University of Groningen Not lean by default Ziengs, Nick

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Not lean by default Ziengs, Nick

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517640-L-bw-Ziengs-SOM 517640-L-bw-Ziengs-SOM 517640-L-bw-Ziengs-SOM 517640-L-bw-Ziengs-SOM Processed on: 14-3-2018 Processed on: 14-3-2018 Processed on: 14-3-2018

Processed on: 14-3-2018 PDF page: 1PDF page: 1PDF page: 1PDF page: 1

Not lean by default:

Exploring practices, their design, and

underlying mechanisms driving

performance

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Publisher: University of Groningen

Groningen, The Netherlands

Printer: Ipskamp Printing

Enschede, The Netherlands

ISBN: 978-94-034-0479-0 (print)

978-94-034-0478-3 (digital)

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system of any nature, or transmitted in any form or by any means, electronic, mechanical, now known or hereafter invented, including photocopying or recording, without prior written permission of the author.

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Not lean by default:

Ex

ploring practices, their design, and underlying

mechanisms driving performance

Proefschrift

ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen

op gezag van de

rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op donderdag 26 april 2018 om 12.45 uur

door

Nick Ziengs

Geboren op 17 december 1985 te Coevorden

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Processed on: 14-3-2018 PDF page: 4PDF page: 4PDF page: 4PDF page: 4 Promotores

Prof. dr. D. P. van Donk Prof. dr. J. Riezebos

Beoordelingscommissie

Prof. dr. ir. C.T.B. Ahaus Prof. dr. ir. J.C. Fransoo Prof. dr. M. Stevenson

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Voor ieders geduld.

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Introduction

1.1 Lean manufacturing

Lean manufacturing has been widely adopted by manufacturers in an effort to improve quality, reduce throughput times, and reduce costs (Albliwi, Antony, Abdul Halim Lim, & van der Wiele, 2014). Lean manufacturing originated in a repetitive manufacturing environment (Hines, Holweg, & Rich, 2004), but was later adopted by manufacturers in non-repetitive manufacturing environments as well (Portioli Staudacher & Tantardini, 2012). Even outside manufacturing industries, the application of lean principles has become widespread. Hospitals (D’Andreamatteo, Ianni, Lega, & Sargiacomo, 2015), service providers (Piercy & Rich, 2009), and governmental institutions (Radnor & Walley, 2008), for example, have adopted lean principles. The widespread adoption of these principles suggests that they are beneficial. Nevertheless, most studies find that the adoption of lean principles often does not result in the expected benefits (see also Albliwi et al., 2014; Stentoft Arlbjørn & Vagn Freytag, 2013 for

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reviews of literature). As such, there is a need to study the relation between lean manufacturing and performance in more detail.

In order to study the relation between lean manufacturing and performance, a definition of lean manufacturing is needed. The problem is, however, that lean manufacturing does not have a clear, concise, or consistent definition (Bhamu, Sangwan, & Singh Sangwan, 2014; Stentoft Arlbjørn & Vagn Freytag, 2013). Lean manufacturing has, amongst others, been characterized as a concept, a philosophy, a set of principles, a set of practices, a set of bundles, or a collection of tools and techniques (Bhamu et al., 2014). More importantly, studies seem to disagree on the operationalization of the concept, the tenets of the philosophy, the principles to include, the practices to incorporate, whether or not these practices should be studied together or in isolation, and which tools and techniques are part of the lean toolkit (Stentoft Arlbjørn & Vagn Freytag, 2013). The lack of a consistent definition might explain why some studies report positive results, whereas others do not. In any case, the lack of a commonly agreed upon definition may explain why literature on lean manufacturing remains inconclusive with respect to whether or not lean manufacturing actually improves performance and, if so, what elements of lean manufacturing contribute most to improved performance.

In light of the absence of a shared definition, we need to rely on a different way to conceptualize lean manufacturing. A number of ways have been suggested. Shah, Chandrasekaran, and Linderman (2008), for example, suggest that it is helpful to distinguish between different levels

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of abstraction when it comes to lean manufacturing. Distinguishing

between various levels of abstraction allows us to separate strategic and operational concerns, general principles and specific tools, long- and short-term interests, and general guidelines and specific goals. The classification suggested by Shah, Chandrasekaran & Linderman (2008) consists of three levels of abstraction, namely: (1) lean manufacturing as a management philosophy, (2) lean manufacturing as a set of manufacturing practices, and (3) lean manufacturing as a collection of specific tools and techniques. Similar classifications have been adopted by others for similar purposes (e.g. Dean & Bowen, 1994; Shah & Ward, 2007; Stentoft Arlbjørn & Vagn Freytag, 2013).

