Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.4 pp.730-743

Linkage Analysis among Factors in Obtaining Return Quantity / Volume: An Interpretive Structural Modelling on Construction Machinery Remanufacturing Industries

Evi Yuliawati*, Pratikto, Sugiono, Oyong Novareza
Department of Industrial Engineering, Faculty of Industrial Technology, Adhi Tama Institute of Technology Surabaya, Jl. Arief Rahman Hakim 100, Surabaya, East-Java, Indonesia Department of Mechanical Engineering, Faculty of Engineering, Brawijaya University
Department of Mechanical Engineering, Faculty of Engineering, Brawijaya University Jl. Mayjen Haryono 167, Malang, East-Java, Indonesia
Department of Industrial Engineering, Faculty of Engineering, Brawijaya University, Jl. Mayjen Haryono 167, Malang, East-Java, Indonesia
*Corresponding Author, E-mail:
November 9, 2019 June 16, 2020 June 5, 2020


Core acquisition management is one of the most crucial activities in remanufacturing industries. The availability of cores is important to support company's operational functions. This study aims to investigate the supporting factors of a successful core acquisition, focusing on return quantity / volume. Several key factors, considered as the root of the acquisition problem, have been identified through Interpretive Structural Model (ISM) method that involves the opinions of experts. From the initial identification, we obtained nine factors, in which two factors are in the dependent cluster, five factors are in the linkage cluster, and two factors are in the independent cluster. The results of the ISMbased model and MICMAC analysis showed that relationship type (factor 1) and convenience of logistics (factor 4) are the keys to successfully obtain an adequate amount of return quantity / volume. The results showed that the ISM method helps to simplify complex problems and suggest the remanufacturing companies to focus and pay more attention to these two factors, for the success of a core acquisition strategy.



    The concept of sustainable manufacturing appears as a topic that is relevant to the condition of limited natural resources. Initially, the company only considered forward supply chains to maximize profits but was not responsible for its end-of-use products. The rapid development of science and technology has boosted the production of heavy equipment in Indonesia. Based on the data from the Association of Heavy Equipment Manufacturers of Indonesia (HINABI), heavy equipment production was 7,981 units in 2018 or an increase of almost 42% compared to 2017. The construction sector with the construction machinery products has the second largest market share, after that forestry 13%, and plantation 12%. The increase has an impact on increasing waste from the construction machinery.

    The concept of Extended Producer Responsibility (EPR) emerged as a solution. The Organization for Economic Cooperation and Development (OECD) defines EPR as an environmental policy approach in which producer responsibility is extended to the post-consumer stage of the product life cycle. The main purpose of EPR is to reduce product waste by encouraging producers to take back old products. As a result, supply chains that consider forward and backward flow or closed-loop supply chain (CLSC) has emerged. The advantage of CLSC lies in how companies recover the value/function of returned products through reuse, recondition, remanufacturing, and recycling (Anityasari and Kaebernick, 2008).

    Remanufacturing emerged as one of the best recovery strategies because it produced better quality products with a lower selling price compared to the newly manufactured products. Alongside, research with topics around remanufacturing is increasing. Lund (1984) defines remanufacturing as a production process where it utilizes used material as its product/component. Then, the material will undergo a recovery process until the resulting product is functioning as good as new one (Östlin et al., 2009;Giuntini and Gaudette, 2003;Atasu et al., 2008).

    Giuntini and Gaudette (2003) found that the costs to remanufacture a used material/product are around 40%- 65% less comparing to the newly production. Besides, the price offered to customers is also 30%-40% cheaper. However, not all products are suitable for remanufacturing. Generally, the products must be durable, contain high-value components, have efficiency and effectiveness in the remake process and it must have market demand (Sundin and Bras, 2005;Ijomah et al., 2007). The United States is the world’s largest remanufacturer, which produced remanufacturing goods totaling 43 billion US dollars and 180,000 jobs in 2011 (Williamson et al., 2012). In India, most remanufacturing activities are limited to printer cartridges. Indian Cartridge Remanufacturers and Recyclers Affiliation (ICRRA) create, advance, and strengthen the remanufacturing business in India (Sharma et al., 2016).

    Östlin et al. (2008) explained that there are three main driving factors in remanufacturing operations, namely profit, company policy, and environment. When these three factors are combined, there is a great potential to be in a win-win situation, where customers get a quality product at a lower price, the remanufacturing process reduces production costs and the environmental impact will also be lower. The high attractiveness results in an increased demand for remanufactured products, which also increases the need for used-materials (cores) in the remanufacturing industries. Then, unbalance returndemand becomes the implication. This has an impact on the uncertainty of the availability of returned products in terms of volume/quantity, quality, and timing (Guide, 2000;Sundin and Dunbäck, 2013). This imbalance in return and demand makes it difficult for remanufacturing companies to carry out their production management and control functions.

    This study is focusing more on the core acquisition management process. Cores acquisition management is an activity to manage and identify potential sources for cores in remanufacturing process to reduce return-demand imbalances by overcoming uncertainty in return volume, timing, and core quality (Guide and Jayaraman, 2000). The survey conducted by Guide and Jayaraman (2000) shows that more than half of companies (61.5%) do not have control over the volume and timing of returns. Remanufacturing companies have implemented various kind of core acquisition strategies to reduce return-demand imbalances. This can be noticed within several remanufacturing companies that offer original spare parts to customers to repair its construction machinery units (Kleber et al., 2011). Although many remanufacturing companies experienced obstacles in the implementation of cores acquisition strategy, efforts to manage cores acquisition must continue. In accordance with Yuliawati et al. (2020), in which they explained that the RL performance in the front-end dimension actually gives lowest level of maturity compared to the engine and back-end dimensions. Hence, the front-end dimension requires a greater support for its development strategy.

