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Int. J. Agile Systems and Management, Vol. 9, No. 2, 2016 89

Copyright © 2016 Inderscience Enterprises Ltd.

Using GreenSCOR to measure performance of the supply chain of furniture industry

Aries Susanty* and Sri Radina Putri Nur Hidayatika Department of Industrial Engineering, Diponegoro University, Prof. H. Soedarto S.H Road, Tembalang District, Semarang, Indonesia Email: [email protected] Email: [email protected] *Corresponding author

Ferry Jie School of Business IT and Logistics, College of Business, RMIT University, Melbourne VIC 3001, Australia Fax: 61-3-99255850 Email: [email protected]

Abstract: The purpose of this study is to measure and evaluate the performance of green supply chain management (later it is abbreviated as GSCM) practice which have been done by some enterprises in the furniture industry in Jepara, Central Java with GreenSCOR approach and propose some feedback to this industry. Data used in this study collected through in personal interview and closed questionnaires. A sample of this study was 20 furniture enterprises, consisted of four large scale enterprises on in-house manufacturing indoor, three large scale enterprises on in-house manufacturing outdoor, seven medium scale enterprises on in-house manufacturing indoor, and six medium scale enterprises on in-house manufacturing outdoor. The result of measurement indicated that the enterprises in the category large scale and in-house manufacturing outdoor have a better aggregate value of the sum total of performance index of each indicator for the implementation of GSCM practices compared with medium scale and in-house manufacturing indoor.

Keywords: GreenSCOR; Jepara; furniture industry; large scale of enterprises; medium scale of enterprises; in-house manufacturing indoor; in-house manufacturing outdoor.

Reference to this paper should be made as follows: Susanty, A., Hidayatika, S.R.P.N. and Jie, F. (2016) ‘Using GreenSCOR to measure performance of the supply chain of furniture industry’, Int. J. Agile Systems and Management, Vol. 9, No. 2, pp.89–113.

90 A. Susanty et al.

Biographical notes: Aries Susanty is a Lecturer in the Department of Industrial Engineering, Diponegoro University. She obtained her Doctoral in Industrial Engineering from the Bandung Institute of Technology. Her research interests include supply chain modelling, supply chain governance, supply chain policy, procurement, and logistics strategy. She has also interests in the field of management and organisation.

Sri Radina Putri Nur Hidayatika is a graduate from the Department of Industrial Engineering, Diponegoro University. During the college, she joined the Laboratory of Optimisation and Industrial System Planning and became one of assistant in that laboratory. Her research interests include supply chain modelling and green supply chain practice. Currently, she works in a private company in Jakarta.

Ferry Jie is a full time Academic Staff at School of Business IT and Logistics, RMIT University. He is currently a Deputy Programme Director, Master of Supply Chain and Logistics Management. He has been in academic career for more than eight years. He has a great value in teaching, research, leadership and community engagement. He has a strong commitment to improving the quality of working life at RMIT in particularly College of Business. In addition, he can make a full commitment to consult with academic staff within college and to represent their interests once the issues are addressed.

1 Introduction

The Indonesia’s wood-based industry has significant contribution to the national income earning through value added products such as furniture. In 2012, the value of furniture export is US$ 1.79 billion. Furniture is a big business involving a large number of workers. Small and medium enterprises (SMEs) have such an important role in the furniture sector that the health of the industry is important for efforts to alleviate poverty and reduce unemployment. In Indonesia, the furniture industry is concentrated in Jepara, Central Java. About 10% of the furniture made in Indonesia comes from Jepara, which has about 12,000 furniture business units and processes an estimated 0.9 million cubic metres of wood each year (Purnomo et al., 2011; Purnomo, 2006).

The significant contribution of the furniture industry to the national income is contrary with problems faced by this industry. In the downstream side, the Jepara furniture industry faces problems of forestry practice problem (e.g., illegal logging). Illegal logging which occurred in forest state owned companies in Java (PERHUTANI) caused timber scarcity, particularly teak and mahogany. PERHUTANI only supply 31.20% out the timber needed by this industry. The rest of the timber needed by this industry (68.8%) is supplied from outside Java and community forests across Java. Excluding timber sourced from PERHUTANI, this means that two thirds of the timber supply comes from outside Jepara. Given this heavy dependence on outside regions, it is important to build relationships with tree farmers in Maluku and Southeast Sulawesi. Timber from within Jepara district accounts for only 0.46% of the total demand, because

Using GreenSCOR to measure performance of the supply chain 91

the district offers little good-quality wood (Melati et al., 2013). In this case, the enterprises in the furniture industry can eliminate the practice of illegal logging by increasing their use of certified timber from suppliers. However, most enterprises in Jepara have very limited knowledge of such certification and their relevance to the sustainability of their business (Yovi et al. 2009). In the midstream sides, the furniture industry faced with solid waste generation problem. Furniture industry throws away an amount of wooden residue from the manufacturing process. The types of residues vary according to types of manufacturing process such as residues from sawmills, residues from plywood mills, and residues from wooden furniture manufacturing (Barua et al., 2014). These untreated residues can cause many damages both economic and environmental. So, treating this residue is important for the furniture industry to overcome the problem related to solid waste generation. In this case, the furniture industry can reuse and recycle the wood waste or use the wood waste as an energy or heat source. The recycling of wood waste into usable products has been studied for many years (Clausen, 2000; Khedari et al., 2004). Then, in the upstream sides, the furniture industry faced fierce competition with China and Vietnam in both the domestic and international markets and the distribution problem. The enterprise and the customer rarely have the same location. The size of finished product and the difficulty in handling make the furniture product have high transport cost. Besides that, there is another issue when the enterprise distributes the final product to their consumers, i.e. the choice of mode of transportation and route of distribution. The choice of mode of transportation and route of distribution not only related to the profit gained by the enterprise, but also the best choice of mode and route distribution will have an impact on the environment since it will be reducing the amount of carbon dioxide emissions discharged into the environment. This condition is also relevant for the process of delivery of raw material (wood) from the suppliers to the enterprise.

Based on the explanation of several problems faced by the furniture industry, it seems that the supply chain of furniture industry is vulnerable to environmental exploitations. So, it has become imperative to integrate the green or sustainable management practices into their supply chain in order to reduce its impact to environmental and maintain competitive advantage. According to Nikbakhsh (2009) incorporating environmental sustainability practices in the supply chain are often referred as green supply chain management (GSCM). GSCM is an approach to improve performance of the process and products according to the requirements of the environmental regulations (Hsu and Hu, 2008). GSCM includes supply chain activities ranging from green purchasing to the integration of life cycle management, all the way through the supplier, manufacturer and customer, to closing the supply chain loop with reverse logistics (Rao and Holt, 2005). Thus, the implementation of GSCM practices needs to be measured. There were various performance metrics have been developed to measure the operation of the entire supply chain. The appropriate performance metrics can be used to measure and evaluate the probability of success in achieving the target, to provide advice or corrective suggestions to the organisation, to provide a feedback system to the manager and to evaluate the internal input and output (Tesoro and Tootson 2000). Specifically, this study will use


green supply chain operations reference model (GreenSCOR) as a performance metrics to measure and evaluate GSCM practice of the furniture industry in Jepara, Central Java. GreenSCOR is a modification of the original SCOR model. GreenSCOR modifies the existing SCOR structure to include environmental processes, metrics, and best practices. By integrating environmental and supply chain management, GreenSCOR provides a foundation for improving operational activities while reducing environmental impacts (SCC, 2010). Hence, the purpose of this study is to measure and evaluate the performance of GSCM practice which have been done by some enterprises in the furniture industry in Jepara, Central Java with greenSCOR approach and propose some feedback to this industry.

The remainder of the paper is structured as follows. The next section describes the literature review and is followed by the discussion of the research methodology. The results are discussed subsequently. Finally, the theoretical and managerial implications and the limitation of the study are presented in the conclusion, along with the future research directions.

