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The impact of market complexity on

operational performance through the

use of just-in-time and mass

customization

Gijsbert Jan Prins (Bart)

S2787628

Master Thesis

MSc Technology and operations management

Rijksuniversiteit Groningen

First supervisor:

Dr. Ir. T. Bortolotti

Second supervisor: Dr. N.B. Szirbik

Word count: 11.041

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Preface

This paper is a result of eight years of studying of which the last three years were devoted to my master’s degree. During those eight years I have had the opportunity to do multiple internships, one of which spiked my interest for manufacturing strategies, in particular lean manufacturing. Because I have done multiple internships I did not feel the need to write my master’s thesis in a corporate environment. Thus the topic of lean and the extensive research base of the chosen topic were a perfect fit for me.

After 5 months of hard work I have learned a lot with regards to extensive literature research and the use of specific statistical analysis tools. What I have learned exceeded what I expected to learn, not only with regards to any new skills I acquired but also getting to know myself a little bit better.

The end result, the paper I will finish my study career with, could not have been achieved without certain people helping, guiding, listening, distracting or giving me feedback. First, and foremost I would like to express my gratitude to Dr. Ir. T. Bortolotti for so clearly explaining the research topic and getting me interested, for his clear feedback and guidance on what subject to tackle next or how to proceed, for answering any simple or difficult questions I had at all times and giving me the opportunity to find my own way allowing me to learn from any mistakes I made before complementing on my progress.I also would like to give thanks to my close friends who heard me out whenever we all met up and dragged me out of my room if I had been there for too long and needed a distraction even though I did not recognise this myself. I also would like to thank the people in my close surrounding who had to put up with me being preoccupied for the largest part of the last five months.

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Abstract

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Table of contents

1. Introduction ... 6

2. Background ... 10

2.1 Market complexity ... 10

2.1.1 Market complexity and just-in-time ... 12

2.1.2 Market complexity and mass customization ... 13

2.2 Just-in-time ... 14

2.2.1 Just-in-time and the effect on operational performance ... 15

2.3 Mass customization ... 18

2.3.1 Mass customization and the effect on operational performance ... 19

2.4 The link between JIT and mass customization ... 20

2.5 Modularity ... 22

2.5.1 Modularity and just-in-time ... 22

2.5.2 Modularity and mass customization ... 23

2.6 Conceptual framework ... 24

3 Methodology ... 25

3.1 Data collection ... 25

3.2 Measures ... 25

3.3 Measurement model ... 27

3.4 Results of the SEM model ... 29

4 Discussion ... 32

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5.1 Managerial implications ... 40

5.2 Limitations and future research ... 41

References ... 43

Appendix A. Measures ... 49

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___________________________________________________________________________

1. Introduction

Generic background of the problem A long time ago craftsmanship was the

predominant method of making goods. This changed during the industrial revolution where the steam engine allowed for low variety and large volumes. Nowadays the demand shifts from standardised, low variety, products towards more customized products (Gilmore & Pine, 1997; Lampel & Mintzberg, 1996; Leffakis & Dwyer, 2014; Salvador & Forza, 2004). This demand for customization is strengthened by customers who are not as easily satisfied because they are more informed on the products and possible alternatives (Salvador, Forza, & Rungtusanatham, 2002). Furthermore, fierce competition drives companies to create a better fit with demand through product customization (Liu, Shah, & Babakus, 2012; Salvador & Forza, 2004). Thus resulting in a complex and dynamic environment where, due to the demand and competition, more elements with dynamic interdependencies have to be managed compared to a more stable market environment (Azadegan, Patel, Zangoueinezhad, & Linderman, 2013; Birkie & Trucco, 2016; Saurin, Rooke, & Koskela, 2013). Market complexity can be characterized as “the different types of products being distributed, the number of organizations involved in marketing the products, the different types of customers served, and the frequency of product changes made in response to its competitors’ actions” (Wong, Lai, & Bernroider, 2015). As Salvador et al. (2002) state “variety typically gives managers a difficult time: inventory levels tend to rise, set-up costs and times tend to deteriorate, material planning and shop floor control tend to become more complex and so on”.

Problem definition and motivation Market complexity can be described as a strategic

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___________________________________________________________________________ the cost of a mass produced product (Fogliatto, Da Silveira, & Borenstein, 2012; Sandrin, Trentin, & Forza, 2018; Trentin, Forza, & Perin, 2012; Tu, Vonderembse, & Ragu-Nathan, 2001). Just-in-time manufacturing is another strategy which is found to have a positive effect on performance (Bhamu & Singh Sangwan, 2014). However, according to some authors such as Hines, Holweg, & Rich (2004); Krishnamurthy & Yauch (2007) & Stump & Badurdeen, (2012), this positive effect on performance is believed to be only achievable if a company finds itself in a stable market environment with low product variety and uncertainty. Thus indicating that in an environment with high market complexity just-in-time might not be an effective strategy. However, the negative effect of market complexity could be diminished by moderating effects such as the level of product modularity (Frandsen, 2017; Krishnamurthy & Yauch, 2007; Stump & Badurdeen, 2012) which would allow just-in-time to be implemented in complex markets and achieving high operational performance.

Literature and managerial gap In their paper Sousa & Voss (2008) state that contingency

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___________________________________________________________________________ often with regards to production strategies. In particular with regards to manufacturing strategies such as just-in-time and mass customization. This is supported by Birkie & Trucco (2016) who state that contextual factors such as complexity and dynamism pose a significant challenge in adapting just-in-time practices and more research is required into this topic. With regards to mass customization Liu et al. (2012) found that the link with complexity has only been studied a few times and needs further research. This is supported by Fogliatto et al. (2012) who found that the majority of mass customization studies are focused on the implementation and the effect on performance. This paper aims to fill the gap with regards to the effect of market complexity on both mass customization and just-in-time.

As mentioned above, complexity and especially dynamic complexity has a significant negative effect on performance of a company. Thus knowing what the effect of market complexity is on just-in-time and mass customization and performance gives managers a tool to deal with complexity and achieve an increase in operational performance in their own markets. Knowing what production strategy to apply is especially important as a company cannot quickly change markets due their specific product or service offering.

The aim of the paper Contingency research states that there is a context to every

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___________________________________________________________________________ inherent increase in variability due to complexity has a negative impact on operational performance and can be reduced by standardizing operations through practices such as modularity (Swaminathan, 2001). Thus to paint a more complete picture modularity is added as a moderator to try and explain the relationship and find more contingency factors related to the framework.

