Drum-buffer-rope in an engineering-to-order system: An analysis of an aerospace manufacturer using data envelopment analysis (DEA)

Int. J. Production Economics xxx (xxxx) xxx

Contents lists available at ScienceDirect

International Journal of Production Economics journal homepage: http://www.elsevier.com/locate/ijpe

Drum-buffer-rope in an engineering-to-order system: An analysis of an aerospace manufacturer using data envelopment analysis (DEA) Eduardo Santos Telles a, *, Daniel Pacheco Lacerda a, b, Maria Isabel Wolf Motta Morandi b, Fabio Antonio Sartori Piran a a b

Production and System Engineering Program, Vale do Rio dos Sinos University – UNISINOS, Brazil Production and System Engineering Graduate Program, Vale do Rio dos Sinos University – UNISINOS, Brazil

A R T I C L E I N F O

A B S T R A C T

Keywords: Theory of constraints (TOC) Drum-Buffer-Rope (DBR) Engineering-to-order (ETO) and Data Envelopment Analysis (DEA)

Increased productivity and efficiency in industries with engineering-to-order (ETO) production systems have attracted growing interest from academia and business. The application of the Drum-Buffer-Rope (DBR) from the Theory of Constraints (TOC) is considered an alternative to traditional improvement programs to achieve these ends. Although research on DBR shows benefits to the companies that use it, empirical evidence about these benefits is uncommon in the literature, especially in ETO production systems. Thus, it is necessary to evaluate the effects that the implementation of DBR produces to contribute to the improvement of efficiency in an ETO system. This study analyses the effects of the implementation of DBR on the efficiency of three ETO production lines of an aerospace manufacturer. The effects were evaluated longitudinally through a case study using Data Envelopment Analysis (DEA), the Wilcoxon test and Analysis of Variance (ANOVA). The results show that the DBR implementation resulted in an increase in efficiency up to 19%. The results also establish that the DBR helps prioritization, improves communication among the productive departments, reduces the lead time and lists other variables and qualitative aspects that contributed, positively or not, to the productive efficiency results.

1. Introduction Overall demand for products with distinguished characteristics re­ inforces the need for companies to look for ways to reduce costs, decrease product development time, and invest in risk management. In this scenario, business activities focused on the development of new products have become strategic factors for companies working with technology (Matt et al., 2015) and requires collaboration with the customer to specify product needs (Chen, 2006). Companies are focused on the development of new products, and contracts for product devel­ opment force companies to operate in a similar business environment and adopt similar operational processes (Chen, 2006; Eidelwein et al., 2018; Lacerda et al., 2010). Response time to market changes should be fast, regardless of the company’s size and its organizational structure, which makes agile lean companies to develop the specific product and produce it with high efficiency (Gosling and Naim, 2009). Engineering-to-Order (ETO) is a product development process that

includes engineering, concept development, architectural development, prototyping, and other activities across the range to manufacturing and final assembly (Chen, 2006). Companies operating in an ETO environ­ ment deliver unique, generally customized products to meet individual requirements. The products have a complex structure and begin the assembly process with several components (Hicks et al., 2000). During the 1980s, to improve the performance of production management, many ETO companies attempted to implement production scheduling and production control systems (Bertrand and Muntslag, 1993). How­ ever, the difficulty faced was that the products can consist of thousands of parts numbers used in many different production operations, ac­ cording to the specifications of each product. In this case, to achieve the best use of resources, an efficient production planning and control (PPC) system is essential. PPC systems can be considered key factors for the success of organizations’ industrial operations (Manikas et al., 2015). The literature presents and compares the difference among PPC systems, such as Just-in-Time (JIT) (Chakravorty and Atwater, 1996;

* Corresponding author. Universidade do Vale do Rio dos Sinos – UNISINOS, Av. Unisinos, 950 – Bairro Cristo Rei, S~ ao Leopoldo/RS, Centro Administrativo, Sala 8010 , CEP: 93.022-000, Brazil. E-mail addresses: [email protected] (E.S. Telles), [email protected] (D.P. Lacerda), [email protected] (M.I.W.M. Morandi), fabiosartoripiran@ gmail.com (F.A.S. Piran). https://doi.org/10.1016/j.ijpe.2019.09.021 Received 24 February 2019; Received in revised form 12 September 2019; Accepted 27 September 2019 Available online 30 September 2019 0925-5273/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Eduardo Santos Telles, Int. J. Production Economics, https://doi.org/10.1016/j.ijpe.2019.09.021

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

Cook, 1994), CONWIP (Koh and Bulfin, 2004); Material Requirements Planning (MRP) (Manikas et al., 2015) and Drum-Buffer-Rope (DBR) (Thürer et al., 2017). As the implementation of PPC systems takes place in complex production environments, the implications of existing vari­ ability, inventory size, and resource capacity are often discussed. Studies about the implementation of TOC in real-world contexts have been made, but either they do not refer to the ETO context or they do not associate the context of high variability in the process and the complexity of ETO products (Gonzalez et al., 2010; Mabin and Balder­ stone, 2003; Wahlers and Cox, 1994; Wu and Liu, 2008). The company where this study was performed is an international ETO aerospace industry that develops new products to meet the specific demand of public and private companies. To increase production effi­ ciency, in 2016 the company started implementation of DBR in opera­ tions processes. Increase productivity and efficiency can be understood as a better use of the resources for the development and production of products, with the purpose of achieving the best manufacturing prac­ tices. Cox and Schleir (2013) show that the use of production scheduling and control mechanisms in the operations area can contribute to process efficiency and quality. The implementation of DBR has optimized the use of productive resources, resulting in better economic results for the company. How­ ever, it is important to measure the performance of operating results over time, including those based on indicators. Goldratt and Cox (1984) highlight that the purpose of the indicators is to motivate those involved to do what is good for the organization as a whole. The comparison of the results in different periods (before/after the DBR) can provide an­ swers to the managers of the company regarding the effects of the implementation of the DBR in the organization. Understanding these effects can support decision-making regarding the best allocation of resources and prioritization of actions. The main contribution of this article is, therefore, to show that the use of DBR in an ETO production system provides improvements in the efficiency of this system of up to 19%, such as in one of the production lines analyzed. The verification and quantification of the effect of the DBR on the efficiency can support its use and reinforce the hypothesis of the literature that its use improves the performance of the companies that use it. Finally, a discussion is made considering the results observed in three production lines of the studied company. This article is divided into five sections plus this Introduction. The following section presents a theoretical overview 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 manage­ ment implications of the results. Finally, the conclusions, work limita­ tions and suggestions for future studies are outlined in Section 6.

