The complementarity between TQM and JIT and the
effect on operational performance: an empirical
investigation
By
Isabelle Smits
S2191806
Master’s Thesis
Technology and Operations Management
Words: 13.165
University of Groningen
ABSTRACT
Purpose - This paper aims to empirically investigate whether two main bundles of lean practices, just-in-time (JIT) and total quality management (TQM), have a linear effect on operational performance or a moderate or extreme complementarity in affecting operational performance. Also this paper explores how the interdependencies between JIT and TQM have to be coordinated.
Methodology - A mixed methodology is used, comprised of a quantitative phase and a qualitative phase. The quantitative phase is based on a statistical analysis and conducted through a survey responded by 125 manufacturing companies in different industries in the Netherlands and China. For the qualitative phase, a case study was conducted with one manufacturing company in the Netherlands.
Findings - This paper proves that JIT and TQM have a linear effect on operational performance, instead of moderate or extreme complementarity. The case study shows competing results in that extreme complementarity exists between JIT and TQM. Complementary JIT and TQM, requires tight rather than loose coordination mechanisms to manage the interdependencies between them. Research implications – Research about lean bundles and the impact on operational performance is usually survey-based, but should be accompanied with multiple case studies. As results between these two methods may differ.
Practical implications – the research suggest that the knowledge about the interaction between JIT and TQM is important, because it provides strategic suggestions for the investment decisions regarding the allocation of scarce resources to JIT and TQM and the impact on operational performance. Further this research suggests that tight coordination is achieved by direct supervision and standardization of work processes in order to implement JIT and TQM hand-in-hand and manage the complex interdependencies between them.
Originality/value – This paper contributes to the complementarity literature by distinguishing between moderate and extreme complementarity. Further it makes it contribution by exploring, which coordination mechanisms have to be put in place in order to manage the interdependencies between complementary JIT and TQM.
TABLE OF CONTENT
PREFACE 5
1. INTRODUCTION 6 2. THEORETICAL BACKGROUND 8 2.1. The linear, multiplicative or constraining factor model 8 2.1.1. Lean, lean bundles and operational performance definitions 8 2.1.2. Linear effect of JIT and TQM on operational performance 9
2.1.3. Moderate and extreme complementarity between JIT and TQM 10
2.2. Coordination of the interdependencies between JIT and TQM 12
2.2.1. Lean production: a system with interdependencies 12
2.2.2. Coordination to manage the interdependencies between lean bundles 13
2.2.3. Mintzberg’s (1980) six coordination mechanisms 13
2.2.4. Coordination mechanisms in lean and lean bundles 15
2.2.5. Development of propositions 16
3. METHODOLOGY 19
3.1. Research design 19
3.2 Quantitative phase – survey 19
3.2.1. Sample and data collection 20
3.2.2. Measurement instrument development 21
3.2.3. Assessing the measurement quality 23
3.2.4. Data analysis 24
3.3. Qualitative phase – case study 24
3.3.1. Studied company and case selection 24
3.3.2. Data collection 25
3.3.3. Data analysis 26
4. ANALYSIS OF THE RESULTS 27
4.1. Quantitative phase - survey 27
4.2. Qualitative phase – case study 30
JIT and TQM or is there a linear effect on operational performance? 31
4.2.3. Interdependency between complementary JIT and TQM 32
4.2.4. Interdependency between JIT and TQM and the resulting coordination mechanisms 33
5. DISCUSSION – LINKING THE QUANTITATIVE PHASE AND THE QUALITATIVE PHASE 35
6. CONCLUSIONS AND FUTURE RESEARCH 38
PREFACE
1. INTRODUCTION
The rapidly changing and highly competitive market of the past twenty years let manufacturing organizations to embrace the principles of lean production (Fullerton, Kennedy, & Widener, 2014). Lean production is characterized as an integrated system, that encompasses a wide variety of practices that could be grouped into lean bundles, two of these are: just-in-time (JIT) and total quality management (TQM) (Bortolotti, Danese, & Romano, 2012; Shah, Chandrasekaran, & Linderman, 2008; Shah & Ward, 2003, 2007). Lean bundles are “sets of interrelated and internally consistent lean practices” (Shah & Ward, 2003:130, 2007:790). Even though research indicated that implementing Total Quality Management (TQM) and Just-in-Time (JIT) together is important for pursuing superior operational performance (Sukarma, Azmi, & Abdullah, 2014), Operations Management literature investigating how JIT and TQM complement each other on operational performance is scarce (Furlan, Dal Pont, & Vinelli, 2011b; Shah & Ward, 2003). “Two activities interact as complements if the marginal benefit of each activity increases in the level of the other activity” (Siggelkow, 2002:901). Complementarity between JIT and TQM is important to study because, “although TQM and JIT function effectively in isolation, their combination yields synergies that lead to further performance improvements” (Flynn, Sakakibara, & Schroeder, 1995:1354). Such synergies result in an overall operational performance that is greater than the sum of the individual impact of TQM and JIT on performance (Sriparavatsu and Gupta, 1997; Mefford, 1989; Flynn et al., 1999; Furlan, Dal Pont & Vinelli, 2011a; Furlan et al., 2011b)
An important question that arises is in what way JIT and TQM complement each other and how do they jointly affect operational performance?
First of all, there might be no complementarity between JIT and TQM, represented by the linear model (Siemsen, Roth, & Balasubramanian, 2008).
when JIT and TQM interact as complements this would mean that in the presence of one lean bundle, the performance gains from the other can be reinforced, regardless the levels of both.
Finally, the constraining factor model suggests a more extreme type of complementarity (Narasimhan et al., 2013; Siemsen et al., 2008). In the lean context, this model argues that when one lean bundle is weak with respect to the other (the constraining factor), focusing on the other lean bundle will not produce a desired impact on operational performance. Which means that maximum performance gain can only be created by focusing on the constraining factor (Narasimhan et al., 2013).
Earlier research investigated JIT and TQM bundles and noticed that these two are complementary, instead of substitutionary (Flynn et al., 1995; Furlan et al., 2011b; Shah & Ward, 2003). This study makes its contribution by considering that complementarity consist of two different types, originating from the two models: moderate and extreme complementarity (Siemsen et al., 2008). This distinction is important because the type of complementarity between JIT and TQM provides different suggestions for the decision in what lean bundle to invest and therefore has consequences for operational performance.
As lean production is related to waste elimination in terms of reducing excess inventory and excess human and machine capacity (Shah & Ward, 2007), knowing where to invest or allocate resources will fit in a lean context. By investigating both forms of complementarity between JIT and TQM, it will give some strategic suggestions for the investment decisions regarding the allocation of scarce resources to JIT and TQM and the impact on operational performance.