Table 1.1 provides an example of the classification. The example lists the principles and tools and techniques associated with the practice of pull production. Pull production practices are used in support of waste elimination efforts as lowering work-in-progress allows potential sources of waste to surface, be identified, and subsequently be eliminated (Hopp and Spearman, 2004). The manner in which the practice of pull production itself is implemented depends on the specific tools and techniques used in support of the practice. Manufactures combine different tools and techniques to create a unique implementation of the practice particularly suited to their needs. For instance, pull production systems with route-specific cards and overlapping work-in-progress restrictions have different properties than pull production systems with product-specific cards and non- overlapping work-in-progress restrictions. These properties make pull systems with route-specific cards and overlapping control loops better

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4 T ab le 1. 1. L ea n m an uf ac tu ri ng clas sif icatio n P rinciples P ra ct ices T oo ls a nd t ec hn iq ues E li m ina te w as te Setu p ti m e red uctio n P ro du ct -s pec if ic ca rd s C on ti nu ou sl y im pr ov e P ull pro du ct io n P ro du ct -a no ny m ou s ca rd s E m po w er e m plo yee s P ro ce ss m an ag em en t Ro ute -s pecif ic ca rds … Qu alit y data an al ys is Sep ar ate w or k-in -p ro gr es s rest rictio ns Ma nag em en t le ad er sh ip Sh ar ed w or k-in -p ro gr ess r estri ctio ns P eo ple m an ag em en t O ver la pp ing w ork -in -pro gre ss re st rict io ns Ju st -in -ti m e deli ver ies fr om s up plier s … Su pp lier q ualit y m an ag em en t … gen er al sp ec if ic co ntex t i nd ep en den t co ntex t-dep en den t in fo rm al fo rm al lo ng -ter m sh or t-ter m str ateg ic op er atio nal 1E xa m ple s of p rin cip les, pr ac tices, an d th eir as so ciate d to ols an d tech niq ues a re sh ow n in bo ld .

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suited for make-to-order manufacturing environments than pull systems

with product-specific cards and non-overlapping work-in-progress restrictions (Riezebos, 2010; Ziengs, Riezebos, & Germs, 2012). The example illustrates the need to consider lean manufacturing on levels of abstraction.

Lean manufacturing, as a management philosophy, provides manufacturers with a set of general principles or guidelines (Bhasin & Burcher, 2006). These principles have their roots in just-in-time and quality management approaches and direct manufacturers to eliminate waste, continuously improve, and empower employees in order to provide value for their customers (Dal Pont, Furlan, & Vinelli, 2008; Stentoft Arlbjørn & Vagn Freytag, 2013). Although these general principles are useful when establishing a long-term operations strategy, they only serve as a guiding principle when trying to implement such a strategy. Therefore, it is not surprising, that manufacturers go about implementing similar strategies in markedly different ways (Bhasin & Burcher, 2006). Different manufacturers emphasize different tenets of the philosophy, implement different practices as part of their strategy, and train employees in the use of particular tools and techniques depending on their specific needs. Nevertheless, the core tenets of the philosophy are remarkably consistent across studies. As such, viewing lean manufacturing as a philosophy helps us to understand what lean adherents attempt to achieve, but not how they will achieve their goals. Lean manufacturing, as a set of manufacturing practices, provides manufacturers with the means to achieve the general goals

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outlined by the philosophy. That is, lean manufacturing practices are used to implement the lean philosophy. Each principle is implemented

through a set of manufacturing practices, such as process management, pull production, or small group problem solving (Furlan, Vinelli, & Dal Pont, 2011). These practices are general enough to be comparable across organizations, yet specific enough to explore to what degree these constituent elements of lean manufacturing relate to performance (e.g. Dean & Bowen, 1994). As such, and perhaps not surprisingly, a large number of empirical studies investigate the relation between these lean manufacturing practices and performance in an effort to determine how they jointly or separately result in performance improvement (see Mackelprang & Nair, 2010; and Nair, 2006 for meta-analyses).

Lean manufacturing, as a collection of tools and techniques, provides manufacturers with the means to support the implementation and use of lean manufacturing practices. These tools and techniques are often highly specific and serve an equally specific purpose. For example, to implement process management practices, such as statistical process control, manufacturers use different types of control charts (see Oakland, 2008 for an overview of different control charts). As another example, manufacturers can use different types of cards and different implementations of work-in-progress restrictions to create a unique implementation of the lean manufacturing practice of pull production (see Thürer, Stevenson, & Protzman, 2016a for an overview of different pull systems).

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In short, to understand how lean manufacturing results in

improved performance, it is important to consider not only the general principles involved but also the manufacturing practices used to implement these principles and the associated tools and techniques used in support of these practices. As such, in line with Shah and Ward (2003), lean manufacturing will be characterized as managerial philosophy implemented through a set of manufacturing practices supported by a large number of specific tools and techniques with their roots in quality management, just-in-time manufacturing, and human resource management. To provide recommendations for lean implementations it is particularly important to study lean manufacturing practices and their associated tools and techniques. The general principles that are shared across lean implementations by themselves provide little guidance with respect to the implementation of lean, whereas insight into how to select and combine practices or even tools and techniques is of great practical value. Consequently, in this dissertation, the focus is on the second and third level of the abstraction outlined above.

1.2 Lean manufacturing practices

As stated, lean manufacturing is often considered as a set of manufacturing practices (e.g. Shah & Ward, 2007; Tortorella, Miorando, & Marodin, 2017). Examples of lean manufacturing practices are process management, pull production, and small group problem-solving. Numerous papers, published in the last thirty years,

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have been dedicated to studying how these lean manufacturing practices affect performance (see Mackelprang & Nair, 2010; Nair, 2006 for

meta-analyses). Implementing selected lean manufacturing practices allows manufacturers to develop their own approach to lean.

