Product modularization and effects on efficiency: An analysis of a bus manufacturer using data envelopment analysis (DEA)

Int. J. Production Economics 182 (2016) 1–13

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Product modularization and effects on efficiency: An analysis of a bus manufacturer using data envelopment analysis (DEA) Fabio Antonio Sartori Piran a, Daniel Pacheco Lacerda a, Luis Felipe Riehs Camargo a, Carlos Frederico Viero a, Aline Dresch a,b,n, Paulo Augusto Cauchick-Miguel b a b

Research Group on Modeling for Learning - GMAP | UNISINOS, Brazil Departament of Production and Systems Engineering - Universidade Federal de Santa Catarina (UFSC), Brazil

art ic l e i nf o

a b s t r a c t

Article history: Received 30 March 2016 Received in revised form 4 August 2016 Accepted 5 August 2016 Available online 6 August 2016

Improvement of efficiency and productivity is a contemporary challenge for industrial organizations. Product modularization is considered a strategic alternative to achieve such objectives. However, empirical evidence supporting these expectations are scarce in the literature. Thus, it is necessary to assess the effects of modularization on the improvement of the efficiency of a company field or a production system. In this context, this study aims to analyze the effects of product modularization on the efficiency of the Product Engineering and the Production Process of a bus manufacturer. The effects were evaluated through a case study using Data Envelopment Analysis, Analysis of Variance and Causal Impact. The results show that product modularization provided statistically significant improvements in efficiency. The results also establish and empirically support a causality (Causal Impact) between modularization and improvement in the efficiency of the Product Engineering and the Production Process of the company analyzed. & 2016 Elsevier B.V. All rights reserved.

Keywords: Modularization Modularity Automotive industry Efficiency Effects of modularization

1. Introduction The variety of products offered by companies has increased over time to meet different customer needs (Lee and Tang, 1997; Thyssen et al., 2006; Park and Kremer, 2015). However, this product variety growth scenario may cause a reduction in the operational performance of organizations (Dekkers, 2006; Patel and Jayaram, 2014) in terms of efficiency, for example. The existing trade-off between an increased variety of products and a decrease in operational performance may be mitigated using modularization (Salvador et al., 2002; Dekkers, 2006). Modularization allows an increased variety of products (Patel and Jayaram, 2014) because it presupposes the planning, development and production of components capable of generating combinations that will result in a wide variety of end-products (Starr, 1965; Salvador et al., 2002). Modularization can be generally described as a set of principles for complexity management (Langlois, 2002). It is important to manage complexity, as studies show a negative impact of complexity on product design and manufacturing systems (Dekkers, 2006; Park and Kremer, 2015). Complexity contributes, for example, to decreased productivity (Dekkers, 2006) and efficiency of manufacturing systems (Perona and Miragliotta, 2004; Xu et al., n Corresponding author at: GMAP | UNISINOS Av. Unisinos, n. 950, CEP: 93.022750, São Leopoldo, RS, Brasil.

http://dx.doi.org/10.1016/j.ijpe.2016.08.008 0925-5273/& 2016 Elsevier B.V. All rights reserved.

2012). Whereas modularization helps to manage complexity, it is understood that a central aspect in research on modularization is the evaluation of its effects on the productivity and efficiency of organizations. In fact, investigating the effects of modularization is primordial. There are a number of opportunities arising from the study of such effects. The use of modularity helps to enable the increase in the variety of products offered without hindering the volume of production and the performance of manufacturing systems (Salvador et al., 2002). There is also a positive relation between product modularization and improvement of the financial performance of companies (Worren et al., 2002). Moreover, a modular product architecture improves the response time to demands and increases the flexibility of companies (Dekkers, 2006). A modular product design allows better management of process complexity and an improvement in performance of Product Engineering by reducing the number of work hours dedicated to the development of a new product (Perona and Miragliotta, 2004). Other studies sought to capture the perception of managers regarding the impacts of modularization. For example, Lau Antonio et al. (2007) suggest that managers should understand modularity as a factor that positively influences the performance of delivery, flexibility and customer service. The authors (Lau Antonio et al., 2009) found similar results, showing that managers found that product modularization increases the level of innovation and flexibility, and also provides better customer service. Product modularization allows

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simultaneous improvements in competitive dimensions regarding cost, quality, flexibility and cycle time (Jacobs et al., 2007). Managers realize that product modularization directly and positively affect the time necessary to develop new products and the individual performance of each product (Danese and Filippini, 2010; 2013; Lau Antonio. et al., 2010). Managers also realize that product modularization positively affects manufacturing performance (Jacobs et al., 2011). Zhang et al. (2014) concluded that product modularization contributed to enabling mass customization by companies. On the one hand, the studies mentioned contribute to a better understanding of the overall effects of product modularization. On the other hand, the evidence was based on subjective evaluations (perceptions), which contribute to the understanding of one dimension of reality, but are subject to positive pressure responses and to different interpretations of the events by the respondents (O’Leary-Kelly and Vokurka, 1998). In this sense, this study aims to overcome these limitations. This is because its objective is to observe, measure and verify (i) the existence of effects of modularization on efficiency, and (ii) the causality regarding the effects of product modularization on the efficiency of Product Engineering and the Production Process in the context of a bus manufacturer. The contribution of this article is, therefore, to verify empirically the effects of product modularization on efficiency. The objective is to analyze existing proposals in the literature in the sense that modularization helps to mitigate the trade-off between variety and efficiency. Another objective is to present a measurement procedure for the effects of modularization and to try to establish a causal relation between modularization and efficiency. Finally, a discussion is made considering the magnitude of the effects observed, both in Product Engineering and the Production Process. This article is divided into five sections and this introduction. The following section presents a theoretical overview of modularization considering the theme under study. Section 3 describes the methodological procedures that supported the planning and conducting of the research. The results are presented and discussed in Section 4. Section 5 explains the management implications of the results. Finally, the conclusions, work limitations and suggestions for future studies are outlined in Section 6.