    In this study, we identify factors that motivate business success in obtaining return quantity / volume in the core acquisition process. The initial factors are obtained and classified from surveys, literature reviews, and remanufacturing expert opinions on the field of construction machinery remanufacturing companies. Furthermore, Interpretive Structural Modeling (ISM) is used as a tool to construct structural relationships among these factors. ISM is a technique that is widely used in developing relationships between factors that affect specific problems. Following is the application of ISM in various fields. Zhou et al. (2019) implemented ISM to model attributes that can improve supply chain sustainability in the end-oflife vehicle (ELV) recycling industry in China. While Vasanthakumar et al. (2016) developed structural models that illustrate the interrelationships between factors which influence the implementation of lean remanufacturing. They succeeded to identify the dominant factors that influence the application of lean remanufacturing principles. Furthermore, Chakraborty et al. (2019) used Fuzzy Inter pretive Structural Modeling (FISM) to develop and evaluate structural relationships between the enablers and the barriers in the management of the automotive remanufacturing product business in India.

    From the above explanation, it can be seen that there is no specific research that examines the interaction of factors that motivate the success of business in obtaining return quantity / volume related to the core acquisition process in the remanufacturing industry. Wei et al. (2015b) detailed the determinants of the success of the cores acquisition process, those factors which are related to volume, timing and quality; but this research did not develop a reciprocal relationship between them.

    Therefore, a study is needed to analyze the structural linkages among the factors involved to assist management in determining and developing the right core acquisition strategy.

    Based on this background, this study aims to identify and analyze the interaction of factors that motivate business achievement in getting a return quantity / volume in the construction machinery remanufacturing industry using the ISM approach. Specifically, the goal is divided into three (1) identifying factors that motivate the success of getting a return quantity / volume in the remanufacturing industry, (2) developing a model that shows structural relationships among recommended factors, (3) determining the driving and dependence power of the factors identified.


    Review of the core acquisition process specifies three categories of activities in the management of acquisition cores (Guide and Jayaraman, 2000), namely (1) core acquisition, (2) strategies to reduce uncertainties, and (3) forecasting core availability. Critical factors play an important role in the cores acquisition process, which considers uncertainty in return quantity / volume, timing, and core quality (Wei et al., 2015b). Initial stage of this study is done by identifying and classifying what factors that motivate business success in getting a return quantity / volume to ensure the availability of cores in the construction machinery remanufacturing industries (see Table 1).

    Followings are the details of each identified factor.

    • 1. Relationship type (the correct form of relationship between remanufacturing companies and customers)

      • Volume returns that are not proportional to the demand for remanufactured products make the company must manage the core acquisition process well (Wei et al., 2015b) There are seven types of structural relationships between customers who act as suppliers of cores and remanufacturers (Östlin et al., 2008). The selection of the correct structural relationship can reduce the risk of unbalance return-demand.

    • 2. The market competition of cores (competition among remanufacturing companies)

      • Increased public awareness of the environment is driving the number of remanufacturing industries in Indonesia to increase. This has an impact on the increasing need for cores. Competition occurs not only between remanufacturing companies, but also with OEMs and OIs (Bulmus et al., 2014).

    • 3. Customer environmental awareness (increasing consumer awareness of the environment)

      • Consumers’ awareness of environmental aspects, including their willingness to buy remanufactured products, can increase return quantity / volume. An environmentally conscious supply chain design is assumed to improve environmental performance and positively influence consumer demand (Altmann, 2015).

    • 4. Logistics convenience (the availability of a used product return location that is easily accessible by consumers)

      • The selection of the correct reverse channel structure, particularly the determination of collection point used products for consumers, is an important consideration for remanufacturing companies. It was found that effectively used product collection activities are those that are close to the customer (Savaskan et al., 2004).

    • 5. Life-cycle stage of the product (the company understands the product life cycle)

      • The success of the remanufacturing process requires information about the market needs of the remanufactured product and information about the amount of return (Thierry et al., 1995). Understanding the life cycle of a remanufactured product is needed because it impacts the differences in the speed of collection and the remanufacturing process (Gan et al., 2015).

    • 6. Return behaviours of the customer (consumer behaviour towards end-of-use (EOU) or end-of-life (EOL) products)

      • There are three categories of customers viewed from the return behaviour: (1) Awareness-Driven Ones are consumers who return used products without rewards, (2) Reward-Driven Ones are consumers who return used products if there is a reward, and (3) Never-Return Ones are consumers who never return their used products (Bai, 2009). Different handling is needed in each customer category to support production management and control in the remanufacturing process.

    • 7. Design for Remanufacturing / DfRem (product design that considers the remanufacturing aspect)

      • The efficiency and effectiveness of the remanufacturing process depend mostly on how the product has been produced (Ijomah et al., 2007). According to the previous research, only a few companies design their products for remanufacturing (Gray and Charter, 2008). However, in the US and Europe, successful companies can be found with remanufacturing operations which maximize the potential of their products through DfRem (Hatcher et al., 2013a;Hatcher et al., 2013b).