2 Literature review

2.1 Green supply chain management

There were several definitions of green supply chain management (GSCM). The idea of GSCM is to eliminate or minimise waste (energy, emissions, chemical/ hazardous, solid wastes) along the supply chain (Hervani et al., 2005). According to Zhu and Sarkis (2004), define of GSCM have ranged from green purchasing to integrated of supply chains starting from supplier, to manufacturer, to customer and reverse logistics, which is ‘closing the loop’. According to Srivastava (2007), GSCM can be defined as integrating environmental thinking into supply chain management, including product design, material sourcing and selection, manufacturing process, delivery of the final product to the consumers as well as end-of-life management of the product after its useful life.

GSCM has emerged in the last few years and covers all phases of a product’s life cycle from design, production and distribution phases to the use of products by the end users and its disposal at the end of a product’s life cycle. Based on this condition, there were a number of approaches for implementing GSCM practice have been proposed in previous literature (Hsu and Hu, 2008). Bowen et al. (2001) identified three types of implementation of GSCM practices, i.e. greening the supply process, product-based green supply, and advanced green supply. The first type of GSCM practices is adaptations made to the company’s supplier management activities in order to incorporate environmental considerations which include collecting environmental information on suppliers, assessing, ranking, and choosing according to supplier’s environmental performance. The second type of GSCM practices is based on the changes in the product supplied which is managing supply inputs such as packaging and recycling which needs cooperation with suppliers. The third type of GSCM practices includes more proactive measures such as introducing environmental criteria into buyers’ performance or entering into joint clean technology programs with suppliers. Not very different with Bowen et al. (2001), according to Hervani et al. (2005), there were four types of implementation of GSCM practices GSCM, i.e. green procurement, green manufacturing/material


management, green distribution/marketing, and reverse logistics. Green procurement is defined as an environmental purchasing consisting of involvement in activities that include the reduction, reuse and recycling of materials in the process of purchasing (Salam, 2008). Green manufacturing is defined as production processes which use inputs with relatively low environmental impacts, which are highly efficient, and which generate little or no waste or pollution (Atlas and Florida, 1998). Green distribution consists of green packaging and green logistics. Packaging characteristics such as size, shape, and materials have an impact on distribution because of their effect on the transport characteristics of the product. Better packaging, along with rearranged loading patterns, can reduce materials usage, increase space utilisation in the warehouse and in the trailer, and reduce the amount of handling required (Ho et al., 2009). Then, according to Rogers and Tibben-Lembke (1999), reverse logistics is the process of planning, implementing, and controlling the efficient, cost-effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal.

Recent studies of GSCM can be separated into two ways: framework for GSCM, and performance measurement. Some frameworks propose how to improve the collaborative relationships between manufacturers and suppliers, to explore the gaps between the framework and the present state, to aid managerial decision making, or to develop a general procedure towards achieving and maintaining the green supply chain (Beamon, 1999; Sarkis, 2003). A set of performance measures is used to determine the efficiency and/or effectiveness of an existing system, to compare competing alternative systems, or to design proposed systems by determining the values of the decision variables that yield the most desirable levels of performance (Beamon, 1999; Hervani et al., 2005). Rather than propose a framework, this study more focus on a set performance measurement for implementation of GSCM by the enterprises in the furniture industry, Jepara, Central Java.

2.2 GreenSCOR

SCOR is organised around five management process (plan, source, make, deliver, and return) that are further subdivided into process categories, elements, tasks and activities (Huang et al., 2005; Hwang et al., 2008; Kasi, 2005; SCC, 2010). According to Li et al. (2011), SCOR enables companies to examine the configuration of their supply chain. It also identifies and eliminates redundant and wasteful practices along supply chains. As a business process architecture, it defines the way these processes interact, how they perform and how they are configured from the supplier’s supplier to the customer’s customer (Min and Zhou, 2002; Huang et al., 2005; SCC, 2010). The GreenSCOR extension of the model was first introduced in its fifth version. Cheng et al. (2010) and Schoeman and Sanchez (2009) note that, GreenSCOR is a modification that integrates environmental considerations through the process, metrics and best practices in SCM processes, while taking into account the impacts of operations at each stage of the product life cycle. Then, based on best practices of GreenSCOR (SCC, 2010), the indicators used in this study for measuring the implementation of GSCM practice by the enterprises in the furniture industry, Jepara, Central Java can be seen in Table 1.


Table 1 Indicators used in this study for measuring the implementation of GSCM which adopted from GreenSCOR

Process category Best practices Description*Establish environmental requirements

The enterprises have established the supplier environmental requirements such as using the certified timber (P1)

Consider environmental production constraints

The enterprises have considered the environmental constraints as part of production capacity, such as planned to use timber substitutes in the process of making the furniture (P2)

Consider environmental impacts

The enterprises have considered environmental impacts when planning the need requirements and production process (P3)

Minimise energy use Plans are created by enterprises to minimise energy use, such as use the waste from timber as fuel for the oven (P4)

Plan

Maximise loads, minimise runs

The enterprises have planned to maximise load size and minimise transportation runs when received or delivered the product (P5)

Select suppliers with environmental management system (EMS)

The enterprises have selected the suppliers with active EMS systems (S1)

Source

Purchase recycled product The enterprises have purchased products from recyclers or remanufactures, such as purchased timber needed from recycles or remanufactures (S2)

Implement an environmental management system (EMS)

The enterprises have implemented an environmental management system (M1)

Schedule production activity The enterprises have implemented schedule production activity to minimise the energy consumed (M2)

Calculate waste produced as % of product produced

The enterprises have calculated the waste produced by the product produced (M3)

Recyclable waste/scrap The enterprises have implemented recycling waste/scrap produced from the production process (M4)

Use recyclable packaging The enterprises have used recycled packaging (M5)

Make

Provide environmental training

The enterprises have provided the environmental training to all employees (M6)

Route to minimise fuel consumption

The enterprises have chosen the best route to minimise the fuel consumption (D1)

Schedule to maximise transportation capacity

The enterprise has made a schedule for delivering the product which can maximise transportation capacity (D2)

Deliver

Enable customer direct shipments

Enable direct shipments between customers to reduce overall transportation and handling (D3)

Note: *Some of the description has been adjusted by the authors based on the field condition.


Table 1 Indicators used in this study for measuring the implementation of GSCM which adopted from GreenSCOR (continued)

Process category Best practices Description*

Replacement of defective product

The enterprises have made communicating with the customer before the return to establish what types of returns are acceptable (R1)

Return

Avoid returns beyond economic repair

The enterprises have estimated the damage to the product and did not physically return product that was beyond economical repair or offers no diagnostic value (R2)

Develop environmental performance standards.

The enterprises have developed the environmental performance standards (E1)

Enable

Integrate environmental considerations into business rules

The enterprises have made integrated environmental considerations into the business rules (E2)

Note: *Some of the description has been adjusted by the authors based on the field condition.

According to Cash and Wilkerson (2003), the adoption of GreenSCOR gives rise to three primary benefits.

1 Improved green management.

The goal of green SCM is to analyse a supply chain to identify opportunities to mitigate waste. GreenSCOR makes it possible to identify the best practices to do so. GreenSCOR requires the use of a clear set of metrics, which improves environmental management. Furthermore, environmental management benefits because it is possible to track intended environmental impacts in the supply chain.

2 Improved SCM performance.

Identifying environmental impacts directly translate into improved SCM. GreenSCOR initiates best practices and metrics, which simplifies the task of improving efficiency throughout the supply chain of a product.

3 Improved green SCM.

By making an allowance for environmental best practices in the supply chain metrics, the impact of the environmental factors is more visible. With improved environmental SCM come improved performance, greater operations efficiency and enhanced customer satisfaction. Improved green supply chain management improves agility, increases adaptability and promotes alignment of business processes.