To fill the gap in current literature and combine market complexity with just-in-time and mass customization the following research will be answered:

“What is the effect of JIT and mass customization on operational performance when influenced by a complex market environment?”

Structure of the paper The first part of this paper explains the relevant literature

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___________________________________________________________________________

2. Background

2.1 Market complexity

Contingency research states that there is a context to every practice which influences the effectiveness of the practices (Sousa & Voss, 2008). The contingency of interest are the current markets which are characterized by more heterogeneous demand, customers being more informed on available alternatives and competitors pushing for larger product variety (Gilmore & Pine, 1997; Liu et al., 2012; Salvador & Forza, 2004). What this means is that it increases the variety of products and it decreases the volume (Jina, Bhattacharya, & Walton, 1997) and thus increases the number and diversity of interdependent elements that have to be managed (Saurin & Gonzalez, 2013). To fulfil the increased demand for customized products is often done by increasing product variety however this results in operational problems such as higher inventory levels, increased set-up cost and material planning becomes more complex (Salvador et al., 2002; Wan & Sanders, 2017; Wong et al., 2015). Furthermore, Jina et al. (1997) state that “the turbulence, as the result of uncertainty and variability, causes the company to experience unpredictable and sub-optimal behaviour as it struggles to achieve the desired outputs”. These factors all boil down to a complex market, which requires companies to adapt their product variety, development plan (Wong et al., 2015) & their manufacturing strategy (Bevilacqua, Ciarapica, & De Sanctis, 2017).

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___________________________________________________________________________ number of elements that have to be managed and dynamic complexity entails the unpredictability of the elements in a system. The term market complexity is defined as environmental uncertainty (Liu et al., 2012) or the “environmental conditions of different types of products, different types of customers and the frequency of product changes in response to competitors” (Wong et al., 2015). In this definition both detailed and dynamic complexity are mentioned in the shape of large and diverse number of products & customers and rapid changes in responses of competitors and unanticipated variability.

Wong et al. (2015) divides market complexity into two factors namely; demand variability & competitive intensity. Both these factors originate from sources downstream or at the same level as the focal company in the supply chain (Bozarth et al., 2009). Market complexity that originates from upstream sources such as supply chain complexity mentioned in the paper of Liu et al. (2012) fall outside the scope of this paper and are therefore not part of this study.

The first measure that falls under market complexity is demand variability which describes demand variation in quantity, customization & timing (Schmenner & Swink, 1998). Dealing with this variability in demand is often done by offering a larger variety of products (Bozarth et al., 2009; Wong et al., 2015). However the law of variability states that increasing levels of variability results in less overall output (Schmenner & Swink, 1998). This is supported by Bozarth et al., (2009) & Ojha, White, & Rogers (2013) who found that variability negatively affects performance, as companies opt for less efficient production methods such as job shops. This effect of demand variability gets larger the further you get from the original end-customer demand through the bullwhip-effect (Bozarth et al., 2009).

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___________________________________________________________________________ offerings of the focal company (Liu et al., 2012; Tsai & Hsu, 2014). This increase in population, i.e. competitors in one market, leads to rivalry and requires companies to find practices that make them stand out from their competition (Mahapatra, Das, & Narasimhan, 2012).

2.1.1 Market complexity and just-in-time

The positive effect of just-in-time on performance is influenced by moderating effects indicating that the context in which JIT is applied is very important for the outcome (MacKelprang & Nair, 2010). However, when it comes to contingency thinking the focus is predominantly on internal factors such as plant size (Azadegan et al., 2013). Applying JIT practices in a complex and dynamic external environment results in misfit of JIT according to some authors. The general believe is that JIT is only applicable in situations where demand is stable and uncertainty low, this is due to the fact that JIT needs a stable takt time1 and low

variation to balance the production process and to allow for precise delivery of products (Bortolotti et al., 2013; Jina et al., 1997; Kolberg, Knobloch, & Zühlke, 2017; Krishnamurthy & Yauch, 2007; Lander & Liker, 2007). When the environmental conditions are dynamic it is found that the performance of JIT is further diminished, further supporting that JIT performs best with low environmental dynamism (Azadegan et al., 2013; Chavez, Gimenez, Fynes, Wiengarten, & Yu, 2013). This is also supported by Bortolotti et al. (2013) who found that uncertainty caused by product variety and dynamism seems to deteriorate the relationship between lean and performance.

This mismatch is said to be caused by the difference in characteristics of a company that uses JIT as their manufacturing strategy and the requirements of the environment, for instance the difference in number of products produced, order-winning criteria and the type of products

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___________________________________________________________________________ produced (Jina et al., 1997). As companies all face different levels of complexity, depending on their position in the system (Saurin et al., 2013) there is no straight forward way to implement just-in-time and a generally applicable solution often does not exist (Jina et al., 1997). Understanding a company’s complex environment is thus key to tailor just-in-time to the specific context and to achieve high performance levels (Lander & Liker, 2007).

Because the general consensus is that just-in-time needs a stable environment the effect of demand variability is expected to be negative. With regards to competitive intensity the effect is expected to be positive as just-in-time differentiates a company through efficient production by removing wasteful activities and increasing, among other performance measures, responsiveness and efficiency (Bennett & Forrester, 1994).

Hypothesis 1a: Demand variability has a negative effect on just-in-time. Hypothesis 1b: Competitive intensity has a positive effect on just-in-time

2.1.2 Market complexity and mass customization

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___________________________________________________________________________ indicates that the effect of demand variability is not significant. Liu et al (2012) tested the effect of market complexity on mass customization and their findings indicate that competitive intensity result in higher levels of mass customization. Furthermore, they found that demand uncertainty is the dominant factor in determining the level of mass customization to achieve high customer satisfaction. However, because Liu et al. (2012) are the only one that found a different effect the hypotheses mentioned below are based on the majority of the literature indicating that both demand variability and competitive intensity have a positive effect on mass customization.

Hypothesis 2a: Demand variability has a positive effect on mass customization. Hypothesis 2b: Competitive intensity has a positive effect on mass customization.

2.2 Just-in-time

Lean or lean production as it is also known, was first popularized in the book of James Womack, Daniel Jones & Daniel Roos in 1990. In their book “The machine that changed the world” the first conceptualization of lean was given by examining the Toyota Production System (TPS). Lean is characterized as a strategy that focuses on removing non-value added activities and by doing more with less (Womack, Jones, & Roos, 1990). For instance using half the human effort, floor space or inventory to produce the same or larger amount of products. The practices of lean that result in a reduction of non-value added activities and an increase in value for the customer are bundled into four bundles by Shah & Ward (2003), namely the following groups;

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___________________________________________________________________________  HRM: Increase effectiveness of employees through training for instance

cross-functional training.