2.2. Drum-Buffer-Rope (DBR) The DBR is a programming and production control mechanism used to spread the concepts of the TOC. Goldratt and Cox (1984) created a tool to deal with one of the problems involving service and production establishments (Cox and Schleir, 2013). In addition, the use of DBR represents a change in the company’s common culture (Umble et al., 2014), as well as in the management of the focus on resources that constitute constraints on the productive process (Betterton and Cox, 2009). 2.3. Analysis of the effects of DBR Wahlers and Cox (1994) present an analysis describing how TOC, which proposes DBR, can influence the choice of competitive factors suitable to increase productive efficiency of an ETO company, but does not associate the context of the high variability of ETO products. Studies about the success of applying the TOC principles in companies are dis­ cussed by Mabin and Balderstone (2003), but do not mention cases about companies with ETO. Wu and Liu (2008) present how to measure the readiness of companies using DBR, emphasize the importance of focusing on existing constraints, but do not discuss readiness capacity in a multiproduct productive environment. Gonzalez et al. (2010) present a case study in a multiproject indus­ trial environment. The performance of the DBR system is positively affected by the rules provided in the steps prior to the bottleneck feature. The research presents a methodology to obtain robustness and to in­ crease the operational performance. However, it does not present which factors influence the result, and which inputs and outputs present greater impact on the efficiency of the system. 2.4. Data envelopment analysis (DEA) DEA is a non-parametric mathematical model that does not use sta­ tistical references or measurement of central tendency. Functional re­ lationships between inputs and outputs are not required in DEA, and are not restricted to single, unique measurements of inputs and outputs (Cook and Seiford, 2009). Cook et al. (2014) mention that analysis using DEA in productive processes tends to have the most easily identifiable inputs and outputs, in which the resources used represent the inputs, and the finished products, the outputs. Regarding the number of input and output vari­ ables, a general rule suggested by Golany and Roll (1989) is that the number of Decision-making units (DMUs) is at least double the number of inputs and outputs combined. Banker, Charnes and Cooper (1989), on the other hand, states that the number of DMUs must be at least three times the number of inputs and outputs combined. Cook et al. (2014) state that this rule has no statistical basis, but is usually imposed for convenience, as, otherwise, it may decrease the efficiency of the DEA analysis. The DEA literature points out two models that are used in the application of the technique. The first is the Constant Returns to Scale (CRS), presented by Charnes et al. (1978), that proposes an input-oriented approach, recommended when the objective is to compare DMU of variables with similar amplitudes. The second is the Variable Returns to Scale (VRS), presented by Banker et al. (1984), states that a DMU cannot be compared with all the DMUs of a given sector, but with the DMUs that operate on a scale similar to theirs, recommended when the object is to compare DMUs of variables with different amplitudes. The DEA model has two orientation possibilities: input or output. If the objective is to maintain the level of resource consumption (e.g. raw materials) and maximize outputs (e.g. production delivery), the model must be output-oriented. On the other hand, if the objective is to keep the outputs constant and to verify better use of inputs used in the pro­ duction process, the model should be input-oriented (Zhu, 2014). The

2. Theoretical background 2.1. Engineering-to-order (ETO) ETO is a product development process that includes engineering, concept development, architectural development, prototyping, and other activities ranging across the spectrum to manufacturing and final assembly (Chen, 2006). A common characteristic of companies that operate in an ETO environment is the delivery of unique customized products to meet individual requirements. The products have a complex structure and begin the assembly process with several components (Hicks et al., 2000). During the 1980s, to improve the performance of production management, many ETO companies attempted to implement production planning and control systems (PPC) (Bertrand and Muntslag, 1993). However, the difficulty faced was that the products can consist of thousands of parts used in different production stages, according to the specifications of each product. In this case, to achieve the best use of resources, an efficient PPC system is essential (Manikas et al., 2015).

2

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

following section presents the methodological procedures used to perform the research.

3.2. Design of data envelopment analysis (DEA) During this stage of the DEA, specialists in the production process of the company under study are assisted. During the conversations with the specialists, preliminary alignments are sought regarding the definition of variables and the process of collecting and processing the data. Table 1 shows the position of each professional, his/her participation in the project and the service time in the company. The studied company has three product lines: computers, displays, and electronics. The implementation of the DBR was carried out, and it covered the production programming of all the product lines. The pro­ cess experts recommended that the study be developed considering the three product lines, since the same importance was given to the different lines. Also, experts advised that engineering products and prototypes should be disregarded, as they used special processes not yet established or under development by the engineering department. It was identified that the company did not present significant management changes in the proposed analysis period, besides having an employee turnover of less than 5% per year. DMUs are the productive elements or units (restaurant, school, hospital, bank, industry among others) (Farrell, 1957). The original idea of DEA is to provide a method that, within a set of DMUs, can identify those that present best practices. The method also allows measurement of the efficiency level of the units outside the efficiency frontier, and to identify benchmarks for comparison (Cook and Seiford, 2009). During the DMU definition stage, it was suggested by process experts to consider monthly deliveries of each product line. It can be analyzed which month had the best or worst result in terms of efficiency. The analyses included 144 DMUs: I) 18 DMUs for the second semester of 2014; II) 36 DMUs for 2015; III) 36 DMUs for 2016; IV) 36 DMUs for 2017; and V) 18 DMUs for the first semester of 2018.