The first studies published in the 1990s conceptualized lean manufacturing as a set of separate or interrelated practices (e.g. Cua, McKone, & Schroeder, 2001; Flynn, 1994; Flynn, Sakakibara, & Schroeder, 1995). These studies were mostly dedicated to determining how subsets of lean manufacturing practices, such as quality management practices (e.g. Flynn, 1994), just-in-time manufacturing practices (e.g. Chang & Lee, 1995), or a combination of both (Cua et al., 2001; Flynn, Sakakibara, et al., 1995) relate to performance. Moreover, these studies not only considered the practice performance relationship, but also the relationships amongst lean manufacturing practices themselves. These studies explored whether certain lean manufacturing practices directly or indirectly, by enabling the effective use of other practices, relate to performance (e.g. Flynn, Sakakibara, et al., 1995). Subsequent studies also addressed the circumstances under which the practice performance relationship was strongest by exploring the role of possible moderators (e.g. White, Pearson, & Wilson, 1999).

These studies attempted to aid manufactures with the selection of lean manufacturing practices and reported mostly positive relations between lean manufacturing practices and performance (see also Mackelprang & Nair, 2010; Nair, 2006 for meta-analyses). However, the strength of the relation between lean manufacturing practices and

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performance reported varied considerably across studies which suggest

that context, as well as other practices, play an important role when it comes to improved performance. In addition, most studies reported positive relations amongst lean manufacturing practices as well which suggests that these practices are mutually reinforcing (e.g. Cua et al., 2001; Flynn, Sakakibara, et al., 1995; Flynn, Schroeder, & Sakakibara, 1994). However, the strength of these relations reported in different studies also varied considerably. Furthermore, different studies included different practices thereby making it difficult to assess the performance implications of lean manufacturing as a whole. As such, it remains difficult to conclusively state which practices are mutually reinforcing and, if so, to what degree and under which circumstances they are. Unfortunately, only a limited number of attempts have been made to explain the differences observed in these studies.

Subsequent studies started to conceptualize lean manufacturing not as a set of interrelated manufacturing practices, but rather as a set of interrelated bundles of practices (Bortolotti, Danese, Flynn, & Romano, 2015; Dal Pont et al., 2008; Shah & Ward, 2003). Conceptualizing lean manufacturing as a set of interrelated bundles acknowledges that lean practices are often not implemented in isolation and that practices within these lean bundles are likely to affect performance in similar ways. That is, studies that considered lean bundles recognized the mutually reinforcing character of lean manufacturing practices within specific bundles. Moreover, these studies also act on the observation that it is becoming difficult to (empirically) distinguish between separate lean manufacturing

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practices which suggests that lean manufacturing implementations have become increasingly integrated. In fact, even more recently, studies

have started to consider lean manufacturing as a whole, rather than constituent bundles, or practices (e.g. Khanchanapong et al., 2014). These developments suggest that it has become more difficult to distinguish between separate lean bundles or practices. A possible explanation might be that lean implementations have become unique due to path dependencies associated with the implementation and refinement of specific practices over time (Netland, 2016). That is, past choices of manufacturers with respect to the implementation and refinement of practices have determined, at least to a degree, what practices can or will be implemented and how they will be implemented. These manufactures have therefore implemented similar practices in markedly different ways making it more difficult to distinguish between practices or even bundles of practices using the same empirical methods.

The most commonly identified bundles are the just-in-time, quality management, and human resource management bundles. According to Dal Pont et al. (2008), the quality management bundle consists of practices such as process management, product design and management, and quality data analysis. The just-in-time bundle consists of practices such as setup time reduction, lot size reduction, schedule adherence, and group technology. The human resource management bundle consists of practices related to small group problem solving, training, involvement, and empowerment.

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The studies which consider lean manufacturing as a set of

practices and those which consider lean manufacturing as a set of bundles address similar questions. These studies consider what lean manufacturing bundles to implement, whether other lean manufacturing bundles are necessary for successful implementation, and under what circumstances implementation is most likely to succeed. Similar as before, the results are inconclusive. Most studies report positive relations between bundles of lean manufacturing practices, but the strength of the relation between lean bundles and performance varies considerably across studies. Moreover, different studies include different practices in similar bundles thereby making it difficult to compare the performance implications of these bundles across studies. Furthermore, some studies consider direct effects of bundles (e.g. Shah and Ward, 2003) whereas others also consider indirect effects (e.g. Dal Pont et al. 2008; Bortolloti et al. 2015) which suggest that the role these bundles play in realizing improved performance is not entirely understood.

In short, despite the wealth of empirical literature on lean manufacturing, a number of important issues remain unresolved. First, the level of abstraction varies across studies. Studies consider lean manufacturing as a set of interrelated practices, a set of interrelated bundles of manufacturing practices, or lean as a whole which makes the comparison across studies difficult. Second, lean manufacturing practices or bundles are often not well defined. Definitions of lean manufacturing practices vary across studies which, again, makes comparison across studies difficult and sheds doubt on the conceptual

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integrity of the measurement instruments used. Notable exceptions notwithstanding, the choice which practices or bundles to include often

appears pragmatic rather than informed by theory. Third, studies often fail to consider alternative models and resort to presenting the model that fits best. There is no consensus as to whether practices or bundles directly or indirectly affect performance. Model comparison, especially when these models are derived from competing theoretical perspectives, aids in the development of theory on lean manufacturing. Nevertheless, competing perspectives are rarely evaluated. As such, it becomes difficult to conclusively state, based on prior literature, whether certain lean manufacturing practices indeed affect performance positively and to what degree. Similarly, it is difficult to assess whether the successful implementation of certain lean manufacturing practices depends on the presence of other lean manufacturing practices and, if so, what the sequence of implementation should be.