    • 8. Refund policy (Remanufacturing companies which have a refund policy)

      • The increasing number of end-of-life (EOL) products poses a danger to the environment. Several refund policy scenarios are implemented in the core acquisition activities to reduce waste while increasing the availability of cores for remanufacturing companies. As done by Heydari et al. (2017) who developed models to improve customer willingness to return used products through discounted offers or direct costs in return. The willingness of customers to return EOL products increases due to government involvement through incentive contributions (tax exemptions and subsidies).

    • 9. Technology change (the availability of technology that supports the remanufacturing process)

      • The effort to balance between return and demand for remanufacturing products involves the function of many variables, including the level of technological innovation and the level of product expectations (Östlin et al., 2009).

    As already mentioned earlier, here we use ISM to construct structural relationships among the identified factors. ISM is a proven methodology that can design complex problem structures that are easier to understand. ISM was first developed by Warfield (1974). Vendor selection research that considers the interrelationships between the criteria carried out by Mandal and Deshmukh (1994) is the first research to implement ISM. Until now, ISM has become a popular tool among researchers and academics to analyse the interrelation of attributes through specific methodologies.

    Many researchers investigate the integration between ISM with Multi Criteria Decision Making (MCSM) methods, such as Analytical Network Process (ANP) and MICMAC. Integration between ISM with other methodologies are conducted by Hussain et al. (2015) which used ISM and Analytical Network Process (ANP); Govindan et al. (2015) which implemented ISM and fuzzy ANP; Vafadarnikjoo et al. (2018) that involved ISM with Fuzzy Delphi Method; and Venkatesh et al. (2015) in which they integrated ISM with fuzzy MICMAC. Many studies also applied ISM in various fields such as reverse logistics (Govindan et al., 2012; Ravi and Shankar, 2005; Bouzon et al., 2015) and other issues in the field of Supply Chain Management (Lim et al., 2017;Verma et al., 2018;Ansari et al., 2018). Furthermore, ISM is also widely implemented in various types of manufacturing industries in India, as done by Govindan et al. (2015) which investigated the interrelationship between remanufacturing factors in the auto parts manufacturing. Other works are done by Venkatesh et al. (2015), in which they structurally analysed the risks associated with apparel retail chain supply chains and Balon (2016) that explored green supply chain barriers in car industries.


    This study is done by a series of stages. The initial stage of this study includes data collection and data validation. Data collection is done to identify factors that motivate the success of obtaining a return quantity / volume in the core acquisition process. Data validation is needed to ensure that the identified factors are relevant to the problem. The ISM-based model is subsequently formed to determine the relationship between these factors. Figure 1 shows the completion methodology of research which is referred to the former study belongs to Govindan and Kannan (2010).

    The following are the details procedure of the ISM methodology which is adopted from Govindan and Kannan (2010).

    • Step 1: Develop the Structural Self-Interaction Matrix (SSIM)

    The first step of ISM Methodology is to determine the paired contextual relationships between identified factors. Determination of relationship is done by experts that coming from both industry, customers, and academics. The following symbols are used to indicate the direction of relationship between Fi factor and Fj factor.

    • - V: if Fi affects Fj

    • - A: if Fj affects Fi

    • - X: if Fi and Fj affects each other

    • - O: if Fi and Fj are not related.

    • Step 2: Develop the Reachability Matrix (RM)

    The second step is to transform SSIM into a binary matrix, by substituting the symbol of V, A, X, O with 1 and 0. The result of the transformation is called the initial reachability matrix. The rules for substitution 1 and 0 are as follows:

    • - If the entry (i, j) in SSIM is V, then the entry (i, j) in the reachability matrix will become 1, and the entry (j, i) becomes 0.

    • - If the entry (i, j) in SSIM is A, then the entry (i, j) in the reachability matrix will become 0, and the entry (j, i) becomes 1.

    • - If the entry (i, j) in SSIM is X, then the entry (i, j) in the reachability matrix will become 1, and the entry (j, i) also becomes 1.

    • - If entries (i, j) in SSIM are 0, then both (i, j) and (j, i) entries in the reachability matrix will become 0.

    In this step, a transitivity concept is then being compared to the initial reachability matrix in order to test the basic assumptions of ISM (Ravi, 2015). The transitivity concept states that if F1 affects F2, and F2 affects F3, then F1 will affect F3. The inconsistency of the relationship between these factors is eliminated through a transitivity testing (Mukherjee and Mondal, 2009). Modifications are needed to obtain the final RM.

    • Step 3: Develop the Level Partitions

    Once the final RM is obtained, the next step is to build the reachability and antecedent sets for each particular factor/variable. The reachability set consists of the factor/variable itself and other factors/variables which it may help to achieve. Whereas, the antecedent set consists of the factor/variable itself and other factors/variables which may help in alleviating it (Kannan and Haq, 2007). The intersection of the reachability and antecedent sets is derived for all factors. Levels for each factor are arranged in a hierarchy, starting from the top-level (level 1) in the ISM model. The next level is obtained by separating the selected factors from the remaining. The partition process is carried out until the levels for all factors are determined.

    • Step 4: Develop the Conical Matrix

    The next step is to convert the Final RM Matrix into a conical matrix. The purpose of this step is to determine the value of the driver and dependence powers of each factor. This is done by determining the total number of rows and columns for each factor.

    • Step 5: Build the ISM-based Model

    ISM-based model is a diagram that shows the direct and indirect relationship between factors. The model represents the factors and their linkages, which are depicted using nodes and line of the edges. The ISM-based model that is based on the Final RM Matrix is build and drawn in this step.