Napper, 2014). Yet, these publications have scarce empirical evidence supporting the relation between modularization and efficiency. There is a need for quantitative evaluations to measure the effects of modularization (Gershenson et al., 2004). Research efforts should concentrate on investigating the effects of the development of a modular architecture of products on the performance of companies (Campagnolo and Camuffo, 2009; Pero et al., 2015). The need for scientific rigor in planning, modeling and measuring the use of modularization in organizations is also highlighted (Starr, 2010; Matsubara and Pourmohammadi, 2010). In short, it is argued that (Boer, 2014) (i) empirical evidence on the benefits of implementing modularization are scarce in the literature, (ii) there is a need for studies addressing the performance of Product Engineering and the Production Process, and (iii) studies evaluating the performance of companies using modularization are inconclusive. Another aspect is that the studies cited point to a need to measure the effects of modularization. However, the approaches to perform such measurements, and which methods, techniques and tools can be used, are not explicit. There are a great number of benefits associated with modularization (Gershenson et al. 2003; Lau Antonio et al., 2007; Jacobs et al., 2011). Increased efficiency, for example, is identified as an expected effect of product modularization (Starr, 1965; 2010; Sushandoyo and Magnusson, 2012; Patel and Jayaram, 2014). However, a search in the literature available did not identify studies and measurements demonstrating such benefits. Therefore, it is necessary to further develop studies in order to measure and analyze the effects of modularization, seeking to verify whether the expected benefits can be observed empirically. Given the above, this work aims to test the hypothesis according to which product modularization improves efficiency, as pointed out in the literature, and also aims to test the hypothesis of causality between improved efficiency and product modularization. Efficiency, in this sense, is related to the ability of a process to produce a certain quantity of designs and products using the lowest number of inputs in relation to other observed processes (Cummins and Weiss, 2013). Initially, this work intends to verify whether product modularization affects the efficiency of the processes analyzed by following the hypotheses below:

2. Theoretical background Product modularity can be defined as a design approach that involves decomposing complex products into different parts or modules. It is characterized by the use of common modules in distinct product projects (Zhang et al., 2014). Modularization has become a common approach to operations management and production in the literature since the 1990s, and currently it is being increasingly used in the automotive industry (Salvador, 2007). In the latter industry, for example, companies such as Fiat, Volkswagen and Citroën (Pandremenos et al.,2009), Ford and Hyundai (MacDuffie, 2013), Scania and Volvo (Sushandoyo and Magnusson, 2012), among others, apply the concepts of modularization. Thus, this industry is an appropriate environment for the study of the effects of modularization (Fine et al., 2005). 2.1. Analysis of the effects of modularization The literature has stressed the need to improve evaluation and measurement of the effects of modularization on organizations (e.g., Gershenson et al., 2003, 2004; Lau Antonio et al., 2007, Campagnolo and Camuffo, 2009; Starr, 2010; Matsubara and Pourmohammadi, 2010; Jacobs et al., 2011; Boer, 2014; Pero et al., 2015). In this sense, among other aspects, studies seeking to measure and evaluate the effects of the implementation of modularization on bus manufacturers were identified (e.g., Sushandoyo and Magnusson, 2012;

H1a. There is a relationship between product modularization and the efficiency of Product Engineering. H1b. There is a relationship between product modularization and the efficiency of the Production Process. Subsequently, it must be assessed whether the identified effects can be attributed or not to product modularization, according to the following hypotheses: H2a. There is a causality between product modularization and efficiency of Product Engineering. H2b. There is a causality between product modularization and efficiency of the Production Process. Therefore, these hypotheses will guide this investigation in order to evaluate whether product modularization helps to reduce the use of resources in design processes and product production. The following section presents the methodological procedures used in performing the research.

3. Research design To develop the research, the methodological approach research adopted was case-based. Case studies are appropriate when an in-

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depth understanding in not fully explored research fields is required (Dubé and Paré, 2003). Specifically, a longitudinal case study was chosen, which is appropriate for single or embedded cases, and it can increase the internal validity of the results (Voss et al., 2002; Barratt et al., 2011). In this sense, the study was conducted by (i) the definition of the case study, (ii) DEA model design, (iii) data collection, (iv) data analysis, and (v) discussion and conclusions. 3.1. Context and selection of analysis units This study was conducted in a bus manufacturing company that designs and produces modular products in its Product Engineering and Production Process. The company does not use modularization throughout its product portfolio. This enables analysis of modularized and non-modularized product designs, which is a necessary condition for conducting a research that seeks to identify causality. Another important aspect was that, even in a modularized product line, the collection of information over time was possible considering pre- and post-modularization. Thus, this company offered proper conditions for the development of the research, which aimed to capture the modularization effects in detail. In this sense, the MPD (modularized product design) and the MP (modularized product) were designed and produced in integral format by the company from January 2011 to October 2013. This same project (MPD) and product (MP) were redesigned. Thus, both its development (product engineering) and production (production process) turned to modular from November 2013 until the end of the analysis period (June 2014). Thus, the transformation from the integral to the modular product enabled analysis of the impact of modularization over time, longitudinally. The commercial automotive industry (in this case, buses) is characterized by the process Engineer-to-Order (ETO). Modularization has become increasingly relevant in this type of industry (Pero et al., 2015). Due to the need to meet different customer requirements, vehicles have a wide range of variation (Palencia and Delgadillo, 2012), for example, size, number of seats, luggage space, optional accessories, among others. In order to facilitate the production of an increased variety of products offered to the market without sacrificing operational performance, in 2007, the company started developing and manufacturing vehicles with modular architectures. Initially, the vehicles were designed and produced in six main modules: (i) front, (ii) rear, (iii) right side, (iv) left side, (v) roof, and (vi) basis. Subsequently, the modules of the lateral parts were also subdivided into smaller modules. In addition, modules of internal parts were developed, such as panels, partitions, seats and the electrical system. The analysis unit for this study is product modularization regarding Product Engineering and the Production Process. These functional areas were chosen because they are the most affected by the modularization process. As the company works with ETO in product design, the Engineering department analyzes the specifications of each application and customizes buses to adapt to customer requirements. In addition to these requirements, the project is developed considering the technical aspects of the manufacturing process (size, number of components, comfort and safety items etc.). During the Production Process, projects are transformed into physical products using resources, such as raw materials, direct and indirect work, among others. The Production Process consists of the following sectors: part/component manufacturing, assembly, plating, painting, finishing, road testing, pre-delivery and inspection. This analysis sought to understand whether modularization influences the efficiency of Product Engineering and the Production

