Measuring the Reverse Logistics Performance of Construction Machinery Remanufacturing Company

第 55 卷 第 3 期

2020年 6 月

JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY

Vol. 55 No. 3

June 2020

ISSN: 0258-2724 DOI：10.35741/issn.0258-2724.55.3.46

Research article Engineering

M

EASURING THE

R

EVERSE

L

OGISTICS

P

ERFORMANCE OF

C

ONSTRUCTION

M

ACHINERY

R

EMANUFACTURING

C

OMPANY

评估工程机械再制造公司的逆向物流绩效

E. Yuliawati a, *, Pratikto b, Sugiono c, O. Novareza c

a _{Department of Industrial Engineering, Adhi Tama Institute of Technology Surabaya} Surabaya, East Java, Indonesia, eviyulia103@gmail.com

b

Department of Mechanical Engineering, Brawijaya University Malang, East Java, Indonesia, pratiktoprawoto@yahoo.com c _{Department of Industrial Engineering, Brawijaya University} Malang, East Java, Indonesia, sugiono_ub@ub.ac.id, novareza15@ub.ac.id

Received: March 3, 2020 ▪ Review: May 20, 2020 ▪ Accepted: May 30, 2020

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

Abstract

This study develops a method to measure the performance level of reverse logistics implementation operated by companies engaged in the construction machinery remanufacturing industry. A framework of reverse logistics performance measurement that focuses on the processes and resources is developed using the Capability Maturity Model approach. The framework was designed in three dimensions: front-end, engine, and back-front-end, with five levels of model maturity, i.e., initial, aware, defined, managed, and optimizing. The assessment was carried out through 25 indicators and model validation was done by implementing the proposed framework on three construction machinery remanufacturing companies in Indonesia. The results show that the three companies were at different maturity levels. Further analysis explains that the front-end dimension has the lowest score of a successful reverse logistics implementation. Therefore, companies that engage in a similar field should focus more on the front-end dimension to improve their overall reverse logistics performance.

Keywords: Construction Machinery, Remanufacturing Industry, Maturity Model, Performance Measurement, Reverse Logistics

摘要 这项研究开发了一种方法来衡量由从事工程机械再制造行业的公司运营的反向物流实施的绩

效水平。使用能力成熟度模型方法开发了以过程和资源为重点的逆向物流绩效衡量框架。该框架 的设计分为三个维度：前端，引擎和后端，具有五个模型成熟度级别，即初始，感知，定义，管 理和优化。通过25个指标进行了评估，并通过对印度尼西亚的三家工程机械再制造公司

实施拟议的框架进行了模型验证。结果表明，这三个公司的成熟度不同。进一步的分析说明，前 端维度在成功实施反向物流中得分最低。因此，从事类似领域的公司应更多地关注前端方面，以 改善其整体逆向物流绩效。

关键词: 工程机械，再制造行业，成熟度模型，绩效评估，逆向物流

I. I

NTRODUCTION

In recent decades, issues surrounding the concept of reverse logistics (RL) have increased in line with the increasing customers’ recognition of usable and recoverable products. In the 1990s, researchers in the related RL area found that RL operation provides benefits in economic, social, and environmental aspects [1]. Economically, the utilization of used products for manufacturing new components can reduce the consumption of material as well as total production costs. On the other hand, the existence of corporate social responsibility (CSR) is regarded as social benefits. Furthermore, RL also has a real impact on the environment in terms of overcoming the inability of final disposal capacity and the inability to process waste with special materials. Besides, it has also been proven that several companies have successfully made products with RL operations, including Xerox, IBM, and BMW [2].

Issues and challenges facing the remanufacturing industries have increased the number of research around the related area. According to [3], process flow on RL is categorized into three aspects: front-end, engine, and back-end. The front-end aspect describes how companies manage to obtain product returns from consumers. Meanwhile, the engine aspect describes how companies perform the process of product return recovery. The last aspect is back-end, which describes how companies understand the market of remanufactured products. Failure in one aspect can result in the disruption to the overall RL process. In the implementation of RL, technical constraints are often being neglected and considered unimportant. Instead, market uncertainty for remanufactured products and the lack of quality cores are more common. Therefore, it becomes important to measure the company performance in all the three aspects.

The Capability Maturity Model (CMM), which was introduced in the early 1990s, has become the starting point for the development of the Maturity Model (MM) concept, which is now being widely accepted by many academics and practitioners [4]. The CMM concept was adopted from an industrial standard framework that used to develop and improve the production processes,

such as the software application to measure human resource performance, production systems, and process modelling and analysis [5]. The purpose of this concept is to measure how companies implement strategic plans, organizations, skills of workers, both individually and as a holistic system.

