull system 2.96 1.05

Continuous flow 3.73 0.69

Setup time reduction 3.26 0.91

Statistical process control 3.21 0.96

Employee involvement 3.52 0.99

Total productive maintenance 3.52 0.88

Note: Total Assets, Total Inventory, and Net Sales figures are reported in million

US$, Sales Growth and ROS in are reported in percentages and ELI is unitless. All

lean production practices are scored on a scale from 1 (no implementation) to 5

(complete implementation).

In line with previous research by Shah and Ward (2003), lean practices are grouped into lean practice bundles using principal component analysis and VARIMAX rotation (Table 6). These results provide a factor structure that is both easy to interpret and theoretically meaningful: All externally-oriented lean pro- duction practices (supplier feedback, supplier JIT, supplier devel- opment, customer involvement) load on factor 1 (termed ‘‘external lean practices’’, ELP) whereas all internally-oriented lean production practices (pull system, continuous flow, setup time reduction, statistical process control, employee involvement, total productive maintenance) load on factor 2 (termed ‘‘internal lean practices’’, ILP). ELP and ILP factor scores were calculated as the average of the factors’ respective constituent items.

In order to test for discriminant validity between the con- structs of internal and external lean practices, we followed the methodology suggested by Shook et al. (2004). The shared variance between factors should be lower than the average variance extracted for each factor. A two-factor model was tested assigning the items to their respective factors and allowing both factors (internal and external lean practices) to covary. For both factors, the variance extracted was higher than the covariance shared by the factors, indicating discriminant validity. In addition, the two factor model presented an excellent fit (CFI¼.981. SRMR¼0.040, RMSEA¼0.043), which according to Kline (2005) is also a precise test of discriminant validity.

Bivariate correlations are shown in Table 7. As expected, both firm size (Net Sales) and sales growth are highly correlated with financial performance. Moreover, both ILP and ELP show positive, albeit insignificant, correlations with ROS and statistically sig- nificant positive correlations with sales growth. The relatively high correlation between ILP and ELP is consistent with the notion that lean production is a system of practices that should be

Setup time reduction 0.39 0.60 Statistical process control 0.34 0.52 Employee involvement 0.17 0.76 Total productive maintenance 0.37 0.67

Note: A two factor solution is retained because the eigenvalues of the first two

factors are greater than 1. The first two factors explain more than 53% of the

variation in the data.

Table 7 Bivariate correlations.

Variable 1 2 3 4 5

1 Net sales

2 Sales growth �0.01

3 Return on sales 0.48 0.30 4 Empirical leanness indicator �0.03 0.13 �0.04

5 Internal lean practices (ILP) 0.07 0.27 0.11 �0.10 6 External lean practices (ELP) 0.15 0.33 0.17 0.14 0.63

Note: Correlation coefficients printed in bold are statistically significant at

po0.05.

Table 8 Mediation tests results for internal and external lean practice bundles.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253248

implemented simultaneously for optimal performance effects (Womack et al., 1990).

Independent Variables

Dependent variables

A B C D

ROS ELI ROS ROS

Intercept �0.19 1.70 0.01 �0.03

lnSales 0.00 0.03 0.01 0.00

SalesGrowth 0.55nnn 0.44 0.63nnn 0.65nnn

ELP �0.02 �0.20 �0.02

ILP 0.08nn �0.40nnn 0.06n

ELI 0.01 0.03

ELI2 �0.13nnn �0.12nnn

n Significant at 10%. nn Significant at 5%. nnn Significant at 1%.

ILP

ELP

Lean Production Bundles

Financial Performance

+

Inventory Leanness

− ELI

ELI2: −

Fig. 2. Summary of mediation test results. Note: The signs ‘‘þ’’ and ‘‘�’’ indicate significant positive and negative relation-

ships, respectively.

4. Empirical analysis and results

4.1. Testing the mediation hypothesis

The mediation hypothesis was tested using the procedure proposed by Baron and Kenny (1986), which is also widely adopted in operations management literature (e.g., Fullerton and Wempe, 2009). First, we test the direct effect of lean practice bundles on financial performance. We then establish a mediation path by showing that a lean practice bundle affects inventory leanness which in turn impacts financial performance. Finally, we show that the direct effect of lean production diminishes when inventory leanness enters the regression model.