3 Method of research

3.1 Instruments and measures

The 20 key performance indicators (KPI) were selected to measure and evaluate the performance of GSCM practice which have been done by some enterprises in the furniture industry in Jepara, Central Java. Out of these 20 indicators, five indicators were used to measure GSCM practices in the planning process, two indicators were used to measure GSCM practice in the sourcing process, six indicators were used to measure GSCM practices in making process, three indicators were used to measure GSCM practices in delivering process, two indicators were used to measure GSCM practices in returning process, and two indicators were used to measure GSCM practice in enabling process of material flow. All of those items were adapted from GreenSCOR v 10 (SCC, 2010). Each kind of KPI must be passed through the validation process from the company so that each KPI is fit with the furniture enterprises condition and the most important is that the KPI’s are measurable at the enterprises. Then each kind of KPI’s that valid is weighted with the concept of analytical hierarchy process (AHP) (Saaty, 1995). After all of that process, we can develop a system measurement for GSCM practices based on GreenSCOR model.

3.2 Questionnaire development

There were two types of questionnaire used in this research. The first type of questionnaire is AHP questionnaire. Here a questionnaire was prepared to compare the level of importance of each process category (plan, source, make, deliver, return, and enable) and the level of importance of each indicator which is belonging to each of process categories. More specifically, the AHP questionnaire was used in order to measure the relative weight of each process category and the relative weight of each indicator that contribute to the quality of implementation of GSCM in the furniture industry as it is perceived by an expert. Figure 1 presents these six process categories and each indicator that was used in the questionnaire. The tree-map constitutes a particularly efficient way to visualise the hierarchy of our problem as a diagram. At the top, the goal is placed, the process category as criteria are placed in the middle and the indicators as sub criteria at the bottom, in smaller rectangular boxes. In detail, each indicator which belongs to each process categories can be seen in Table 1. Then, for ease in answering the questionnaire, Saaty’s nine-point scale is used in AHP questionnaire, ranging from 1 (=equal importance between element I and j) to 9 (=absolute dominance of me over j), and reciprocal values, respectively. All values within the range of 1 to 9 and 1/9 to 1 are possible; the respondents were not restricted to the integer data points 1, 2 etc. and their reciprocals (Saaty, 1995). For example, if an expert replies that plan is absolute important than source, the plan is said to have a relative weight of nine times that of the source.

By structure, AHP questionnaire comprised of two sections. The first part included the demographic and operational characteristics designed to determine the fundamental issues, including the demographic characteristics of the respondent. The second part was devoted to pairwise comparison between the process categories (plan, source, make, deliver, return, and enable) and between the indicators. In this case, each process and indicators are paired and decision makers are called to note their preference between the


two processes and indicators. An example of the second part of AHP questionnaire can be seen in Table 2.

Figure 1 AHP hierarchical tree for assessing the priority weight of process category and indicators of implementation GSCM in the furniture industry

Assessment the priority weight of process category and indicators of implementation GSCM in furniture industry

Plan

Establish environmental requirements

Consider environmental production constraints

Consider environmental impacts

Minimize energy use

Maximize loads, minimize runs

Source

Select suppliers with Environmental Management System (EMS)

Purchase recycled product

Make

Implement an Environmental Management System (EMS)

Schedule production activity

Recyclable waste/scrap

Use recyclable packaging

Provide environmental training

Deliver

Route to minimize fuel consumption

Schedule to maximize transportation capacity

Enable customer direct shipments

Return

Replacement of defective product

Avoid returns beyond economic repair

Enable

Develop environmental performance standards

Integrate environmental considerations into business rules

Table 2 An example of the second part of AHP questionnaire: the pairwise comparison between the process categories

1 3 5 7 9 9 7 5 3 1

Plan Source

Plan Make

Plan Deliver

……. …..

Return Enable

Notes: 1 (= equal importance between element I and j) to 9 (= absolute dominance of me over j).

The second of the questionnaire is GSCM questionnaire. Although this questionnaire uses the same indicators as the AHP questionnaire, specifically, this questionnaire is used to measure the degree of implementation of GSCM practice by each SMEs in furniture industry which is represented by the score value of each indicator. For assessing the score value of each indicator, a five-point scale is used, ranging from 1 (= not considering it) to 5 (= implementing successfully). For example, if the enterprises have fully established the environmental requirements for their suppliers such as using the certified timber, then, the SMEs will get score five for this indicator. By structure, GSCM questionnaire


comprised of two sections. The first part of GSCM questionnaire consists of the demographic and operational characteristics which is designed to determine the fundamental issues, including the demographic characteristics of the respondent. The second part of GSCM questionnaire was devoted to the identification of the degree of the current practice of GSCM by the SMEs in the furniture industry. An example of the second part of GSCM questionnaire can be seen in Table 3. Table 3 An example of the second part of the GSCM questionnaire for assessing the degree of

implementation of indicators in plan category

1 2 3 4 5 1 The enterprises have established the supplier environmental

requirements such as using the certified timber

2 The enterprises have considered the environmental constraints as part of production capacity, such as planned to use timber substitutes in the process of making the furniture

3 The enterprises have considered environmental impacts when planning the need requirements and production process

4 Plans are created by enterprises to minimise energy use, such as use the waste from timber as fuel for the oven

5 The enterprises have planned to maximise load size and minimise transportation runs when received or delivered the product

Notes: (1= not considering it) to 5 (=implementing successfully).

3.3 Sample of the research

According to type of questionnaire used in this research, the sample of this research can be divided into two categories. The first category is an individual sample for AHP questionnaire and the second category is an SME sample for assessing the implementation of GSCM practice in the furniture industry.

3.3.1 Individual sample of AHP questionnaire

One of the major advantages of the AHP is that the analysis does not always require statistically significant sample size (Baby, 2013), so we do not require a large sample number to fill out the AHP questionnaire. Although the number of respondents who filled out a questionnaire is not important, but the quality of the respondents who filled out questionnaires need to be considered because the data set obtained from the survey of AHP questionnaire may reflects each respondent’s subjective judgment. Based on this condition, this study uses a non-probability, a purposive sampling technique for choosing the respondent to fill out the AHP questionnaire. Non-probability sampling is a sampling method where the samples are grouped in a process that does not give all the individuals in the population an equal chance of being selected (Ligthelm et al., 2005). Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher. In purposive sampling, the researcher will choose the samples with a purpose in mind (Zikmund and Babin, 2010). In the other words, in the purposive sampling, the respondents were selected in the sample is based upon some appropriate characteristic of the sample members. In this case, the inclusions


characteristic of the sample members for filling out the AHP questionnaire were as follows:

• people who have knowledge about the process production in the furniture industry

• people who have knowledge about the implementation of GSCM practice in the furniture industry

• people that are willing and that have the time to participate in the study.

Based on that characteristic, chairman of Indonesian Furniture and Handicraft Association (ASMINDO) Central Java, representatives of the Indonesian Environmental Forum (WALHI), and the owners of a furniture enterprise was chosen as the respondent to fill out the AHP questionnaire.

3.3.2 SMEs sample of GSCM questionnaire

A non-probability, a purposive sampling technique also used for choosing several SMEs as a respondent to fill out the GSCM questionnaire. In this case, the SMEs who became a respondent were selected based on the following criteria:

• The first criterion was to choose the enterprises who are connected with the main component of this research, i.e. GSCM and furniture industry.

• The second criterion was to choose the enterprises from various levels of the furniture industry in order to ensure that the role of GSCM practice of the process of making furniture performance can be identified comprehensively. For this reason, the enterprise will be chosen from large scale and also from medium scale.

• The third criterion employed was to choose only those enterprises which were associated with the process of making furniture from the sawmill process until the process of making a product and then delivering the product to the final customer. As the focus of the research is towards GSCM practices so it was important to choose the enterprise associated with the furniture manufacture in order to ensure compliance with the research theme.