These unresolved issues give rise to a number of problems. First, these issues make it difficult to build on prior literature to develop a comprehensive theory on lean manufacturing. The different levels of abstraction used, the inconsistent use of definitions and lack of comparison hinder model building, testing, and refinement. Second, these unresolved issues also make it difficult for practitioners to derive guidelines for the implementation of lean manufacturing practices from these studies. As such there is a need to synthesize the current literature base.

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The previous discussion leads us to formulate the following

general research objective which highlights the need to study lean at the second level of abstraction outlined previously. The first objective is to better understand how lean manufacturing practices jointly affect performance.

1.3 Lean manufacturing practices and their design

The first research objective addresses the relationship between lean manufacturing practices and performance. As such, the first research objective is expressed at the second level of abstraction outlined previously. However, to truly understand how lean manufacturing practices affect performance, it is important to consider the third level of abstraction as well. That is, the design of these lean manufacturing practices should be taken into account by looking at the associated tools and techniques used in support of the lean manufacturing practices. Considering lean manufacturing as a set of interrelated practices only aids us in understanding how constituent elements of lean manufacturing affect performance. However, considering lean manufacturing practices on the practice level only offers limited insight into why these practices affect performance as these practices can be implemented in various ways. In other words, studying lean manufacturing practices on the level of the practices does not shed light on the mechanisms by which these practices affect performance.

The design of lean manufacturing practices is often tailored to address specific needs even though these designs share a similar intent.

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Here, the design of lean manufacturing practices refers to the selection of tools and techniques and the way in which they are used in support

of the practices themselves. Consider, for instance, the lean manufacturing practice of pull production. Pull production is of particular interest because pull production can be implemented in markedly different ways (see Thürer, Stevenson, & Protzman, 2016a for an overview of different pull production systems). KANBAN (Sugimori, Kusunoki, & Cho, 1977), CONWIP (Spearman, Woodruff, & Hopp, 1990), and POLCA (Suri, 1998) all provide different ways to implement pull production. Each of these pull production systems uses different types of cards (e.g. product-specific, product-anonymous, and route-specific) and relies on different types of work-in-progress restrictions (e.g. separate, shared, or overlapping). The design of a pull production system can be tailored to match the specific circumstances manufacturers find themselves in by, for instance, selecting and implementing different types of cards and work-in-progress restrictions. To understand how to tailor lean manufacturing practices such as pull production, a thorough understanding of the mechanisms by which these lean manufacturing practices affect performance is required. Furthermore, a thorough understanding of the circumstances under which these mechanisms drive performance is also necessary. This sentiment is not new, others have also argued that it is important to move away from approaches that consider lean manufacturing as a set of practices to approaches which take the actual design and use of these practices on the shop floor into account (Hasle, Bojesen, Jensen, & Bramming, 2012).

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The previous discussion leads us to formulate the following

general research objective and highlights the importance of studying lean manufacturing practices at the second and third level of abstraction outlines previously. The second objective is to better understand how the design of lean manufacturing practices affects performance by studying the underlying mechanisms that drive performance.

1.4 Outline

The above discussion led us to formulate the two general research objectives outlined above: (1) to better understand how lean manufacturing practices jointly affect performance and (2) to better understand how the design of lean manufacturing practices affects performance by looking at the underlying mechanisms that drive performance. Each research objective is addressed in one of the following chapters. The chapters address a specific problem within the confines of the stated research objectives.

In chapter 2, we address the first research objective. To explore the interrelatedness of lean manufacturing practices, we look at a subset of lean manufacturing practices, namely core and infrastructural quality management practices. In the chapter, we use meta-analysis and structural equation modeling techniques to address the role that core and infrastructural quality management practices play when it comes to realizing improved performance. As such, in the second chapter, we address how these practices jointly affect performance. In chapter 2,

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we, therefore, consider the second level of the classification, namely lean manufacturing practices.

In chapter 3, we address the second research objective. To explore how the design of lean manufacturing practices affects performance, we look at the design of pull production systems. In the chapter, we use discrete-event simulation to address how the placement of work-in-progress restrictions affects the effective workload balancing capability of a unit-based pull production system. As such, in the third chapter, we address both the design and the underlying mechanism, namely workload balancing, that drives performance. In chapter 3, we, therefore, consider both the second and the third level of the classification.

In chapter 4, we also address the second research objective. To explore how the design of lean manufacturing practices affects performance, we, again, take the design of pull production systems as an example. In chapter 4, however, we use a controlled experiment to address how work-in-progress restrictions affect performance by influencing the behavior of individuals on the shop floor. Two mechanisms which direct the behavior of individuals on the shop floor are considered. As such, in the fourth chapter, we address both the design and the underlying mechanisms that drive performance. In the chapter, we, therefore, consider the third level of the classification.

In chapter 5, we address the contribution of each study to the associated research objective. In addition, we provide suggestions to

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explore each research objective further. In the next section, we provide

an extended overview of each chapter.

1.4.1 Chapter 2 - The Distinctive Roles of Core and

Infrastructural Quality Management Practices: A Meta-Analytical Structural Equation Modeling study

In chapter two, we explore the relationship between a subset of lean manufacturing practices and performance and address the first research objective. More specifically, we address the relationship between quality management practices and performance. The relation between quality management practices and performance is addressed because a considerable amount of empirical studies, dating back to the seminal work by Saraph, Benson, & Schroeder (1989), have reported on this relationship with various results. In addition, only a limited number of studies relate quality management practices to just-time time and associated human resource management practice thereby limiting the feasibility of including practices from other lean bundles in a single study.