    4.1 Data Collection and Validation

    Data in this study are collected through surveys and interviews. The respondents are coming from three companies that are members of the Heavy Equipment Manufacturer Association of Indonesia (HINABI). These three companies carry out the remanufacturing process on several components of its construction machinery such as excavators, dump trucks, and bulldozers. The remanufactured spare parts include: engine, power train, electrical components, etc. Table 3 shows a general description of the remanufacturing companies involved in this qualitative study.

    Theoretical factors that motivate the success of a core acquisition process are traced through literature review within several reputable scientific databases, such as Springer, Elsevier, Emerald, Taylor & Francis, etc. After these factors are identified, they are then evaluated by experts, in order to eliminate factors that have little or no influence on return quantity / volume. In addition, the likelihood distances of each factor relative to other factors are also assessed. The experts were coming from both industry, academics, and customers with the distribution of each class is 50%, 25%, and 25%, respectively.

    Finally, nine factors were identified based on survey results, literature reviews, and expert opinions. Furthermore, these factors are classified into two aspects: (1) external factors, which are factors that motivate remanufacturing companies to conduct core acquisition and (2) internal factors, which are factors that inspire management to ensure remanufacturing operations run effectively.

    • Step 1: Develop the Structural Self-Interaction Matrix (SSIM)

    The aim of this step is to build a matrix of paired relationships between the identified factors that are represented in SSIM Matrix (Azar and Bayat, 2013). The identification process of the paired relationship is conducted based on experts’ assessment. The linkage between factors is represented in an SSIM matrix by consi- dering the mode of expert’s assessment (Azar and Bayat, 2013). The mode will be used as a value in the form of symbols in SSIM, which is summarized in Table 4.

    • Step 2: Develop the Reachability Matrix (RM)

    The result of the transformation is called the initial reachability matrix, as shown in Table 5.

    Modifications are needed to obtain the final Reachability Matrix, as shown in Table 6.

    • Step 3: Develop the Level Partitions

    The identified factors are divided into four levels. There are two factors at level 1, four factors at level 2, one factor at level 3, and two factors at level 4. Table 7, 8, 9, and 10 show the partitioning results of the identified factors at each level.

    • Step 4: Develop the Conical Matrix

    Result of the Conical Matrix is shown in Table 11.

    • Step 5: Build the ISM-based Model

    The ISM-based model shows a multi-level structural relationship between nine factors that motivates the success of efforts to obtain return quality/volume. In this problem, the identified factors are divided into four levels which can be seen in Figure 2.


    An analysis of Cross-Impact Matrix Multiplication Applied to Classification (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement/MICMAC) is employed in this study. MICMAC analysis aims to compare the driving and dependence powers of each factor (Kannan and Haq, 2007). The driving power is obtained from the sum of the rows for each factor, while the dependence power is calculated from the sum of the columns for each factor. In MICMAC analysis, the factors are classified into four clusters, namely:

    • 1. Autonomous cluster, which is a cluster consisting of factors that have weak driving power and weak dependence power

    • 2. Dependent cluster which consists of factors that have weak driving power and strong dependence power

    • 3. Linkage cluster, which is a cluster that consists of factors that have strong driving power and strong dependence power

    • 4. Independent cluster, which consists of factors that have strong driving power and weak dependence power

    The diagram of driving power-dependence power (see Figure 3) indicates that there are no factors included in the autonomous cluster. This shows that all factors affect the success of the business in getting return quantity / volume. Furthermore, it is important for a remanufacturing company to pay attention to all factors in order to ensure the availability of cores. Besides, this diagram also shows that there are two factors included in the dependent cluster, five factors included in the cluster linkage, and two factors included in the independent cluster.

    The results of the ISM Model and MICMAC Analysis are summarized in Table 12. Each factor is categorized in the appropriate level and cluster.

    Based on the result in Table 12, increasing competition between remanufacturing companies (F2) and con-sumer return behaviour towards end-of-use and end-oflife products (F6) are included in the dependent cluster. Both factors are at the top level of the ISM-based model. Factors with strong dependence power will depend on many other factors. Thus, the management of these factors will be influenced by the management of other relating factors. Therefore, the remanufacturing company should be focusing more on these factors.

    The increasing consumer awareness towards environment (F3), company’s understanding of product life cycle (F5), product design that considers remanufacturing aspects (F7), the availability of technology that supports the remanufacturing process (F9), and remanufacturing companies with a refund policy (F8) are included in the linkage cluster. The factors in these clusters tend to be unstable because they have strong driving and dependence powers. This indicates that the dependence on other factors is also high. These results are in line with the description of the factors in the ISM-based model; the five factors are at level 2 and 3, which is the link between the most basic level and the highest level.

    Finally, the independent cluster is consisting of two factors, which are the proper form of relationship between remanufacturing companies and customers (F1) and the availability of a product return location that is easily accessible to customers (F4). Both of these factors are key variables in the success of getting return quantity / volume. Relationship type (F1) influences the acquisition of return quantity / volume. The success of a remanufacturing company depends on the relationship between the company itself and the customer (F1). Return of used products is a critical factor for remanufacturing companies (Wei et al., 2015a). Therefore, the management of the relationship between remanufacturing companies and customers becomes very important. The location facilities for returning used products that are accessible to customers (F4) has important consequences on return quantity / volume. Designing the right closed-loop supply chain structure has the potential to increase both profits, market demand, and product returns (Savaskan et al., 2004).