Lean production implementation is observed for all firms in the data set (N¼229), while ELI and firm performance are only observed for public firms that have provided company names (N¼82). In order to use the full sample and obtain unbiased estimates, we specify a selection model (Greene, 2008, p. 882) as follows:

Zni ¼b10þb11LPBiþe1i, i ¼1,2,. . .,229 ð1Þ

Zi ¼ 1 if Zni 40

0 if Zni r0

( ð2Þ

FinancialPerf ormancei

¼

b20þb21lnSalesiþb22SalesGrowthi þb23LPBiþb25 ELIiþb26ELI

2 i þgkþe2i if Zi ¼1

unobserved if Zi ¼0

8>< >: ð3Þ

e1i e2i

! �N

0

0

� � ,

1 rs rs s2

!" # ð4Þ

Eq. (1) describes the relationship between a (internal or external) lean production bundle LPBi for firm i and the likelihood of observing a firm’s financial data Zi

n, which is a continuous latent variable. Eq. (2) indicates that a firm’s financial data is observed (Zi¼1) when Zi

n is above a threshold. Eq. (3) expresses a firm’s financial performance conditional upon the observing its financial data. Note that inventory leanness ELIi enters this equation in linear and quadratic terms. As noted previously, we also include lnSalesi (the natural logarithm of net sales), SalesGrowthi (annual percentage change in net sales) and gk (industry fixed effect) as control variables (e.g., Rumyantsev and Netessine, 2007). The error terms e1i and e2i of Eqs. (1) and (3) follow a bivariate normal distribution specified in Eq. (4). By simultaneously estimating these equations as a system, one can control for potential selection bias in the data set. The estimation results are presented in Table 8.

The results in Table 8 Column A show that internal lean practices have a significant and positive effect on financial performance (po0.05). External lean practices, however, do not impact financial performance. Furthermore, internal lean practices have a significant (albeit negative) effect on inventory leanness (Table 8 Column B), while external lean practices do not significantly affect inventory leanness. Consistent with the findings of Eroglu and Hofer (2011), the results in Column C show that the relationship between inventory leanness and financial performance follows an inverted U-shape. When lean practice bundles and inventory leanness enter the regression equation simultaneously (Column D), the effect of internal lean practices decreases in magnitude and significance level (po0.1). Collectively, these findings support the mediation

hypothesis H1a for internal lean practices. However, the mediation hypothesis H1b for external lean practices is not supported. These results are summarized in Fig. 2.

4.2. Post-hoc analysis of mediation relationships

An unexpected result in Table 8 calls for further exploration. Specifically, the significant negative effect of internal lean prac- tices on inventory leanness appears to imply that implementation of internal lean practices leads firms to hold greater amounts of inventory. This is not only counterintuitive but it also contradicts the positive direct effects that internal lean practices have on financial performance. Mediation analysis results in which the direct path and the mediated path exhibit opposite signs are considered a type of ‘‘inconsistent mediation’’ (MacKinnon et al., 2000). Shrout and Bolger (2002) note that the presence of partial or unexpected mediation effects ‘‘suggest[s] that the causal mechanism is more, rather than less, complicated. [y] [T]hese complications have the potential of enriching both theory and practice’’ (p. 434). Hence, we further explore the finding of inconsistent mediation in this post-hoc analysis.

As noted by MacKinnon et al. (2012), timing and longitudinal effects may be a potential reason for such inconsistent mediation. Specifically, it is conceivable that it may take some time for internal lean practice implementation to yield the desired bene- fits in terms of greater inventory leanness. Indeed, there is evidence that lean production may, at least in some instances, lead to (temporary) increases in inventory holdings and asso- ciated costs (Wu, 2002). The investigation of such time-varying effects, which will require a time series data set, is suggested for future research.