Quality management practices are often divided into two categories, namely core and infrastructural quality management practices. Process Management, Product Design and Management, and Quality Data Analysis are examples of core quality management practices. The core quality management practices closely resemble the practices within the quality management lean bundle (Dal Pont et al., 2008; Shah & Ward, 2003). People management, Management

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Leadership, Supplier Quality Management, and Customer Focus are infrastructural practices. The infrastructural quality management

practices closely resemble practices included in other lean bundles such as the human resource management bundle (Dal Pont et al., 2008), the supply chain management bundle (Tortorella et al., 2017) or the fitness bundle (Bortolotti, Boscari, & Danese, 2015).

The role of infrastructural quality management practices, in particular, has been, and still is, the topic of considerable debate. Proponents of an indirect view argue that infrastructural quality management practices indirectly affect performance by providing support for core quality management practices and that infrastructural quality management practices need to be in place in order for core quality management practices to be effective. Proponents of a direct view argue that infrastructural practices directly affect performance and, therefore, quality management practices can be implemented in isolation. Both perspectives assume that core and infrastructural quality management practices are distinct which in itself is contested by a third perspective of which adherents suggest that such a distinction is not possible. These perspectives have been implicitly and explicitly used in previous studies and each has gathered considerable empirical support. Nevertheless, each perspective provides different suggestions with respect to the implementation of these practices. As such, it is necessary to settle this debate.

To evaluate these perspectives, we combine meta-analysis and structural equation modeling. First, we use meta-analysis to synthesize

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the empirical evidence documented in more than sixty studies. Second,

we use the meta-analytically derived correlation matrix as an input for structural equation modeling. Confirmatory factor analysis allows us to evaluate whether core and infrastructural quality management practices are distinct (two-factor model) or indistinct (single-factor model). Structural equation modeling allows us to assess the relationship between quality management practices and performance.

The results of the meta-analysis show that core and infrastructural quality management practices are positively related to each other and performance. However, these relationships are subject to considerable heterogeneity which suggests moderators are at play. The results of the confirmatory factor analysis supports a single-factor model which suggests that it is difficult to distinguish between core and infrastructural quality management practices. The results of the structural equation model, in turn, suggest that quality management practices positively affect quality, operational and business performance. The results of the study further the debate on the role of infrastructural and core quality management practices and suggests that a universal yet customized approach to quality management is warranted.

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1.4.2 Chapter 3 – The Placement of Effective Work-In-Progress Restrictions in Route-Specific Unit-Based Pull Production Systems: A Discrete-Event Simulation Study

In chapter 3, we explore the relation between production planning and control system design and performance and thereby address the second research objective. More specifically, we evaluate the workload balancing capability of unit-based pull production systems. Unit-based pull production systems regulate the release and dispatching of work by restricting the number of orders on the shop floor. Unit-based pull production systems are often used by manufacturers in an attempt to achieve shorter and more reliable throughput times.

To achieve short and reliable throughput times, manufacturers make use of the workload balancing capability of pull production systems, especially when confronted with routing variability. The workload balancing capability of a unit-based pull production system depends on its structure and configuration. The structure refers the number, size, and placement of work-in-progress restrictions. The configuration refers to the extent or the degree of the work-in-progress restrictions. In this study, we explore to what degree the effective workload balancing capability of a unit-based pull production system depends on the placement of work-in-progress restrictions on the shop floor.

Discrete-event simulation was used to explore the relation between the placement of work-in-progress restrictions and the effective workload balancing capability of a unit-based pull production

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system. Discrete-event simulation is suitable because it allows us not

only to study the placement of work-in-progress restrictions, but also how the placement of these restrictions is affected by circumstances manufacturers find themselves as characterized by various levels of interarrival variability, processing time variability, utilization, and batch size. The structure of POLCA, a unit-based pull production system, was used because the multiple overlapping work-in-progress restrictions allowed us to consider the placement of work-in-progress restrictions whereas other structures would not.

The results of the simulation suggest that the placement of work-in-progress restrictions influences the effective workload balancing capability of the unit-based pull production system. Workload balancing on the shop floor is shown to be more effective than balancing at the moment of release especially for shorter routings. Workload balancing at the moment of release becomes more important once the routing becomes longer. The study underpins the importance of considering the design of the lean manufacturing practice, rather than whether or not the practice itself has been implemented.

1.4.3 Chapter 4 – Motivational Mechanisms in

Work-In-Progress Restricted Production Systems: An Experimental Study

In chapter 4, we explore the relationship between coordination losses and motivation gains in work-in-progress restricted production systems and thereby address the second research objective. More specifically,

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we explore how pull production system design influences both coordination losses as well as motivation losses and gains. Pull

production systems restrict work-in-progress on the shop floor which results in lower work-in-progress levels and shorter shop floor throughput times. However, low work-in-progress levels also result in an increase in idle time, or coordination loss, by creating interdependencies between resources on the shop floor. Unsurprisingly, most pull production systems are therefore designed to reduce work-in-progress to a degree which does not increase the interdependencies between shop floor resources.

A number of authors have argued that the negative consequences of increased interdependencies are overstated in production systems where workers are the primary determinant of processing times and suggest that the interdependencies should be exploited, rather than avoided. In work-in-progress restricted production systems, individuals adjust their effort in response to changes to the state of the system. Individuals are motivated to prevent idle time. That is, individuals decrease their effort if they themselves are likely to become idle and increase their effort when they are likely to cause others to become idle. Although the speed up (motivation gains) and slow down effects (motivation losses) that occur in work-in-progress restricted production systems have been demonstrated, the mechanisms that drive motivation gains in work-in-progress restricted systems and the subsequent consequences for production system design and performance are less well understood.