3. Research method This paper intends to empirically test the hypotheses proposed in the literature that the DBR provides beneficial effects on the efficiency of the companies that use it, focusing on the ETO environment. For this, the following hypotheses are tested: H0:. There is no relationship between the implementation of DBR and the effects on the production process efficiency. H1:. There is a relationship between the implementation of DBR and the effects on the production process efficiency. Therefore, these hypotheses will guide this investigation to evaluate whether DBR contributes to the improvement of technical efficiency in an ETO system. The objective of this work is to analyze the effects of DBR imple­ mentation in an aerospace manufacturer’s ETO production system, thus, it is necessary to develop an in-depth case study and not only based on existing perceptions. Case studies are appropriate to provide a detailed knowledge of the process (Barratt et al., 2011). In this sense, the study was conducted by (i) definition of the case study, (ii) modelling using DEA, and (iii) data analysis and statistics. 3.1. Definition of the case study The company in which the study is developed is an electronic aero­ space industry that, in the pursuit of increased productivity and effi­ ciency, in 2016, started the implementation of DBR in its industrial processes. Before the implementation of DBR, the number of products developed and sold by the company was smaller, the products them­ selves used analog technologies that were less complex compared to the electronics developed today. In previous projects, after signing the product development contracts, the engineering department had a period for product design and production of the prototypes according to customers’ requirements. When the product qualification stages were completed, and the final development received customer approval, the production phases were started. The production planning and control (PP&C) department, responsible for analysis of the demand for orders and production scheduling, used the ERP system to perform the master production planning. This method of working was effective for more than 30 years in the company’s operations department. In 2011 when new products were developed there was an increase in the number of parts and production processes. At this time, the company identified that the current method of production prioritization began to rise some difficulties. The product mix demanded fine programing of PP&C, due to the high variability of part numbers and low volume. After a production order was received, the material was put aside by the warehouse and delivered to the first stage of the process. Then the product followed the steps in a pushed flow, and the prioritization was given by PP&C. Communication among areas was faulty, and priorities changed according to the supervisors, even when against PP&C definitions. The electronic assembly process is divided into assembly of printed circuit board (PCB) and assembly of final products. The PCB assembly process begins with the SMT (surface mount technology) assembly and goes on to manual assembly, electrical testing, final inspection, and lastly storage. Subsequently, the PCBs components are separated into kits and delivered to the mechanical assembly lines of computers, dis­ plays and electronics, followed by the tests at room temperature, envi­ ronmental testing and lastly the final inspection. Thus, to develop the investigation of the company’s production process, the performance of the operational results over time is analyzed, considering the periods before and after the implementation of the DBR.

3.2.1. Variables, data collection, and DEA analyses The selection of the variables for this work is based on the framework proposed by Jain et al. (2011), whose initial phase structures the need to select a standard list of inputs and outputs of the system under study, and perform the later validation of the data. Additionally, the literature is analyzed in search of similar variables already used to select the DEA model and start the data analysis. After defining the variables, it was decided which would be used as inputs and outputs of the model. Table 2 presents the variables list and their references used in the DEA model. The details of each selected Table 1 Experts’ profiles. Function

Support to the project

Time in the company (years)

PP&C analyst (I)

Support in the definition of the model, data collection of the productive process and interpretation of the results. Support in the definition of the model and data collection of the productive process. Support in the definition of the model, data collection of the productive process and interpretation of the results. Support in the definition of the model, data collection of the productive process and interpretation of the results. Support in the definition of the model and data collection of the productive process. Support in defining the model and validating the model.

8

PP&C analyst (II) Manufacturing engineer (I) Manufacturing engineer (II) Co-ordinator of Manufacturing Engineering Industrial Director

3

7 15

12

15 18

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

variable are shown in Appendix 1. To create a DEA analysis, it is necessary to define the model (CRS or VRS) and the orientation (input or output). The model used in this work is the CRS, since an internal comparative analysis is performed. Thus, the amplitude and scale of the chosen variables are similar among the DMUs, indicating the use of the CRS model. Regarding the orientation, this work is orientated to input. Equation (1) shows the DEA model used in the analysis. Pm j¼1 uj ​ yj0 ​ ​ MAXeff 0 ¼ Pn (1) i¼1 vi ​ xi0 ​ ​ Subject to: Pm uj yjk ​ ​ Pj¼1 � 1; 8k n i¼1 vi xik ​ ​

and management of productivity and efficiency) available at . Initially, the productive efficiency is analyzed over the defined time, and for this the data of the compound efficiency is used to calculate the minimum, maximum, average and standard Deviation values. This analysis demonstrated a variation due to DBR implementation. The choice of compound efficiency is due to the discrimination problem when performed with other efficiencies. Com­ pound efficiency is an aggregate index, which corresponds to the composition situated between standard efficiency and inverted frontier efficiency (Souza et al., 2018). Compound efficiency has been used for analyses in the operations management field (Barbosa et al., 2017; Gilsa et al., 2017; Piran et al., 2017, 2016; Souza et al., 2018). In order to identify if there are significant differences among the means of the analysis, the Analysis of Variance (ANOVA) test is per­ formed. This test makes multiple comparisons of treatment groups, determining whether the complete set of sample means suggests that the samples were obtained from the same general population (Hair et al., 2009). Before performing the ANOVA test, its assumptions were veri­ fied. For the normality test, Shapiro Wilk was conducted, and for ho­ moscedasticity, Levene was conducted (Hair et al., 2009). If any of the assumptions were violated, a non-parametric test - the Wilcoxon – was performed. Fig. 1 flow chart summarizes the data analysis performed.