    According to Östlin et al. (2008), there are seven structural relationships, namely ownership-based, servicecontract, direct-order, deposit-based, credit-based, buyback, and voluntary-based. Some studies implement structural relationships to collect returned products. The most widely applied relationship types are buy-back and credit-based. In line with this, Yuliawati et al. (2018) emphasized that there are important structural relationships because the reverse supply chain network structure determines the level of closeness of the customer to the remanufacturer, the level of OEM control over the customer, and the operational risk of the remanufacturing process. Jung and Hwang (2011) developed a buy-back strategy for core acquisition in two scenarios, namely competition and coordination between an OEM and a remanufacturer. Wei et al. (2015a) developed a mathematical model that focuses on deposit-based relationships on three types of refund policies, including single refund, multiple refund, and perfect refund policies. A mathematical model that considers two types of take-back systems was developed by Kwak and Kim (2013). The two systems are wastestream system and market-driven system. In the first system, the remanufacturer passively receives the returned product, while in the second system, the remanufacturer gives an incentive to the customer to get the returned product. Yamzon et al. (2016) developed a closed-loop supply chain model that integrates incentive levels and three take-back program types, which are discount, cash payment, and voluntary product return. The integration was carried out to increase product quantity and quality and also maximize supply chain profits.

    Logistics convenience (F4) has a large influence on return quantity / volume. Several scenarios are developed to determine the location of the return collector that is considered beneficial for the company and consumers. Modak et al. (2018) analyzed the model that develops three scenarios of collection activities, namely retailer led collection, manufacturer led collection, and the third party led collection. Feng et al. (2017) discusses three scenarios in reverse supply chain with dual-recycling channels, namely traditional single, single online, and hybrid in a centralized and decentralized scheme. Numerical calcul-ations showed if consumers prefer online-recycling channels. Qiaolun et al. (2011) showed the importance of determining the parties that have to do the collecting and processing activities in the reverse supply chain. Investigations were carried out on four models. The first model was the MCP, where the collecting and processing activities were carried out by the manufacturer. Then, the TCMP, where the collecting activities were carried out by the third party and the manufacturer performs the processing. Afterward, in RCMP, where collecting activity was carried out by retailers and manufacturers doing the processing. The last was TCTP, where collecting and processing activities were carried out by third parties. The implication of the model is the optimal pricing decision.

    5.1 Theoretical Implication

    In terms of theoretical implication, this study contributes through an effective investigation of obtaining return quantity / volume in a core acquisition process. Previously, the importance of product returns to remanufacturing companies, especially those dealing with construction machinery, was systematically explained. The ISM approach helps decision makers, especially for problems that involve many factors so that it becomes easier to understand. The interrelationships and interrelationships between factors are described in a multi-level hierarchy in the decision making process. The results revealed that relationship type and logistics convenience, which are included in the category of internal aspects, became a key variable of the success of a core acquisition strategy.

    5.2 Managerial Implication

    This study of an exploration towards a core acquisition process of the construction machinery remanufacturing industry has implications on the supply chain sustainability. The factors considered in the ISM model are built from both internal and external aspects. Internal aspects are interpreted as variables that originate from the remanufacturing company. While factors from external aspects are things that come from the side of stakeholders. The ISM approach illustrates the interrelationships between all of these factors in the process of core acquisition from different perspectives.

    The results of this study are useful for decision makers to better understand the complexity of the return quantity / volume problem in the core acquisition process. Furthermore, structured direction is also given to overcome these problems in order to improve system performance. Two key factors of concern are relationship type and convenience logistics. First, the type of relationship can be improved by determining the appropriate form of relationship between remanufacturing companies and suppliers of end-of-use and end-of-life products (in this case the consumers themselves).

    In a construction machinery industry, which requires a very large investment, causing the market for remanufacturing companies to be wide open, especially for units that have been out of warranty period. Remanufacturing companies can take advantage of this opportunity by offering a form of mutually beneficial relationship. There are seven types of structural relationships that can be applied according to Östlin et al. (2008). Secondly, convenience logistics can be improved by providing end-of-use and end-of-life product collection channels that are accessible. Heavy equipment products are products with large sizes and volumes, users often have difficulty if there is trouble with the machine. In order to improve performance on this factor, remanufacturing companies may consider collaborating with third parties or building workshops close to the location of the consumers. Both of these factors are included in the internal aspect, which means that improving the performance of remanufacturing companies depends on the decision makers within the company. Handling of these factors will affect the performance of other factors.

    Achieving sustainable performance in the construction machinery remanufacturing industry is important. This research can help achieve this performance, by focusing its performance improvement on the two key factors that have been identified.


    This research provides guidance for remanufacturing companies engaged in the field of construction machinery in prioritizing managerial efforts in the core acquisition process. The result of this study shows that nine factors determined the success of a core acquisition process, especially in the aspect of return quantity / volume. Seven factors were categorized as drivers or key variables, and two factors were dependent variables. The relationship between remanufacturing companies and customers and the location facilities of product return that are easily accessible to customers (logistics convenience) were two main factors that should be gained full attention from the management. The contribution of the ISM methodology and MICMAC analysis provides direction for decision makers to understand the complexity of the relationships between key factors, and provide structured direction for the success of a core acquisition strategy.