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Process. Therefore, efficiency indicators for these areas were evaluated for 3.5 years, considering a period before and after product modularization. To conduct the research, two types of product design were selected, one modularized and the other non-modularized. For Product Engineering, a non-modularized product design (nMPD) was considered as the control variable, i.e., a product design that did not undergo intervention (modularization). The modularized product design (MPD) was considered as the response variable, i.e., a product design that underwent intervention (modularization). The same procedures were performed for the Production Process, with a nonmodularized product (nMP) as the control variable and a modularized product (MP) as the response variable. Observing designs and products (modularized and non-modularized) considering control and response variables is a necessary condition to better evaluate the effects of modularization on efficiency. Moreover, this decision may increase the possibility of establishing causality between the effect and the studied phenomenon. This procedure is commonly used in Economics (Abadie, 2005), Marketing (Antonakis et al., 2010; Brodersen et al., 2015) and Health Sciences studies (Kleinberg, Hripcsak, 2011), among others. 3.2. Design of data envelopment analysis (DEA) The primary data measured used in this phase were obtained by a non-parametric programming approach to data envelopment analysis, characterized as a time series. From the literature (Jain et al., 2011), a monthly lot of developed modularized (MPD) and non-modularized (nMPD) product designs was defined as a Decision Making Unit (DMU) for Product Engineering. For the Production Process, the DMU was the monthly lot of produced modularized (MP) and non-modularized (nMP) products. The monthly lot consists of the total of completed projects and manufactured products within one month using Product Engineering and the Production Process, respectively. The relative efficiency of each DMU is defined as the ratio between the weighted sum of its products (outputs) and the weighted sum of the inputs needed to generate them (Cook et al., 2014). All equations representing the DEA model used in the analysis can be seen in Appendix 1. The types of efficiency calculated by the DEA were (i) standard efficiency, (ii) inverted frontier, and (iii) composed efficiency (Yamada et al., 1994). The composed efficiency was used for this analysis. It includes weighting between DMUs and the best and worst performances (Yamada et al., 1994). The use of composed efficiency seeks to confer greater rigor to the assessment, given that the worst performers are also included in the metric. For the DEA model project, the support of the company’s professional experts was requested, as suggested by Jain et al. (2011) and Park et al. (2014), considering experience, knowledge of modularization and the extent of the support during the development of the research. Table 1 shows the profiles of the experts. 3.2.1. Variables, data collection and DEA analysis Data were collected directly from the database of the company’s management system. The company has data collectors and sensors in the engineering and production process. This data collectors and sensors are used by operators to take notes of the engineering and production events and accomplish controls during the processes. The resulting information introduced by the operators (collectors and sensors) was fed into a database. This database has been accessed by researchers through the company's management system, enabling appropriate data collection for this investigation. In addition, data were also collected with the company’s measurement systems and from spreadsheets used in the organization. The data collection period was from January 2011 to June

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Table 1 Experts’ profiles. Function

Support to the project

Time in the company (years)

Product engineer Support to the definition of the model Product engineer Support to the definition of the model Co-ordinator of Information Technology (IT) Support to the definition of the model and Product Engineering and the Production Process data collection Product Engineering Manager Support to the definition of the model Engineering Director Support to the definition of the model, data collection, model validation and contribution to interpreting the results Industrial Director Support to the definition of the model, data collection, model validation and contribution to interpreting the results

8 13 10 10 7 7

Table 2 List of variables used for Product Engineering. Variable

References

Function in the model

Commercial lead time (negotiation) Engineering lead time (specification of request) Engineering lead time (product configuration) Number of parts Number of items purchased Number of items produced Number of persons employed in the process (Product Engineering) Number of reported technical problems Number of products with customer complaints Number of items with customer complaints Number of projects developed

Trappey and Chiang (2008) Joneja and Lee (2007); Trappey and Chiang (2008); Jacobs et al. (2011); Lin et al. (2012); Napper (2014) Chakravarty and Balakrishnan (2007); Napper (2014) Salvador et al. (2002); Chakravarty and Balakrishnan (2007); Napper (2014)

Input1 Input2 Input3 Input4 Input5 Input6 Input7

Chandra et al. (1998); Zhu (2000); Duzakin and Duzakin (2007) Starr (2010); Jacobs et al. (2011); Feng and Zhang (2014) Starr (2010); Jacobs et al. (2011); Feng and Zhang (2014) Trappey and Chiang (2008)

2014. This type of collection reduces the possibility of perception bias by operators and/or managers. The analyses include 42 DMUs: (i) 12 DMUs for 2011, (ii) 12 DMUs for 2012, (iii) 12 DMUs for 2013, and (iv) 6 DMUs for the first half of 2014. Table 2 lists the variables used for Product Engineering, and Table 3 lists the variables for the Production Process. The variables of all the projects developed and products produced during the collection period were analyzed. To select the variables shown in Tables 2 and 3, studies were analyzed considering modularization and data envelopment analysis (e.g., Jacobs et al., 2011; Napper, 2014; Feng and Zhang, 2014;

Table 3 Final list of the production process variables. Variable

Theoretical basis

Function in the model

Aluminum Fiber Car mat Fabrics Glasses Manufacturing lead time Assembly lead time

Jain et al. (2011); Park et al. (2014); Cook et al. (2014)

Input1 Input2 Input3 Input4 Input5 Input6 Input7

Number of items (number of parts) purchased Number of items (number of parts) produced Number of persons employed in the process (Production) Number of reported technical problems Number of products with customer complaints Number of products produced

Joneja and Lee (2007); Starr (2010); Jacobs et al. (2011); Lin et al. (2012); Napper (2014) Salvador et al. (2002); Chakravarty and Balakrishnan (2007); Napper (2014)