Based on those three criteria, 20 enterprises that belong to large or medium scale, in-house manufacturing indoor and outdoor furniture were chosen as the respondent to fill out the GSCM questionnaire. These enterprises were consisted of four large scale of enterprises in-house manufacturing indoor, three large scale of enterprises in-house manufacturing outdoor, seven medium scale of enterprises in-house manufacturing indoor, and six medium scale of enterprises in-house manufacturing outdoor (see Table 4). Table 4 Sample of SMEs as respondent to fill out the GSCM questionnaire

In-house manicuring outdoor In-house manufacturing indoor

Large scale Four enterprises Three enterprises Medium scale Seven enterprises Six enterprises

In detail, according to three criteria, the background of those sample selection to become a respondent to fill out the GSCM questionnaire can be described as follows. Based on


Prestvik’s (2009) study of the Jepara furniture industry, there were seven categories of business units: workshops, log parks, sawmills, showrooms, warehouses, dry kilns and ironmongeries. Furniture workshops are then further categorised according to their types of production process: those that produce unfinished items from unprocessed round wood; those that purchase components, pieces and sets and then assemble them into a finished product; those that combine both these stages of furniture making; and those that produce only parts of furniture (Prestvik, 2009). Then, based on those that combine all the stages of furniture making and those that produce only parts of furniture, in brief, the furniture workshops in Jepara can be divided into in-house manufacturing and subcontracting manufacturing. In house manufacturing done all the stage of production process of making furniture, from receipt an order until delivering the final product. At in-house manufacturing, the supply process starts at the arrival of an order from a customer. The order is processed and entered into the enterprise’s production system. In processing the order, stages of work to be conducted for each position of the order are specified. If a new product is concerned, the offer calculation department will calculate prices for the products. This is the procedure with regards to part of customer’s orders, if prices for the orders have not been previously calculated. Different with in-house manufacturing, in the subcontracting manufacturing, enterprises just involve in the finishing of the final product, while the production process itself has been done by the others craftsmen based on the orders from the enterprises. These craftsmen usually have their specialty products to make, such as table, chair, cupboards, and so forth. The relationship between the enterprises and the craftsmen are only for short-term relationships, limited to the purchase of semi-finished product. Therefore, the enterprises didn’t have knowledge and also didn’t care about GSCM practices by the craftsmen, including the source of timber used by the craftsmen. Then related with type of product resulted, most furniture workshops in Jepara (in house manufacturing and subcontracting manufacturing) produce indoor furniture (89.5%), 7.8% produce outdoor furniture, and the remaining produce carvings, handicraft and calligraphy (Melati et al., 2013). Indoor furniture made of various furniture and equipment used to fulfil the function of a room in the house, such as for a terrace, living room, family room, dining room, study room, bedroom, kitchen, library, and others. Outdoor furniture (or garden furniture) manufactures furniture for outdoor use; but, recently, the outdoor furniture is also producing furniture that will be used in the indoor as on the terrace, living room, dining room, and others. Although the type of furniture produced is similar, the motif and finishing process of outdoor and indoor furniture is definitely different and it will be affect the waste of timber resulted.

3.4 Data collection procedure

This study utilised both primary and secondary sources of data. The primary sources of data consisting of questionnaires and personal interviews. The 20 copies of questionnaires and personal interviews were administered to the chairman of ASMINDO Central Java, representatives from the WALAHI, and the owners of a furniture enterprise belongs to large or medium scale, in-house manufacturing indoor and outdoor furniture. The secondary sources of data consisting of enterprise document as a complementary of the result of questionnaire and personal interviews.


4 Result of research

4.1 Priority weight

After obtaining the individual pairwise judgments of each respondent (there were three respondents chosen to fill out the AHP questionnaire), the next step is the computation of a vector of priorities or weighting of element (process category and indicators of each process category) in the matrix. In terms of matrix algebra, this consists of calculating the ‘principal vector’ of the matrix by adding the members of each column to find the total. In the next step, in order to normalise each column to sum to 1.0 or 100%, divide the elements of that column by the total of the column and sum them up. Finally, add the elements in each resulting row and divide this sum by the number of elements in the row to get the average. The results (principal vectors) show us the approximate priority weight of each element in process category and the approximate priority weight of each indicator by the individual members of the group of respondent. Then, the final priorities weight of each element in process category and the final priorities weight of each indicator given by the individual members is aggregated by arithmetic mean method to arrive at the group consensus. This study use arithmetic mean as a method to get the group consensus of priority weight because, according to results of research conducted by Yedla and Shrestha (2007), the arithmetic mean is the most appropriate and efficient method to be applied in AHP for group aggregation. The result of the calculation with arithmetic mean shows us that the elements in process category have the following approximate priority weights: plan (0.380), make (0.299), source (0.151), deliver (0.071), enable (0.055), and return (0.045). In detail, the results of group aggregation of priority weights for each process category (plan, make, source, deliver, enable, and return) and for each indicator are shown in column 4 in Table 5. Table 5 Composite priority weight for each process category and indicators

Process category (1)

Local weight

(2) Indicators (3) Local

weight (4)

Global weight

(5)

Establish environmental requirements (P1) 0.312 0.118 Consider environmental production constraints (P2) 0.101 0.038

Consider environmental impacts (P3) 0.374 0.142 Minimise energy use (P4) 0.134 0.051

Plan 0.380

Maximise loads, minimise runs (P5) 0.079 0.030 Select suppliers with environmental management 0.667 0.101 Source 0.151

Purchase recycled product (S2) 0.333 0.050 Implement an environmental management system 0.350 0.105

Schedule production activity (M2) 0.056 0.017 Waste produced as % of the product produced (M3) 0.099 0.029

Recyclable waste/scrap (M4) 0.291 0.087 Use recyclable packaging (M5) 0.132 0.040

Make 0.299

Provide environmental training (M6) 0.072 0.021 Route to minimise fuel consumption (D1) 0.333 0.024

Schedule to maximise transportation capacity (D2) 0.333 0.024 Deliver 0.071

Enable customer direct shipments (D3) 0.333 0.024


Table 5 Composite priority weight for each process category and indicators (continued)

Process category (1)

Local weight

(2) Indicators (3) Local

weight (4)

Global weight

(5) Replacement of defective product (R1) 0.250 0.011 Return 0.045

Avoid returns beyond economic repair (R2) 0.750 0.033 Develop environmental performance standards (E1) 0.667 0.037 Enable 0.055

Integrate environmental considerations (E2) 0.333 0.018

The result of group aggregation of priority weights for each indicator in column 4 Table 5 represents a local weight of each indicator. The global weight of each indicator depends on the weight of its corresponding process category, so the global weight of each indicator is calculated by multiplying the local weight with weight of corresponding process category. The global weight of each indicator can be seen in column 5 in Table 5. Thus, based on rearranged in descending order of priority, the first rank of indicator is occupied by considering environmental impacts (0.142). Establish environmental requirement (0.118) is in the top five ranking include implementing an environmental management system (0.105), select suppliers with environmental management system (0.101), recyclable waste/scrap (0.087), and minimise energy use (0.051).

4.2 Result of measurement of GSCM practice with GreenSCOR

The aggregate value of GSCM practice of each enterprise is the sum of the performance index of each indicator, which representing multiplication between the score value of indicators with the weight of the indicator. As the scoring system, the score of each indicator needs to be equalised using normalisation process (Snorm) of DeBoer (Trienekens and Hvolby, 2000), so the measurement scale from 0 – 100 for each indicator could be obtained. This normalisation process is aimed at obtaining the comparable scales of the indicator values because the GreenSCOR use multi-indicators with Likert Scale for evaluation the implementation of the practice of GSCM in the furniture industry. In this case, Likert scaling used for qualitative indicators and this qualitative indicator value which is obtained from survey data must be normalised to a scale that is common for all indicators. This is accomplished by constructing a normalising function for each indicator. Normalisation aims at obtaining comparable scales that allow inter indicator comparison (Watts, 1997). So, Snorm normalisation is needed in order to create a standard scale of the indicators that call now being used. This is the Snorm equation of DeBoer:

( ) ( )( )( ) ( )( )

i min max min

max x max min

Larger is better :Snorm S – S S – S 100

Lower is better :Snorm S – S S – S 100

= ×

= ×

where

Si the actual score of each indicator

Smin minimum scale

Smax maximum scale.