The MM concept describes the phase of increasing qualitative and quantitative capacity changes to assess how progress is being made in a specified focus area or domain [6]. According to [7], the MM concept has been widely used in many domains, such as new product development, risk management, Supply Chain Management (SCM), etc. Meanwhile, [8] argued that the concept of MM is designed to assess maturity (i.e., competence, ability, and level of sophistication) based on comprehensive criteria. The scope of MM can be either specific (for example, a model specified for raw material inventory information systems) or generic (for example, a comprehensive model in a production process information system).

The best-known representation of the CMM concept is a model with five maturity levels, namely initial, managed, defined, quantitatively managed, and optimizing. These five levels describe an evolutionary plateau of an organization [9], which identify the maturity level of organizational process improvement. At each level of maturity, the process area is defined as a description of capability and maturity. The level of maturity illustrates the extent to which the process is defined, managed, and measured explicitly. This study adopts the CMM model with modifications based on our research’s objective, which is to consider an RL system that focuses on both resources and processes.

The main element in an RL system is the flow of material and information [10]. An RL process generally consists of the integration of the following activities: collection, evaluation, storage, recovery, and redistribution. Along with RL resources, the five RL activities are then considered as fundamentals in developing the framework of RL-CMM. In this case, what is meant by RL resource is every resource owned by the company that contributes to the achievement of RL objectives. Resources

involved in this study include human, financial, physical, relational and information, organizational, and legal resources.

Based on the above discussion, the purpose of this study is to develop a framework to measure and assess the RL performance in construction machinery remanufacturing enterprises using the CMM approach. Through the proposed RL-CMM model, the RL performance of remanufacturing construction machinery enterprises can be assessed and explained in detail along with suggestions and recommendations to improve its RL performance. To achieve these research objectives, we define the following research questions:

 RQ1: What dimensions and RL indicators should be accommodated in the performance measurement of a construction machinery remanufacturing company?

 RQ2: How well the Indonesian construction remanufacturing companies implement the RL operations?

 RQ3: How are the performance of each RL dimension? Are there any dimensions that needs to be improved?

Studies discussing the RL performance evaluation using MM approaches are still very few, especially those focusing on the construction machinery remanufacturing industry. In this study, we intend to answer the question of how well the construction machinery remanufacturing industry companies have implemented their RL operations by developing a framework of performance measurement based on the RL-CMM model. This study is organized in the following order: Section 1, explains the research background, including the concept of MM and reverse logistics as well as its implementation in the previous studies. Section 2 describes the establishment of the reverse logistics maturity model framework that was applied to this study. Section 3 describes the research methodology and stages of the developed Reverse Logistics-Capability Maturity Model (RL-CMM) model. Section 4 discusses the implementation and managerial implications of the proposed RL-CMM performance evaluation framework of construction machinery remanufacturing companies. Finally, the last section contains the study’s conclusions and directions for further research.

II. R

ESEARCH

M

ETHODS

We try to answer our research questions by developing and validating a framework of a RL

performance measurement model using the procedure shown in Figure 1.

Maturity Dimension

Maturity Level

Indicator

Data Collection

Convert Ordinal Data to Interval Data

Maturity Level Assessment

Analysis of RL Dimensions Data Validation &

Reliability

No

Yes Stage 1: Development of

the RL-CMM Framework

Stage 2: Assessment of Construction

Machinery Remanufacturing Company
s Maturity Level

Figure 1. The methodology of research

As shown in Figure 1, this study was conducted in two stages. The first stage was the development of the RL-CMM framework and the second stage is the assessment and evaluation of the RL maturity level.

A. Development of the Framework of Reverse Logistics-Capability Maturity Model (RL-CMM)

The first stage of our study was to develop the framework that will be used to assess and evaluate the performance level of RL implementation in the specified industry. Here, we improved the framework of [18] by making several adjustments to the construction machinery remanufacturing companies.

The framework of the Capability Maturity Model (CMM) was originally designed to assess the maturity level of a specified system through comprehensive assessment criteria. We developed the CMM further by integrating it with a RL approach. We used both qualitative and quantitative approaches to develop the RL-CMM framework for construction machinery remanufacturing companies.

In order to answer the RQ1 research question, a literature review was carried out on the RL system along with an empirical study. We conducted surveys and interviews on construction machinery remanufacturing companies in Indonesia. All of the information that we gathered was regarding the real RL implementation carried out by companies to support the development of the framework. Subsequently, the data processing stage was carried out using a statistical methodology to validate the capabilities of the model. This framework development technique is similar to [11].