Input8 Input9

Chandra et al. (1998); Zhu (2000); Duzakin (2007)

Input10

Starr (2010); Jacobs et al. (2011); Feng and Zhang (2014)

Input11 Input12

Jain et al. (2011); Cook et al. (2014)

Output1

Input8 Input9 Input10 Output1

Park et al., 2014). The listed variables were validated with the experts listed in Table 1. The resources used in the process under evaluation are defined as inputs and the result of the use/transformation of such resources are defined as outputs (Cook et al., 2014). After a discussion with experts, the number of product designs developed in Product Engineering was defined as an output. As for other variables, there was agreement on their use as DEA inputs. For a better discrimination of the model, the Stepwise variable selection method, developed by Wagner and Shimshak (2007), was applied in order to verify the impact and the relevance of each variable for the overall efficiency of the model. After applying the Stepwise method, the variables considered not relevant were excluded from the elaborated models. The details of each selected variable are shown in Appendix 2. Table 3 shows the Production Process variables. The output was defined as the quantity of products produced (Cook et al., 2014). The other variables were considered as inputs to the DEA. Appendix 3 presents a detailing of each variable selected for the Production Process. Among the models used in the DEA analysis, the model, Constant Returns to Scale (CRS) was chosen. The use of the CRS model is recommended in this analysis of efficiency because of its objective, i.e., to test the ability to avoid waste, that is, to use minimal resources to perform the operation (Kleine, 2004). Finally, the orientation to input was used. The orientation to input is recommended in this case because the resources used in the processes (inputs) are more controllable than the outputs, which depend on market demand, for example. In this case, the reduction of resources used in inefficient units is proposed (Hamdan and Rogers, 2008). The number of designs and products analyzed is summarized in Table 4. The developed designs and products produced under examination relate to the same bus model. Thus, a comparative analysis of the magnitude of the effects observed for Product Engineering and the Production Process becomes feasible.

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Table 4 Number of designs of the products analyzed. Period Product engineering (MPD)

Product engineering (nMPD)

Total product engineering

Productive process (MP)

Productive process (nMP)

Total productive process

2014 2013 2012 2011 TOTAL

129 165 110 113 517

178 270 245 253 946

211 500 450 645 1,806

148 296 300 401 1,145

359 796 750 1.046 2,951

49 105 135 140 429

3.3. Statistical analysis and analysis of causal inference in a time series The time series, concerning the efficiency scores obtained from the calculations using data envelopment analysis, were subjected to statistical analysis. First, Shapiro-Wilk and Levene tests were performed. They demonstrated that the assumptions of Anova were treated at a significance level of 5%, thus allowing its use (Nataraja and Johnson, 2011). The Anova was used to verify significant differences in the isolated efficiency of groups between the periods before (January 2011 to October 2013) and after (November 2013 to June 2014) modularization. Subsequently, the causal inference in a time series was analyzed. This is done to measure the effects of a given cause, for which the Causal Impact technique was used (Brodersen et al., 2015). The causal impact of an intervention in a time series can be understood as the difference between the observed response value and the value that would be obtained without using the treatment (Brodersen et al., 2015). In this study, the treatment is “modularization”. The causal impact is the difference between efficiency scores obtained after the implementation of modularization and the efficiency scores that would be obtained if modularization had not been implemented. In order to use Causal Impact, a set of data related to a control time series is needed (Brodersen et al., 2015). The control time series is represented by efficiency during a non-modularized product design time, considering Product Engineering and the Production Process. In addition, a set of data related to a response time series is needed. In this case, it is represented by efficiency during modularized product design time in Product Engineering and the Production Process. Thus, the time series related to efficiencies over time of nMPD, MPD, nMP and MP allowed estimation of the confractual scenario, i.e., how the response metric (efficiencies of MPD and MP) would have evolved over time without modularization. The equations that represent the time series model used in the analysis are detailed in the Appendix 4.

4. Results In this section, the results for Product Engineering and, subsequently, the Production Process are presented and discussed. 4.1. Analysis and discussion of product engineering results Fig. 1 shows the results for the composed efficiency of modularized product designs and non-modularized product designs. This analysis enables observation of the evolution of efficiency scores over time for Product Engineering isolated (black dashed line) during the transition period, i.e., a time period during which the modularization was implemented in the company’s Product Engineering. By analyzing Fig. 1, there is an increase in MPD efficiency,

Fig. 1. Evolution of efficiency of modularized and non-modularized product designs.

whereas DMUs with the best performance efficiency scores occur after modularization. It can also be seen that, after an increase in efficiency scores during the period after modularization, there was a decrease in the scores of DMU 39 (March 2014) and 40 (April 2014). This decrease is related to a design problem in the doors and divisions of the product, a situation that led to increased customer complaints (Input10). However, it is possible to see different effects for nMPD efficiency, considering that the best performing DMUs in this time sequence are concentrated in the period before modularization (2011). Table 5 shows the composed efficiency scores illustrated in Fig. 1. Product Engineering resources are shared, i.e., any improvement action performed by Engineering affects both types of projects evenly (MPD, nMPD and automation, for example). According to the discussions with process experts, it was found that there was no action prioritizing MPD over nMPD (prioritization of one product design over the other). Table 6 summarizes the averages for product design efficiency scores covering the periods before and after modularization, and the general average. Upon examining Table 6, MPD increased in average efficiency after modularization (before: 0.428; after: 0.700). The minimum efficiency of MPD occurs before modularization (February 2013) and the maximum efficiency score occurs after modularization (December 2013). It is understood that the position of the maximum efficiency score after product modularization indicates the positive effects of modularization. The nMPD efficiency behaves differently, considering that a significant increase in efficiency between the periods (before and after modularization) is not observed. Regarding the minimum (0.433) and the maximum (0.591) efficiency of nMPD, both occur before modularization, covering February 2012 and November 2011, respectively. Based on data presented in Fig. 1 and in Tables 5 and 6, the

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Table 5 Composed efficiency of modularized and non-modularized product designs. DMU