Larger is better is used when the higher observed value represents better performance, as in case of indicator P1 (enterprises have established the supplier environmental requirements such as using the certified timber). This is an example of the calculation to get the standard scale of indicators P1 for CV. Majawana with larger is better equation.

( ) ( ) ( )i min max minLarger is better :Snorm S – S S – S 100 (4 –1) (5 –1) 100 75= × = × =

Lower is better used when the lower value represents better performance. In this study, lower is better is not used to create a standard scale of indicators since the best condition of each indicator achieved when the indicator can get the highest value.

The Tables 6, 7, 8, and 9 is a list of scores (after normalisation process) and performance index of each indicator for each surveyed enterprise which is consist of six large scale of enterprise in-house manufacturing indoor, seven large scale of enterprise in-house manufacturing outdoor, four medium scale of enterprise in-house manufacturing indoor, three medium scale of enterprise in-house manufacturing indoor outdoor. Thus, Table 10 shows us the average of score and performance index of each indicator from all surveyed enterprises in the each category (large-medium, indoor-outdoor). This study uses the interval value from Trienekens and Hvolby (2000) for grouping the aggregate value of GSCM practice from all the surveyed enterprises (see the value in the bottom row in Table 6 to Table 9) in order to differentiate those enterprises between poor, marginal, average or excellent. This interval value is used because Trienekens and Hvolby (2000) also used this interval value for measuring the performance in supply chain although they didn’t use GreenSCOR as a framework.

The major findings are discussed as follows. The aggregate value of GSCM practice from each surveyed enterprises shows us that PT. Kota Jati has the highest aggregate value; The major findings are discussed as follows. The aggregate value of GSCM practice from each surveyed enterprises shows us that PT. Kota Jati has the highest aggregate value; whereas PT. Jepara Original has the lowest aggregate value. PT Jati belongs to large-scale enterprises in-house manufacturing outdoor, whereas PT. Jepara Orginal belongs to medium-scale enterprises in-house manufacturing indoor. Compared with medium scale, the enterprises in the category large scale have better aggregate value. Most of enterprise in this category has more concern with doing GSCM practice than medium scale enterprises, such as the origin of the timber used in the production process. Most of them also encouraging the reduction of a waste from sawmilling and furniture production process by introducing more efficient wood processing and manufacturing methods, decreasing wood drying degrade, utilizing small dimension timbers and wood off-cuts for various wood components, and introducing new technologies such as wood bending and laminating. Then, compared with in-house manufacturing indoor, in-house manufacturing outdoor have better aggregate value. All the large-scale enterprises in-house manufacturing outdoor included in a good category with an aggregate value ranging from 79 – 85. Most of in-house manufacturing outdoor produce furniture with simple design. This design makes less waste wood resulted and more product can be made for each piece of wood. Besides that, the dimension and shape of waste wood generated from production processes are suitable for producing a waste product that is based on the furniture which utilize the waste. In this case, the waste product would utilize lower quality and smaller dimension timbers. Now, this product is exported to other countries, such as Italy, Singapore, Malaysia, and China.


Table 6 Result of measurement of GSCM practice of large-scale enterprises in-house manufacturing indoor

Scor

e P

erfo

rman

ce in

dex

Indi

cato

rs

CV

. M

ajaw

ana

PT.

Ken

t D

evon

P

T. M

axim

In

doow

od

PT.

Tal

enta

ja

va d

esig

n W

eigh

t C

V.

Maj

awan

a P

T. K

ent

Dev

on

PT.

Max

im

Indo

owod

P

T. T

alen

ta

java

des

ign

Est

ablis

h en

v re

quir

emen

ts (P

1)

75

75

100

100

0.11

8 8.

850

8.85

0 11

.800

11

.800

C

onsi

der e

nv p

rodu

ctio

n co

nstr

aint

s (P

2)

75

100

100

100

0.03

8 2.

850

3.80

0 3.

800

3.80

0

Con

side

r env

impa

cts

(P3)

10

0 10

0 10

0 10

0 0.

142

14.2

00

14.2

00

14.2

00

14.2

00

Min

imis

e en

ergy

use

(P4)

10

0 10

0 10

0 10

0 0.

051

5.10

0 5.

100

5.10

0 5.

100

Max

load

s, m

in ru

ns (P

5)

100

100

100

100

0.03

0 3.

000

3.00

0 3.

000

3.00

0 Se

lect

sup

plie

rs w

ith E

MS

(S1)

75

75

10

0 10

0 0.

101

7.57

5 7.

575

10.1

00

10.1

00

Purc

hase

recy

cled

pro

duct

(S2)

0

0 0

0 0.

050

0.00

0 0.

000

0.00

0 0.

000

Impl

emen

t an

EM

S (M

1)

75

75

100

100

0.10

5 7.

875

7.87

5 10

.500

10

.500

Sc

hedu

le p

rod

activ

ity (M

2)

75

50

75

75

0.01

7 1.

275

0.85

0 1.

275

1.27

5 W

aste

pro

duce

d as

% o

f the

pr

oduc

t pro

duce

d (M

3)

100

100

100

100

0.02

9 2.

900

2.90

0 2.

900

2.90

0

Rec

ycla

ble

was

te/s

crap

(M4)

0

0 0

0 0.

087

0.00

0 0.

000

0.00

0 0.

000

Use

recy

clab

le p

acka

ging

(M5)

10

0 10

0 10

0 10

0 0.

040

4.00

0 4.

000

4.00

0 4.

000

Prov

ide

env

trai

ning

(M6)

75

25

10

0 75

0.

021

1.57

5 0.

525

2.10

0 1.

575

Rou

te to

min

fuel

con

sum

ptio

n (D

1)

100

100

100

100

0.02

4 2.

400

2.40

0 2.

400

2.40

0

Sche

dule

to m

ax tr

ansp

orta

tion

capa

city

(D2)

10

0 10

0 10

0 10

0 0.

024

2.40

0 2.

400

2.40

0 2.

400

Ena

ble

cust

omer

dir

ect

ship

men

ts (D

3)

100

0 10

0 10

0 0.

024

2.40

0 0.

000

2.40

0 2.

400

Rep

lace

men

t of d

efec

tive

prod

uct (

R1)

75

0

0 75

0.

011

0.82

5 0.

000

0.00

0 0.

825

Avo

id re

turn

s be

yond

eco

nom

ic

repa

ir (R

2)

0 0

0 0

0.03

3 0.

000

0.00

0 0.

000

0.00

0

Dev

elop

env

per

form

ance

st

anda

rds

(E1)

75

50

10

0 10

0 0.

037

2.77

5 1.

850

3.70

0 3.

700

Inte

grat

e en

v co

nsid

erat

ions

(E

2)

75

75

100

100

0.01

8 1.

350

1.35

0 1.

800

1.80

0

Agg

rega

te v

alue

of G

SCM

pra

ctic

e of

eac

h en

terp

rise

71

67

81

82

Cat

egor

y*)

Ave

rage

M

argi

nal

Goo

d G

ood

Not

es: *

) acc

ordi

ng to

Tri

enek

ens

and

Hvo

lby

(200

0): ≤

40:

poo

r; 4

1-60

: mar

gina

l; 51

–70:

ave

rage

; 71–

90: g

ood;

>90

: exc

elle

nt.


Table 7 Result of measurement of GSCM practice of large-scale enterprises in-house manufacturing outdoor

Scor

e Pe

rfor

man

ce in

dex

Indi

cato

rs

CV.

Dut

a Je

para

C

V. M

andi

ri

Abad

i PT

. Kot

a Ja

ti W

eigh

t C

V. D

uta

Jepa

ra

CV.

Man

diri

Ab

adi

PT. K

ota

Jati

Esta

blis

h en

v re

quire

men

ts (P

1)

100

100

100

0.11

8 14

.200

14

.200

14

.200

C

onsi

der e

nv p

rodu

ctio

n co

nstra

ints

(P2)

10

0 10

0 10

0 0.