Given the statements above, a second question arises how managers have to deal with JIT and TQM. Managers have to coordinate the interdependencies between two bundles, if they want to maximize operational performance (Furlan et al., 2011a; Furlan et al., 2011b). However, research on this topic is scarce (Furlan et al., 2011b).
Furlan et al., 2011b). Whether a coordination mechanism is tight or loose, depends on the degree to which employees handle the coordination, instead of a supervisor or standardized systems who controls them (Mintzberg et al., 2003). Since complementarity implicates that complex interdependencies are shared by lean bundles (Milgrom and Roberts, 1995), I argue that in case JIT and TQM are complementary, tight coordination is required to manage this interdependency. On the other hand, when lean bundles lack complementarity, the interdependencies are not so complex (Furlan et al., 2011a). Therefore, lean bundles may be implemented as separate modules (Furlan et al., 2011a), assuming a linear effect exists on operational performance. As such, lean bundles consist of smaller and simpler tasks, which are easier to manage (Furlan et al., 2011a). Consequently, I argue that in case JIT and TQM have a linear effect on operational performance, loose coordination is required to manage their interdependency. This leads to the following research question: How should the interdependencies between JIT and TQM be coordinated?
This study will combine quantitative and qualitative research. A survey among 125 manufacturing companies in the Netherlands and China is conducted to investigate the type of complementarity between TQM and JIT. Next, a case study within a Dutch manufacturing company will gather data about the organizational consequences of complementarity and gives deeper insight into the degree of coordination required.
2. THEORETICAL BACKGROUND
2.1. The linear, multiplicative or constraining factor model
2.1.1. Lean, lean bundles and operational performance definitions
A JIT bundle consists of practices intended to reduce or eliminate waste along the value chain (Shah and Ward, 2003). JIT practices are often related to four components: kanban, lot size reduction, scheduling, setup time reduction (Flynn et al., 1995). Sakakibara, Flynn and Schroeder (1993) identify four JIT practices; equipment layout, pull system support, supplier quality level and kanban. Shah and Ward (2003) include additional practices like lot size reduction, cycle time reduction, quick changeover and production process reengineering.
A TQM bundle encompasses lean practices aimed at continuous improvement and sustainability of quality products and processes (Shah & Ward, 2003). TQM is a set of practices often divided into three components: customer focus, employee involvement and process focus (Dean and Bowen, 1994). For example Cua, McKone, & Schroeder (2001) included practices like cross-functional product design, process management, supplier quality management, customer involvement, cross-functional training, and employee involvement. On the other hand, Dal Pont, Furlan and Vinelli (2008) included practices, which reduce process variance, like poka-yoke, standard operations procedure, statistical process control, 5s, and proprietary design of equipment.
Taken together, it can be said that JIT is related with techniques to reduce waste and TQM is associated with continuous improvement of products and processes.
Operational performance is defined as the five basic performance objectives quality, speed, dependability, flexibility and costs (Slack et al., 2010).
2.1.2. Linear effect of JIT and TQM on operational performance
H1: JIT has a positive linear effect on operational performance.
H2: TQM has a positive linear effect on operational performance.
2.1.3. Moderate and extreme complementarity between JIT and TQM
Since lean production can be characterized as a system (Shah & Ward, 2003, 2007), organizations have to bear in mind that interactions among JIT and TQM may appear (Furlan et al., 2011b). Implementing JIT and TQM in combination, will lead to greater operational performance improvements than the sum of their individual impact on operational performance (Sriparavatsu and Gupta, 1997; Mefford, 1989; Flynn et al., 1999; Furlan et al., 2011a; Furlan et al., 2011b). Although it is acknowledged that such synergies exist among distinct bundles of practices, few studies investigated them (Shah and Ward, 2003), as the idea stays rather vague (Furlan et al., 2011b). This paper uses the multiplicative model (Narasimhan et al., 2013) and the constraining factor model (Siemsen et al., 2008) to clarify and define the different forms of complementarity that could exists among JIT and TQM and their impact on operational performance.
Literature highlights the interaction between JIT and TQM (Shah & Ward, 2003; Furlan et al., 2011b). In my paper it is stated that a complementarity interaction between JIT and TQM could consist of two different types, extreme complementarity and moderate complementarity (Siemsen et al., 2008). The multiplicative model (Narasimhan et al., 2013) and constraining factor model (Siemsen et al., 2008) explain these interactions types.
The multiplicative model considers two different forms of interaction: substitutionary and complementary (Narasimhan et al., 2013). According to Siemsen et al. (2008) this form of complementarity is called: moderate complementarity.
characterized as less restrictive for a company. This is because trade-offs can be made between the investments (Jinhui Wu et al., 2013). For example, investment decision-makers can choose to invest relatively more in JIT than in TQM and the other way around.
On the other hand, “two activities interact as complements if the marginal benefit of each activity increases in the level of the other activity” (Siggelkow, 2002:901), indicating a positive interaction (Narasimhan et al., 2013) or moderate complementarity (Siemsen et al., 2008). Put differently, when adding one activity in the presence of another activity, the increase in operational performance will be higher than adding the activity in isolation (Furlan et al., 2011b). So, if a company starts with JIT when TQM is already in place, a company will get greater benefits from JIT and vice versa. According to Flynn et al. (1995) JIT and TQM are commonly coupled together because one practice builds upon another. These authors described that a lot of TQM practices decrease process variance, which can be seen as precondition for JIT to be used effectively. On the other hand, JIT practices reducing inventory will lead to the exposure of quality problems (Flynn et al., 1995). Moreover, when JIT and TQM interact as moderate complements this would mean that in the presence of one lean bundle, the performance gains from the other lean bundle can be reinforced, regardless the levels of both. This leads to the following hypotheses, where JIT and TQM have a multiplicative influence on operational performance:
H3: JIT and TQM positively interact on operational performance
H4: JIT and TQM negatively interact on operational performance
TQM, will constrain operational performance. Not meeting this this minimum level might even have a negative impact on operational performance (Jinhui Wu et al., 2013). According to Narasimhan et al. (2013) the constraining factor model seems to be more representative than the multiplicative model, as it resolves its conflicting conclusions. This leads to the following hypothesis:
H5: JIT and TQM are extreme complementary bundles; the constraining bundle determines operational performance.
For a summary of all the hypotheses and clarification of the different effects, see Appendix I, table 9.1.1.
2.2 Coordination of the interdependencies between JIT and TQM
2.2.1. Lean production: a system with interdependencies
Lean production can be characterized as an integrated system encompassed of lean bundles, aimed to eliminate waste by decreasing internal, supplier and customer variability (Shah & Ward, 2003, 2007). A lean production system consists of elements, i.e. lean bundles, which interact and have some degree of interdependency (Shah and Ward, 2007). Further, such a system should effectively be managed, if organizations want to pursue lean production and minimize inventory (Shah and Ward, 2007).