Mediation analysis further assumes that the causal relationships between all variables have been properly identified (MacKinnon

Internal Lean Practices

External Lean Practices

Inventory Leanness

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253 249

et al., 2012). Potential simultaneous bidirectional effects may result in inconsistent estimation results. In this particular case, it is conceivable that the relationship between lean production and inventory leanness is bidirectional and simultaneous. In other words, while lean practices increase a firm’s inventory leanness, a firm that is experiencing excess inventory problems (low inventory leanness) may be more likely to adopt lean practices in an effort to improve its inventory efficiency and financial performance. Furthermore, internal and external lean practices may affect each other as well. For example, a firm that implements internal lean practices may be more likely to also implement external lean practices and vice versa (Furlan et al., 2011a, b). Thus, the relationship between lean produc- tion and inventory leanness may be more than a simple unidirec- tional relationship.

The central tenet of this post-hoc analysis is that the simulta- neous bidirectional relationships outlined above may better capture the true nature of the relationship between lean produc- tion implementation and inventory leanness and, ultimately, provide an explanation for the unexpected result shown in Section 4.1. To capture these complex relationships, a system of simultaneous linear equations is formulated as follows:

ELIi ¼l11þl12ILPiþl13ELPiþl15ln Salesiþl16SalesGrowthiþe1i, ð5Þ

ILPi ¼l21þl23ELPiþl24ELIiþl25ln Salesiþl26SalesGrowthiþe2i, ð6Þ

ELPi ¼l31þl32ILPiþl34ELIiþl35ln Salesiþl36SalesGrowthiþe3i: ð7Þ

In the system of Eqs. (5)–(7), inventory leanness is a function of both internal and external lean practices. At the same time, internal and external lean practices are functions of inventory leanness as well as of each other. Table 9 presents estimation results using two-stage least squares and three-stage least squares methods which allow for simultaneous estimation of the endogen- ous relationships between lean practices and inventory leanness.

The estimation results in Table 9 suggest that the implementa- tion of internal lean practices is positively related to the imple- mentation of external lean practices, and vice versa. Moreover, external lean practices and inventory leanness have significant positive effects on each other. That is, firms that implement external lean practices to a greater degree exhibit greater inventory leanness. Likewise, firms with greater inventory leanness tend to implement external lean practices to a greater extent. Thus, when

Table 9 Interrelationships among inventory leanness, internal and external lean practices.

Independent Dependent variables

ELI ILP ELP

Variables 2SLS 3SLS 2SLS 3SLS 2SLS 3SLS Intercept �1.46 �2.07nnn �0.15 �1.05nn 1.79nnn 1.11nnn

lnSales �0.07 �0.07 0.02 �0.02 0.04n 0.03

SalesGrowth 0.22 0.14 0.19 0.09 0.27 �0.03

ILP �0.46n �0.97nnn 0.47nnn 0.70nnn

ELP 0.98nnn 1.61nnn 0.93nnn 1.24nnn

ELI �0.19n �0.42nnn 0.21nnn 0.41nnn

Note: 2SLS and 3SLS represent two-stage least squares and three-stage least

squares methods, respectively. In the 2SLS results, the regression models are all

significant at the 5% level and the R2 for ELI, ILP and ELP models are 0.13, 0.39 and

0.45, respectively. Since 3SLS estimates the regression equations as a system,

significance levels are not calculated. In the 3SLS results, the system weighted R2

is 0.61. n Significant at 10%. nn Significant at 5%. nnn Significant at 1%.

the bidirectional and simultaneous nature of the relationships between lean practices and inventory leanness is taken into account, the effect of external lean practices on inventory leanness is positive and significant as expected.

Finally, the estimation results from the simultaneous equation model (Table 9) indicate that internal lean practices have a signifi- cant negative impact on inventory leanness, suggesting that greater internal lean practice implementation reduces inventory leanness. However, it should be noted that inventory leanness also has a significant negative effect on internal lean practices. In other words, firms with low inventory leanness, i.e., those that hold above-average inventories, tend to adopt internal lean practices to a greater extent than firms with high inventory leanness. This may be an explanation for the negative coefficient estimate of internal lean practices on inventory leanness in the mediation analysis results (Table 8).

In summary, the post-hoc analysis achieves two important objectives: First, it provides more detailed insights into the simul- taneous and bidirectional relationships between lean practice bundles as well as between lean production and inventory leanness. Second, in so doing it offers an explanation for some unexpected results found in the initial empirical analysis. Specifically, the relationship between ELP and inventory leanness is found to be positive indeed. Moreover, the post-hoc analysis presents evidence that the negative relationship between ILP and inventory leanness may be due to the fact that firms with low inventory leanness tend to increase their efforts to implement (internal) lean production practices. These findings are summarized in Fig. 3.