(1.1)

uj � 0; 8j vi � 0; 8i

(1.2)

where: eff0 ¼ efficiency of DMU 0 under analysis uj ¼ weight calculated for the output j, j ¼ 1, … m vi ¼ weight calculated for the input i, i ¼ 1, … n yj0 ​ ​ ¼ quantity of output j for DMU under analysis xi0 ​ ​ ¼ quantity of input i for DMU under analysis yjk ​ ​ ¼ quantity of output j for DMU k, k ¼ 1, … n xik ​ ​ ¼ quantity of input i for DMU k, k ¼ 1, … n k ¼ number of DMU under analysis m ¼ number of outputs n ¼ number of inputs

4. Results In this section, the results of the productive efficiency are presented. They consider a total period of four consecutive years, the interval being from July 2014 to June 2018. This period allowed consideration of 144 DMUs referring to the three product lines (computers, displays, and electronics). During development of the statistical analyses, the data corresponding to the efficiency of the computers and displays lines di­ verges from a normal distribution (Appendix 4), at a level significantly lower than 0.05 (5%). Thus, it is was necessary to use a non-parametric test instead of ANOVA. For the electronics production line, the Shapiro Wilk test demonstrated normality, and the Levene test, homogeneity of the variances, which made it possible to proceed to the ANOVA test.

The objective function represents efficiency. The first set of con­ straints (1.1) ensures that the reduction of inputs does not exceed the boundary defined by efficient DMUs. The second group of constraints (1.2) ensures that the reduction of inputs does not change the current level of outputs. This work compares the use of inputs by the DMUs, and, conse­ quently, the efficiency during the period of analysis before and after the implementation of the DBR. The general definitions of the developed DEA model are summarized in Appendix 3. To review and validate the use of the chosen model, the company’s experts were consulted. 3.3. Data analysis and statistics After the data had been collected, the evaluation process started. The data obtained were organized on a spreadsheet to calculate the perfor­ mance of each DMU using the free software SAGEPE (system for analysis Table 2 DEA model variables list. Function in the model

Variable

References

Input01

Production time

Input02

Lead time

Input03

Number of employees

Input04

Number of complaints Work in process On-time order delivery Finished product Scrap

Friedman and Sinuany-Stern (1998); Jain et al. (2011); Park et al. (2014). Jain et al. (2011); Park et al.(2014); Cook et al. (2014). Chandra et al. (1998); Keh and Chu (2003); Düzakin and Düzakin (2007); Wu et al. (2017). Nanci et al. (2004).

Input05 Output01 Output02 Output03

Keh and Chu (2003); Jain et al. (2011). Keh and Chu (2003). Jain et al. (2011); Park et al.(2014); Cook et al. (2014); Nanci et al.(2004). Jain et al. (2011); Wu et al.(2017).

Fig. 1. Data analysis process. 4

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

efficiency results in the first 9 consecutive months post-transposition of the DBR implementation are close to those in the period prior to DBR implementation, with results that are related to the adaptation phase of the production system to DBR. However, the data indicate that DBR implementation increased production efficiency in the displays line. The chart in Fig. 4 illustrates the results of the electronics line. Analyzing Fig. 4, it is possible to view a small increase in the effi­ ciency of the electronics line in the period after the implementation of DBR (after DMU 25 on Jul/2016), since the linear trend line of the whole series (orange dotted line) has a steeper slope (in the sense of higher efficiency) than the linear trend line before the DBR (blue dotted line). Although the linear trend line shows evolution, the average growth verified in the electronics line in the analysis period was approximately 4%.

4.1. DEA production lines efficiency Table 3 lists the results that represent the efficiency scores for the compound efficiency calculations performed in the DEA. The DMUs are segregated into three periods: prior to the implementation of DBR (DMU 1 - DMU 18, in gray), transition (DMU 19 - DMU 24, in yellow), and after the implementation of DBR (DMU 25 - DMU 48, in green). The complete results are available in Appendix 2. Regarding the computers line, the DMUs with the best performance in the analyzed series were in 2017, with 8 months of good results located in the green area of Table 3, in the period after implementation of the DBR. DMU 4 and 22 presented the worst performance results in the entire time series. DMU 4 was prior to DBR implementation, and DMU 22 in the transition period. In the displays line, the best perfor­ mance of the composite efficiency is attributed (in order of magnitude) to the DMUs 47, 37, 42, 43, 46, 39, 41, 45, 48 and 10. It is emphasized that, of the 10 DMUs with the best performance in the analyzed series, 9 of them are located in the period after the DBR implementation (Table 3 green area). As for the electronics line, the best efficiency performance (in order of magnitude) is attributed to DMUs 43, 30, 48, 34, 12, 19, 25, 47, 6 and 20. Of the 10 DMUs with the best performance in the series analyzed, 2 are located in the previous period (Table 3 gray area) and 2 are located in the transition period of DBR implementation (Table 3 yellow area). DMUs 16 and 23 had the worst performance results in the entire time series. To facilitate understanding of the efficiency results, the computers line results are shown in chart form in Fig. 2. This format makes it possible to visualize the tendency of the evolution of the scores throughout the time series. The transition period of DBR implementa­ tion is illustrated by the light brown area. According to Fig. 2, there is an increase in the efficiency of the computers line in the period after the DBR implementation (after DMU 25 in Jul/2016), since the linear trend line of the whole series (orange dotted line) has a steeper slope (in the sense of higher efficiency) than the linear trend line before the DBR (blue dotted line). In the first month post-transition of the DBR implementation (Jul/ 2016) the efficiency score was among the lowest recorded, which shows the adaptation phase of the productive system to DBR. However, in the following months, there was an indication that DBR implementation had increased production efficiency. The chart in Fig. 3 illustrates the results of the displays line. In Fig. 3, it is possible to observe an increase in the efficiency of the displays line in the period after the implementation of the DBR (after DMU 25 on Jul/2016), since the linear trend line of the whole series (orange dotted line) has a steeper slope (in the sense of higher efficiency) than the linear trend line before the DBR (blue dotted line). The