038

5.10

0 5.

100

5.10

0 C

onsi

der e

nv im

pact

s (P3

) 10

0 10

0 75

0.

142

3.00

0 3.

000

2.25

0 M

inim

ise

ener

gy u

se (P

4)

100

100

100

0.05

1 10

.100

10

.100

10

.100

M

ax lo

ads,

min

runs

(P5)

0

0 0

0.03

0 0.

000

0.00

0 0.

000

Sele

ct su

pplie

rs w

ith E

MS

(S1)

10

0 10

0 10

0 0.

101

10.5

00

10.5

00

10.5

00

Purc

hase

recy

cled

pro

duct

(S2)

75

75

75

0.

050

1.27

5 1.

275

1.27

5 Im

plem

ent a

n EM

S (M

1)

100

100

100

0.10

5 2.

900

2.90

0 2.

900

Sche

dule

pro

d ac

tivity

(M2)

0

75

75

0.01

7 0.

000

6.52

5 6.

525

Was

te p

rodu

ced

as %

of t

he p

rodu

ct p

rodu

ced

(M3)

10

0 10

0 10

0 0.

029

4.00

0 4.

000

4.00

0 R

ecyc

labl

e w

aste

/scr

ap (M

4)

100

25

100

0.08

7 2.

100

0.52

5 2.

100

Use

recy

clab

le p

acka

ging

(M5)

10

0 10

0 10

0 0.

040

2.40

0 2.

400

2.40

0 Pr

ovid

e en

v tra

inin

g (M

6)

100

100

75

0.02

1 2.

400

2.40

0 1.

800

Rou

te to

min

fuel

con

sum

ptio

n (D

1)

100

100

100

0.02

4 2.

400

2.40

0 2.

400

Sche

dule

to m

ax tr

ansp

orta

tion

capa

city

(D2)

75

75

75

0.

024

0.82

5 0.

825

0.82

5 En

able

cus

tom

er d

irect

ship

men

ts (D

3)

0 0

0 0.

024

0.00

0 0.

000

0.00

0 R

epla

cem

ent o

f def

ectiv

e pr

oduc

t (R

1)

100

100

100

0.01

1 3.

700

3.70

0 3.

700

Avo

id re

turn

s bey

ond

econ

omic

repa

ir (R

2)

100

100

100

0.03

3 1.

800

1.80

0 1.

800

Dev

elop

env

per

form

ance

stan

dard

s (E1

) 10

0 10

0 10

0 0.

037

14.2

00

14.2

00

14.2

00

Inte

grat

e en

v co

nsid

erat

ions

(E2)

10

0 10

0 10

0 0.

018

5.10

0 5.

100

5.10

0

Aggr

egat

e va

lue

of G

SCM

pra

ctic

e of

eac

h en

terp

rise

79

84

85

Cat

egor

y*)

Goo

d G

ood

Goo

d

Not

es: *

) acc

ordi

ng to

Trie

neke

ns a

nd H

volb

y (2

000)

: ≤ 4

0: p

oor;

41–6

0: m

argi

nal;

51–7

0: a

vera

ge; 7

1–90

: goo

d; >

90.


Table 8 Result of measurement of GSCM practice of medium-scale enterprises in-house manufacturing indoor

Scor

e P

erfo

rman

ce in

dex

Indi

cato

rs

PT.

E

MI

CV

. Je

para

or

igin

al

PT.

H

EI

PT.

M

onta

igne

fu

rnitu

re

CV

. A

ldon

a fu

rnitu

re

Den

smar

t fu

rnitu

re

UD

. Pri

ma

Rid

ha

Citr

a

Wei

ght

PT.

E

MI

CV

. Je

para

or

igin

al

PT.

H

EI

PT.

M

onta

igne

fu

rnitu

re

CV

. A

ldon

a fu

rnitu

re

Den

smar

t fu

rnitu

re

UD

. P

rim

a R

idha

C

itra

Est

ablis

h en

v re

quir

emen

ts

(P1)

75

75

75

75

75

75

75

0.

118

8.85

0 8.

850

8.85

0 8.

850

8.85

0 8.

850

8.85

0

Con

side

r en

v pr

oduc

tion

cons

trai

nts

(P2)

75

10

0 10

0 50

10

0 50

75

0.

038

2.85

0 3.

800

3.80

0 1.

900

3.80

0 1.

900

2.85

0

Con

side

r en

v im

pact

s (P

3)

100

100

100

100

100

100

100

0.14

2 14

.200

14

.200

14

.20

0 14

.200

14

.200

14

.200

14

.200

Min

imis

e en

ergy

use

(P

4)

0 0

100

100

100

100

0 0.

051

0.00

0 0.

000

5.10

0 5.

100

5.10

0 5.

100

0.00

0 M

ax lo

ads,

min

run

s (P

5)

75

0 10

0 10

0 10

0 10

0 75

0.

030

2.25

0 0.

000

3.00

0 3.

000

3.00

0 3.

000

2.25

0 Se

lect

sup

plie

rs w

ith E

MS

(S1)

75

75

75

75

75

75

75

0.

101

7.57

5 7.

575

7.57

5 7.

575

7.57

5 7.

575

7.57

5

Purc

hase

rec

ycle

d pr

oduc

t (S

2)

100

0 0

0 0

0 0

0.05

0 5.

000

0.00

0 0.

000

0.00

0 0.

000

0.00

0 0.

000

Impl

emen

t an

EM

S (M

1)

75

75

75

75

75

75

75

0.10

5 7.

875

7.87

5 7.

875

7.87

5 7.

875

7.87

5 7.

875

Sche

dule

pro

d ac

tivity

(M

2)

75

50

50

75

50

50

0 0.

017

1.27

5 0.

850

0.85

0 1.

275

0.85

0 0.

850

0.00

0 W

aste

pro

duce

d as

% o

f th

e pr

oduc

t pro

duce

d (M

3)

100

75

100

75

75

75

75

0.02

9 2.

900

2.17

5 2.

900

2.17

5 2.

175

2.17

5 2.

175

Rec

ycla

ble

was

te/s

crap

(M

4)

0 75

0

0 0

0 0

0.08

7 0.

000

6.52

5 0.

000

0.00

0 0.

000

0.00

0 0.

000

Use

rec

ycla

ble

pack

agin

g (M

5)

100

100

100

100

100

100

100

0.04

0 4.

000

4.00

0 4.

000

4.00

0 4.

000

4.00

0 4.

000

Pro

vide

env

trai

ning

(M

6)

50

25

25

25

25

25

25

0.02

1 1.

050

0.52

5 0.

525

0.52

5 0.

525

0.52

5 0.

525

Rou

te to

min

fue

l co

nsum

ptio

n (D

1)

100

0 10

0 10

0 10

0 10

0 50

0.

024

2.40

0 0.

000

2.40

0 2.

400

2.40

0 2.

400

1.20

0

Sche

dule

to m

ax

tran

spor

tatio

n ca

paci

ty (

D2)

75

0

100

100

100

100

75

0.02

4 1.

800

0.00

0 2.

400

2.40

0 2.

400

2.40

0 1.

800

Ena

ble

cust

omer

dir

ect

ship

men

ts (

D3)

10

0 0

100

0 10

0 0

75

0.02

4 2.

400

0.00

0 2.

400

0.00

0 2.

400

0.00

0 1.

800

Rep

lace

men

t of

defe

ctiv

e pr

oduc

t (R

1)

75

75

75

0 0

75

75

0.01

1 0.

825

0.82

5 0.

825

0.00

0 0.

000

0.82

5 0.

825

Avo

id r

etur

ns b

eyon

d ec

onom

ic r

epai

r (R

2)

0 0

0 0

0 0

0 0.

033

0.00

0 0.

000

0.00

0 0.

000

0.00

0 0.

000

0.00

0

Dev

elop

env

per

form

ance

st

anda

rds

(E1)

25

25

75

85

50

50

50

0.

037

0.92

5 0.