A system can be distinguished as a loosely coupled system or tightly coupled system (Sanchez & Mahoney, 1996). As such, the lean production system can be tightly or loosely coupled. “In loosely coupled systems the interdependence between activities is low, while in tightly coupled systems the interdependence is high” (Siggelkow, 2002:903). Shah and Ward (2007) view lean production as a tightly coupled system, because of the mutual dependence that exists among the lean bundles.
2.2.2. Coordination to manage the interdependencies between lean bundles
Coordination is needed for managing an interdependency among activities, so that variability and inventory can be decreased (Simatupang, Wright, & Sridharan, 2002). Coordination means “the act of managing interdependencies between activities performed to achieve a goal” (Malone & Crowston, 1990: 358). However, the required coordination differs according to the system and the degree of interdependency between the elements of that system. According to Sanchez & Mahoney (1996) tightly coupled systems have to be coordinated by managerial authority, while loosely coupled systems do not require this authority. Stieglitz and Heine (2007) indicated that the higher the interdependencies involved in the production, the higher the need for centralized coordination. Raposo and Fuks (2002) point that activities that depend on each other to start, perform or end require sophisticated coordination. In stead, the coordination of low interdependent activities does not have to occur explicitly and could be coordinated by a “social protocol” (Raposo & Fuks, 2002).
Coordination may also be explained in terms of mechanisms (Melin & Axelsson, 2005), often used to explain coordination between lean bundles. A coordination mechanism can be seen as the manner in which an organization aligns its employees in performing activities (Mintzberg et al., 2003). For example, Thompson (1967) and Mintzberg (1980) suggested well-known sets of mechanisms (Melin & Axelsson, 2005). Because Mintzberg’s (1980) coordination mechanisms are characterized as the most well-established sets of coordination mechanisms (Melin & Axelsson, 2005) and are often used in describing the way in which lean production and lean bundles have to be managed (Niepce & Molleman, 1998; Furlan et al., 2011a; Furlan et al., 2011b), these have been chosen to use in this paper.
2.2.3. Mintzberg’s (1980) six coordination mechanisms
• Direct supervision achieves coordination by one supervisor (M) who manages the employees (O) by giving them specific orders and monitor their actions (Mintzberg, 1980; Melin & Axelsson, 2005).
• The standardization of work processes achieves coordination by specifying the content of the work processes, typically done by work schedulers or long-range planners (A) (Mintzberg, 1980; Melin & Axelsson, 2005). For example, standard rules and routines can direct the doing of the work itself (Mintzberg, 1980).
• The standardization of outputs achieves coordination by specifying the results of the work, typically done by work schedulers or long-range planners (A) (Mintzberg, 1980; Melin & Axelsson, 2005). For example, specifications of the output or specific performance measures (Mintzberg, 1980).
• A looser way to realize coordination is by means of the standardization of skills. Here the employee (O) is standardized, instead of the work itself or the outputs (Mintzberg et al., 2003). Before employees join the organization, employees get a specified training to acquire a set of standards skills and knowledge (Mintzberg, 1980; Melin & Axelsson, 2005), which they apply to their work later on (Mintzberg et al., 2003). As a consequence, the standards are not specified by the schedulers, but are learned by the employee as an input to the job (Mintzberg et al., 2003). Coordination is realized by the fact that the employees know what to expect of each other (Mintzberg et al., 2003), reducing the need to continuously align them.
• The standardization of norms achieves coordination by employees (O) having a common set of values; beliefs and expectations, which make them, work together (Mintzberg et al., 2003).
Figure 2.1: Coordination mechanisms (Melin & Axelsson, 2005) 2.2.4. Coordination mechanisms in lean and lean bundles
In lean production, Niepce and Molleman (1998) used Mintzberg’s (1980) coordination mechanisms to identify the way in which lean activities are controlled on the production floor. These authors described that coordination is achieved by the standardization of work processes and needs first-line supervisors.
Within the context of lean bundles, Furlan et al. (2011a) point that the existence of
complementarity between lean bundles implicates complex interdependencies are shared by these
two bundles (Milgrom and Roberts, 1995). These interdependencies need to be coordinated and a tight coordination between lean bundles is required, to acquire the positive impact that each lean bundle has on the marginal benefit of the other (Furlan et al., 2011a). These authors identified Thompson’s (1967) coordination mechanisms process standardization and mutual adaption to achieve this coordination. Also Furlan et al. (2011b) indicate the need to use coordination mechanisms to manage the interdependencies between the lean bundles JIT and TQM.
On the other hand, Furlan et al. (2011a) denote that a lack of complementarity between lean bundles implicates the possibility for a modular-wise design approach, because these lean bundles do not create enough synergy to be complementary. In this situation the interdependencies between bundles are not so complex and, as such, lean bundles can be implemented as separate modules (Furlan et al., 2011a), or a loosely coupled system. Here, such modules can perform autonomously and concurrently (Sanchez & Mahoney, 1996). Therefore, without such complex interdependencies, there is relatively no need to align these bundles. As a consequence, the entire process can be broken down into smaller and simpler tasks, which makes their management and implementation easier for an organization (Furlan et al., 2011a), reducing the need for managerial authority (Sanchez & Mahony, 1996).
From a managerial perspective, this means that lean bundles or different modules should be implemented sequentially by an organization (Furlan et al., 2011a).
2.2.5. Development of propositions
If JIT and TQM are complementary bundles, how does such interdependency between these two arise? It is assumed that JIT and TQM have a reciprocal interdependency, which means that the use of JIT is shaped and constrained by the use of TQM and vice versa, thus having a mutual dependence. On the operational level, lean bundles are translated in sets of different manufacturing practices (Cua et al., 2002), therefore, to understand the reciprocal
interdependency between JIT and TQM, the supposed one-way dependency between JIT and
TQM practices has to be explained. I assume that this dependency is expected to be sequential; one practice is the input for the use of another practice (Galeazzo et al., 2013).
In this context, “practices (input) are approaches used by managers and workers with the goal of achieving certain types of performance (output)” (Flynn, et al., 1995:1326). JIT practices are aimed to minimize and eliminate forms of waste (Jinhui Wu et al., 2013), like pull production and lot size reduction (Flynn et al., 1995). The main goal of TQM practices is to continuously improve and sustain the quality of processes and products (Cua et al., 2001), like statistical process control, supplier quality management and product and process design (Flynn et al., 1995).
a prerequisite for effective use of JIT’s waste elimination (Flynn et al., 1995), like pull production. For example, “TQM practices lead to a less variable (better-controlled) manufacturing process that, in turn, reduces the need for safety stock buffers” (Flynn et al., 1995:1329). In other words, without involvement in controlling the process with statistics, machines will have to stop frequently because of quality problems that may appear. As a consequence these problems require a buffer by using more safety stock, if they want to maintain a constant workflow and meet customers needs and without possible “stockouts” (Flynn et al., 1995). As process variance is a prerequisite for reducing waste in the form of excess inventory, it can be said JIT can only perform pull production effectively by performing statistical process control first. So, statistical process control is the input for pull production.