DMU1 DMU2 DMU3 DMU4 DMU5 DMU6 DMU7 DMU8 DMU9 DMU10 DMU11 DMU12 DMU13 DMU14 DMU15 DMU16 DMU17 DMU18 DMU19 DMU20 DMU21 DMU22 DMU23 DMU24 DMU25 DMU26 DMU27 DMU28 DMU29 DMU30 DMU31 DMU32 DMU33 DMU34 DMU35 DMU36 DMU37 DMU38 DMU39 DMU40 DMU41 DMU42

Month/Year Composed efficiency of MPD

Composed efficiency of nMPD

Jan/11 Feb/11 Mar/11 Apr/11 May/11 Jun/11 Jul/11 Aug/11 Sep/11 Oct/11 Nov/11 Dec/11 Jan/12 Feb/12 Mar/12 Apr/12 May/12 Jun/12 Jul/12 Aug/12 Sep/12 Oct/12 Nov/12 Dec/12 Jan/13 Feb/13 Mar/13 Apr/13 May/13 Jun/13 Jul/13 Aug/13 Sep/13 Oct/13 Nov/13 Dec/13 Jan/14 Feb/14 Mar/14 Apr/14 May/14 Jun/14

0.461 0.541 0.475 0.500 0.553 0.571 0.524 0.550 0.535 0.534 0.591 0.561 0.529 0.433 0.497 0.523 0.524 0.500 0.531 0.500 0.496 0.486 0.500 0.489 0.548 0.500 0.500 0.449 0.440 0.559 0.481 0.492 0.500 0.500 0.500 0.521 0.450 0.553 0.504 0.541 0.557 0.483

0.305 0.285 0.324 0.518 0.512 0.501 0.522 0.410 0.455 0.505 0.314 0.386 0.500 0.506 0.500 0.432 0.500 0.487 0.524 0.351 0.353 0.326 0.390 0.288 0.324 0.279 0.470 0.417 0.556 0.364 0.451 0.466 0.534 0.494 0.751 0.782 0.754 0.750 0.492 0.561 0.761 0.743

Table 7 Analysis of efficiency of groups before and after modularization of product engineering. Period

Average efficiency of MPD

Average efficiency of nMPD

Before modularization After modularization Difference in efficiencies F p-value

0.428 0.700 0.272 57.688 0.000

0.511 0.513 0.002 0.032 0.858

regarding the periods before and after modularization has a significance level higher than 0.05 (before Sign.¼ 0.639; after Sign.¼ 0.220). As for the Levene test, the result obtained was Sign. ¼0.566. Thus, it was found that such data have a normal distribution and are homogeneous. As for the non-modularized product design, the Shapiro-Wilk test (before Sign. ¼0.596; after Sign. ¼0.703) and the Levene test (Sign. ¼0.958) show that such data also have a normal distribution and are homogeneous. Thus, the assumptions for the use of Anova were met, and the Anova statistical test was performed (Table 7). Anova results show that the average efficiency of Product Engineering increased from 0.428 to 0.700 after product modularization for modularized product design. The F score of the average of the periods considered in the modularized product design is 57.688. The p-value ¼0.000 allows us to conclude that the detected difference is significant. As for the non-modularized product design, the same evaluations were made for confirmatory purposes. It was noticed that the efficiency before modularization was 0.511 and after modularization, 0.513. However, an F¼0.032 and a p-value ¼ 0.858 do not allow us to conclude that there was a significant difference among the averages of these periods. Based on the analysis performed, it is not possible to refute hypothesis H1a, according to which there are effects of product modularization on the efficiency of Product Engineering. As shown above, such effects are positive, i.e., modularization helped to increase efficiency. This observation shows that the improvement in efficiency over time, as observed for Product Engineering, can be attributed to the implementation of modularization. In the next section, we will try to establish a causality, and the effects of modularization on Product Engineering efficiency will be measured.

CAPTION: Before modularization Transition period After modularization

analyses of composed efficiencies of MPD and nMPD arranged in a time series provide evidence that product modularization increased Product Engineering efficiency. However, to accept or refute the hypotheses established, statistical tests (Anova and assumptions) and analyses of causal inference on Product Engineering efficiency (modularization) were performed (see below). 4.1.1. Assumptions and analysis of variance (ANOVA) of efficiency of product engineering As for the modularized product design, the Shapiro-Wilk test

4.1.2. Causality and dimensioning of the effects of product modularization on the efficiency of product engineering The data envelopment analysis technique allowed verification of the effect of modularization on the efficiency of Product Engineering in the company studied. However, the DEA did now allow accurate measurement of the magnitude of the observed effect, since data regarding the behavior of the efficiency of modularized product design was not available from November 2013 to July 2014 (modularization period), i.e., it was not possible to verify whether the company had implemented modularization. To conduct this dimensioning, the

Table 6 Analysis of efficiency of groups before and after modularization. Modularized product design (MPD)

Average Standard deviation Minimum Maximum

Non-modularized product design (nMPD)

Before modularization

After modularization

General average

Before modularization

After modularization

General average

0.428 0.087 0.279 0.556

0.700 0.109 0.492 0.782

0.480 0.140 0.279 0.782

0.511 0.037 0.433 0.591

0.513 0.037 0.450 0.557

0.512 0.037 0.433 0.591

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Fig. 2. Dimensioning of the effect of modularization on product engineering.