Each of these bundles consist of multiple practices and those that are applied for just-in-time are internally focused where they form an integrated manufacturing strategy (Chavez et al., 2013). Internal practices of lean seem to have a positive impact on operational performance (cost, quality, flexibility & delivery) in different contexts (Abdallah & Matsui, 2009; Chavez et al., 2013).

From these four bundles just-in-time has been researched the most (Abdallah & Matsui, 2009; Shah & Ward, 2003). Just-in-time consists of practices that are related to production flow with the intent of removing any non-value added activities in the production process. The practices that fall under just-in-time are; cellular layout, bottleneck identification and removal or production smoothing, cycle time reduction, reengineering setup processes, single-minute exchange of dies (SMED), Kanban and pull production, daily schedule adherence, flow and just-in-time supply (Bortolotti et al., 2013; Shah & Ward, 2003). Apart from production processes, just-in-time can be also applied to non-production processes such as packaging (Bortolotti et al., 2013). According to Shah & Ward (2003), Just-in-time practices have a positive effect on performance. This is supported by MacKelprang & Nair (2010) who, in their meta-analysis, state that just-in-time practices positively affects one or more of the following performance measures; quality, manufacturing cost, inventory, cycle time, flexible manufacturing and delivery performance.

2.2.1 Just-in-time and the effect on operational performance

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___________________________________________________________________________ quality, manufacturing cost, inventory, cycle time, flexible manufacturing and delivery performance (MacKelprang & Nair, 2010). The operational performance measure, responsiveness, is measured on the following dimensions; on-time delivery, fast delivery, volume flexibility & mix flexibility. Responsiveness itself can be defined in different ways and is sometimes mistakenly referred to as flexibility. According to Reichhart & Holweg (2007) responsiveness is a factor of both internal and external events (e.g. customer orders), however, since this study is focused on external events the focus lies on the external customer orders. When it comes to the definition Reichhart & Holweg (2007) state the following;

“The responsiveness of a manufacturing or supply chain system is defined by the speed with

which the system can adjust its output within the available range of the four external flexibility types: product, mix, volume and delivery, in response to an external stimulus, e.g. a customer order”.

Based on the performance increase of just-in-time in areas such as cycle time, flexible manufacturing and delivery performance, which closely relate to the measurement of responsiveness and the definition of Reichhart & Holweg (2007), it is expected that just-in-time has a positive effect on responsiveness. This positive effect on responsiveness is achieved through the practices mentioned in chapter 2.2 by increasing machine flexibility & delivery capabilities and efficiency by manufacturing cost, cycle time and inventory levels (Bortolotti et al., 2013). This is supported by Tu et al. (2001) who state that time based methods, which share many practices that fall under just-in-time, have a positive influence on the delivery time, cost and speed of the manufacturing process forming the competences for customization responsiveness.

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___________________________________________________________________________ The definition of efficiency given by Farrell (1957) is: “A firms success in producing

as large as possible output from a given set of inputs”. Efficiency is thus using resources such

as machines, people or material to produce as much as possible in the given time that is available. When this is compared to just-in-time it is found that it reduces any wasteful activities from the production process, thus only focusing on creating value. This closely resembles the idea of using the inputs in such a way to produce as much or only products that are important or add value for the customer. According to Farrell (1957) efficiency consists of two parts, the first part is technical efficiency which has to do with the amount of products that are produced with regards to its inputs or resources. Bortolotti et al. (2013) mention that efficiency is increased by reducing cycle time and inventory levels which is in line with the definition of technical efficiency. The second part relates to price efficiency which is more difficult to determine as it depends on the pricing levels with regards to the industry and is thus more inherent to change (Farrell, 1957). According to Bortolotti et al. (2013) price efficiency is increased when the manufacturing cost is decreased.

Hypothesis 3b: Just-in-time has a positive effect on efficiency.

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___________________________________________________________________________ competition. Based on this it is expected that just-in-time mediates between competitive intensity and both operational performance measures.

Hypothesis 4a: The relationship between demand variability and operational performance is mediated by just-in-time.

Hypothesis 4b: The relationship between competitive intensity and operational performance is mediated by just-in-time.

2.3 Mass customization

Mass customization is a reaction to the ever-changing market. The move from craft production which was the dominant production mode before the industrial revolution, to the current open markets which face increased competitiveness, globalization and short life cycles requires a stable strategy that allows for production of large product variety (Abdallah & Matsui, 2009; Liu et al., 2012). The definition of mass customization is difficult to give as it is an exotic strategy with only a few companies applying it to their manufacturing environment (Piller, 2005; Salvador, Holan, & Piller, 2009). A simple and clear definition is given by Piller (2005); “mass customization is meeting the individual customers’ needs with the same level of efficiency of a mass producer”. Merle, Chandon, Roux, & Alizon (2010) support this by mentioning the unique feature of mass customization to break the trade-off between customization and dimensions of performance.

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___________________________________________________________________________ vary in the description, for instance Gilmore & Pine (1997) description varies from full cooperation with the customer throughout the design process, changes made by the customer after purchase of the product, changing the packaging & finally to a level where the customer does not know the product is fully customized for them. The description of Lampel & Mintzberg (1996) is more easily understood and is solely based on the level of influence of the customer from the full design of the product, fabrication, assembly or transport.

2.3.1 Mass customization and the effect on operational performance

When it comes to mass customization and performance authors have found that mass customization improves cost, quality, flexibility and delivery (Kumar, 2004). Furthermore, it improves the number of customers that are reached (increased product variety) in competitive environments (Abdallah & Matsui, 2009) and high levels of mass customization has a positive impact on the perceived value by the customer (Tu et al., 2001; Tu, Vonderembse, Ragu-Nathan, & Ragu-Ragu-Nathan, 2004). When it comes to mass customization and the effect on performance it is clear that mass customization adds value for the customer and allows for larger product variety.

As explained in 2.2.1 responsiveness is being able to react to a customer order in terms of product, volume, mix & delivery. As explained in 2.3, mass customization is a manufacturing strategy that is focused on delivering customized products in terms of design, assembly, packaging or delivery. Furthermore, mass customization allows to manufacture customized products quickly (Tu et al., 2001). This seems to indicate that mass customization allows a company to quickly adapt their products, volume, mix and delivery to customer demand.