Consumer / User

Distributor / 3PL

Secondary Market

Engine Back End

Distributor / 3PL Front End Remanufacturer Remanufacturer Remanufacturer Distributor / 3PL Distributor / 3PL Distributor / 3PL

Figure 2. Reverse logistics system mechanism

1)

Dimensions and Indicators of Reverse

Logitics

The initial stage in developing the maturity model framework was the determination of the dimensions that describe a company’s RL system. The framework is developed in three dimensions: front-end, engine and back-end [12]. Figure 2 shows the structure of the three dimensions in the RL system mechanism. An assessment was conducted to determine the level of success of the company in implementing an RL.

As mentioned earlier, these dimensions were identified based on inputs from several stakeholders. Following are descriptions of the role of each dimension:

a) Front-End

This dimension is related to the collection of product returns. The level of each indicator is then developed and increased to reach a condition where unbalanced supply and demand in the RL system can be reduced.

b) Engine

This dimension refers to all activities of product returns recovery, where the used product is recovered into a product that can function as good as new. Several recovery strategies can be done, such as reusing, reconditioning, remanufacturing, and recycling [13].

c) Back-End

This dimension deals with remarketing products in the secondary market from planning remarketing strategies and pricing to assessing the availability and demand of the secondary market.

2) Classification of the Maturity Level

The maturity level classification of the proposed RL-CMM framework refers to the model developed by [14]. The concept states that there are five levels of maturity. Starting from the lowest maturity of level 1, a condition in which the company only understands the system being developed, and ending at the highest maturity of level 5, where the company has long-term knowledge and awareness about the system’s sustainability.

In this research, the proposed framework was designed to assess the maturity of a company's reverse logistics performance. Maturity level is developed in five clusters of capability. Company is rated with a highest performance, when it reaches the fifth level of maturity. We adopt the maturity level developed by [14], with the following development and adjustments:

a) Level 1: Initial

At this level, the reverse logistics system has not yet been implemented. The company only operates forward logistics, so that raw materials are purely derived from natural resources obtained from suppliers. At this stage, company does not aware with the product sustainability and it does not yet understand the existence of reverse logistics system.

b) Level 2: Aware

At level 2, company has become more aware of the product sustainability and the existence of processes and resources in reverse logistics system. Company's management team begins to calculate the potential benefits, not only in term of financial, but also social and

environmental, which can be obtained from the implementation of RL operations.

c) Level 3: Defined

At this level, company starts to implement the RL operations. The management of RL processes and resources is defined as a standardized process. Performance measurements of both process and resource utilization have not been done because the application of reverse logistics system itself is still limited.

d) Level 4: Managed

At this level, recovery processes and utilization of resources has been managed, monitored and controlled. Performance measurement has been done quantitatively based on the needs of customers and company. However, company has not yet managed the processes and resources that have reached the final stages of their life cycle.

e) Level 5: Optimizing

This stage is the highest maturity level of a reverse logistics system. At this stage, the process of RL has been performed continuously. The company has carried out a complete RL process by using used products as 100% of its production material. Implementation of the RL operations has provided maximum benefits to the company.

3) Indicators for Each Dimension

For this research project, we developed assessment indicators based on literature review and empirical study. Several previous researchers have described the specific indicators for each dimension. [15] listed the different areas and responsibilities of the remanufacturing process: purchasing (supplier analysis, negotiation, contract execution, proposal solicitation, procurement), production (production scheduling, product design, disassembly strategies, remanufacturing operations, reassembly strategies, forecasting, production strategy), and marketing (pricing, buying, advertising, merchandising, packaging, customer service). Research conducted by [16] showed that there are 25 aspects of the collection process, including collection points, reasons for returns, knowledge about returns, forecast of supplies, and return policy. [17] explained the indicators that represent the efficiency of remanufacturing operations: parts management, technological proficiency, costs, information flow, material flow, quality management, technical cleanliness, and resource efficiency.

We then surveyed practitioners (regional manager, sales division head, HR department head, and supervisor) from remanufacturing companies and original equipment manufacturers (OEMs). We also consulted individual academics majoring in supply chain management and remanufacturing fields of research. They were coming from several different institutions. Based on input from the stakeholders above, we developed 25 assessment indicators for the three dimensions of RL: front-end, engine, and back-end.

For the front-end dimension (the collection of product returns) there are nine indicators that measure the success of an RL system. These include: location of product return collection, product return mechanism, product return classification, refund policy, product return planning and control, availability of information about product returns (lead time, return rate, and volume), reverse logistics network, coordination and communication among parties in the supply chain, and compliance with laws and regulations. Attention to each indicator ensures the optimal balance of supply and demand in an RL system.

The engine dimension incorporates 11 indicators: availability and reliability of technology for product recovery, availability and application of IT, availability and optimum use of infrastructure, worker specialization, inventory control, product return testing mechanisms, product recovery quality, product recovery mechanisms, recovery process management, utilization of used products as production material, and recovery process management. In this dimension, the remanufacture of used products results in products equal in quality to new ones.