Conversely, as JIT practices aim to reduce waste, like lot size reduction; this decreases the need for inventory buffers, which results in an improved TQM performance (Flynn et al., 1995). More specifically, JIT’s inventory reduction practices expose quality problems by immediate parts starvation (Furlan et al., 2011b). In other words, if inventory levels are high, this allows processing at a less-than-optimal quality level, without direct consequences for a constant workflow (Flynn et al., 1995). Lot size reduction, lowering the inventory levels, will expose the quality problems at a particular machine, by the immediate starvation of parts at the subsequent machines (Flynn et al., 1995; Furlan et al., 2011b). Reducing the inventory further, will expose more quality problems, and so on, referring to the river and rocks analogy (Slack et al., 2010). Without reducing the inventory, quality problems stay undetected and unsolved, and therefore, lot size reduction can be seen as an input for TQM’s exposure of problems, applying approaches for determining and prioritizing process quality problems (Flynn et al., 1995).
Thus, TQM’s statistical process control seems to be an input for JIT’s pull production, but JIT’s lot size reduction, is yet another input for TQM’s detection of quality problems. It is assumed that these one-way dependencies, on an operational level, will lead to the reciprocal interdependency between JIT and TQM.
Taken together, it is proposed that if complementarity exists between JIT and TQM, direct
supervision and standardization of work processes are expected to coordinate their simultaneous
In this situation, production teams should not work autonomously, because employees on the production floor often fail to fully take into account the interdependencies (Stieglitz & Heine, 2007) that could appear between lean bundles. Therefore, a supervisor is needed to direct teams and take responsibility for their activities (Niepce & Molleman, 1998). Secondly, complementary bundles need common sets of rules and routines, pursued by each bundle of practices (Furlan et al., 2011a). Employees have to adapt to a fixed pace, because the minimization of inventory buffers and solving quality problems makes employees more dependent on workflow time sequencing, determined by schedulers (Niepce & Molleman, 1998).
On the other hand, it is assumed that if JIT and TQM lack complementary, they a linear effect exists on operational performance and mutual adjustment and standardization of norms are expected to coordinate their sequential implementation and the interdependency between these lean bundles.
For example, Nakamura et al., (1998) provide evidence that JIT practices, such as, set-up time reduction, schedule flexibility, pull-system support and JIT supplier relationships positively impact operational performance on several dimensions. Besides a positive and direct impact of JIT, Dal Pont et al. (2008) also point to the positive and direct impact of TQM practices, such as, statistical process control, proprietary design of equipment and 5s on operational performance. In case of a linear effect on operational performance, it is assumed that JIT and TQM can be seen as separate modules with not so complex interdependencies. This means that both JIT and TQM consist of smaller and simpler tasks, which are easier to manage for the organization (Furlan et al., 2011a), allowing for employees coordinating themselves instead of a supervisor who coordinates them (Mintzberg, 1980). Therefore, mutual adjustment and standardization of skills is expected to coordinate these two bundles. By mutual adjustment and standardization of norms employees know what to expect of each other and share the same values and beliefs (Mintzberg et al., 2003), by which they correct, stimulate and motivate each other in one organizational culture. This reduces the need to continuously align them (Mintzberg et al., 2003) and allow them to work independent from each other in separate JIT or TQM.
3. METHODOLOGY
3.1 Research design
This paper studies the impact of JIT and TQM on operational performance within manufacturing companies. More particularly, it studies different forms of complementarity between JIT and TQM and the impact on operational performance. Further, this paper explores, how the interdependencies between JIT and TQM have to be coordinated. The empirical results of this study were first acquired through a quantitative phase and second a qualitative phase. The underlying structure of the research is illustrated in appendix II, table 9.2.1.
As such, a mixture of statistical results and an in-depth analysis through a case study is generated. Investigating the quantitative part before the qualitative part gives a more detailed explanation of previously validated hypothesis or rejected research hypothesis (Jabbour, Santos, & Nagano, 2010). In addition, a mixed methodology aims to gain multiple perspectives on the same theme (Cunningham, 1997) in order to acquire an improved validated result (Modell, 2005). Also, doing surveys before a case study favors the uncovering of behavioral patterns and hidden interactions of the observed phenomenon, beneficial to case studies (Sieber, 1973). For these reasons, the mixed method is used for this study, meeting the suggestion that measuring lean bundles and operational performance should be supported by qualitative research (Sousa and Voss, 2002) and the recommendation of investigating the coordination of their interdependencies with a case study (Furlan et al., 2011b). Consequently, this empirical research consists of two phases: the quantitative phase and qualitative phase.
3.2. Quantitative phase – survey
Figure 3.1: conceptual model and hypotheses quantitative phase. 3.2.1. Sample and data collection
To handle missing data the first approach is to prevent it by increasing respondent involvement, giving clear instruction, a well-designed questionnaire (Forza, 2002). However, some data will be missed and the deletion strategy was used (Forza, 2002). Next to incomplete information, it is possible that respondents are not knowledgeable enough and cannot be trusted, which leads to random or even bias error (Karlsson, 2009). To further overcome these problems, a qualitative research next to a quantitative research is conducted (Karlsson, 2009).
3.2.2. Measurement instrument development
In this paper, a mail questionnaire is chosen to collect the data. A group of 15 students refined and translated a pre-developed questionnaire. The students were possible to adjust the wording, scaling, respondent identification and made it a complete questionnaire, as Forza et al. (2002) recommended as key tasks in developing the measurement instrument. The validity of the questions was guaranteed by translating the questions from Dutch to English and back by different students. The questionnaire consist of seven parts: plant description, plant context, Just-in-Time (JIT) production techniques, Total quality management (TQM) techniques, Supply chain management (SCM) techniques, Human resource management (HRM) techniques and plant performance. Furthermore, the questionnaire is comprised of 20 questions. The different lean practices were questioned with the multiple choice scaling technique on a 5-point scale. This scaling technique is appropriate to use when a small amount of separate, mutually exclusive categories should classify a full range of response (Karlsson, 2009). Each major variable in this research is defined, specified and operationalized below and summed up appendix III, table 9.9.4, 9.3.5, and 9.3.6.