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___________________________________________________________________________ efficiency and low manufacturing cost (Abdallah & Matsui, 2009; Kumar, 2004). This is in line with providing both technical and price efficiency and therefore it is expected that mass customization increases the level of efficiency (Farrell, 1957).

Hypothesis 5a: Mass customization has a positive effect on responsiveness.

Hypothesis 5b: Mass customization has a positive effect on efficiency.

Demand variability is a complexity on which mass customization thrives. Through the use of mass customization a higher level of customization is possible, thus reaching more customers. Literature has shown that mass customization has a positive effect on both measures of operational performance. Thus it is expected that mass customization mediates the relationship between demand variability and operational performance. Just like the case with just-in-time, mass customization is a strategy to differentiate a company from its competitors through delivering higher performance. Through the use of mass customization, higher competitive intensity can result in higher levels of operational performance.

Hypothesis 6a: The relationship between demand variability and operational performance is mediated by mass customization.

Hypothesis 6b: The relationship between competitive intensity and operational performance is mediated by mass customization.

2.4 The link between JIT and mass customization

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___________________________________________________________________________ believed to not fit the paradigm of a manufacturing strategy because it combines both high levels of customization and low production (Duray, Ward, Milligan, & Berry, 2000) or in other words it combines differentiation and cost leadership (Blecker & Abdelkafi, 2006). The inherent differences increases the difficulty in finding the link between just-in-time and mass customization. The study of Alford, Sackett, & Nelder (2000) suggests that in an automotive environment, which requires large product variety and customization of products, requires companies to adjust their capabilities. Mass customization on a first glance seems to fit this complex environment, however how the practices of just-in-time are to be implemented in this environment is not so straight forward.

If and how both manufacturing strategies are linked has not received much attention in literature according to Abdallah & Matsui (2009). Their findings suggest that companies combining just-in-time and mass customization perform better than those who only apply mass customization thus linking the two strategies. This idea that just-in-time can, and is used in combination with mass customization is supported by Tu et al. (2001) who found that companies that apply based practices also apply mass customization practices. The time-based practices mentioned in their paper closely relate to just-in-time with common practices such as cellular layout, employee involvement, reengineering setups & pull production. Furthermore, Trentin et al. (2012) concluded that cellular layout of the production facility reduces the amount of information that has to be processed which is found to be beneficial for a mass customization strategy. Based on the limited literature concluding that just-in-time is beneficial for mass customization it is hypothesized that mass customization has a positive effect on just-in-time.

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2.5 Modularity

As markets get inherently more complex due to increased demand for product variety and uncertainty there is an increasing need for a manufacturing strategy to maintain or increase operational performance. However, not all strategies that result in increased performance are as easily adaptable to these complex conditions. A practice to diminish the inherent complexity that results from a complex market is modular manufacturing (Frandsen, 2017; Lau, 2011). Modular manufacturing standardises components allowing these components to be shared between products, thus reducing the amount of unique elements and the interdependencies between these elements both in the same system as well as across multiple systems (Frandsen, 2017). It technically splits a complex system, task or product into smaller segments reducing complexity (Mikkola & Skjøtt-Larsen, 2004). Modularity is viewed as the manufacturing practice that gives a company the capability to manufacture products in a repetitive and efficient fashion while still providing large amounts of product variety through combinations of standard modules (Duray et al., 2000; Frandsen, 2017). This is supported by Salvador et al. (2002) who state that modularity mitigates the relationship between product variety and operational performance.

2.5.1 Modularity and just-in-time

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___________________________________________________________________________

Hypothesis 8a: Modularity positively moderates the relationship between demand variability and just-in-time.

To be successful in a competitive environment requires strategies that allow a company to outperform or deliver something their competitors cannot (Mahapatra et al., 2012). Modularity allows companies to produce larger product variety at lower cost than developing fully customized products. Apart from larger product variety it also reduces engineering times bringing new products faster to the market and thus the customer (Custódio, Roehe Vaccaro, Nunes, Vidor, & Chiwiacowsky, 2017).

Hypothesis 8b: Modularity positively moderates the relationship between competitive intensity and just-in-time.

2.5.2 Modularity and mass customization

From the previous paragraph we know that modularity reduces variability, increases product variety and allows for efficient manufacturing. Mass customization as a manufacturing strategy is believed to deliver large product variety at the cost of mass production. The literature review of Frandsen (2017) found that 13 of the 68 papers in their literature review were focused on the link between modularity and mass customization. Gilmore & Pine (1997) support the outcome of Frandsen (2017) and state that modularity is closely linked or even part of mass customization. Duray et al. (2000) take this even further by stating that modularity is critical in achieving mass customization as it makes sure ‘mass’ in mass production is achieved.

Hypothesis 9a: Modularity positively moderates the relationship between demand variability and mass customization.

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2.6 Conceptual framework

The direct and moderation effects hypothesised in previous sections are all shown in the conceptual model in figure 1. The indirect hypothesis, or the mediation effects, are not shown as this would make the model unnecessarily unclear. These mediation effects originate from market complexity and flow through either mass customization or just-in-time to operational performance. Operational performance Just-in-time Mass customization Modularity H3 H5 Market complexity H1 H7 H2 Modularity H9 H8

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3

Methodology

3.1 Data collection

The data is collected by means of a survey which is part of the third round of the high performance manufacturing (HPM) project (Schroeder & Flynn, 2001). The surveys are sent to companies that are active in any of the three different industries; machinery, electronics and transportation components with a minimum of 100 employees. These plants operate in nine different countries (Japan, US, Korea, Germany, Italy, Sweden, Austria, Spain, Finland and the UK). The complete survey consisting of multiple areas of expertise is first developed in English before the questions were translated into the native language of each of the nine countries. Translation into the native language is done by a different person helping to control for country-level bias (Peng, Liu, & Heim, 2011). The complete survey wis sent to a total of 303 plants, 15 of the answer had more than 30% of the questions not filled in and are thus discarded. For each plant the survey questions were answered by respondents with the most knowledge regarding the subject, with a total of 12 different areas of expertise for each plant. The survey used for this particular study were focused on market complexity, mass customization, just-in-time, modularity and operational performance

3.2 Measures

The measures of MC, JIT, modularity and operational performance are based on previously used scales from the literature. Market complexity is measured based on extensive literature research. Multi-item scales were used to test the variables, these scales are shown in Appendix A.