    However, this research is limited to certain case studies because the results obtained cannot yet represent the construction machinery remanufacturing industry in general. These limitations provides direction for further research, which involves other aspects in the core acquisition process, such as quality and timing aspects. In addition, it is also necessary to consider involving more other factors according to the latest references in obtaining return quantity / volume. Consideration of the involvement of statistical data testing techniques and methods can also be considered in order to improve the quality of research on the related topic.



    Flow diagram of the solution methodology.


    The ISM-based model developed in this study.


    Theoretical factors of a successful core acquisition strategy in remanufacturing companies

    Company’s general description involved in this study

    Factors that motivate the success of core acquisition in remanufacturing business

    Level 1 of reachability matrix interaction

    Level 2 of reachability matrix interaction

    Level 3 of reachability matrix interaction

    Level 4 of reachability matrix interaction

    Conical matrix

    Factors Clustering Result of the ISM-based Model and MICMAC Analysis


    1. Altmann, M. (2015), A supply chain design approach considering environmentally sensitive customers: The case of a German manufacturing SME, International Journal of Production Research, 53(21), 6534-6550,
    2. Anityasari, M. and Kaebernick, H. (2008), A concept of reliability evaluation for reuse and remanufacturing, International Journal Sustainable Manufacturing, 1(1-2), 3-17,
    3. Ansari, Z. N. , Kant, R. , and Shankar, R. (2018), Prioritizing the performance outcomes due to adoption of critical success factors of supply chain remanufacturing, Journal of Cleaner Production, 212, 779-799,
    4. Aras, N. , Güllü, R. , and Yürülmez, S. (2011), Optimal inventory and pricing policies for remanufacturable leased products, International Journal of Production Economics, 133(1), 262-271,
    5. Atasu, A. , Guide, V. D. R. , and Van Wassenhove, L. N. (2008), Product reuse economics in closed-loop supply chain research, Production And Operations Management, 17(5), 483-496,
    6. Atasu, A. , Toktay, L. B. , and Van Wassenhove, L. N. (2013), How collection cost structure drives a manufacturer’s reverse channel choice, Production and Operations Management, 22(5), 1089-1102,
    7. Azar, A. and Bayat, K. (2013), Designing a model for “Business process-orientation” using interpretive structural modeling approach (ISM), African Journal of Business Management, 7(26), 2558-2569,
    8. Bai, H. (2009), Reverse supply chain coordination and design for profitable returns: An example of ink cartridge, Master Thesis, Worcester Polytechnic, Institute, Worcester, MA, 2009.
    9. Balon, V. (2016), Assessment of barriers in green supply chain management using ISM : A case study of the automobile industry in India, Global Business Review, 17(1), 116-135,
    10. Bouzon, M. , Govindan, K. , Manuel, C. , and Rodriguez, T. (2015), Reducing the extraction of minerals: Reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach, Resources Policy, 46(1), 27-36,
    11. Bulmus, S. C. , Zhu, S. X. , and Teunter, R. (2014), Competition for cores in remanufacturing, European Journal of Operational Research, 233(1), 105-113,
    12. Chakraborty, K. , Mondal, S. , and Mukherjee, K. (2019), Critical analysis of enablers and barriers in extension of useful life of automotive products through remanufacturing, Journal of Cleaner Production, 227, 1117-1135,
    13. Chen, D. , Ignatius, J. , Sun, D. , Zhan, S. , Zhou, C. , Marra, M. , and Demirbag, M. (2018), Reverse logistics pricing strategy for a green supply chain: A view of customers’ environmental awareness, International Journal of Production Economics, 217, 197-210,
    14. Chuang, C. , Wang, C. X. , and Zhao, Y. (2014), Closed-loop supply chain models for a high-tech product under alternative reverse channel and collection cost structures, International Journal of Production Economics, 156, 108-123,
    15. Feng, L. , Govindan, K. , and Li, C. (2017), Strategic planning: Design and coordination for dual-recycling channel reverse supply chain considering consumer behavior, European Journal of Operational Research, 260(2), 601-612,
    16. Gan, S. S. , Pujawan, I. N. , Suparno, and Widodo, B. (2015), Pricing decision model for new and remanufactured short-life cycle products with time-dependent demand, Operations Research Perspectives, 2, 1-12,
    17. Gaur, J. , Subramoniam, R. , Govindan, K. , and Huisingh, D. (2018), Closed-loop supply chain management: From conceptual to an action oriented framework on core acquisition, Journal of Cleaner Production, 167, 1415-1424,
    18. Ghoreishi, N. , Jakiela, M. J. , and Nekouzadeh, A. (2011), A cost model for optimizing the take back phase of used product recovery, Journal of Remanufacturing, 1(1), 1,
    19. Giri, B. C. and Bardhan, S. (2016), Coordinating a two-echelon supply chain with environmentally aware consumers, International Journal of Management Science and Engineering Management, 11(3), 178-185,
    20. Giuntini, R. and Gaudette, K. (2003), Remanufacturing: The next great opportunity for boosting US productivity, Business Horizons, 46(6), 41-48, 21.