Just-in-time (JIT): A JIT bundle consists of practices intended to reduce or eliminate waste along
suppliers who are able to deliver frequently and in small amounts, resulting in reduced internal inventory and costs (Lee and Ebrahimpour (1984). Lastly, the customer JIT link is included, as for example involving the customer early in the product development process will decrease the lead time (Trygg, 1993).
Total quality management (TQM): A TQM bundle encompasses lean practices aimed at
continuous improvement and sustainability of quality products and processes (Shah & Ward, 2003). Cua, McKone, & Schroeder (2001) included practices like cross-functional product design, process management, supplier quality management and customer involvement. On the other hand, Dal Pont et al. (2008) included practices, which reduce process variance, like poka-yoke, standard operations procedure, statistical process control, 5s, and proprietary design of equipment. In this survey, TQM is measured through 30 items underlying the practices: statistical process control, proprietary equipment development, cleanliness and organization, process management, product design and management, quality data analysis, supplier quality management and customer quality management. Nair's (2006) meta-analysis suggests the positive influence of including supplier quality management and customer focus in QM practices on operational performance. Customers are for example an important input to the product design process, by explaining their needs and desires (Flynn et al., 1995). The supplier is included because it is important they can assure the quality of the incoming materials and parts (Flynn et al., 1995).
Operational performance: in this study operational performance underpins the five basic performance objectives quality, speed, dependability, flexibility and costs (Slack et al., 2010). The operational performance compared to 3 years ago is measured with the following 23 items: capacity utilization, product quality, environmental performance, delivery speed, product performance, product innovativeness, overhead costs, volume flexibility, mix flexibility, inventory turnover, time to market, employee satisfaction, customer service, procurement lead time, labour productivity, product flexibility, delivery reliability, unit manufacturing costs, procurement costs, manufacturing lead time, return on investment, profit, market share.
Control variables: the results were controlled for the age of the plant (measured as the years since
the number of employees) industry code and country in which the plant is located.
Table 3.1 presents the descriptive statistics of all the bundles. There was a significant positive relationship between JIT and TQM, r = 0,802, p < 0,01. There was also a significant positive relationship between JIT and operational performance, r = 0,236, p < 0,01. Finally, the correlation table shows that an increase in TQM is significantly related with an increase in operational performance (r = 0,295, p < 0,01).
MEAN SD JIT TQM PERF
JIT 3,794 0,432 1 0,802** 0,236**
TQM 3,752 0,50 0,802** 1 0,295**
PERF
** p < 0,01
3,601 0,338 0,236** 0,295** 1
Table 3.1: descriptive statistics
3.2.3. Assessing the measurement quality
al., 2011b; Dal Pont et al., 2008) and proved valid earlier, the original constructs and underlying items were used to do the analysis.
3.2.4. Data analysis
Before the data analysis started, data was put in a database. The raw data obtained from the questionnaire was converted in useful information by statistical analysis. The type of data that were obtained from the questionnaire was ordinal data. Data analysis is divided into two parts: preliminary data analysis and hypothesis testing (Forza, 2002). “Before the hypotheses can be tested, it is useful to check the underlying assumptions of the test and to get a feeling with the data in order to better interpret the results” (Forza, 2002:181). Second, data is analyzed for hypothesis testing through a regression analysis, researching four different models: linear model, multiplicative model, constraining factor model and the combined model.
3.3. Qualitative phase – case study
3.3.1. Studied company and case selection
Research about the relation between lean best practices and performance improvements are usually survey-based and thus often leave out the organizational consequences of such relations and the impact that they may have on operational performance (Sousa & Voss, 2008). Therefore, this study contributes by conducting a case study and explores the phenomenon in-depth in it’s own environment (Yin, 2013). Therefore, a qualitative approach, i.e. case study, is used to explore how the interdependencies between JIT and TQM have to be coordinated, in case JIT and TQM are complementary or lack complementarity. And consequently, considers which coordination mechanisms have to be put in place.
The second part of the research includes a “how” question and according to Yin (2013), this question is suitable for a case study research design. According to Voss, Tsikriktsis and Frohlich (2002), a case study gives the advantage that the phenomenon can be studied in its natural setting and meaningful and relevant insights can be generated by observing the degree of coordination in practice.
Company X continuously improved its production process, including different practices of TQM and JIT, which also were the selection criteria for this study. Moreover, the last couple of years it developed it’s own lean philosophy within the organization. Implementing these lean initiatives requires the right way of managing the interdependencies. Although the company aware for it, top management and employees on the production floor not always seemed to be aligned. Therefore, this company is very suitable for this research.
In this study the choice is made to conduct a single case study, which allows for deeper observation of the organization Voss et al. (2002). Further, a case study is according to Yin (2013) suitable for developing hypotheses or proportions, testing, or expanding already formulated theories. Two possible scenarios were considered that might appear in Company X: JIT and TQM are complementary or lack complementarity. This study tried to expand the already formulated theory about complementary bundles by investigating how to manage the consequent interdependencies between JIT and TQM, as the study of Furlan et al. (2011b) did not tackled this question. Further this study tried to explore how to coordinate the interdependencies between JIT and TQM if they lack complementarity. Despite research is done about the coordination of the interdependencies between complementary upstream and downstream JIT and internal and external JIT bundles which lack complementarity (Furlan, et al., 2011a), knowledge about the coordination between JIT and TQM is scarce.
Therefore, propositions are made about how to manage the interdependencies between complementary JIT and TQM and when JIT and TQM lack complementarity and which coordination mechanisms to implement. Because Mintzberg’s (1980, 2003) coordination mechanisms are characterized as the most well-established sets of coordination mechanisms (Melin & Axelsson, 2005) and are often used in describing the way in which lean production and lean bundles have to be managed (Niepce & Molleman, 1998; Furlan et al., 2011b; Furlan et al., 2011a), these have been chosen to use in this paper. Nevertheless, a single case study has the disadvantage of a limited generalizability of the conclusions that can be drawn, from such a single event (Voss et al., 2002).
3.3.2. Data collection
aim was to interview one production manager, one manager responsible for TQM, and one responsible for JIT, and possibly employees on the production floor. When collecting data, it’s important to consider validity and reliability (Karlsson, 2009). To avoid subjective assessments and to ensure construct validity, several sources of evidence have to be used (Yin, 2013), therefore, the interviews were complemented by a plant tour, direct observation, and additional business documents from Company X. As a result, the triangulation is guaranteed (Karlsson, 2009).
According to Yin (2013) external validity could be assured, when the outcomes are generalizable in other contexts. A single-case study limits generalizability of the conclusions compared to multiple-case studies (Sousa & Voss, 2008). However, this study will give a greater depth compared to multiple case studies (Sousa & Voss, 2008), and is used to compensate the lack of generalizability as much as possible.