Fig. 3. Interrelationships among inventory leanness and lean practices. Note: The signs ‘‘þ’’ and ‘‘�’’ indicate significant positive and negative relation-

ships, respectively.

4.3. Testing the interaction hypotheses

The interaction hypothesis posits that when internal and exter- nal lean practices are implemented together, they will generate a positive interaction effect that will increase the financial and inventory performance effect of internal and external lean practice bundles. To test the interaction effect, Eq. (8) was estimated.

Perf ormancei ¼b0þb1lnSalesiþb2SalesGrowthi þb3ILPiþb4 ELPiþb5ILP � ELPiþ

X k

gkIndkþei ð8Þ

The estimation results are summarized in Table 10 where the dependent variable is financial performance (ROS, column A) and inventory leanness (ELI, column B). The respective baseline estima- tion results without the added interaction effects are shown in Table 8. For both ROS and ELI as dependent variables, the interaction

Table 10 Moderation tests results for internal and external lean practice bundles.

Independent variables Dependent variables

A ROS B ELI

Intercept 0.24 9.41nnn

lnSales 0.001 0.04

SalesGrowth 0.53nnn 0.29

ILP �0.20 �2.75nnn

ELP �0.29 �2.47nnn

ILP�ELP 0.08a 0.67nnn

a Marginally significant in a one-tailed test. nnn Significant at 1%.

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253250

effects carry positive and at least marginally significant coefficient estimates, suggesting that the performance benefits of lean produc- tion are greater when both practice bundles are implemented simultaneously. Hence, there is some statistical support for the interaction hypotheses H2 and H3.

5. Discussion and concluding remarks

Collectively, the analyses presented here draw a more com- plete picture of the lean production-inventory leanness-financial performance triangle. This research adds to the theory of lean production by highlighting and investigating the mediating role of inventory management efficiency in deriving the financial performance benefits that are commonly associated with lean production implementation. As such, this study underscores the importance of inventory management within the broader realm of operations management.

5.1. Main findings

A major finding of this research is the mediating role of inventories in the relationship between lean practices and firm performance. Internal lean practices, in particular, have a positive effect on financial performance. This direct effect, however, decreases in magnitude by 25% when inventory leanness enters the regression equation, thus suggesting that inventory leanness partially mediates the link between internal lean practices and financial performance. This result is consistent with Fullerton and Wempe (2009) who presented evidence that non-financial per- formance measures partially mediate the lean production-finan- cial performance relationship. This finding further implies that internal lean practices affect firm performance not only through improved inventory leanness, but also through other mechan- isms. Most notably – and consistent with prior research – internal lean production practices may directly contribute to greater financial performance by lowering operating costs. In this vein, multiple studies have established that internal lean production practices such as TQM and TPM are associated with greater financial performance (e.g., Cua et al., 2001).

In contrast to internal lean practices, the direct effect of external lean practices on financial performance is statistically insignificant. However, the post-hoc analysis reveals that external lean practice implementation is positively associated with inven- tory leanness which, in turn, is linked to financial performance. While prior research has found evidence that greater implemen- tation of external lean practices (e.g., JIT adoption) is associated with greater financial performance (e.g., Inman and Mehra, 1993; Callen et al., 2000; Fullerton et al., 2003) our results indicate that much of the performance enhancing effect of external lean

production may be due to cost reductions that are derived from greater inventory leanness.

A second major implication of this study is the identification of bidirectional and simultaneous effects among internal lean prac- tices, external lean practices and inventory leanness. The empiri- cal results indicate that a firm that implements internal lean practices is also likely to implement external lean practices. In other words, firms seem to view and implement lean production as a comprehensive system instead of a collection of loosely related practices that can be individually adopted. In addition, firms with greater levels of inventory leanness are more likely to adopt external lean practices. Likewise, we find evidence that greater external lean practice implementation is associated with greater inventory leanness. Moreover, we find evidence that lower levels of inventory leanness are associated with greater implementation of internal lean practices. This finding may indicate that firms recognizing their lack of inventory leanness implement internal lean practices in an effort to reduce inven- tories. Hence, this is a possible explanation for the negative estimate of the effect of internal lean practices on inventory leanness. Although this finding remains somewhat surprising, it shows that lean practices vary in their performance effects.