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In this study, we consider two mechanisms which motivate

individuals to adjust their effort in work-in-progress restricted production systems, namely social comparison and social indispensability. To evaluate the social comparison and social indispensability explanations, we conducted a controlled experiment.

The results provide evidence in support of both social comparison and social indispensability explanations. Furthermore, the results suggest that sequential interdependencies can be determinantal. In the presence of sequential interdependencies, social comparison and indispensability mechanisms seemingly mitigate the detrimental effects associated with these sequential interdependencies. The results also suggest that in the absence of the sequential interdependencies, social comparison results in improved performance. The study underpins the importance of considering the design of the lean manufacturing practices, rather than whether or not the the practice has been implemented.

1.4.4 Chapter 5 – Conclusion and Discussion

Chapter 2, 3, and 4 each report on a study which addresses one of the two research objectives. The first study addresses how lean manufacturing practices jointly affect performance. The second and third studies address how the design of lean practices affects performance. Each study relies on a different research approach to explore a specific question related to lean manufacturing practices, their design, and the underlying mechanisms that drive performance. In

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chapter five, we address the contribution of each study in relation to the general research objectives introduced in this chapter. In addition, we

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Chapter 2

The Distinctive Roles of Core and

Infrastructural Quality Management

Practices: A Meta-Analytical

Structural Equation Modeling Study

2.1 Introduction

To ensure competitiveness firms need to improve their quality standards relentlessly (Schroeder, Linderman, & Zhang, 2005; Zhang & Xia, 2013). To do so, firms rely heavily on both core and infrastructural quality management practices (Flynn, Sakakibara, et al., 1995; Naor, Goldstein, Linderman, & Schroeder, 2008; Zu, 2009). Core quality management practices refer to the implementation and use of statistical tools and techniques such as control charts, cause-and-effect diagrams, or histograms (Sousa & Voss, 2002). Infrastructural quality management practices, such as quality related training programs and empowering employees to make quality-related decisions, aim to

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facilitate the effective use of core quality management practices (Sousa & Voss, 2002). Managers tasked to improve quality face an arduous decision – to prioritize core quality management practices, infrastructural quality management practices, or invest equally in both. Although the distinction between core and infrastructural practices is widely used (Bortolotti, Danese, et al., 2015; Flynn, Sakakibara, et al., 1995; Naor et al., 2008; Zu, 2009), the role of infrastructural quality management practices, in particular, is subject to considerable debate (Sousa & Voss, 2002; Zu, 2009). In short, there are three competing perspectives. Proponents of an indirect view argue that both core and infrastructural practices are necessary to maintain and improve quality performance (Flynn, Schroeder, & Sakakibara, 1995; Zu, 2009). In fact, these authors argue that infrastructural quality management practices support core quality management practices and therefore only indirectly contribute to quality and organizational performance. In contrast, proponents of the direct view argue that infrastructural quality management practices directly contribute to performance and will positively affect performance even in the absence of core quality management practices (Dow, Samson, & Ford, 1999; Powell, 1995; Samson & Terziovski, 1999). Proponents of the first two perspectives subscribe to the idea that core and infrastructural practices can be distinguished which is contested as well by the proponents of a third perspective. Here the argument is that no distinction can be made between the core and infrastructural practices. As such, the debate surrounding the role of infrastructural quality management practices, first voiced by Sousa and Voss (2002), is not yet resolved as indicated

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by recent studies in support of both the indirect (Bortolotti, Danese, et

al., 2015; Dal Pont et al., 2008; Zu, 2009; Zu, Robbins, & Fredendall, 2010), the direct perspective (Naor et al., 2008), and indistinct perspective (Choi and Eboch, 1998) . In this study, we take the first step to resolve this debate by examining the relationships between core and infrastructural quality management practices and evaluating these alternative perspectives.

To study the relation between core and infrastructural quality management practices and their relation to performance, we use meta-analytical techniques (Borenstein, Hedges, Higgins, & Rothstein, 2009; Cooper, Hedges, & Valentine, 2009; Hunter & Schmidt, 2004) in combination with structural equation modelling (Viswesvaran & Ones, 1995). Meta-analysis is suitable to resolve contradictory findings when a large number of studies have been published and when additional individual studies are not likely to further the debate. Meta-analytical techniques allow us to provide an accurate estimate of the strength of the relationship between infrastructural quality management practices, core quality management practices, and performance. Confirmatory factor analysis and structural equation modeling techniques allow us to assess whether core and infrastructural practices can be considered distinct and to what degree these practices predict performance.

The study contributes to the literature and to manufacturing practice in the following ways. First, we examine the direct relation between core quality management practices and performance and the direct relation between infrastructural quality management practices

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and performance. In doing so, we update Nair’s (2006) meta-analytical study by adding over a decade’s worth of research on quality management. Second, we examine the relationship between core and infrastructural quality management practices, thereby extending prior meta-analysis which only addressed the relation between quality management practices and performance, but not between the core and infrastructural practices themselves (Nair, 2006; White, 1996). Third, by assessing the relation between infrastructural and core quality management practices we reflect on both the direct and indirect nature of the relationship between core quality management practices, infrastructural quality management practices, and performance. Understanding the role of infrastructural quality management practices will enable manufacturing managers to make an informed decision when investing in quality management practices.