Market complexity is measured based on the demand variability and competitive intensity that

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___________________________________________________________________________ 4 items measuring the competitive intensity that is used in the paper of Liu et al. (2012). Both scales for market complexity are measured using a 7 point Likert scale (1 ‘strongly disagree’ and 7 ‘strongly agree’). Mass customization is measured based on the scales used by (Tu et al., 2001) consisting of 7 items with outcomes ranging from 1 (strongly disagree) to 7 (strongly agree). Just-in-time is measured based on the scales applied in the study of Bortolotti et al. (2013). This scale consists of 6 items where respondents can answer on a 7 point Likert scale (1 ‘strongly disagree’ and 7 ‘strongly agree’) where respondents agree or disagree on their JIT implementation. Modularity is measured on the scale previously used by (Forza & Salvador, 2000) which is a scale based on 5 items. Operational performance is measured with 3 items for

responsiveness and 4 items for effectiveness based on the scales used in the paper of Bortolotti

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___________________________________________________________________________

3.3 Measurement model

Construct validity was tested through confirmatory factor analysis using Lisrel 8.80 and SPSS for the Cronbach’s alpha. The results of the convergent validity using the Cronbach’s alpha showed that all scales scored higher than 0.70 showing that the items assigned to each construct are indeed related. Because all scales are tested and established in literature it is recommended that each scale scores above the cut-off point of 0.70 (Christmann & Van Aelst, 2006). This is further strengthened by the output of the confirmatory factor analysis showing that almost all the items load higher than 0.40 for their specific construct as shown in table 2. Selecting a cut-off point for factor loadings depends a couple of aspects. According to Hair, Babin, Anderson, & Tatham (1998) mention that the sample size determines the cut-off point, with a sample size of 304 the cut-off falls between 0.30 and 0.35 to find a significant effect. According to Stevens (1992) regardless of the

sample size a factor loading of 0.40 is sufficient. As an addition to this Comrey & Lee (2013) advice to use more stringent cut-off points ranging from 0.32 (poor), 0.45 (fair), 0.55 (good), 0.63 (very good) to 0.71 (excellent). Using these guidelines to validate the chosen cut-off point of 0.40 and all the factor loadings it can be concluded that based on the

Latent variable Indicator

Factor loading t -value

Demand variability (DV) DV1 0.92 -DV2 0.90 28.12 Competitive intensity (CI) CI1 0.63

-CI2 0.58 12.17 CI3 0.67 13.21 CI4 0.64 12.91 Just-in-time (JIT) JIT1 0.61

-JIT2 0.67 8.61 JIT3 0.48 6.79 JIT4 0.45 6.34 JIT5 0.66 8.56 JIT6 0.37 5.41 Mass customization (MC) MC1 0.56 -MC2 0.61 7.54 MC3 0.42 5.90 MC4 0.69 8.08 MC5 0.67 7.98 MC6 Removed MC7 0.59 7.41 Modularity (MOD) MOD1 0.67

-MOD2 0.61 13.20 MOD3 0.66 13.81

MOD4 Removed

MOD5 0.66 13.84 Responsiveness (RESP) RESP1 0.71

-RESP2 0.72 10.01 RESP3 0.57 8.28 RESP4 0.68 9.58 Efficiency (EFF) EFF1 0.38

-EFF2 0.71 5.42 EFF3 0.78 5.38

Table 2. CFA results

Latent variable Minimum Maximum Mean

Standard deviation DV 1.33 6.62 4.04 1.09 CI 3.50 7.00 5.58 0.68 JIT 3.08 6.95 4.79 0.64 MC 2.58 6.61 4.99 0.61 MOD 2.13 6.75 4.73 0.60 RESP 2.00 5.00 3.81 0.60 EFF 1.67 5.00 3.34 0.61

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___________________________________________________________________________ sample size all factor loadings are large enough to show significant effects (Hair et al., 1998). Furthermore, based on the cut-off point of 0.40 only 2 loadings fall slightly below this point but were kept to ensure that all aspects of those specific scales were measured (Stevens, 1992). The chosen cut-off point roughly translates into fair factor loadings using the scale of Comrey & Lee (2013). Looking at the factor loadings separately we see that 83% of the factor loadings is >0.55 and 17% drops below <0.55 further supporting that convergent validity is met. Divergent validity was tested by checking the factor loadings of the items for all constructs. Items that did not load significant for their intended construct or loaded significant for an unintended construct were removed from the measurement model. This resulted in the removal of items MC6 (reverse coded) and MOD4 which loaded significantly lower than the cut-off point. It is, however, decided to keep the item JIT6 regardless of the factor loading only 0.38. This is done because the items of just-in-time specifically measure the most important aspects of JIT, thus removing one would prohibit fully measuring just-in-time. After removal the items that are left will be ordered again, this results in for instance MOD5 being numbered MOD4 in the subsequent chapters.

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___________________________________________________________________________

3.4 Results of the SEM model

Testing the hypothesis requires to test direct, moderation and mediation effects. This is done using structural equation modelling, and subsequently path analysis of the results through Lisrel 8.80. Following the methodology of Bortolotti et al. (2013) the moderating effects of modularity and the product customization are tested using Ping’s (1995) two-step method. The first step of Ping's (1995) method requires that first the direct effects of modularity and product customization on MC and JIT are tested. The resulting effects (lambda) of the constructs and their loading and errors (theta-delta and phi) have to be recorded. The second step combines the variables into one interaction variable by creating interactions between the lambda and theta-delta of both constructs resulting in the interaction variables DV-MOD, DV-DC, CI-MOD & CI-DC. For a more detailed and specific description of the method and required functions see appendix B.

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___________________________________________________________________________ to calculate the mediation effect, it is calculated by hand using the model output as input for the Sobel function.

Overall model fit was determined using fit indices Chi-square, RMSEA, CFI (Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011). The results show that the model fit is acceptable;

[𝜒2(408) = 859.77 ; 𝜒2⁄𝑑. 𝑓.= 2.11 < 3 ; 𝑅𝑀𝑆𝐸𝐴 = 0.062 (0.056; 0.067) < 0.08 ; 𝐶𝐹𝐼 = 0.921 > 0.9]. Demand variability DV x MOD Competitive intensity CI x MOD Mass customization Just-in-time Responsiveness Efficiency γ = .17 (2.23)* γ = .45 (4.91)** γ = .60 (4.35)** γ = .16 (2.52)* γ = .34 (2.96)** γ = .26 (3.30)** γ = .21 (1.98)*

Figure 2. SEM results (*p<0.05 & **p<0.01)

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___________________________________________________________________________ regards to mass customization only one direct hypothesis 5a is significant where mass customization positively effects responsiveness (γ=0.174; t-value=2.225; p-value <0.05) rejecting hypothesis 5b as mass customization has no significant effect on efficiency. The relationship between mass customization and just-in-time is tested positively significant in that increased levels of mass customization result in increased levels of just-in-time, supporting hypothesis 7 (γ=0.256; t-value=3.297; p-value <0.01).