4.2. Wilcoxon and analysis of variance (ANOVA) To identify if there were significant differences among the means of the analyzed periods, the ANOVA test was performed. However, for the use of ANOVA, tests such as Shapiro Wilk and Levene are applied to verify if the data represent a normal homogeneous distribution. Other­ wise, the Wilcoxon non-parametric test is performed. The tests for validation of ANOVA assumptions are summarized in Table 4. According to the Shapiro-Wilk normality test on computers and displays production lines, the data were normal (homogeneous) with a significance level greater than 0.05 (Sign. ¼ 0.2454 and 0.7763). In the period after DBR, the data differed from a normal distribution, meaning that the data were not normal (heterogeneous) with a level significantly lower than 0.05 (Sign. ¼ 0.0089 and 0.0008). Thus, one can not accept the hypothesis that the data constituted a normal distribution, and for this reason, the Wilcoxon non-parametric test was performed, as shown in Table 5. Analyzing the data in Table 5, according to the Wilcoxon test, the null hypothesis H0 is rejected and the alternative hypothesis H1 is accepted, p-value for computers ¼ 0.0002 (0.02%) <0.05 (5%) and pvalue for displays ¼ 0.0068 (0.68%) <0.05 (5%). There are statistically significant effects of the DBR implementation on the efficiency of computers and displays production lines. The compound efficiency in the period after the implementation of the DBR is higher than in the period before it, the confidence interval being 95%. For the electronics production line, according to the Shapiro-Wilk test (Table 4), the data were normal (homogeneous) with a signifi­ cance level higher than 0.05 (before Sign. ¼ 0.4982 and after Sign. ¼ 0.1064). Consequently, the hypothesis that the data constituted a normal distribution was acceptable. As for the Levene test, the positive result (Sign. ¼ 0.7072) also makes the hypothesis that the data were

Table 3 Production lines compound efficiency.

5

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

Fig. 2. Evolution of efficiency in the computers line.

Fig. 3. Evolution of efficiency in the displays line.

Fig. 4. Evolution of efficiency in the electronics line. Table 4 Analysis of normality and homogeneity. Period

Efficiency data before DBR Efficiency data after DBR Total sample of data

Table 5 Analysis of the non-parametric test for computers and displays production lines.

Computers Shapiro-Wilk (Sign.)

Displays ShapiroWilk (Sign.)

Electronics Shapiro-Wilk (Sign.)

Electronics Levene (Sign.)

0.2454

0.7763

0.4982

–

0.0089

0.0008

0.1064

–

–

–

–

0.7072

Information

Computers values

Displays values

Statistic p-value Null Hypothesis Lower Limit (Pseudo) Median Upper Limit Confidence Interval

24 0.0002 0 0.1400 0.1099 0.0750 0.95

61.5 0.0068 0 0.1650 0.0993 0.0300 0.95

is presented in Table 6. Following the ANOVA test, we accepted the null hypothesis H0 and rejected the alternative hypothesis H1, p-value ¼ 0.4596 (45.96%) > 0.05 (5%). The F-score of the compound efficiency was

homogeneous acceptable. So we did not reject the hypothesis of equality of variances. Thus, the assumptions for the use of ANOVA to compare the means of compound efficiencies were made. The ANOVA test result 6

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

0.5562 and p-value 0.4996, which allowed affirmation that it was smaller than the critical F 4.0517, so there was no significance in the difference detected. Therefore, considering a 95% confidence interval, there was no evidence of the effects of the implementation of DBR on the production process efficiency in the electronics line.

Table 7 Variation in the efficiency of the three product lines.

4.3. Production process efficiency and ANOVA analysis

Table 6 ANOVA test of the electronics line. MQ

F

p-value

F critical

Among groups Within the groups

0.0017 0.1422

1 46

0.0017 0.0031

0.5562

0.4596

4.0517

Variation (Δ%)

Computers Displays Electronics

0.47 0.53 0.51

0.58 0.63 0.53

19% 16% 4%

This research contributes to the knowledge about the TOC and DBR, and shows that the use of DBR in an ETO production system provides improvements in the efficiency of this system. Although many types of research have been developed to show the results of applying the DBR (Betterton and Cox, 2009; Mabin and Balderstone, 2003), there are few applications of DBR in ETO production systems. The results observed show empirical evidence of the benefits pro­ vided using DBR in an ETO production system. These benefits can be understood as an improvement in the productive efficiency of the computers and displays lines, and are aligned with the authors, Little et al. (2000), which identify the need for planning and programming as a key factor in the performance of ETO companies. As in the work of Rhee et al. (2010), it is shown how the practical application of DBR in process management can bring positive results in the efficiency of the company. The ETO literature indicates that the companies in this segment need agile efficient internal processes to remain in the competitive environ­ ment (Chen, 2006), and, in many cases, they suffer from lead time and different cycle times among products, not allowing adequate synchro­ nization of production (Matt et al., 2014). Therefore, the results of the productive efficiency after implementation of the DBR presented in this research emphasize that it is possible to keep the company in the competitive environment, and use DBR as a tool to prioritize and syn­ chronize the production of different products. Evidence of these benefits helps to fill the gap identified in the literature. As regards the contributions to the company, this research supports the measurements of efficiency and proves the benefits with the implementation of DBR in the three production lines. It should be noted that these benefits have not yet been measured. When presenting the