    21. Gönsch, J. (2014), Buying used products for remanufacturing: negotiating or posted pricing, Journal of Business Economics, 84(5), 715-747,
    22. Govindan, K. and Kannan, D. , Haq, A. N. (2010), Analyzing supplier development criteria for an automobile industry, Industrial Management & Data Systems, 110(1), 43-62,
    23. Govindan, K. , Palaniappan, M. , Zhu, Q. , and Kannan, D. (2012), Analysis of third party reverse logistics provider using interpretive structural modeling, International Journal of Production Economics, 140(1), 204-211,
    24. Govindan, K. , Shankar, K. M. , and Kannan, D. (2015), Application of fuzzy analytic network process for barrier evaluation in automotive parts remanufacturing towards cleaner production: A study in an Indian scenario, Journal of Cleaner Production, 114, 119-213,
    25. Gray, C. and Charter, M. (2008), Remanufacturing and product design, International Journal of Product Development, 6(3-4), 375. Available from: https://do
    26. Guide Jr., V. D. R. and Van Wassenhove, L. N. (2001), Managing product returns for remanufacturing, Production and Operations Management, 10(2), 142-155.
    27. Guide, V. D. R. (2000), Production planning and control for remanufacturing: Industry practice and research needs, Journal of Operations Management, 18(4), 467-483.
    28. Guide, V. D. R. and Jayaraman, V. (2000), Product acquisition management: Current industry practice and a proposed framework, International Journal of Production Research, 38(16), 3779-3800,
    29. Hatcher, G. D. , Ijomah, W. L. , and Windmill, J. F. C. (2013a), Integrating design for remanufacture into the design process: The operational factors, Journal of Cleaner Production, 39, 200-208,
    30. Hatcher, G. D. , Ijomah, W. L. , and Windmill, J. F. C. (2013b), Design for remanufacturing in China: A case study of electrical and electronic equipment, Journal of Remanufacturing, 3(1), 3,
    31. He, Y. (2015), Acquisition pricing and remanufacturing decisions in a closed-loop supply chain, International Journal of Production Economics, 163, 48-60,
    32. Heydari, J. , Govindan, K. , and Jafari, A. (2017), Reverse and closed loop supply chain coordination by considering government role, Transportation Research Part D: Transport and Environment, 52(A), 379-398,
    33. Hussain, M. , Awasthi, A. , and Tiwari, M. K. (2015), An ISM-ANP integrated framework for evaluating alternatives for sustainable supply chain management, Applied Mathematical Modelling, 40(5-6), 3671-3687,
    34. Ijomah, W. L. , McMahon, C. A. , Hammond, G. P. , and Newman, S. T. (2007), Development of robust design-for-remanufacturing guidelines to further the aims of sustainable development, International Journal of Production Research, 45(18-19), 4513-4536,
    35. Jung, K. S. and Hwang, H. (2011), Competition and cooperation in a remanufacturing system with take-back requirement, Journal of Intelligent Manufacturing, 22(3), 427-433,
    36. Kannan, G. and Haq, A. N. (2007), Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment, International Journal of Production Research, 45(17), 3831-3852,
    37. Kleber, R. , Zanoni, S. , and Zavanella, L. (2011), On how buyback and remanufacturing strategies affect the profitability of spare parts supply chains, International Journal of Production Economics, 133(1), 135-142,
    38. Kwak, M. and Kim, H. (2013), Market positioning of remanufactured products with optimal planning for part upgrades, Journal of Mechanical Design, 135(1), 011007,
    39. Li, J. , Wang, Z. , Jiang, B. , and Kim, T. (2017), Coordination strategies in a three-echelon reverse supply chain for economic and social benefit, Applied Mathematical Modelling, 49, 599-611,
    40. Lim, M. K. , Tseng, M. , Hua, K. , and Dat, T. (2017), Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach, Journal of Cleaner Production, 162, 806-816,
    41. Liu, L. , Wang, Z. , Xu, L. , Hong, X. , and Govindan, K. (2017), Collection effort and reverse channel choices in a closed-loop supply chain, Journal of Cleaner Production, 144, 492-500, Available from: https://do
    42. Lund, R. T. (1984), Remanufacturing: The Experience of the United States and Implications for Developing Countries, World Bank, Washington, DC.
    43. Mandal, A. and Deshmukh, S. G. (1994), Vendor selection using interpretive structural modelling (ISM), International Journal, 14(6), 52-59,
    44. Miao, Z. , Fu, K. , Xia, Z. , and Wang, Y. (2017), Models for closed-loop supply chain with trade-ins, Omega , 66(B), 308-326,
    45. Modak, N. M. , Modak, N. , Panda, S. , and Sana, S. S. (2018), Analyzing structure of two-echelon closed-loop supply chain for pricing, quality and recycling management, 171, 512-528, Journal of Cleaner Production,
    46. Mukherjee, K. and Mondal, S. (2009), Analysis of issues relating to remanufacturing technology: A case of an Indian company, Technology Analysis & Strategic Management, 21(5), 639-652,
    47. Östlin, J. , Sundin, E. , and Björkman, M. (2008), Importance of closed-loop supply chain relationships for product remanufacturing, International Journal of Production Economics, 115(2), 336-348,
    48. Östlin, J. , Sundin, E. , Björkman, M. , and Strategies, R. (2009), Product Lifecycle Implications for Remanufacturing Strategies, Journal of Cleaner Production, 17(11), 999-1009.
    49. Qiaolun, G. , Jianhua, J. , and Tiegang, G. (2011), Pricing decisions for reverse supply chains, Kybernetes, 40(5/6), 831-841,