The second step is to look at the moderating effect of modularity through the use of Ping’s method. A detailed explanation of the exact procedure of Ping’s method is given in Appendix B. Before the effect of the interaction variable can be measured Ping’s method requires to test the direct effect first. Even though the direct effect is not part of the hypothesis mentioned in chapter 2 it is still interesting to mention them. The results show that modularity does not significantly affect either just-in-time or mass customization in a direct relationship. With regards to just-in-time, modularity only seems to positively moderate the relationship between competitive intensity supporting hypothesis 8b (γ=0.208; t-value=1.977; p-value <0.05). When it comes to mass customization the moderation effect of modularity shows that it has a positive significant effect on the relationship between competitive intensity and mass customization (γ=0.340; t-value=2.961; p-value <0.01).

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___________________________________________________________________________ intensity and operational performance is measured by multiplying the unstandardized regression coefficient of competitive intensity to just-in-time and from just-in-time to responsiveness (MacKinnon et al., 2002). Not a part of the hypothesis testing but still interesting to check is the direct effect of market complexity on operational performance. The results shows that competitive intensity does not significantly affect performance but demand variability does significantly affect responsiveness in a negative way. One thing to mention with regards to the Sobel (1982) test is that it is only capable of finding if a relationship is significant and is not capable of calculating the direction of the relationship. Therefore, the numbers in figure 3 & 4 could be deceiving.

Figure 3. Mediation CI-JIT-RESP (*p<0.05)

Figure 4. Mediation CI-JIT-EFF (*p<0.05)

4

Discussion

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___________________________________________________________________________ In their paper Sousa & Voss (2008) argue that contextual factors which are relevant to operations management and operational performance should be studied more to advance the still relatively small subject in literature. This chapter not only discusses the relevance of market complexity but also the relationship between just-in-time and mass customization and the moderating effect of modularity to create a more complete image of contingency effects related to the framework.

With regards to the framework of Bortolotti et al. (2013) mass customization, modularity and market complexity are added. In line with the expected outcome and the results of (Bortolotti et al., 2013) just-in-time has a significant positive effect on both responsiveness and efficiency. This indicates that the practices of just-in-time allow for quick and efficient response to customer orders through the use of practices such as small lot sizes, SMED, cellular layout and Kanban and Heijunka (Bortolotti et al., 2013).

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___________________________________________________________________________ The positive effect of mass customization on just-in-time is evident that mass customization requires other practices to break the trade-off between responsiveness and efficiency. This is supported by Bortolotti, Danese, Flynn & Romano (2015) who found that competitive performance (quality, delivery, flexibility & cost) is cumulative as shown in the sand code model. To achieve any of the performance measures a high enough level has to be achieved on the previous measure. For instance improving delivery requires a certain level of quality and to achieve more flexibility requires a certain level of quality and delivery. Taking a look at the performance measures shows us that efficiency is the equivalent of cost and responsiveness is closely related to deliverability and flexibility in the sand cone model. The results of mass customization only significantly affecting responsiveness, just-in-time significantly affecting both responsiveness & efficiency and mass customization significant positively affecting just-in-time could thus be explained by the sand cone model (Bortolotti et al., 2015). First mass customization is implemented to achieve responsiveness before just-in-time is implemented to increase responsiveness further and subsequently increasing the level of efficiency.

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___________________________________________________________________________ 2004; Van Hoek, 2001) all said to have a negative effect on just-in-time (Azadegan et al., 2013; Chavez et al., 2013).

The second practice that is closely related to mass customization and removing obstacles for just-in-time is modularity. Using modular products results in relatively simple obtainable and cost effective product variety and allowing for easier postponement when assembly or manufacturing of the modular parts that differentiate the product is postponed (Feitzinger & Lee, 1997; Piller et al., 2004). When modularity is implemented to achieve mass customization it results in the standardisation of components and reducing complexity. This standardisation of components allows for just-in-time production in a high variety low volume environment as it allows for standardisation of the components (Jina et al., 1997). This is supported by Bennett & Forrester (1994) who state that modularity allows for “a single identifiable piece of work to be produced in a single cell thus supporting just-in-time production”.

With regards to the contingency effect of market complexity on mass customization the results show that demand variability does not have a significant direct effect on either mass customization or just-in-time. This seems to indicate that variability in quantity & timing does not influence the level of each of the manufacturing strategies which is surprising. The majority of the literature shows that mass customization offers product customization which allows it to deal with both variability in customization, quantity & timing of demand (Abdallah & Matsui, 2009). However, the results of the SEM model suggest otherwise and confirm the results of Liu et al. (2012) who found that there is no direct effect of demand variability. Instead it moderates between mass customization and performance where increased levels have a positive effect on this relationship.

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___________________________________________________________________________ advantage delivering customized products in an efficient way. Liu et al. (2012) found in their paper that competitive intensity does result in an increase in mass customization which is said to be due to the increased performance that mass customization delivers. Liu et al. (2012) used the same HPM dataset however the result of competitive intensity on mass customization is different. The difference in outcome is expected to result from the fundamental difference in framework and variables. The framework of Liu et al. (2012) focused on only mass customization and customer satisfaction where the framework consists of more variables such as market complexity, just-in-time and operational performance. Thus the fundamental differences of both frameworks might explain the differences in results generated by the statistical program LISREL.

Just as with mass customization, demand variability does not significantly affect just-in-time. This result goes against the literature that states that just-in-time requires a stable environment in terms of demand. When demand is uncertain, the amount, timing and customization changes dynamically. Due to the reduced inventory any large fluctuations in demand could be problematic when just-in-time is applied (Bortolotti et al., 2013). However, in highly complex market environments companies tend to produce to customer order resulting in manufacturing getting closer to the pure pull form of one piece flow. Furthermore, just-in-time is found to have a positive effect on responsiveness, indicating it can handle a certain degree of demand variability. The explanation for the lack of significance could be that just-in-time can handle a certain degree of variability, however there are other strategies that perform better in these environments.

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___________________________________________________________________________ themselves from competitors.. Through just-in-time companies are able to quickly respond to demand by reducing any non-value added activities in the production process, production based on pull and Kanban & by reducing cycle time and setup times.