925

2.77

5 3.

145

1.85

0 1.

850

1.85

0

Inte

grat

e en

v co

nsid

erat

ions

(E

2)

50

50

75

50

50

50

50

0.01

8 0.

900

0.90

0 1.

350

0.90

0 0.

900

0.90

0 0.

900

Agg

rega

te v

alue

of G

SCM

pra

ctic

e of

eac

h en

terp

rise

67

59

71

65

68

64

59

Cat

egor

y*)

Ave

rage

Ave

rage

G

ood

Ave

rage

A

vera

ge

Ave

rage

A

vera

ge

Not

es: *

) ac

cord

ing

to T

rien

eken

s an

d H

volb

y (2

000)

: ≤ 4

0: p

oor;

41–

60: m

argi

nal;

51–7

0: a

vera

ge; 7

1–90

: goo

d; >

90: e

xcel

lent

.


Table 9 Result of measurement of GSCM practice of medium-scale enterprises in-house manufacturing outdoor

Scor

e Pe

rfor

man

ce in

dex

Indi

cato

rs

PT. B

uana

M

ulti

Prat

ama

CV.

K

alin

gga

Jati

PT.

Zakr

a

CV.

Is

tana

Pe

rabo

t

CV.

Q

ueen

N

atur

e ha

bita

t W

eigh

t PT

. Bua

na

Mul

ti Pr

atam

a

CV.

K

alin

gga

Jati

PT.

Zakr

a

CV.

Is

tana

Pe

rabo

t

CV.

Q

ueen

N

atur

e ha

bita

t

Esta

blis

h en

v re

quire

men

ts (P

1)

100

75

75

75

100

75

0.11

8 11

.800

8.

850

8.85

0 8.

850

11.8

00

8.85

0 Es

tabl

ish

env

requ

irem

ents

(P1)

0

0 75

75

50

0

0.03

8 0.

000

0.00

0 2.

850

2.85

0 1.

900

0.00

0 C

onsi

der e

nv p

rodu

ctio

n co

nstra

ints

(P2)

10

0 10

0 10

0 10

0 10

0 10

0 0.

142

14.2

00

14.2

00

14.2

00

14.2

00

14.2

00

14.2

00

Con

side

r env

impa

cts (

P3)

100

100

0 10

0 10

0 10

0 0.

051

5.10

0 5.

100

0.00

0 5.

100

5.10

0 5.

100

Min

imis

e en

ergy

use

(P4)

10

0 10

0 10

0 0

0 10

0 0.

030

3.00

0 3.

000

3.00

0 0.

000

0.00

0 3.

000

Max

load

s, m

in ru

ns (P

5)

100

75

75

75

100

75

0.10

1 10

.100

7.

575

7.57

5 7.

575

10.1

00

7.57

5 Se

lect

supp

liers

with

EM

S (S

1)

0 0

75

75

0 10

0 0.

050

0.00

0 0.

000

3.75

0 3.

750

0.00

0 5.

000

Purc

hase

recy

cled

pro

duct

(S2)

10

0 75

10

0 75

75

75

0.

105

10.5

00

7.87

5 10

.500

7.

875

7.87

5 7.

875

Impl

emen

t an

EMS

(M1)

75

75

75

75

75

75

0.

017

1.27

5 1.

275

1.27

5 1.

275

1.27

5 1.

275

Sche

dule

pro

d ac

tivity

(M2)

10

0 10

0 10

0 75

10

0 10

0 0.

029

2.90

0 2.

900

2.90

0 2.

175

2.90

0 2.

900

Was

te p

rodu

ced

as %

of t

he p

rodu

ct

prod

uced

(M3)

0

75

75

75

75

75

0.08

7 0.

000

6.52

5 6.

525

6.52

5 6.

525

6.52

5

Rec

ycla

ble

was

te/s

crap

(M4)

10

0 10

0 10

0 10

0 10

0 10

0 0.

040

4.00

0 4.

000

4.00

0 4.

000

4.00

0 4.

000

Use

recy

clab

le p

acka

ging

(M5)

75

10

0 75

75

75

50

0.

021

1.57

5 2.

100

1.57

5 1.

575

1.57

5 1.

050

Prov

ide

env

train

ing

(M6)

10

0 10

0 10

0 10

0 0

100

0.02

4 2.

400

2.40

0 2.

400

2.40

0 0.

000

2.40

0 R

oute

to m

in fu

el c

onsu

mpt

ion

(D1)

10

0 10

0 10

0 0

0 10

0 0.

024

2.40

0 2.

400

2.40

0 0.

000

0.00

0 2.

400

Sche

dule

to m

ax tr

ansp

orta

tion

capa

city

(D

2)

100

100

100

100

100

100

0.02

4 2.

400

2.40

0 2.

400

2.40

0 2.

400

2.40

0

Enab

le c

usto

mer

dire

ct sh

ipm

ents

(D3)

75

10

0 75

75

75

75

0.

011

0.82

5 1.

100

0.82

5 0.

825

0.82

5 0.

825

Rep

lace

men

t of d

efec

tive

prod

uct (

R1)

0

100

0 0

0 0

0.03

3 0.

000

3.30

0 0.

000

0.00

0 0.

000

0.00

0 A

void

retu

rns b

eyon

d ec

onom

ic re

pair

(R2)

75

10

0 10

0 75

75

50

0.

037

2.77

5 3.

700

3.70

0 2.

775

2.77

5 1.

850

Dev

elop

env

per

form

ance

stan

dard

s (E1

) 10

0 75

10

0 75

75

50

0.

018

1.80

0 1.

350

1.80

0 1.

350

1.35

0 0.

900

Aggr

egat

e va

lue

of G

SCM

pra

ctic

e of

eac

h en

terp

rise

77

80

81

76

75

78

Cat

egor

y*)

Goo

d G

ood

Goo

d G

ood

Goo

d G

ood

Not

es: *

) acc

ordi

ng to

Trie

neke

ns a

nd H

volb

y (2

000)

: ≤ 4

0: p

oor;

41–6

0: m

argi

nal;

51–7

0: a

vera

ge; 7

1–90

: goo

d; >

90: e

xcel

lent

.


Table 10 Average score and performance index of GSCM practice from all surveyed enterprises

Larg

e sc

ale

of e

nter

pris

e in

-ho

use

man

ufac

turi

ng in

door

La

rge

scal

e of

ent

erpr

ise

in-

hous

e m

anuf

actu

ring

out

door

M

ediu

m s

cale

of e

nter

pris

e in

-ho

use

man

ufac

turi

ng in

door

Med

ium

sca

le o

f ent

erpr

ise

in-

hous

e m

anuf

actu

ring

out

door

In

dica

tors

W

eigh

t Av

erag

e sc

ore

Aver

age

perf

orm

ance

in

dex

Aver

age

scor

e Av

erag

e pe

rfor

man

ce

inde

x Av

erag

e sc

ore

Aver

age

perf

orm

ance

in

dex

Av

erag

e sc

ore

Aver

age

perf

orm

ance

in

dex

Esta

blis

h en

v re

quire

men

ts

(P1)

0.

118

87.5

00

10.3

25

100.

000

11.8

00

75.0

00

8.85

0

83.3

33

9.83

3

Con

side

r env

pro

duct

ion

cons

train

ts (P

2)

0.03

8 93.7

50

3.56

3 25.0

00

0.95

0 78.5

71

2.98

6

33.3

33

1.26

7

Con

side

r env

impa

cts

(P3)

0.

142

100.

000

14.2

00

100.

000

14.2

00

100.

000

14.2

00

10

0.00

0 14.2

00

Min

imis

e en

ergy

use

(P4)

0.

051

100.

000

5.10

0 10

0.00

0 5.

100

57.1

43

2.91

4

83.3

33

4.25

0 M

ax lo

ads,

min

runs

(P5)

0.

030

100.

000

3.00

0 91.6

67

2.75

0 78.5

71

2.35

7

66.6

67

2.00

0 Se

lect

sup

plie

rs w

ith E

MS

(S1)

0.