3.3.3. Data analysis
4. ANALYSIS OF THE RESULTS 4.1. Quantitative phase – survey
In the previous methodology section, the reliability and validity of the measures were demonstrated and consequently a sum variable was constructed by creating scale averages for each scale. Next, these scores were standardized. Here, the lowest standardized score on JIT and TQM is operationally defined as the minimum of the two constructs for each respondent. Further the dummy JIT variable (DJIT) that is set to 1 if JIT is the minimum and 0 if TQM is the minimum. And the dummy TQM variable (DTQM) is set to 1 of TQM is the minimum and to 0 if JIT is the minimum (Siemsen et al., 2008).
The correlation table 3.1 shows that an increase in JIT is significantly related with increase in TQM and vice versa (r =0,802, p < 0,01), however it does not say something about its assumed complementarity. Four models were evaluated (see table 4.1), to test the hypotheses (Siemsen et al., 2008) and to see whether moderate or extreme complementarity exists between JIT and TQM. Model 1 equals the linear model and includes the basic linear effects of JIT and TQM on operational performance (H1 and H2). Model 2 is the constraining factor model (H4) and includes the minimum of JIT or TQM, which changes the standardized values of JIT and TQM. Model 3 equals the multiplicative model (H3) and allows for the interaction effects between JIT and TQM, next to the standardized constructs. Lastly, model 4 is the combined model (Siemsen et al., 2008). The control variables are included in each model. Controls for the number of employees, time of lean implementation and plant age are all absolute values. A dummy variable is made for the different industry codes that participated. The dummy variable is set to 1 if it was the industry code and 0 if it was another industry code. Further, the dummy variable for country is set to 1 if the plant is located in China and 0 if the plant is in The Netherlands.
and performance best? Where according to Siemsen et al. (2009:437), “the higher the R², the better the fit”
The linear model is de only model that is significant compared to the other models (R² = 0,201, p < 0,05). Although the constraining factor model (R² = 0,214, p > 0,05), the multiplicative model (R² = 0,214, p > 0,05) and the combined model (R² = 0,229 p > 0,05) have a higher explanatory value than the linear model, they are not significant. For model 3 this means that there was not a significant interaction between JIT and TQM on performance, B = - 0,032, p >0,01. This means that an increase in JIT will not lead to an increase in performance, and that this relation won’t be even more stronger when TQM is high as opposed to low. For model 2 this means that either JIT or TQM are not the constraining factor in their relation on performance.
Therefore the linear model dominates all other models and can give an R² 20,1% explanation for the dataset. It can be concluded that these tests do not give evidence to support the hypothesis and thus is rejected.
Second, although the linear model was significant, the relation between JIT itself on performance was not significant.
These results are contrary to previous and comparable studies, which have noticed that JIT and TQM are complementary bundles and indicate a significant relation between JIT and operational performance (Shah & Ward, 2003; Dal Pont et al., 2008; Furlan et al., 2011b).
However, a possible explanation for the fact that these results do not hold in this study can be range reduction in the questionnaire. It seems that little to no variance exists between companies in performing JIT practices, where the answers did not range between a scale of 1 (strongly disagree) to 5 (strongly agree) on the Likert scale, but a scale of 3 (agree) to 4 (strongly agree). This could suggest that JIT has become an order qualifier instead of an order winner. Improved JIT alone is no longer regarded as an aid to differentiate its internal processes and products from those of its competitors (Roh, Hong, & Min, 2014). As an order qualifier, JIT also does not make the difference on the relation between TQM and operational performance (multiplicative model) or as an constraining factor (constraining factor model). Instead, JIT may not be the competitive factor leading to success, but has become a necessity.
requires two separate constructs, JIT and TQM stayed interwoven during the tests. As a consequence no complementarity can be detected.
Model Model 1 (linear) Model 2 constra ining factor Model 3 multipli cative Model 4 (combined)
Variable B S.E. B S.E. B S.E. B S.E
Total quality management (TQM) 0,271* 0,12 0 0,265 0,246 0,280* 0,120 0,383 0,2 58 JIT x TQM -0,032 0,025 -0,042 0,0 29 DJ (JIT) -0,190 0,370 -0,167 0,3 69 DT (TQM) -0,266 0,370 -0,219 0,3 70 DJ x JIT 0,086 0,617 0,160 0,6 16 DJ x TQM -0,099 0,205 -0,174 0,2 11 DT x JIT 0,022 0,588 -0,019 0,5 86 DT x TQM - - - - R 0,449 0,462 0,462 0,478 R Square 0,201** 0,214 0,213 0,229 * p < 0,05 ** p < 0,01 Table 4.1
4.2. Qualitative phase – case study
4.2.1. What Lean has brought for Company X
order-winning factors together with its suppliers in the supply chain; the reaction time to the customer, delivery time, flexibility and cost price. This is in contrast to the order qualifiers of the process: quality and delivery performance. In other words, the focus had to move to the customer and the delivery time had to adapt to last minute ordering, as a consequence of the financial crisis. Further, the company had to be flexible in its production, because no reliable forecasts could be made and they had to decrease the direct- and indirect costs. The lean philosophy played and still plays an important role in reaching these targets. Following this philosophy, the culture based on continues improvements was created; continuous improvement based on the model of Kaizen (TQM); tools related to continuous improvement based on Lean (TQM) and a logistic model based on the principles of Pull (JIT) were implemented. Shifting the focus from Push to Pull, resulted in shifting the ownership of inventory before assembly for a big part from Company X to its suppliers. The JIT principle already starts at the suppliers, to reduce stock at Company X. Tools and projects used and still in use is communication on the floor by organization daily morning starts. Further, the process changed its focus from push to pull by introducing flow lines, pull supply, planning daily and order intake as soon as possible. Embracing the lean philosophy allowed in 2014 for a decrease in reaction time from 2 weeks to 3 till 1 day. Delivery time decreased from 3 weeks to 1 week till 1 day. Flexibility on forecast increased from 5% to 25%. Indirect FTE’s were decreased to minus 3 FTE’s. With the introduction of lean, JIT, kanban and daily planning allowed for a decrease of the inventory turns from 6 to 12,2 a month. As a result stock decreased by half from August 2011-January 2015. Still major issues are present on the quality side, such as the quality of processes and the change from quality control to quality assurance.
Appendix IV, table 9.5.1 shows a smaller version of organization chart of the management of the lean production system.