The remainder of this chapter is structured as follows. In section 2.2, we will discuss the three perspectives proposed in the literature and provide three alternative models which fit these perspectives. In section 2.3, we will detail the selection of papers, coding of papers, and the meta-analytical techniques used. The results are presented in section 2.4 and the implications for theory and practice and suggestions for future research are discussed in section 2.5. In the final section, we will conclude.

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2.2 Background

According to Dean and Bowen (1994), quality management is best characterized as a managerial philosophy based on the principles of customer centrality, continuous improvement, and organizational-wide effort. These principles provide managers with a set of guidelines for quality improvement initiatives. Quality management’s most noticeable proponents claimed that adherence to these principles would allow any organization to deliver high-quality products and services without compromising cost or delivery performance (e.g. Crosby, 1979; Deming, 1982; Juran, 1988).

Unsurprisingly, claims by quality management proponents and early examples of success gained a foothold with practitioners and led to a surge of quality management initiatives. More often than not, the outcome of these quality management initiatives was disappointing. The apparent contradiction led researchers to investigate quality management in a more rigorous empirical manner to discern what makes quality management initiatives successful (e.g. Flynn, 1994; Krafcik, 1988; Saraph, Benson, & Schroeder, 1989) resulting in the publication of a larger number of studies which address the relationship between quality management practices and performance.

There is considerable agreement in literature as to what the most common or important quality management practices are (Nair, 2006; Sousa & Voss, 2002; Zu et al., 2010), namely (1) process management, (2) product design and management, (3) quality data and analysis, (4) management leadership, (5) customer quality management, (6) supplier

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quality management, and (7) people management. Table 2.1 provides an overview of these practices and their descriptions as proposed by Zu et al. (2008, p.632) based on their assessment of the literature.

In a further effort to explain the successful and unsuccessful quality management practices, researchers classified quality management practices as either core or infrastructural (Flynn, Schroeder, et al., 1995; Naor et al., 2008; Sousa & Voss, 2002; Zu et al., 2008). To maintain and improve quality, core quality management practices focus on the application of statistical tools and techniques. Infrastructural quality management practices, on the other hand, are intended to support and facilitate the effective use of core quality management practices and emphasize managerial, employee, supplier, and customer involvement. Implementation of core quality management practices without consideration of infrastructural quality management practices has been suggested as an important reason for the failure of many of the early quality management initiatives (Powell, 1995).

The role of infrastructural quality management practices when it comes to improving performance, however, is highly debated. Not all scholars agree on the supporting role of infrastructural quality management practices. In fact, a large number of studies found infrastructural quality management practices to be of greater importance than core quality management practices. These studies go as far as stating that quality improvement can be realized without the use of core quality management practices through reliance on

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31 T ab le 2. 1. C or e an d in fr astr uct ur al qu alit y m an ag em en t p rac tices Cla ss P ra ct ice 1 Descript io n 2 co re pr oce ss m an ag em en t T her e is an e m ph as is o n m is tak e-pr oo f pr oce ss d esig n. T her e is co ns is ten t us e of statis tical pr oce ss co ntr ol, pr ev en tiv e m ai nte nan ce . Ma nag er s an d em plo yee s m ak e ef fo rt s to m ai ntai n clea n sh op f lo or s an d m ee t s ch ed ules. pr od uct desig n an d m an ag em en t T her e is th or ou gh r ev ie w b ef or e pr od uctio n. Desig n tea m s in vo lv e peo ple fr om dif fer en t fu nctio ns su ch as m an uf ac tu rin g, m ar keti ng , pu rch asi ng dep ar tm en ts . Si m pli fied d esi gn a nd s ta nd ar dizatio n ar e en co ur ag ed f or m an uf ac tu rab ilit y. qu alit y data an al ys is Q ualit y data ar e av ailab le to m an ag er s an d em plo yee s. T her e is an e ff or t to co llect ti m el y qu ali ty d ata. Qu alit y da ta ar e us ed f or im pr ov em en t. in fr astru ct ur al cu sto m er f ocu s C us to m er n ee ds a nd ex pec tat io ns ar e as ses sed . C us to m er s ar e in vo lv ed i n qu ali ty im pr ov em en t p ro jec ts . C us to m er s atis fac tio n is m ea su red . T her e is a clo se co ntact w it h ke y cu sto m er s. m an ag em en t le ad er sh ip T op m an ag em en t ac ce pts r es po ns ib ilit y fo r qu alit y an d is ev alu ated b ased o n qu al it y per fo rm an ce . T op m an ag em en t p ar ticip ates in q ualit y im pr ov em en t e ff or ts a nd m ak es str ateg ie s an d go al s fo r qu alit y im pr ov em en t. peo ple m an ag em en t E m plo yee s ar e in vo lv ed i n qu alit y dec is io ns . E m plo yee s ar e ev al uated b ased o n th eir qu alit y per fo rm an ce a nd t heir co ntr ib utio ns to q ualit y ar e rec og nized a nd r ew ar ded . Ma nag er s en co ur ag e tea m wo rk in g. T her e is tr ain in g on QM f or m an ag er s an d em plo yee s. su pp ly q ua lit y m an ag em en t A s m all nu m ber o f su pp lier s ar e us ed . Su pp lier s ar e in vo lv ed in p ro du ct dev elo pm en t an d qu alit y im pr ov em en t pr oj ec ts . Su pp lier s ar e ev al uat ed b ased o n qu alit y. T he or gan izatio n pr ov id es su pp lier s tr ain in g an d tech nical as si sta nce . 1C on str uct lab els fr om Nair ( 20 06 ) 2C on str uct descr ip tio ns f ro m Z u et al. ( 20 08 , p .6 32 )