causal impact of modularization was analyzed using Causal Impact. Using the control variable (nMPD efficiency scores), the response variable (MPD efficiency scores) and the history of the response variable before the treatment, the Causal Impact technique performs statistical calculations including the average absolute effect and the relative effect caused by the intervention (Brodersen et al., 2015). Fig. 2 illustrates the behavior of the efficiency of modularized product design over time and the estimated confractual scenario considering all 42 DMUs analyzed. The dotted line between the DMUs 30 and 40 is the period during which the series was modularized. The blue-shaded stripe represents the variations in efficiency over time considering the period before modularization. The blue dotted line represents the average behavior of the efficiency series before modularization and estimates the behavior without modularization. By analyzing Fig. 2, the “original” behavior related to the current behavior of the efficiency of the modularized product design and the projection of performance without modularization. The current behavior, represented by the black line, is the same efficiency behavior as shown in Fig. 1. Thus, upon analyzing the “original” behavior of the time series considering the estimated modularization of the period, the behavior of the current efficiency (black line), which is above the blue-shaded stripe, represents the increase in efficiency obtained by implementing modularization of Product Engineering. If the modularization had not been implemented, the variation in efficiency after modularization would have been restricted to variations within the blue-shaded stripe and not above it, as noted. Considering the “pointwise” behavior of the efficiency series in Fig. 2, the Causal Impact technique used estimated the effect of the behavior at a 95% confidence interval. Thus, the blue-shaded stripe shows how the variation within this range would be. Table 8 presents a summary of the amplitude of the effects of the implementation of modularization on Product Engineering. By analyzing the results in Table 8, it is possible to observe the current scenario. The average composed efficiency after Product Engineering modularization is 0.700. It is also possible to observe a confractual scenario after modularization. In other words, if modularization had not been implemented, the average composed efficiency after modularization would be 0.430. It can be concluded that,

by implementing modularization, there was a 0.270 increase in the composed efficiency of Product Engineering. It was also possible to observe the relative effect, which increased by 63%. The probability of causal effect was 99.88901%, i.e., above the 95% stipulated as a premise for the model. Given the above, it is not possible to refute hypothesis H2a, according to which there is causality between product modularization and the efficiency of Product Engineering. 4.2. Analysis and discussion of production process results The analyses related to Production Process were performed using the same procedures previously presented for Product Engineering. First, the behavior of efficiency of the Production Process and the behavior of modularized and non-modularized products were verified, as shown in Fig. 3. These analyses allow observation of the evolution of efficiency scores over time. This evolution was isolated during the transition period (black dashed line), that is, during the period in which the product was transformed from integral to modular. As shown in Fig. 3, if the period before modularization is considered, lower variations are observed from January 2011 to July 2012. There was a decrease in the performance of the DMUs related to the second half of 2012, and there was an increase in them in 2013. However, when considering the efficiency of the modularized product, the best performance scores occurred after modularization (November 2013 to June 2014). Regarding nonmodularized products, the best efficiency scores were obtained for

Table 8 Effect of modularization on product engineering. Behavior of efficiency

Average efficiency

Current scenario (modularized) Confractual scenario (non-modularized) Absolute effect Relative effect Probability of causal effect

0.700 0.430 0.270 63% 99.88901 Fig. 3. Evolution of efficiency of modularized and non-modularized products.

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design efficiency before and after modularization and the general average. By examining Table 10, MPs presented an increase in average efficiency after modularization (before: 0.501; after: 0.533). The minimum efficiency of the MPs (0.433) occurs before modularization (January 2013) and the maximum efficiency score (0.591) occurs after modularization (November 2013). It is understood that the position of the maximum efficiency score after modularization is indicative of positive effects of modularization. The efficiency of nMPs does not show the same behavior considering that a significant increase in efficiency before and after modularization is not observed. Regarding the minimum (0.454) and the maximum (0.537) efficiency of nMPs, they both occur before modularization, i.e., June 2012 and August 2011, respectively. Based on data presented in Fig. 3 and in Tables 9 and 10, the analyses of composed efficiencies of MP and nMP arranged in time series present evidence suggesting improvements, derived from modularization in the efficiency of Production Process. However, to accept or refute the hypotheses suggested, statistical tests and analyses of causal inference in the efficiency of Production Process were performed.

Table 9 Composed efficiency of modularized and non-modularized products. DMU

Month/year Composed efficiency of MP

Composed efficiency of nMP

DMU1 DMU2 DMU3 DMU4 DMU5 DMU6 DMU7 DMU8 DMU9 DMU10 DMU11 DMU12 DMU13 DMU14 DMU15 DMU16 DMU17 DMU18 DMU19 DMU20 DMU21 DMU22 DMU23 DMU24 DMU25 DMU26 DMU27 DMU28 DMU29 DMU30 DMU31 DMU32 DMU33 DMU34 DMU35 DMU36 DMU37 DMU38 DMU39 DMU40 DMU41 DMU42

Jan/11 Feb/11 Mar/11 Apr/11 May/11 Jun/11 Jul/11 Aug/11 Sep/11 Oct/11 Nov/11 Dec/11 Jan/12 Feb/12 Mar/12 Apr/12 May/12 Jun/12 Jul/12 Aug/12 Sep/12 Oct/12 Nov/12 Dec/12 Jan/13 Feb/13 Mar/13 Apr/13 May/13 Jun/13 Jul/13 Aug/13 Sep/13 Oct/13 Nov/13 Dec/13 Jan/14 Feb/14 Mar/14 Apr/14 May/14 Jun/14

0.519 0.523 0.512 0.471 0.494 0.467 0.513 0.537 0.510 0.518 0.511 0.519 0.493 0.486 0.500 0.490 0.518 0.510 0.496 0.500 0.508 0.493 0.500 0.503 0.479 0.504 0.505 0.500 0.500 0.454 0.508 0.477 0.467 0.477 0.474 0.500 0.482 0.492 0.500 0.500 0.528 0.500

0.459 0.500 0.516 0.502 0.525 0.472 0.518 0.530 0.514 0.528 0.519 0.525 0.501 0.488 0.515 0.483 0.521 0.518 0.505 0.516 0.510 0.463 0.473 0.450 0.433 0.470 0.501 0.527 0.546 0.551 0.438 0.521 0.521 0.500 0.591 0.564 0.509 0.589 0.500 0.512 0.500 0.500

CAPTION: Before modularization Transition period After modularization

the DMUs before modularization (during 2011). Table 9 shows the composed efficiency scores illustrated in Fig. 3. The resources of the Production Process are shared, i.e., any improvement action performed in operations affects both products (MP, nMP) uniformly and simultaneously. According to the discussions with process experts, there were no actions prioritizing MPs over nMPs. Table 10 summarizes the averages of product