In addition to providing the efficiency scores of the three product lines, the DEA allows an evaluation of the clearances. The order of the largest mean gap variation is shown in Table 8. The evaluation of the clearances shows that the variable Output02 “Finished product” presents the largest average overall variation, with the greatest impact on the displays line and electronics. However, in

gl

After

5. Management implications

4.4. Analysis of the clearance of the DEA model

SQ

Before

each product line, in absolute terms, the variation was small and does not have significant results that contribute to the analysis. Thus, it is not possible to affirm that the DBR influenced the reduction of the output gap variable Output02. The Input05 “Work in process” variable presents the second largest overall variation, with the greatest impact on the displays line. The re­ sults indicate that there was a reduction in the quantity of processed products (WIP) in the three product lines, and this can be considered the highest benefit provided by the implementation of DBR in the ETO system studied. In practice, the slower production rate of each line sets the pace of deliveries, and, thus, the later steps are adjusted according to the “beat” and thus avoid establishment of intermediate stocks. It is worth mentioning that, despite the reduction of WIP products, produc­ tion lines keep quantities of products in process (buffers) to protect against any problems that may occur at some stage of the process, especially regarding the critical resources. In addition, by establishing the slower production rate, production line supervisors along with PP&C analysts and manufacturing engineers encourage employees to use the five TOC focus steps. The result is evi­ denced in the input01 variable, “Production time”, which can be considered the second highest benefit provided by the DBR imple­ mentation. Given the characteristics of the ETO system, with personal­ ized products, different stages and delivery dates (Bertrand and Muntslag, 1993), the contribution from employees to minimize re­ strictions is important. This point can be understood as a continuous improvement tool that has impacted the results of manufacturing pro­ duction cycles times.

Table 7 presents a synthesis of the efficiency results of the three product lines in the period before and after the implementation of the DBR. The best performance obtained after implementation of DBR was in the computers line, followed by displays and electronics. As for the analyses performed in the computers line, the alternative hypothesis H1 was accepted, since it was identified that there is evidence of the effects of the implementation of DBR on the productive process efficiency. The effects can be considered positive, the implementation of the DBR increased the productive efficiency of the computers line. To understand the effects that led the computers line to obtain this result, evaluation of the results was performed by process experts. It was reported that the computers line was considered to be the most complex, with different stages during the assembly process and tests, a charac­ teristic found in customized ETO systems (Hicks et al., 2000). The communication among the distinct stages of the process contributed to the efficiency result, since, before to implementation of the DBR, there were difficulties in prioritizing which products should be delivered. To minimize this difficulty, use of the DBR reports, classified by a color chart (black, red, yellow and green), was started, which facilitated the understanding of those involved in the process, and led them to focus their efforts on the priority production orders. Thus, it is possible to understand that the DBR reports improved communication in the three product lines, but the greatest impact was evident in the computers line. Concerning the analyses performed on the displays line, the alter­ native hypothesis H1 was accepted, since it was identified that there was evidence of the effects of the DBR implementation on the production process efficiency. As in computers line, the perceived effects can be considered positive, that is, the implementation of the DBR increased the productive efficiency of the displays line. For electronics line, the null hypothesis H0 was accepted, since it was identified that there was no evidence of the effects of DBR imple­ mentation on the production process efficiency. The effects perceived can be considered impartial, that is, the implementation of the DBR did not significantly increase the productive efficiency of the electronics line. In the electronics line, the lower efficiency growth, when compared to the others, is attributed to the great variability of the products. This line has products considered complex and others simple, which causes difficulties in managing the resources. The association of families of products with a similar level of complexity tends to provide benefits in the efficiency result in companies that operate in ETO systems.

Variation Source

Line

7

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

Table 8 Order of average variation of clearances.

results to the company’s specialists, they indicated that the guidelines suggested by this analysis can be useful as parameters for devising new projects to improve the productive efficiency of the three product lines. Finally, the use of the DEA to compare the three different product lines is relevant for the company to make comparative measurements. Such a technique can be replicated over the next few years, and provide comparisons throughout a longer period. From the results of the pro­ ductive efficiency, managers can establish goals for improvement of the organizational results. The process specialists indicated that the moni­ toring of efficiency over time provides a concrete basis for assessing the impact of resource use.

computers and displays lines. However, the effects of the implementa­ tion of the DBR in the electronic products line did not present significant perceivable results. The main limitations of this work were: in consid­ ering only one industrial plant, it was not possible to replicate the results for other international industrial sites; and not considering engineering and prototype products, the production processes were not completely established and were likely to distort the results of the DEA model. Future works could perform analyses on the effects of DBR on other ETO production systems. An external benchmarking could also be carried out among other international industrial sites of the company under study, and, thus, develop the best practices that would positively influence the DBR implementation.

6. Conclusion

Acknowledgments

The results allow us to state that, after DBR implementation, the compound efficiency of the computers production line increased on average by 19%, the displays production line increased by 16% and the electronics increased by 4%. For confirmatory purposes, Wilcoxon and ANOVA statistical tests were developed. When evaluating the DEA re­ sults, it was verified that there are indications that the implementation of the DBR assisted improvement in the company’s production system, as well as an increase in the scores of the productive efficiency in the

The authors thank CNPq, the Brazilian government agency, for supporting our research, for the provision of financial support. The au­ thors also express their appreciation of the company that made this study possible. However, any analysis is the responsibility of the au­ thors, and, therefore, does not represent the company’s position. Finally, the authors thank the reviewers for their comments to improve the work.

Appendix 1. Variables selected in DEA Function in the model

Variable

Unit of measurement

Description/Definition

input01 input02 input03 input04 input05 output01 output02 output03

Production time Lead time Number of employees Number of complaints Work in process On-time order delivery Finished product Scrap

Hours Days People Units Units Percentage Units Units

Time for product manufacture. Total production time. Number of employees in the production process. Quantity of customer complaints about products. Quantity of products being processed. Percentage of products delivered on time. Total number of products produced. Quantity of products rejected and discarded during production.

8

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

Appendix 2. Production lines efficiency

9

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

Appendix 3. Definitions of the developed DEA model Assumption

Description/Detail

Definition of products to be analyzed

� Defined three product lines: computers, displays and electronics. � Ignored engineering products and prototypes. � Defined period of analysis July 2014–June 2018. � Considered the monthly lot a product line produced. � The analysis of the production process considers 144 DMUs. � It presents variables informed by process experts. � Analyzed the literature, considering works on DBR in surveys that use the DEA. � The model used in this work is CRS. � An internal comparative analysis is carried out in the company being studied. � The orientation used in this work is input. � To compare the efficiency of the DMUs during the period of analysis before and after DBR implementation.