    50. Ravi, V. , Shankar, R. (2015), Analysis of interactions among the barriers of reverse logistics, Technological Forecasting & Social Change, 72, 1011-1029,
    51. Ray, S. , Boyaci, T. , and Aras, N. (2005), Optimal prices and trade-in rebates for durable, remanufacturable products, Manufacturing & Service Operations Management, 7(3), 208-228,
    52. Sasikumar, P. , Kannan, G. , and Haq, A. N. (2010), A multi-echelon reverse logistics network design for product recovery-a case of truck tire remanufacturing, International Journal of Advanced Manufacturing Technology, 49(9-12), 1223-1234,
    53. Savaskan, R. C. , Bhattacharya, S. , and Van Wassenhove, L. N. (2004b), Closed-loop supply chain models with product remanufacturing, Management Science, 50(2), 239-252.
    54. Sharma, V. , Garg, S. K. , and Sharma, P. B. (2016), Identification of major drivers and roadblocks for remanufacturing in India, Journal of Cleaner Production, 112, 1882-1892,
    55. Sun, X. , Li, Y. , Govindan, K. , and Zhou, Y. (2013), Integrating dynamic acquisition pricing and remanufacturing decisions under random price-sensitive returns, International Journal of Advanced Manufacturing Technology, 68(1-4), 933-947,
    56. Sundin, E. and Bras, B. (2005), Making functional sales environmentally and economically beneficial through product remanufacturing, Journal of Cleaner Production, 13(9), 913-925,
    57. Sundin, E. and Dunbäck, O. (2013), Reverse logistics challenges in remanufacturing of automotive mechatronic devices, Journal of Remanufacturing, 3(1), 2.
    58. Thierry, M. , Salomon, M. , Van Nunen, J. , and Van Wassenhove, L. (1995), Strategic issues in product recovery management, California Management Review, 37(2), 114-135.
    59. Umeda, Y. , Kondoh, S. , and Sugino, T. (2005), Proposal of “marginal reuse rate” for evaluating reusability of products, International Conference on Engineering Design ICED 05, Melbourne.
    60. Vafadarnikjoo, A. , Mishra, N. , Govindan, K. , and Chalvatzis, K. (2018), Assessment of consumers’ motivations to purchase a remanufactured product by applying fuzzy Delphi method and single valued neutrosophic sets, Journal of Cleaner Production, 196, 230-244,
    61. Vasanthakumar, C. , Vinodh, S. , and Ramesh, K. (2016), Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices, International Journal of Production Research, 54(24), 7439-7452,
    62. Venkatesh, V. G. , Rathi, S. , and Patwa, S. (2015), Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling, Journal of Retailing and Consumer Services, 26, 153-167,
    63. Verma, A. , Seth, N. , and Singhal, N. (2018), Application of interpretive structural modelling to establish interrelationships among the enablers of supply chan competitiveness, Materials Today: Proceedings, 5(2), 4818-4823.
    64. Warfield, J. N. (1974), Developing Subsystem Matrices in Structural Modeling, IEEE Transactions on Systems, Man and Cybernetics, SMC-4(1), 74-80.
    65. Wei, S. , Tang, O. , and Liu, W. (2015a), Refund policies for cores with quality variation in OEM remanufacturing, International Journal of Production Economics, 170, 629-640,
    66. Wei, S. , Tang, O. , and Sundin, E. (2015b), Core (product) acquisition management for remanufacturing: A review, Journal of Remanufacturing, 5(1), 4,
    67. Williamson, I. A. , Pearson, D. R. , Aranoff, S. L. , Pinkert, D. A. , Johanson, D. S. , and Broadbent, M. M. (2012), Remanufactured Goods: An Overview of the U.S. and Global Industries, Markets, and Trade, United States International Trade Commission, USITC Publication 4356, Washington, 2012.
    68. Xiong, Y. , Li, G. , Zhou, Y. , Fernandes, K. , Harrison, R. , and Xiong, Z. (2014), Dynamic pricing models for used products in remanufacturing with lost-sales and uncertain quality, International Journal of Production Economics, 147, 678-688,
    69. Xu, Y. and Xie, H. (2016), Consumer Environmental Awareness and Coordination in Closed-Loop Supply Chain, Open Journal of Business and Management, 4(3), 427-438,
    70. Yamzon, A. , Ventura, V. , Guico, P. , and Sy, C. (2016), Optimal planning of incentive-based quality in closed-loop supply chains, Clean Technologies and Environmental Policy, 18, 1415-1431,
    71. Yan, W. , Xiong, Y. , Xiong, Z. , and Guo, N. (2015), Bricks vs. clicks: Which is better for marketing remanufactured products ? European Journal of Operational Research, 242(2), 434-444,
    72. Yi, P. , Huang, M. , Guo, L. , and Shi, T. (2016), Dual recycling channel decision in retailer oriented closed-loop supply chain for construction machinery remanufacturing, Journal of Cleaner Production, 137, 1393-1405,
    73. Yuliawati, E , Pratikto, P. , Sugiono, S. , and Novareza, O. (2018), A review of structural relationships in closed-loop supply chain model, IPTEK Journal of Proceedings Series, 6,
    74. Yuliawati, Evi, Pratikto, Sugiono, & Novareza, O. (2020). Measuring the reverse logistics performance of construction machinery remanufacturing company, Journal of Southwest Jiaotong University, 55(3),
    75. Zeng, A. Z. (2012), Coordination mechanisms for a three-stage reverse supply chain to increase profitable returns, Naval Research Logistics, 60(1), 31-45,
    76. Zhou, F. , Lim, M. K. , He, Y. , Lin, Y. , and Chen, S. (2019), End-of-life vehicle (ELV) recycling management: Improving performance using an ISM approach, Journal of Cleaner Production, 228, 231-243,
    Do not open for a day Close