4.2.2. Does moderate or extreme complementarity exists between JIT and TQM or is there a linear effect on operational performance?
reliability or suppliers, which do not stick to the quality agreements. “As such, JIT and TQM work together, and TQM is the prerequisite for JIT.” When the question is asked if JIT and TQM reinforce each other, head JIT, answers that JIT does not reinforce TQM, but TQM does reinforce JIT. Reason for this lies in the possibility that JIT has to wait to make further steps because TQM is not sufficient enough. Head of TQM, responsible for the conversion from Quality Control (QC) to Quality Assurance (QA) feels more for the extreme complementarity between JIT and TQM. He noticed a cross over point from a situation were they invested in both JIT and TQM, but now TQM gets the most attention. Head of TQM: “When the production comes to a certain level, you realize that the quality level has to increase first before further steps can be taken in JIT.” Also, the director of Company X’s production business unit sees that JIT and TQM extremely complement each other. “We first invest in our growth point TQM, before we further invest in JIT”. “Although we have made a responsible start with JIT, as our suppliers performed hundred per cent, we quickly saw JIT requiring for a better quality.” On the other hand, all the three did not feel anything for the JIT substituting TQM or the other way around. Head JIT said: “JIT and TQM are too independent from each other and have such unique characteristics, to compensate for one another.” All the three agree JIT and TQM stimulate and motivate each other to improve operational performance and they achieve more together, rather than individually.
4.2.3. Interdependency between complementary JIT and TQM
According to director of the production business unit, the complementarity between JIT and TQM involves a strong interdependency between them. He described this as the essence of lean: “If you decide to create a continuous improvement process, it results in excluding that non-complementary things will happen”. A situation he described as one where people and practices do not have dependency at all. “If this would be the case, no need is created between JIT and TQM and one becomes too dominant and opportunistic. Further, if you ensure that they are required to grow together to higher levels by implementing them together, than you maintain the lean culture of continuous improvement. Although, there is of course always a leading one, they really need each other.”
likely to do. Instead, decreasing the inventory level, by reducing our lot sizes, exposes the quality issues in our process. In a situation of fewer inventories, the cause of these quality issues can be detected by employees of the TQM department analysing the process and using different graphic
tools.” Head of TQM gives another example: “the process quality of the supplier is a important
input for us to perform pull production. If the supplier cannot guarantee to deliver on time in full (OTIF), you always have to go back to a certain level of safety stock inventory to meet delivery reliability”. Also, TQM’s supplier quality management will increase JIT’s daily schedule
adherence. “Making clear agreements and holding close contact with the suppliers about their
product quality will decreases the need for employees to inspect products and consequently allows us to complete the schedule as planned.” According to the director of the production business unit, tolerance deviation is another situation where this strong dependency comes forward. He explained that their assembly department does not have the measurement instruments to measure the half fabricate completely. Therefore, he said, it is important to inform and explain to the suppliers how their half fabricate has to be designed to fit in our assembled end product, a part of TQM’s focus on supplier quality management, by involving them in the product quality. If this is not communicated well, the possibility exist that assemblies do not fit. “If this is the case, the people from quality control are not able to locate the failure and consequently the production line has to stop”. According to him, this can even lead to one-day stoppage and failure to deliver our customers on time. “Such things happen in here and we should secure this with good supplier quality management”.
4.2.4. The interdependency between JIT and TQM and the resulting coordination mechanisms
To align JIT and TQM and allowing them to be implemented together, manufacturing engineers own and manage a product line and can intervene when something goes wrong or improvements are initiated. “In such a situation, they can establish a team incorporating all disciplines and they can decide to create a project”, as mentioned by the direct of the production business unit. According to him the interdependency between JIT and TQM requires communicative skills between both groups.
about what to do and, what time and when. For example, “An important standard is the use of the poka yoke technique, to show at certain moment that something is likely to go wrong”, as explained by the head of JIT. “Also, we use standardized bins for us and our suppliers”. Director of the production unit sees the daily morning start as an important routine, because employees are stimulated to continuously seek to do things better. Further he said: “Coordination by rules and routines leads to a better control and working together as one”.
According to the director the production business unit, KPI’s are important for production, but for him it’s more a sign to take action, than that it can be seen as a goal. He explained that coordination by manufacturing engineering and the specific rules and routines maintain the chosen direction and the KPI’s are more a sign of not following up the agreements made in the first two.
The three do not favour looser coordination mechanisms. The head of TQM disagrees that the production employees themselves achieve coordination, because one person cannot oversee the whole process or the interdependencies between JIT and TQM. However, he said: “If it means that own initiatives serve as feedback for the top, then I certainly support that”. The head of JIT views it as a mix of tight and loose coordination: “We give our employees the possibility to give feedback on the process, but they are controlled by manufacturing engineering and the rules and routines they develop.” Also the director of the production is not a supporter of the informal circuit taking actions on their own, resulting from discussions on the lowest hierarchy level. However, he said, “If they come together, make a suggestion for improvement and introduce it to Manufacturing Engineering, than I agree”. For me, he said, “A supervising team and specific rules and routines cover standard performance measures (KPI’s), standard skills and knowledge of the workers, mutual adjustment and norms and values.”
The reason the fill in the coordination in this way is to create a fixed underlying structure in which the output stays constant. This tight coordination makes sure that everyone continuously thinks in terms of improvement, as said by the director of the production unit. “As such, nobody can be hidden from the problem.”
direct supervision with the department of Manufacturing Engineering, intervening at production lines with cross-functional teams only when something goes wrong.
Standardization of work processes is filled in by specific rules and routines developed by Manufacturing Engineering. This gives clarity and guidance on the production floor without the need for engineering teams to continuously give direct orders.
Loose coordination is not favoured to control the interdependency between JIT and TQM, because production employees cannot oversee this interdependency. However, this does not mean that employees cannot participate in the decision-making. Employees can introduce possible process improvements, which are handled as feedback for Manufacturing Engineering in their decision-making, problem solving and development of standards.
5. DISCUSSION – LINKING THE QUANTITATIVE PHASE AND THE QUALITATIVE PHASE
Earlier research investigated the interaction between JIT and TQM bundles and noticed the existence of complementarity (Furlan et al., 2011b). However, this study stated that two different forms, namely moderate and extreme complementarity, could explain complementary bundles. Although this difference is made in other contexts, this hasn’t been done yet in the context of lean bundles. This research aims to shed light on the difference between these two forms of complementarity and tests whether extreme complementarity holds for the interaction between JIT and TQM. Moreover, this study explores how the interdependency between JIT and TQM has to be coordinated.
Results indicated that a linear model explains the dataset the best. This means that JIT and TQM have both a direct effect on operational performance and neither moderate nor extreme complementarity exists. Thus, H1 and H2 are accepted and H3, H4 and H5 are rejected. Earlier research from Dal Pont et al. (2008) also indicated a direct and positive effect of JIT and TQM on operational performance. However, Flynn et al. (1995) and Furlan et al. (2011b) point to complementarity between JIT and TQM.
factor, which directly leads to a winning business. Therefore, it may not be the competitive factor leading to success (Slack et al., 2010). The idea of JIT as an order qualifier may have managerial implications. Although JIT may not directly improve performance, it needs a particular level of performance to compete with other companies.