The role of modularity on the relationship between market complexity and the manufacturing strategies is expected to diminish complexity caused by demand variability and increase the ability to deliver larger product variety while still maintaining efficiency. The results show that modularity does not significantly moderate the relationship between demand variability and mass customization or just-in-time. Through the use of modularity a company can produce large product variety to reach more customers. However, modularity does not deal with varying levels of demand as this is done by buffering inventory, time or capacity. Neither of these are achieved by modularity as it merely enables the production of multiple different products through the use of modules. Modularity is believed to be a part of mass customization (Frandsen, 2017; Gilmore & Pine, 1997) and might thus have a direct or mediation effect on mass customization instead of a moderating effect.

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___________________________________________________________________________ reduces some of the inherent complexity that results from larger product variety (Duray et al., 2000) allowing for higher levels of just-in-time and mass customization in a competitive environment.

The path analysis and subsequent manual calculation of the Sobel test result in only two out of the eight mediation hypothesis to test significant. Furthermore, not hypothesized but still helpful in explaining the effect are the direct effects of market complexity on operational performance. The direct effects show that demand variability has a direct negative effect on responsiveness. This effect is expected as increased complexity tends to deteriorate lead times, material planning and shop floor control (Salvador et al., 2002). The direct effect of competitive intensity on operational performance is not significant. As competition increases a company has a decreased opportunity to grow and competitors can quickly follow match product offerings (Liu et al., 2012; Tsai & Hsu, 2014). However, when a company does not differentiate themselves from competitors this does not negatively affect operational performance as they will still have the same strategic manufacturing capabilities. Based on Sobel’s test the indirect effect of competitive intensity through just-in-time on both responsiveness and efficiency is found to be significant. When mediation is tested it is inferred that a mediator explains the effect an independent variable has on a dependent variable. However, without the direction it is difficult to explain the results.

5

Conclusion & future research

This study tested 21 hypotheses with regards to market complexity, manufacturing strategies and operational performance through direct, moderation and mediation effects to answer the following research question;

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___________________________________________________________________________ The hypothesis are aimed at increasing knowledge with regards to contingency theory, in particular market complexity and the effect it has on manufacturing strategies and operational performance. The framework for this research topic is largely based on that of Bortolotti et al. (2013) and the literature review in this paper.

Market complexity is viewed as a contingency with significant impact on performance according to the literature, however the effect is less pronounced then was hypothesized. Overall market complexity only seems to have a direct effect on just-in-time with regards to competitive intensity. Even though the general idea is that just-in-time needs a stable environment the results do not support this. Based on this it seems that demand variability is a moderator instead of an antecedent and seems to diminish the effect of just-in-time on operational performance (Bortolotti et al., 2013). We find that mass customization is not affected by demand variability or competitive intensity indicating that mass customization is neither a strategy to be affected by or deal directly with market complexity. Instead Liu et al. (2012) show that demand variability has a positive effect on the relationship between mass customization and operational performance.

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___________________________________________________________________________ The results of the Sobel test show that just-in-time mediates the relationship between competitive intensity and operational performance. However, the Sobel test only shows the significance level and not the direction of the effect. Since this paper is a first step in explaining the contingency of market complexity this significant result is still considered important for the literature.

Modularity is added to framework as it is believed to diminish complexity through the use of modular products. However, as a moderator the effect is only significantly positive for competitive intensity and just-in-time/mass customization. The expected reduction in complexity by modularity is thus only achieved for competitive intensity.

5.1 Managerial implications

Managers have limited resources available, choosing the right manufacturing strategy for the right situation is thus important. The literature on contingency theory indicates that the context in which a strategy is implemented has a significant effect on performance.

When demand variability is large, just-in-time or mass customization are not the strategies that positively affect the outcome on performance. This indicates that there are different strategies or practices that will result in high performance under high demand variability. However, both strategies are also not negatively affected which means that they can still be implemented to achieve other performance benefits not tested in this framework.

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___________________________________________________________________________ not differentiate the company from its competitors. However, as before it does not negatively affect it either thus allowing for mass customization to be implemented without having a negative effect on performance.

When the goal is to improve operational performance managers have to consider the sand cone model (Bortolotti et al., 2015). To achieve both responsiveness and efficiency a company first requires to improve quality and delivery. Furthermore, the results indicate that mass customization increases flexibility indicating it has to be implemented first. To also improve efficiency, just-in-time has to be implemented second according to the sand cone model. This is supported by the notion that mass customization is related to both postponement and modularity, both of which allow for higher levels of just-in-time, resulting in a better fit of just-in-time when mass customization is already implemented. Furthermore, when managers want to adopt a mass customization manufacturing strategy it could be beneficial to find the right practices other than just-in-time to achieve both high product variety and efficiency.

5.2 Limitations and future research

Mediation is tested using the Sobel (1982b) test, however as a mediation test bootstrapping is generally accepted as a statistically stronger method. Even though our sample size consisted of 303 companies this still only allows to find a medium mediation effect with the Sobel test. Bootstrapping would allow for finding small effects and thus allowing for a more precise test. Furthermore, the Sobel test only tests the significance level and not the direction. Knowing the direction creates a better picture of the contingency effect of market complexity.

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___________________________________________________________________________ With regards to complexity as a whole, this study included external complexity, so demand and competitive intensity. As mentioned in the background there is also internal complexity such as product or process complexity. To create a complete picture of the impact of complexity on manufacturing strategies future research could try different combinations of strategies and complexity.

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___________________________________________________________________________

References

Abdallah, A. B., & Matsui, Y. (2009). The Impact of Lean Practices on Mass Customization and Competitive Performance of Mass-Customizing plants, 1–30.

Alford, D., Sackett, P., & Nelder, G. (2000). Mass customisation — an automotive perspective.

International Journal of Production Economics, 65(1), 99–110.

Azadegan, A., Patel, P. C., Zangoueinezhad, A., & Linderman, K. (2013). The effect of environmental complexity and environmental dynamism on lean practices. Journal of Operations

Management, 31(4), 193–212.

Bennett, D., & Forrester, P. (1994). Product Variety and Just in Time - Conflict and Challenge. The

Internal Journal of Logistics Management, 5(1), 73–80.

Bevilacqua, M., Ciarapica, F. E., & De Sanctis, I. (2017). Lean practices implementation and their relationships with operational responsiveness and company performance: an Italian study.

International Journal of Production Research, 55(3), 769–794.

Bhamu, J., & Singh Sangwan, K. (2014). Lean manufacturing: literature review and research issues.

International Journal of Operations & Production Management, 34(7), 876–940.