Definition of the period of analysis Definition of decision-making units (DMUs) Definition of DEA model variables Definition of the DEA model Definition of the DEA model orientation

Appendix 4. Histogram of efficiency in the production lines

10

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

In the computers line it is possible to identify that the efficiency of the DMUs in the period before the DBR implementation was concentrated in the central value, with an average of 0.47, justifying the data normality by the Shapiro Wilk test. In the period after DBR, the average efficiency of the DMUs presented an average value of 0.57, and data were concentrated on the right side of the histogram. Thus, the data were considered hetero­ geneous, and, therefore, it was not possible to continue the ANOVA parametric test, since one of the criteria for choosing the statistical test could not be met.

Equivalent results and heterogeneous data were found when performing the Shapiro Wilk test to verify normality in the displays line.

For the electronics line, the Shapiro Wilk test demonstrated normality, and the Levene test, homogeneity of the data, thus enabling application of the ANOVA test.

11

E.S. Telles et al.

International Journal of Production Economics xxx (xxxx) xxx

References

Jain, S., Triantis, K.P., Liu, S., 2011. Manufacturing performance measurement and target setting : a data envelopment analysis approach q. Eur. J. Oper. Res. 214, 616–626. https://doi.org/10.1016/j.ejor.2011.05.028. Keh, H.T., Chu, S., 2003. Retail productivity and scale economies at the firm level: a DEA approach. Omega 31, 75–82. https://doi.org/10.1016/S0305-0483(02)00097-X. Koh, S.-G., Bulfin, R.L., 2004. Comparison of DBR with CONWIP in an unbalanced production line with three stations. Int. J. Prod. Res. 42, 391–404. https://doi.org /10.1080/00207540310001612026. Lacerda, D.P., Cassel, R.A., Rodrigues, L.H., 2010. Service process analysis using process engineering and the theory of constraints thinking process. Bus. Process Manag. J. 16, 264–281. https://doi.org/10.1108/14637151011035598. Little, D., Rollins, R., Peck, M., Porter, J.K., 2000. Integrated planning and scheduling in the engineer-to-order sector. Int. J. Comput. Integr. Manuf. 13, 545–554. htt ps://doi.org/10.1080/09511920050195977. Mabin, V.J., Balderstone, S.J., 2003. The performance of the theory of constraints methodology. Int. J. Oper. Prod. Manag. 23, 568–595. https://doi.org/10.1108/0 1443570310476636. Manikas, A., Gupta, M., Boyd, L., 2015. Experiential exercises with four production planning and control systems. Int. J. Prod. Res. 53, 4206–4217. https://doi.org/10 .1080/00207543.2014.985393. Matt, D.T., Dallasega, P., Rauch, E., 2015. On-site oriented capacity regulation for fabrication shops in Engineer-to-Order companies (ETO). Procedia CIRP 33, 197–202. In: https://doi.org/10.1016/j.procir.2015.06.036. Matt, D.T., Dallasega, P., Rauch, E., 2014. Synchronization of the manufacturing process and on-site installation in ETO companies. Procedia CIRP 17, 457–462. In: https ://doi.org/10.1016/j.procir.2014.01.058. Nanci, L.C., Azevedo, S.M., Soares de Mello, J.C.C.B., 2004. Estudo da efici^ encia de empresas distribuidoras de jornais usando an� alise envolt� oria de dados. Prod. Produç~ ao 7, 27–35. Park, J., Lee, D., Zhu, J., 2014. An integrated approach for ship block manufacturing process performance evaluation: case from a Korean shipbuilding company. Int. J. Prod. Econ. 156, 214–222. https://doi.org/10.1016/j.ijpe.2014.06.012. Piran, F.A.S., Lacerda, D.P., Camargo, L.F.R., Viero, C.F., Dresch, A., Cauchick-Miguel, P. A., 2016. Product modularization and effects on efficiency: an analysis of a bus manufacturer using data envelopment analysis (DEA). Int. J. Prod. Econ. 182, 1–13. https://doi.org/10.1016/j.ijpe.2016.08.008. Piran, F.A.S., Lacerda, D.P., Camargo, L.F.R., Viero, C.F., Teixeira, R., Dresch, A., 2017. Product modularity and its effects on the production process: an analysis in a bus manufacturer. Int. J. Adv. Manuf. Technol. 88, 2331–2343. https://doi.org/10.100 7/s00170-016-8906-8. Rhee, S.-H., Cho, N.W., Bae, H., 2010. Increasing the efficiency of business processes using a theory of constraints. Inf. Syst. Front. 12, 443–455. https://doi.org/10.100 7/s10796-008-9145-9. Souza, I.G., Lacerda, D.P., Camargo, L.F.R., Dresch, A., Piran, F.S., 2018. Do the improvement programs really matter? An analysis using data envelopment analysis. BRQ Bus. Res. Q. 21, 225–237. https://doi.org/10.1016/j.brq.2018.08.002. Thürer, M., Stevenson, M., Silva, C., Qu, T., 2017. Drum-buffer-rope and workload control in High-variety flow and job shops with bottlenecks: an assessment by simulation. Int. J. Prod. Econ. 188, 116–127. https://doi.org/10.1016/j.ijpe.2017.0 3.025. Umble, M., Umble, E., Murakami, S., 2014. International journal of production implementing theory of constraints in a traditional Japanese manufacturing environment : the case of hitachi tool engineering. Int. J. Prod. Econ. 37–41. htt ps://doi.org/10.1080/00207540500381393. Wahlers, J.L., Cox, J.F., 1994. Competitive factors and performance measurement: applying the theory of constraints to meet customer needs. Int. J. Prod. Econ. 37, 229–240. https://doi.org/10.1016/0925-5273(94)90173-2. Wu, H., Lv, K., Liang, L., Hu, H., 2017. Measuring performance of sustainable manufacturing with recyclable wastes: a case from China’s iron and steel industry. Omega (U. K.) 66, 38–47. https://doi.org/10.1016/j.omega.2016.01.009. Wu, H.H., Liu, J.Y., 2008. A capacity available-to-promise model for drum-buffer-rope systems. Int. J. Prod. Res. 46, 2255–2274. https://doi.org/10.1080/002075406010 39783. Zhu, J., 2014. Quantitative Models for Performance Evaluation and Benchmarking, International Series in Operations Research & Management Science. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-06647-9.

Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078–1092. Banker, R.D., Charnes, A., Cooper, W.W., Swarts, J., Thomas, D., 1989. An introduction to data envelopment analysis with some of its models and their uses. Res. Gov. 5, 125–163. Non-Profit Account. Barbosa, L.M., Lacerda, D.P., Piran, F.A.S., Dresch, A., 2017. Exploratory analysis of the variables prevailing on the effects of product modularization on production volume and efficiency. Int. J. Prod. Econ. 193, 677–690. https://doi.org/10.1016/j.ijpe.20 17.08.028. Barratt, M., Choi, T.Y., Li, M., 2011. Qualitative case studies in operations management: trends, research outcomes, and future research implications. J. Oper. Manag. 29, 329–342. https://doi.org/10.1016/j.jom.2010.06.002. Bertrand, J.W.M., Muntslag, D.R., 1993. Production control in engineer-to-order firms. Int. J. Prod. Econ. 31, 3–22. Betterton, C.E., Cox, J.F., 2009. Espoused drum-buffer-rope flow control in serial lines: a comparative study of simulation models. Int. J. Prod. Econ. 117, 66–79. https://doi. org/10.1016/j.ijpe.2008.08.050. Chakravorty, S.S., Atwater, J.B., 1996. A comparative study of line design approaches for serial production systems. Int. J. Oper. Prod. Manag. 16, 91–108. https://doi.org/10. 1108/01443579610119117. Chandra, P., Cooper, W.W., Li, S., Rahman, A., 1998. Using DEA to evaluate 29 Canadian textile companies - considering returns to scale. Int. J. Prod. Econ. 54, 129–141. https://doi.org/10.1016/S0925-5273(97)00135-7. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444. https://doi.org/10.1016/0377-2217 (78)90138-8. Chen, C.S., 2006. Concurrent engineer-to-order operation in the manufacturing engineering contracting industries. Int. J. Ind. Syst. Eng. 1, 37–58. https://doi. org/10.1504/IJISE.2006.009049. Cook, D.P., 1994. A simulation comparison of traditional, JIT and TOC manufacturing systems in a flow shop with bottlenecks. Prod. Inventory Manag. J. Cook, W.D., Seiford, L.M., 2009. Data envelopment analysis (DEA) - thirty years on. Eur. J. Oper. Res. 192, 1–17. https://doi.org/10.1016/j.ejor.2008.01.032. Cook, W.D., Tone, K., Zhu, J., 2014. Data envelopment analysis: prior to choosing a model. Omega (U. K.) 44, 1–4. https://doi.org/10.1016/j.omega.2013.09.004. Cox III, J.F., Schleir, J.G., 2013. Handbook da Teoria das Restriç~ oes. Bookman, Porto Alegre. Düzakin, E., Düzakin, H., 2007. Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: an application of 500 major industrial enterprises in Turkey. Eur. J. Oper. Res. 182, 1412–1432. https://doi. org/10.1016/j.ejor.2006.09.036. Eidelwein, F., Piran, F.A.S., Lacerda, D.P., Dresch, A., Rodrigues, L.H., 2018. Exploratory analysis of modularization strategy based on the theory of constraints thinking process. Glob. J. Flex. Syst. Manag. 19, 111–122. https://doi.org/10.1007/s40171-0 17-0177-1. Farrell, M.J., 1957. The measurement of productive efficiency. J. R. Stat. Soc. Ser. A 120, 253. https://doi.org/10.2307/2343100. Friedman, L., Sinuany-Stern, Z., 1998. Combining ranking scales and selecting variables in the DEA context: the case of industrial branches. Comput. Oper. Res. 25, 781–791. https://doi.org/10.1016/S0305-0548(97)00102-0. Gilsa, C. Von, Lacerda, D.P., Camargo, L.F.R., Souza, I.G., Cassel, R.A., 2017. Longitudinal evaluation of efficiency in a petrochemical company. Benchmark 24, 1786–1813. https://doi.org/10.1108/BIJ-03-2016-0044. Golany, B., Roll, Y., 1989. An application procedure for DEA. Omega 17, 237–250. https://doi.org/10.1016/0305-0483(89)90029-7. Goldratt, E.M., Cox, J., 1984. The Goal. North River Press, Croton-on-Hudson, NY. Gonzalez, -R.,P.L., Framinan, J.M., Ruiz-Usano, R., 2010. A multi-objective comparison of dispatching rules in a drum-buffer-rope production control system. Int. J. Comput. Integr. Manuf. 23, 155–167. https://doi.org/10.1080/09511920903440362. Gosling, J., Naim, M.M., 2009. Engineer-to-order supply chain management: a literature review and research agenda. Int. J. Prod. Econ. 122, 741–754. https://doi.org/10.10 16/j.ijpe.2009.07.002. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Athan, R., 2009. An� alise Multivariada de Dados, sixth ed. Bookman Editora, Porto Alegre. Hicks, C., McGovern, T., Earl, C.F., 2000. Supply chain management: a strategic issue in engineer to order manufacturing. Int. J. Prod. Econ. 65, 179–190. https://doi. org/10.1016/S0925-5273(99)00026-2.

12