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infrastructural quality management practices alone. For instance, Powell (1995) was the first to argue, based on the resource-based view of the firm, that infrastructural practices are more likely to result in a competitive advantage because of their intangible nature and the belief that infrastructural quality management practices are difficult to imitate. Powell’s results showed infrastructural quality management practices to be positively associated with performance whereas core quality management practices were not. Dow et al. (1999) and Samson et al. (1999) draw similar conclusions based on a larger sample. More recently, Lakhal et al. (2006), Naor et al. (2008), and Fotopoulos (2009) showed that infrastructural practices alone can play a vital and direct role in improving quality performance. The hypothesized direct relation between infrastructural quality management practices and performance suggests that infrastructural practices are not merely supportive. Managers willing to implement quality practices could suffice with just the implementation of infrastructural quality management practices (Powell, 1995) or, at least, infrastructural quality management practices should require greater attention than core quality management practices (e.g. Naor et al., 2008; Samson & Terziovski, 1999). The direct perspective is summarized in Figure 2.1.

In contrast, the second group of scholars argues in support of an indirect relationship between infrastructural quality management practices and performance. For instance, Flynn et al. (1994) were the first to find support for the notion that the success of core quality management practices was likely to depend on infrastructural quality management practices. Later studies, for example, Dal Pont et al.

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(2008), Flynn et al. (1995), Lakhal et al. (2006) and Zu et al. (2008),

provided additional support for the claim that infrastructural quality management practices are necessary to support core quality management practices. These studies suggest that infrastructural quality management practices serve as a foundation for core quality management practices. The indirect perspective is summarized in Figure 2.2.

Both of the aforementioned perspectives assume that core and infrastructural quality management practices are distinct. Other studies were not able to find support for the distinction between core and infrastructural quality management practices (e.g. Choi & Eboch, 1998). In these studies, the authors consider core and infrastructural quality management practices jointly as a single construct. Sousa and Voss (2002) suggest that the practices themselves are not difficult to imitate, however the combination or integration of infrastructural and core quality management practices might be. This perspective is in line with theory on resource complementarity (Laursen & Foss, 2000) and socio-technical systems theory (Zu, 2009). Figure 2.3 summarizes the final perspective.

Studies that address the relation between quality management practices and performance conceptualized performance in different ways. A number of studies focus on quality performance measured using indicators such as conformance to specifications (Phan et al. 2011a; Phan et al. 2011b), rework (Flynn et al. 1994), or scrap and defect levels (Mahadevappa and Kotreshwar, 2004). Others focus on

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the impact of quality management practices on a broader range of operational performance dimensions such as cost (Flynn et al. 1994; Noar et al. 2008), flexibility (Swink et al., 2005), dependability (Curkovic et al, 2000), or speed (Lau, 2000). Still others consider the implications of quality management practices on business performance using indicators such as increase in market share (Su et al. 2008), profit (Boyer et al. 1997), or firm performance (Curkovic et al., 2000). In this chapter, we consider quality management practices to influence both quality and operational performance relatively directly whereas business performance is influence indirectly through quality and operational performance. Amongst others, Curkovic et al. (2000) and Kaynak (2003) have argued similarly. In addition, we consider the possibility that quality performance bolsters operational performance or hinders it through potential trade-offs.

To add to the confusion, results from studies using either one of the three theoretical perspectives are difficult to compare because of differences in study design. First, quality management practices are inconsistently categorized as either core or infrastructural. For example, Flynn et al. (1995) classify quality data analysis as an infrastructural practice, whereas Samson and Terziovski (1999), Dow et al. (1999), and Powell (1995) label it as a core quality management practice. Second, different studies use different measures of performance. Some studies use measures of financial performance (Powell, 1995), others use combined measures of operational performance (Samson & Terziovski, 1999), still others focus on quality performance (Forza & Filippini, 1998).

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Infrastructural

quality management practices:

1. Management Leadership 2. People Management 3. Supply Quality Management 4. Customer Focus

Core

1. Process Management 2. Quality Data Analysis 3. Product Design and Management

Performance:

1. Quality Performance 2. Operational Performance 3. Business Performance

Figure 2.1. Direct perspective

Infrastructural

1. Management Leadership 2. People Management 3. Supply Quality Management 4. Customer Focus

Core

1. Process Management 2. Quality Data Analysis 3. Product Design and Management

Performance:

Figure 2.2. Indirect perspective

Quality management practices:

1. Management Leadership 2. People Management 3. Supply Quality Management 4. Customer Focus 5. Process Management 6. Quality Data Analysis 7. Product Design and Management

Performance:

University of Groningen Not lean by default Ziengs, Nick

Not lean by default:

Exploring practices, their design, and

underlying mechanisms driving

performance

Not lean by default:

Ex

ploring practices, their design, and underlying

mechanisms driving performance

Proefschrift

Nick Ziengs

Contents

Chapter 1

Introduction

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Chapter 2

The Distinctive Roles of Core and

Infrastructural Quality Management

Practices: A Meta-Analytical

Structural Equation Modeling Study

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