Birkie, S. E., & Trucco, P. (2016). Understanding dynamism and complexity factors in engineer-to-order and their influence on lean implementation strategy. Production Planning and Control,

27(5), 345–359.

Blecker, T., & Abdelkafi, N. (2006). Complexity and variety in mass customization systems: analysis and recommendations. Management Decision, 44(7), 908–929.

Bortolotti, T., Danese, P., Flynn, B. B., & Romano, P. (2015). Leveraging fitness and lean bundles to build the cumulative performance sand cone model. International Journal of Production

Economics, 162, 227–241.

Bortolotti, T., Danese, P., & Romano, P. (2013). Assessing the impact of just-in-time on operational performance at varying degrees of repetitiveness. International Journal of Production Research,

51(4), 1117–1130.

(44)

___________________________________________________________________________

operational performance. International Journal of Operations & Production Management, 33(5), 562–588.

Christmann, A., & Van Aelst, S. (2006). Robust estimation of Cronbach’s alpha. Journal of Multivariate

Analysis, 97(7), 1660–1674.

Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology press.

Custódio, D. T., Roehe Vaccaro, G. L., Nunes, F. L., Vidor, G., & Chiwiacowsky, L. D. (2017). Variant product configuration of industrial air handling units in a MTO environment. International

Journal of Advanced Manufacturing Technology, 1–13.

Duray, R., Ward, P. T., Milligan, G. W., & Berry, W. L. (2000). Approaches to mass customization: Configurations and empirical validation. Journal of Operations Management, 18(6), 605–625. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical

Society. Series A (General).

Feitzinger, E., & Lee, H. L. (1997). Mass Customization at Hewlett-Packard: The Power of Postponement. Harvard Business Review, 75(1), 116–121.

Fogliatto, F. S., Da Silveira, G. J. C., & Borenstein, D. (2012). The mass customization decade: An updated review of the literature. International Journal of Production Economics, 138(1), 14–25. Forza, C., & Salvador, F. (2000). Assessing some distinctive dimensions of performance feedback

information in high performing plants. International Journal of Operations & Production

Management, 20(3), 359–385.

Frandsen, T. (2017). Evolution of modularity literature: a 25-year bibliometric analysis. International

Journal of Operations & Production Management, 37(6), 703–747.

Gilmore, J. H., & Pine, B. J. (1997). The Four Faces of Mass Customization - HBR. Harvard Business

Review (1997), 75(1), 91–101.

Hair, J. F., Babin, W. C., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (fifth ed). Upper Saddle River: Prentice Hall.

Heene, M., Hilbert, S., Draxler, C., Ziegler, M., & Bühner, M. (2011). Masking Misfit in Confirmatory Factor Analysis by Increasing Unique Variances: A Cautionary Note on the Usefulness of Cutoff Values of Fit Indices. Psychological Methods, 16(3), 319–336.

(45)

___________________________________________________________________________

Production Management (Vol. 24).

Hitt, M. A., Keats, B. W., & DeMarie, S. M. (1998). Navigating in the new competitive landscape: Building strategic flexibility and competitive advantage in the 21st century. Academy of

Management Perspectives, 12(4), 22–42.

Jacobs, M., Vickery, S. K., & Droge, C. (2007). The effects of product modularity on competitive performance. International Journal of Operations & Production Management, 27(10), 1046– 1068.

Jina, J., Bhattacharya, A. K., & Walton, A. D. (1997). Applying lean principles for high product variety and low volumes: some issues and propositions. Logistics Information Management, 10(1), 5– 13.

Kolberg, D., Knobloch, J., & Zühlke, D. (2017). Towards a lean automation interface for workstations.

International Journal of Production Research, 55(10), 2845–2856.

Krishnamurthy, R., & Yauch, C. A. (2007). Leagile manufacturing: a proposed corporate infrastructure.

International Journal of Operations & Production Management, 27(6), 588–604.

Kumar, A. (2004). Mass customization: Metrics and modularity. International Journal of Flexible

Manufacturing Systems, 16(4 SPEC. ISS.), 287–311.

Lampel, J., & Mintzberg, H. (1996). Customizing Customization. Sloan Management Review, 38(1), 21–30.

Lander, E., & Liker, J. K. (2007). The Toyota Production System and art: Making highly customized and creative products the Toyota way. International Journal of Production Research, 45(16), 3681– 3698.

Lau, A. K. W. (2011). Critical success factors in managing modular production design: Six company case studies in Hong Kong, China, and Singapore. Journal of Engineering and Technology

Management - JET-M, 28(3), 168–183.

Leffakis, Z. M., & Dwyer, D. J. (2014). The effects of human resource systems on operational performance in mass customisation manufacturing environments. Production Planning &

Control, 25(15), 1213–1230.

Liu, G. (Jason), Shah, R., & Babakus, E. (2012). When to Mass Customize: The Impact of

(46)

___________________________________________________________________________

MacKelprang, A. W., & Nair, A. (2010). Relationship between just-in-time manufacturing practices and performance: A meta-analytic investigation. Journal of Operations Management, 28(4), 283–302.

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104.

Mahapatra, S. K., Das, A., & Narasimhan, R. (2012). A contingent theory of supplier management initiatives: Effects of competitive intensity and product life cycle. Journal of Operations

Management, 30(5), 406–422.

Merle, A., Chandon, J. L., Roux, E., & Alizon, F. (2010). Perceived value of the mass-customized product and mass customization experience for individual consumers. Production and

Operations Management, 19(5), 503–514.

Mikkola, J. H., & Skjøtt-Larsen, T. (2004). Supply-chain integration: Implications for mass

customization, modularization and postponement strategies. Production Planning and Control,

15(4), 352–361.

Ojha, D., White, R. E., & Rogers, P. P. (2013). Managing demand variability using requisite variety for improved workflow and operational performance: The role of manufacturing flexibility.

International Journal of Production Research, 51(10), 2915–2934.

Peng, X. D., Liu, G. (Jason), & Heim, G. R. (2011). Impacts of information technology on mass customization capability of manufacturing plants. International Journal of Operations &

Production Management, 31(10), 1022–1047.

Piller, F. T. (2005). Mass Customization - Reflections on the State of the Concept. The International

Journal of Flexible Manufacturing Systems, 313–334.

Piller, F. T., Moeslein, K., & Stotko, C. M. (2004). Does mass customization pay? An economic approach to evaluate customer integration. Production Planning and Control, 15(4), 435–444. Ping, R. A. (1995). A Parsimonious Estimating Technique for Interaction and Quadratic Latent

Variables. Journal of Marketing Research, 32(3), 336.

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