4.2.1. Assumptions and Analysis of Variance (ANOVA) of Efficiency of the Production Process As for the modularized product design, the Shapiro-Wilk test before and after modularization has a significance level higher than 0.05 (before modularization Sign. ¼ 0.212; after modularization Sign. ¼0.110). As for the Levene test, the result obtained was Sign. ¼0.555. Thus, the data have a normal distribution and are homogeneous. As for the non-modularized product design, the Shapiro-Wilk test (before modularization Sign. ¼ 0.375; after modularization Sign. ¼0.703) and the Levene test (Sign. ¼ 0.434) suggest that the data also have a normal distribution and are homogeneous. Thus, the conditions for using Anova to compare averages of composed efficiencies in modularized and non-modularized products were met, and thus an Anova test was performed (Table 11). The Anova results show that, for the modularized product, the average composed efficiency of Production Process increased from 0.501 to 0.533 after product modularization. An F score of the average between the periods regarding modularized product design (6.425552) and a p-value of 0.015264 allow affirmation that the difference detected is statistically significant. As for the non-modularized product, the same evaluations were conducted for confirmatory purposes. It is noticed that the efficiency before modularization was 0.499 and after modularization, 0.497. However, an F¼0.063992 and a p-value ¼0.80159 do not allow us to state that there was a significant difference among the averages of the periods. In this sense, the efficiency of the nonmodularized product did not change significantly before and after modularization. This shows that the improvement in efficiency over time, as observed for the Production Process, can be attributed to the implementation of modularization.

Table 10 Analysis of efficiency of groups before and after modularization. Modularized product (MP)

Average Standard deviation Minimum Maximum

Non-modularized product (nMP)

Before modularization

After modularization

General average

Before modularization

After modularization

General average

0.501 0.029 0.433 0.551

0.533 0.041 0.500 0.591

0.508 0.034 0.433 0.591

0.499 0.018 0.454 0.537

0.497 0.016 0.474 0.528

0.498 0.018 0.454 0.537

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Table 11 Analysis of efficiency of groups before and after modularization regarding the Production Process. Period

Before modularization After modularization Difference of efficiencies F p-value

Average Efficiency of MP

Average Efficiency of nMP

0.501 0.533 0.032 6.425552 0.015264

0.499 0.497  0.002 0.063992 0.80159

Therefore, it is not possible to refute hypothesis H1b, according to which there are effects of product modularization on the efficiency of the Production Process. As shown, these effects are positive, i.e., modularization helps to increase efficiency. 4.2.2. Causality and Dimensioning of the effects of product modularization on the efficiency of the Production Process The Causal Impact technique was used to dimension the effects of product modularization on the Production Process. Fig. 4 illustrates the behavior of modularized product efficiency over time. By analyzing the “original” behavior of the time series considering the post-modularization estimate, the current efficiency, indicated by the black line above the blue-shaded stripe, increased with the implementation of modularization in the Production Process. If modularization had not been implemented, the curve of efficiency variation after modularization would have been within the blue-shaded stripe and not above it, as noted. As for the “pointwise” behavior, an effect at a 95% confidence interval is estimated. The blue-shaded stripe shows how the variation above and below this range would be. Table 12 shows a summary of the amplitude of the effects of implementation of product modularization on the Production Process. By analyzing the results shown in Table 12, the average product modularization after modularization was 0.533. It is also possible to observe the confractual scenario of the same period. If modularization had not been implemented, the average composed efficiency of the period after modularization would be 0.501. Therefore, the implementation of product modularization increased the efficiency of the Production Process by 0.032. The results are statistically significant by both Anova and Causal Impact. It was also possible to observe the relative effect, which increased by 6.3%. The probability of a causal effect was 99.88901%, i.e., above the 95% stipulated as a premise for the model. Thus, it is not possible to refute hypothesis H2b, according to which there is causality between product modularization and efficiency of the Production Process. In the next section, the results are discussed.

9

Table 12 Effects of modularization on the production process. Behavior of efficiency

Average efficiency

Current scenario (modularized) Confractual scenario (non-modularized) Absolute effect Relative effect Probability of causal effect

0.533 0.501 0.032 6.38% 99.88901

5. Management implications This research contributed to observation of the effects produced by product modularization on the efficiency of two functional areas (Product Engineering and the Production Process) of a bus manufacturer. This discussion is important because previous studies on the benefits achieved by companies using modularization are scarce in the literature (Jacobs et al., 2011; Boer, 2014). The observed results show that product modularization increased efficiency, which supports the theoretical proposition according to which modularization helps to eliminate the trade-off between variety and efficiency (Salvador et al., 2002; Dekkers, 2006; Patel, Jayaram, 2014). A measurement of the effects produced by modularization using DEA combined with Anova was also presented. It is understood that the suggestion of this measurement procedure is relevant due to a need to measure the effects of modularization on businesses (Starr, 2010; Matsubara and Pourmahammadi, 2010). However, the literature does not explain how to perform such analyses (Thyssen et al., 2006). The developed DEA model may be used as a support for other research on the effects of modularization by presenting a set of variables that can be used. In addition, this study sought to establish causality between modularization and efficiency using Causal Impact. It is understood that the technical suggestion of Causal Impact to evaluate performance contributes to the operational management area. Studies using this method were conducted previously only in Marketing (Brodersen et al., 2015). Finally, there was discussion considering the magnitude of the effects observed both in Product Engineering and the Production Process. Product modularization affected Product Engineering more than the Production Process. This result may provide support to company managers in their decision-making processes regarding product modularization, as it provides indications of which department or area may expect benefits. It is noteworthy that similar discussions regarding the extent of the benefits of modularization were not identified in the literature. In the seminal work by Starr (1965), product modularization

Fig. 4. Effects of modularization on the Production Process.