If JIT might not be an order winner anymore, which lean bundles are? Can JIT become an order winner again, supported by other concepts? For example, Roh et al. (2014) argued that to become an order winner in the current global manufacturing environment, a manufacturing company needs to integrate its customers, suppliers and use advanced manufacturing technology, as a prerequisite to JIT.
In the qualitative phase, Company X saw an extreme complementary between JIT and TQM bundles. All interviewees agreed that one has to invest first in TQM, before one can start or further invest in JIT. This fits the constraining factor model, which assumes that in the absence of an appropriate TQM, increasing JIT is unlikely to improve operational performance. Although, TQM is the leading one, JIT and TQM really need each other to perform. Implementing JIT and TQM simultaneously, forces them to grow together to higher levels, which maintains the lean culture of continuous improvement.
Moreover, the interviewees emphasized quality of products and processes as a prerequisite for just-in-time delivery. An alternative model to approach this is the Sand cone model of Ferdows & De Meijer (1990). By using this model, Bortolotti, Danese, Flynn, & Romano (2015:238) said: “Once product quality has reached a sufficient level, JIT practices can be developed, to further foster quality performance and improve delivery performance”. Although the same principle is shared that TQM is a prerequisite for JIT, the manner of implementing the lean bundles differs. In contrast to a simultaneous implementation, Bortolotti et al. (2015) implicate that managers should first implement TQM before implementing JIT, in order to achieve excellence on multiple dimensions of performance. However, this contrasts earlier research of Furlan et al. (2011b) where JIT and TQM should be implemented hand-in-hand to gain maximum performance.
For Company X the complementarity between JIT and TQM involves a strong interdependency
without each other”, pointed by the director of the production unit. Within the company, JIT is shaped and constrained by the use of TQM and vice versa, referring to a reciprocal interdependency. For example, Company X mentioned that JIT and TQM need, motivate and stimulate each other, which creates this interdependency between them. Such interdependency can best be explained at the level of the practices on the production floor, by the appearing one-way dependencies between the different practices accompanied with JIT and TQM on the production floor.
For example, according to the head of JIT, without controlling their process with statistics the production line has to stop frequently because of quality issues. This requires high safety stocks to meet the delivery reliability. The employees who perform activities involving TQM’s statistical process control, which reduce process variance, allows them, to minimalize inventory with JIT’s pull production activities. Therefore, statistical process control can be described as an input for pull production. Also, TQM’s supplier quality management seems to be an input for JIT’s daily schedule adherence and just-in-time delivery to the customer. On the other hand, lowering the inventory levels by reducing lot sizes exposes the quality issues in their process and the possibility to analyse the process by using graphic tools.
The interdependency between JIT and TQM makes coordination essential, because “coordination ensures that no one gets dominant”, as said by the director of the production unit. “We want to develop a process where JIT and TQM continuously activate each other to perform better and better and we cannot reach this without tight and structured coordination achieved by direct supervision and standardization of work processes.”
managed by the responsible manufacturing engineer. In a project they try to find a better way to align the JIT and TQM practices and the involved employee activities and redirect them. Company X believes in management-by-exception, where the employees can operate without much oversight, while disturbances of the process, i.e. the exceptions, are closely coordinated and managed by an engineering team. Although Mintzberg’s (1980) coordination mechanisms are well-known and often used in the context of lean, it may be also be regarded as old-fashioned, therefore, management-by-exception may be another, additional approach for coordinating lean bundles.
If the production is performing, as it should be, employees can be monitored on a certain distance. In such a situation, tight coordination is achieved by standard rules and routines that are applied, and which each lean bundle has to pursue. According to the head of TQM these standards allows employees to know what is expected from them and on which moment in time. Director of the production unit: “Because employees from JIT and TQM all have to pursue the same procedures, the coordination between them is better controlled.”
Within Company X, interdependency between JIT and TQM is coordinated by tight rather than loose coordination mechanisms. Reason for this is that employees cannot oversee the interdependencies between JIT and TQM, because they are operating too locally. “If employees are allowed to coordinate themselves and make informal decisions, the coordination agreements of the engineering team on a higher level might be disturbed.”
However, this does not mean that employees on the production floor cannot participate in the decision-making. Employees are expected to give feedback on the process and suggest improvements to Manufacturing Engineering. According to the director of the production unit this ensures that everyone, also on the production floor, thinks continuously in terms of improvement.
6. CONCLUSIONS AND FUTURE RESEARCH
interdependencies between them. Direct supervision and standardization of work processes achieve this coordination, filled in by Manufacturing Engineering and rules and routines respectively. Company X believes in management-by-exception, where the employees can operate without much oversight by coordinating with rules and routines, while disturbances of the process, i.e. the exceptions, are closely coordinated and managed by an engineering team.
Although these outcomes were against my expectations, I think it resulted in some new and interesting points to research in the future. In future research it is important to reconsider the relation between JIT, TQM and operational performance and how they have to be implemented. Do JIT and TQM have a linear effect on operational performance or does complementarity still exists, but can it not be detected in this dataset? Does complementarity between JIT and TQM always requires simultaneous implementation or can they be implemented sequentially?
Secondly, the question has to be asked: should researchers actually want to separate JIT and TQM? According to Khanchanapong et al. (2014) more researchers had difficulty to precisely separate JIT and TQM by listing unique practices that can be allocated to JIT or TQM. Reason for this lays in the extensive overlap that the two philosophies have (Cowton and Vail, 1994; Dean Jr. and Snell, 1991; Flynn et al., 1995; Khanchanapong et al., 2014). As a solution Khanchanapong et al. (2014) decided to take JIT and TQM together in one lean bundle and research the complementarity between lean practices and manufacturing technologies on operational performance.
This can possibly be tested in future research with a more extensive dataset and other statistical tests, which can prove the nature of complementarity. However, it is maybe even more important to conduct multiple case studies, because exactly at this point, results differed. It is worth noticing that case research resulted in many of the breakthrough concepts and theories in lean production (Sousa & Voss, 2008).
Lastly, future research should investigate management-by-exception as a coordination approach for lean bundles. Also, companies with a customer order decoupling point other than at the assembly might be investigated, like design or manufacturing.
7. LIMITATIONS
This study is subject to a couple of limitations. First, this study has poor construct validity, because it was not possible to develop a factor analysis. However, this also implicates a high inter-correlation between JIT and TQM. The underlying JIT and TQM practices are so inextricably interwoven that they cannot be measured as two separate constructs, therefore, no complementary interaction may be detected in spite of its possible presence in real-life.