A third major finding of this research is the positive interaction between internal and external lean practices. From its inception, lean production was designed as a system of distinct activities that collectively work to reduce waste and associated costs (Huang, 1991; Roth and Miller, 1992; Imai, 1998). Our results support this notion. Specifically, we find that the concurrent implementation of external and internal lean practices carries greater performance benefits, both in terms of financial perfor- mance and inventory leanness, than the implementation of only one set of lean practices.

5.2. Limitations and future research opportunities

As any research, this study has a number of limitations which may present interesting future research opportunities. First, the empirical analyses rely on a cross-sectional data set. The use of longitudinal data will allow researchers to capture learning effects in lean production. It is plausible that firms become more proficient in implementing lean practices over time, which may result in even better financial performance. Future research could address this issue by assessing the changes in the effects of lean practices over time. Second, the negative coefficient estimate for the effect of internal lean practices on inventory leanness remains an unresolved issue. Could this finding be a statistical artifact of the present data set or do internal lean practices really negatively impact inventory leanness? The replication of this study with a greater sample size may bring greater clarity to this issue.

5.3. Implications for research and practice

This study has a number of implications for further research. First, our findings corroborate the conceptualization of lean production as a system of management practices that may have different operational and financial implications for a firm. The majority of existing research has focused on the study of JIT practices and has concluded that greater implementation of such practices results in lower inventories (e.g., Huson and Nanda, 1995; Balakrishnan et al., 1996). However, there is limited research examining the relationship between non-JIT lean pro- duction practices and inventory leanness. Yet, there is theoretical and anecdotal evidence that lean production may, in at least some instances, even lead to increases in inventory holdings and associated costs (Wu, 2002). Moreover, prior research has found that different lean practices differentially impact non-financial

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253 251

performance measures (Fullerton and Wempe, 2009). Empirical research, therefore, must allow for such differential effects. In addition, as lean production facets are part of a system of potentially co-dependent practices, the individual effects cannot be clearly discerned without controlling for potential interactions.

Second, this study underscores the importance of investigating lean production within the operational context of a firm. As the results of this study show, inventory leanness mediates, and thereby, drives the effect of lean production implementation on financial performance. As inventory leanness varies significantly across indus- tries and firms (Eroglu and Hofer, 2011), it is evident that the financial effects of lean production implementation may vary accordingly.

An interesting implication of this research for managers relates to the positive interaction effect of internal and external lean practices. In other words, the implementation of a particular lean practice will not only have a direct performance benefit, but it can also improve the contribution of other existing lean practices in a firm. Given the synergistic interaction among various lean

Table 11 Confirmatory factor analysis results for the lean production survey.

External lean practices Supplier feedback We frequently are in close contact with our supplier

We give our suppliers feedback on quality and delive

We strive to establish long-term relationships with o

Supplier JIT Suppliers are directly involved in the new product d

Our key suppliers deliver to our plants on JIT basis.

We have a formal supplier certification program.

Supplier development Our suppliers are contractually committed to annual

Our key suppliers are located in close proximity to o

We have corp. level communication on important iss

We take active steps to reduce the number of suppli

Our key suppliers manage our inventory.

We evaluate suppliers on the basis of total cost and

Customer involvement We frequently are in close contact with our custome

Our customers give us feedback on quality and deliv

Our customers are actively involved in current and f

Our customers are directly involved in current and f

Our customers frequently share current/future dema

Internal lean practices Pull Production is ‘‘pulled’’ by the shipment of finished g

Production at stations is ‘‘pulled’’ by the current dem

We use a ‘‘pull’’ production system.

We use kanban, squares, or containers of signals for

Flow Products are classified into groups with similar proc

Products are classified into groups with similar routi

Equipment is grouped to produce a continuous flow

Families of products determine our factory layout.

Setup Our employees practice setups to reduce the time re

We are working to reduce the setup times in our pla

We have low setup times in our plants.

SPC Large number of equipment/processes on shop floor

We make extensive use of statistical techniques to r

Charts showing defect rates are used as tools on the

We use fishbone type diagrams to identify causes of

We conduct process capability studies before produc

Employee involvement Shop-floor employees are key to problem solving tea

Shop-floor employees drive suggestion programs.