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and other processes are suggested as alternatives to improve productivity and efficiency. The importance of productivity and efficiency, as well as modularization, was also discussed by this author (Starr, 2010). Five decades have passed since the study by Starr (1965), and the analysis performed in this study contributes to Starr’s work (1965), with a great research effort in this regard. The results of this investigation were presented and discussed with company experts. In this sense, from the company's perspective, this work contributes to its business because the assessments prove the benefits expected from the implementation of modularization, considering that such benefits and the use of the techniques addressed were not measured to that extent. When confronted with the results, the company’s experts indicated that the orientations obtained by the analysis might be managerially useful and relevant as parameters for developing additional strategies, pursuing improvements in efficiency and productivity for the development of bus designs using modularization. The company’s experts indicated that the efficiency scores obtained by DEA may help to understand the performances of Product Engineering and the Production Process due to the simplification of information. This is because the DEA helped in proving the improvements achieved in efficiency due to: reduction of lead time (engineering and production), increased use of common items between products (reduction in the amount of developed and manufactured items), reduced the labor involved in the processes, improvement in product quality (reducing the number of complaints from clients). Furthermore, according to these experts, all the performance measurements carried out by the organization were focused on local and specific analyses that had not yet been made, that is, with the depth developed by this research. Finally, this work contributes to bus manufacturers in general, for it may serve as a basis for the implementation of modularization in other organizations in this segment. This is because it shows empirically that modularization has significant benefits. The next section presents the conclusions and the limitations of this work, in addition to indicating future research opportunities.

6. Conclusions The results allow us to state that the composed efficiency of Product Engineering increased on average by 0.270, and the composed efficiency of the Production Process increased by 0.032 after product modularization. There is a causality between product modularization and improved efficiency. For confirmatory purposes, non-modularized designs and products were also evaluated. The evolution of efficiency of nMPD and nMP over time did not have the same improvements as observed for MPD and MP. By assuming that designs and products were produced under the same conditions, the possibility of relating the positive effects observed to the implementation of modularization was reinforced. The main limitations of this work were the variables defined for the DEA analysis. It was not possible to include all variables defined originally due to a lack of availability of company data (e.g., working hours spent on each design and the production of each product). For future works, analyses considering the effects of modularization from an economic point of view are suggested. The cost efficiency can be studied in companies that use modularization. Another suggestion is to make comparative analyses between companies using and not using modularization, and extend the discussion regarding the effects observed in Product Engineering and the Production Process. Acknowledgements:

Acknowledgements Authors thank CNPq, Brazilian government agency for research support, for providing grants and financial support. The authors also appreciate the company that made this study possible. However, any analysis is the responsibility of the authors and, thus, it does not represent the position of the company. Finally, the authors acknowledge the reviewers for their comments to enhance the paper.

Appendix A. Data envelopment analysis (DEA) equations

m

MAXpo=

∑i =1ui yio n

∑ j =1vj xjo

(1)

Subjected to: m

∑i = 1ui yik n

∑ j = 1vj xjk

≤1 for k=1, 2…z

ui and vj>0 Where: ui ¼weight calculated for the output i yi0 ¼quantity of output i for the unit under analysis vj ¼weight calculated for the input j xj0 ¼quantity of input j for the unit under analysis yik ¼quantity of output i for unit k of a given sector xjk¼ quantity of input j for unit k of a given sector z ¼number of evaluation units m ¼number of outputs n ¼number of inputs

(2)

(3)

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11

Appendix B. Variables selected in DEA for product engineering

Variable Name

Description

Input1

Time, in number of days, referring to entering the budget into the system until By design the formalization of the sale Time, in number of days, referring to detailing and refinement of sold order By design

Input4

Commercial lead time (negotiation) Engineering lead time (specification of request) Engineering lead time (product configuration) Part numbers

Input5

Number of items purchased

Input6

Number of items produced

Input2 Input3

Input7

Number of persons employed in Product Engineering Input8 Number of reported technical problems Input9 Number of products with customer complaints Input10 Number of items with customer complaints Output1 Number of projects developed

Indicator

Time, in number of days, for drawing the bus design

By design

Number of items (parts) not shared with other products included in the structure of each design Number of items (parts) included in the structure of each project purchased from external suppliers Number of items (parts) included in the structure of each project produced internally Number of people involved in Product Engineering processes

By design

Number of times Production reported production design problems detected in the manufacturing of the products analyzed Number of vehicles with customer complaints to be referred to technical assistance among the products analyzed Total number of items with customer complaints among the products analyzed (vehicle x items per vehicle) Total number of projects completed by Product Engineering in the particular period (month)

By design By design Monthly Monthly Monthly Monthly Monthly

Appendix C. Variables selected in DEA for the production process

Variable Name

Description

Input1

Aluminum

Quantity in kg of aluminum used in the manufacturing of the bus

Input2

Fiber

Input3

Car mat

Input4

Fabrics

Input5

Glass

Input6

Manufacturing lead time

Input7

Assembly lead time

Input8

Number of items purchased

Input9

Number of items produced

Input10

Number of persons employed in the process (Production) Input11 Number of reported technical problems Input12 Number of products with customer complaints Output1 Number of products produced

Indicator

By product Quantity in kg of fiber used in the manufacturing of the bus By product Quantity in m2 of car mat used in the manufacturing of the bus By product Quantity in m2 of fabric used in the manufacturing of the bus By product Quantity in glass units used in the manufacturing of the bus By product Time, in number of days, referring to the product from the beginning of By production until completion at the Manufacturing sector product Time, in days, from the beginning to the end of the assembly of the bus By product Number of items (parts) included in the structure of each product purchased By from external suppliers product Number of items (parts) included in the structure of each product produced By internally product Number of people involved in the product production process Monthly Number of times Production reported design/product problems detected in Monthly the manufacturing of the products analyzed Number of vehicles with customer complaints to be referred to technical as- Monthly sistance among the products analyzed Total number of products completed by Production in the defined period Monthly (month)

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Appendix D. Causal impact analysis equations

γt =ZtT zt +∈t

(4)

zt +1 = Tt zt +Rt nt

(5)

(

)

∈t ~N 0,σt2 and nt ~N ( 0,Q t

)

(6)

Where: Yt ¼scale observation Zt ¼d  1 vector Tt ¼ d  d matrix Et ¼scale observation error with σt and nt noise variance Rt ¼d  q matrix nt ¼dimension for q

Appendix E. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ijpe.2016.08.008.

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