101

87.5

00

8.83

8 10

0.00

0 10. 1

00

75.0

00

7.57

5

83.3

33

8.41

7

Purc

hase

recy

cled

pro

duct

(S

2)

0.05

0 0.

000

0.00

0 0.

000

0.00

0 14.2

86

0.71

4

41.6

67

2.08

3

Impl

emen

t an

EMS

(M1)

0.

105

87.5

00

9.18

8 10

0.00

0 10.5

00

75.0

00

7.87

5

83.3

33

8.75

0 Sc

hedu

le p

rod

activ

ity (M

2)

0.01

7 68.7

50

1.16

9 75.0

00

1.27

5 50.0

00

0.85

0

75.0

00

1.27

5 W

aste

pro

duce

d as

% o

f the

pr

oduc

t pro

duce

d (M

3)

0.02

9 10

0.00

0 2.

900

100.

000

2.90

0 82.1

43

2.38

2

95.8

33

2.77

9

Rec

ycla

ble

was

te/s

crap

(M4)

0.

087

0.00

0 0.

000

50.0

00

4.35

0 10.7

14

0.93

2

62.5

00

5.43

8 U

se re

cycl

able

pac

kagi

ng

(M5)

0.

040

100.

000

4.00

0 10

0.00

0 4.

000

100.

000

4.00

0

100.

000

4.00

0

Prov

ide

env

train

ing

(M6)

0.

021

68.7

50

1.44

4 75.0

00

1.57

5 28.5

71

0.60

0

75.0

00

1.57

5 R

oute

to m

in fu

el

cons

umpt

ion

(D1)

0.

024

100.

000

2.40

0 10

0.00

0 2.

400

78.5

71

1.88

6

83.3

33

2.00

0

Sche

dule

to m

ax

trans

porta

tion

capa

city

(D2)

0.

024

100.

000

2.40

0 91.6

67

2 .20

0 78.5

71

1.88

6

66.6

67

1.60

0

Enab

le c

usto

mer

dire

ct

ship

men

ts (D

3)

0.02

4 75.0

00

1.80

0 10

0.00

0 2.

400

53.5

71

1.28

6

100.

000

2.40

0

Rep

lace

men

t of d

efec

tive

prod

uct (

R1)

0.

011

37.5

00

0.41

3 75.0

00

0.82

5 53.5

71

0.58

9

79.1

67

0.87

1

Avo

id re

turn

s be

yond

ec

onom

ic re

pair

(R2)

0.

033

0.00

0 0.

000

0.00

0 0.

000

0.00

0 0.

000

16.6

67

0.55

0

Dev

elop

env

per

form

ance

st

anda

rds

(E1)

0.

037

81.2

50

3.00

6 10

0.00

0 3.

700

51.4

29

1.90

3

79. 1

67

2.92

9

Inte

grat

e en

v co

nsid

erat

ions

(E

2)

0.01

8 87.5

00

1.57

5 10

0.00

0 1.

800

53.5

71

0.96

4

79.1

67

1.42

5


Then, the modified importance-performance analysis (IPA) is used as a technique for identifying those indicators that are most in need of improvement. The application of the IPA is introduced by Martilla and James (1977). The IPA consists of a pair of coordinate axis where the ‘importance’ (y-axis) and the ‘performance’ (x-axis) of the different indicators involved are compared. In this research, the importance of indicators is expressed by the weight of each indicator and the performance of indicators is expressed by the score of each indicator. Thus, the mean of weight and the mean of average performance index and are used as coordinates for plotting individual indicators on a two-dimensional matrix which has four quadrants as shown in Figure 2 until Figure 5. This matrix will be used to prescribe prioritisation of indicators for improvement and can provide guidance for strategy formulation. Specifically, the four quadrants in an IPA are characterised as follows: concentrate here, keep up with the good work, low priority, and possible overkill (Martilla and James, 1977). Concentrate here is a quadrant which has high importance, but low performance; the indicators belong to this quadrant requires immediate attention for improvement and are major weaknesses. Keep up the good work is a quadrant which has high importance and high performance; the indicators belong to this quadrant have opportunities for achieving or maintaining competitive advantage and are major strengths. Low priority is a quadrant which has low importance and low performance; the indicators belong to this quadrant are minor weaknesses and do not require additional effort. Possible overkill is a quadrant which has low importance, but high performance; this quadrant indicates that business resources committed to these indicators would be overkill and should be deployed elsewhere.

Figure 2 Result of plotting individual indicators for large scale of enterprise in-house manufacturing indoor (see online version for colours)

Figure 3 Result of plotting individual indicators for large scale of enterprise in-house manufacturing outdoor (see online version for colours)


Figure 4 Result of plotting individual indicators for medium scale of enterprise in-house manufacturing indoor (see online version for colours)

Figure 5 Result of plotting individual indicators for medium scale of enterprise in-house manufacturing outdoor (see online version for colours)

In all categories of enterprises (large and medium scale –indoor and outdoor), indicator recyclable waste/scrap (M4) was included in quadrant 1. It means this indicator had high importance for GSCM practice, but the performance of this indicator still low. Although both were in quadrant 1, score for recycling waste/scrap for in-house manufacturing outdoor was higher than in-house manufacturing indoor. As stated before, most of in-house manufacturing outdoor had implemented recycling waste/scrap. This condition was related to the type of the product resulted. Most of the product produced by in-house manufacturing outdoor has a simple design, so this product would have less waste wood. The waste wood would also have size and dimension which were easier to use in making the other product. Besides indicator recyclable waste/scrap, indicator purchase recycled product (S2) was also included in quadrant 1 for medium scale of enterprise in-house manufacturing indoor and outdoor; whereas indicator minimise energy use (P4) was included in quadrant 1 only for medium scale of enterprise in-house manufacturing indoor.

5 Conclusions

It seems that the supply chain of furniture industry is vulnerable to environmental exploitations. So, it has become imperative to integrate the green or sustainable management practices into their supply chain in order to reduce its impact to environmental and maintain competitive advantage. Incorporating environmental sustainability practices in the supply chain are often referred as green supply chain management (GSCM) and the implementation of GSCM practices of furniture industry


need to be measured. So, based on this condition, the purpose of this study is to measure and evaluate the performance of GSCM practice which have been done by some enterprises in the furniture industry in Jepara, Central Java with greenSCOR approach and propose some feedback system to this industry.

The result of measurement of the implementation of GSCM practices with greenSCOR approach indicates that the enterprises in the category large scale have better aggregate value compared with medium scale. The result of measurement also indicates that in-house manufacturing outdoor has better aggregate value compared with in-house manufacturing indoor. Then, indicator recyclable waste/scrap was included in quadrant 1 in all categories of enterprises (large and medium scale –indoor and outdoor). It means this indicator had high importance for GSCM practice, but the performance of this indicator still low. Although both were in quadrant 1, score for recycling waste/scrap for in-house manufacturing outdoor was higher than in-house manufacturing indoor. As stated before, most of in-house manufacturing outdoor had implemented recycling waste/scrap. This condition was related to the type of the product resulted.

Although the research conducted in this paper is based on data collected by researcher from the different category of furniture enterprises (in-house manufacturing large and medium, indoor and outdoor), the methodology would advise a much larger geographical applicability on assessing the implementation of GSCM practices with GreenSCOR approach. In this study, sample of furniture enterprises only taken from one region. In connection to this limitation, it is possible to carry out further research to see how the issues look like in the other region by taking the furniture enterprises outside Jepara as a sample of research. This study also has limitations due to the fact that this study does not capture the differences between the GSCM practices in the furniture industry with their economic or social performance. The next step for upcoming research is trying to examine the relationship between the variations of GSCM practices with the economic and social performance and conduct the statistical hypothesis testing about the significance of those relationships.

Acknowledgements

This work is supported by the Directorate .of Higher Education, Ministry of Research and Higher Education (Hibah Strategis Nasional No. 148-05/UN7.5.1/PG/2015).

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