Shop-floor employees lead product/process improvem

Shop-floor employees undergo cross-functional train

TPM We dedicate a portion of everyday to planned equip

We maintain all our equipment regularly.

We maintain excellent records of all equipment mai

We post equipment maintenance records on shop flo

Note: All lambda values are significant at po0.01 level.

practices, managers should look at the system-wide effects of practices that are adopted in their operations. Also, interactions between various practices should be assessed to determine the total performance effect of a given lean practice. Moreover, our research highlights the importance of considering operational performance outcomes, such as inventory leanness, as a precursor to enhanced financial performance.

Appendix A. Confirmatory factor analysis

See Appendix Table 11.

Appendix B. Empirical leanness indicator (ELI)

The ELI is based on the concept of turnover curves popularized by Ballou (1981, 2000, 2005) that describes firm size-adjusted

Lambda Fit statistics s. 0.6137 w2(8)¼9.5238 ry performance. 0.6405 p¼0.3000

ur suppliers. 0.7226 CFI¼0.9931

RMSEA¼0.0293

evelopment process. 0.5648 SRMR¼0.0312

0.5648

0.3356

Lambda Fit statistics cost reductions. 0.4570 w2(32)¼41.7159

ur plants. 0.3057 p¼0.1168

ues with key suppliers. Dropped CFI¼0.9818

ers in each category. 0.5068 RMSEA¼0.0370

0.5661 SRMR¼0.0495

not per unit price. 0.5191

rs. 0.6435

ery performance. 0.7505

uture product offerings. 0.5829

uture product offerings. 0.5504

nd info with marketing dept. 0.6875

Lambda Fit statistics oods. 0.7831 w2(17)¼26.7066 and of the next station. 0.7112 p¼0.0625

0.9589 CFI¼0.9887

production control. 0.6409 RMSEA¼0.0507

SRMR¼0.0492

essing requirements. 0.5612

ng requirements. 0.5774

of families of products. 0.7767

0.6473

Lambda Fit statistics quired. 0.8206 w2(8)¼14.9280 nts. 0.6568 p¼0.0606

0.5258 CFI¼0.9850

RMSEA¼0.0625

are currently under SPC. 0.7946 SRMR¼0.0448

educe process variance. 0.9481

shop floor. 0.6655

quality problems. Dropped

t launch. Dropped

Lambda Fit statistics ms. 0.8001 w2(8)¼9.4532

0.8219 p¼0.3055

ent efforts. 0.8000 CFI¼0.9974

ing. Dropped RMSEA¼0.0286

SRMR¼0.0247

ment maintenance related activities. 0.6037

0.8416

ntenance related activities. 0.8011

or for active sharing with employees. Dropped

Sales

Firm Size-Adjusted Industry Average

ELI

In ve

nt or

y

Fig. 4. The sales–inventory relationship. Note: Figure adapted from Eroglu and Hofer (2011).

C. Hofer et al. / Int. J. Production Economics 138 (2012) 242–253252

industry average inventory levels (Fig. 4). A firm’s deviation from this curve represents its level of ‘‘leanness’’ or ‘‘unleanness.’’

More formally, ELI is calculated as the error term from the regression model lnInvif ¼a0iþa1ilnSalesif þeif , where Invif is the average of the firm’s total inventories reported at the end of 2008 and 2009, and Salesif is the total sales volume of firm f in industry i. Fitting this model by industry provides some control for industry- specific idiosyncrasies of the inventory-sales relationship. The remaining differences in the inventory-sales relationship across firms within an industry, thus, primarily arise from systematic differences in firms’ inventory management practices. The residual eif is studentized and rescaled such that relatively lower inventory holdings translate to higher positive ELI values, and vice versa (Eroglu and Hofer, 2011). The model shown above was estimated using the ordinary least squares method for each of the 24 four-digit NAICS industries represented in the survey sample. Detailed regression results are not reported here due to space constraints but are available from the authors upon request. The average value of the coefficient estimate of the lnSales variable, an indicator of scale economies in inventory management, was 0.91. The average value of the R-squared statistic was 0.89, indicating a very good model fit.

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