The back-end dimension comprises the marketing and resale of recovered products in the secondary market. The assessment indicators for this dimension include: remarketing strategy, pricing strategy for remanufactured products, consumer’s willingness-to-pay for remanufactured products, secondary market competitors, and warranty for remanufactured products. Table 1 summarizes the 25 indicators used to assess the maturity level of the construction machinery remanufacturing companies.

Table 1.

Dimensions and indicators to assess the maturity level of construction machinery remanufacturing company

Dimension Indicator Front-end

F1 Collection point

F2 Product return mechanism F3 Product return classification

Dimension Indicator

F4 Refund policy

F5 Product return planning and control F6 Information availability of product

returns

F7 Customer environmental awareness F8 Coordination and communication

among parties

F9 Compliance with legislations

Engine

E1 Availability and reliability of technology for product recovery E2 Availability and application of IT E3 Availability and optimal use of

infrastructure

E4 Specialization of workers E5 Inventory control

E6 Product return testing mechanism E7 Quality of product recovery E8 Product recovery mechanism E9 Recovery process management E10 Utilization of used products as

production materials E11 Waste management

Back-end

B1 Remarketing strategy B2 Pricing strategies for

remanufactured products

B3 Consumer’s willingness-to-pay for remanufactured products

B4 Secondary market competitors B5 Warranty and after sales services

The assessment criteria for each indicator are presented in the Appendix.

B. Maturity Level Assessment and Evaluation of Each Construction Machinery

Remanufacturing Company

Assessment of the RL maturity level of each company was carried out through the following questionnaire. The questionnaire used the following Likert scale:

1 = The company is not aware about and does not understand the RL system;

2 = The company is aware of the RL system but has not yet implemented it;

3 = The company conducts RL management, but the implementation is still limited;

4 = The company implements RL management properly and in measurable terms;

5 = The company follows the RL system continuously.

1) Data Collection

In this step, data were collected through questionnaires. An assessment of the implementation of the RL system was carried out by distributing questionnaires to company managers.

2) Validity and Reliability Testing

After conducting the survey, we analyzed the result by statistically checking its validity and reliability. Validity refers to the precision of the study, the intention being to achieve the

hypothesized value, while reliability refers to the ability to reproduce the result via subsequent independent studies [18]. Data are considered to be valid if rcount > rtable. Moreover, data are

considered reliable if the resulting Cronbach’s Alpha value is greater than 0.6 [19].

3) Conversion of Ordinal Data into Interval Data

Data obtained through a questionnaire with a Likert scale are considered to be ordinal data. Statistical data processing requires quantitative data. Thus, to meet this requirement, the obtained ordinal data must be converted into interval data. In this study, data conversion was accomplished using the Successive Interval Method with the help of a spreadsheet.

4) Determination of the Company’s Maturity Level

The company’s level of success in implementing the RL system was determined by the average value of 25 assessment indicators. The average value, which still exists in the form of interval data, was then categorized into one of the following five levels of maturity:

1) Initial level 2) Aware level 3) Defined level 4) Managed level 5) Optimized level

5) Analysis of the RL Dimension

The last step was carried out to answer research question RQ3, namely to identify which RL dimensions need to be improved. Identification was based on the assessment of all participant companies for the relevant dimensions, namely the front-end, engine, and back-end dimensions.

III. R

ESULTS

A. Case Study: Indonesian Construction Machinery Remanufacturing Companies

As explained earlier, this study investigates the implementation of RL in Indonesian construction machinery remanufacturing companies. As a developing country, in the last 5 years, Indonesia has focused on massive and equitable infrastructure development. Such development is expected to create strong connectivity between regions, reduce logistical costs, improve quality of life, and reduce economic disparities. This large-scale development raises the demand for construction materials and heavy equipment.

According to the Association of Indonesian Heavy Equipment Manufacturers (HINABI), the market demand for heavy equipment and

construction machinery was quite high until the end of 2019. The market needs are attributed to the mining sector (40%), construction sector (20– 25%), and either the plantation sector or the forestry industry (35%). The high utility of heavy equipment in the mining sector results in a high equipment turnover. In the construction sector, heavy equipment can be utilized for quite a long time, up to 3–5 years. This will necessarily impact environmental aspects, such as by increasing the amount of waste generated. In addition, high investment in heavy equipment necessitates the development of RL systems in the related industries. Therefore, it is crucial to develop a framework for RL performance in all related areas of business.

In this study, model validation was conducted by implementing RL-CMM in the remanufacturing construction machinery companies. The participating companies were construction machinery remanufacturing companies located in Indonesia. The characteristics of the participating companies are presented in Table 2.

Data from HINABI indicate that not many HINABI members are engaged in the remanufacturing industry. Therefore, the three participating companies are considered to be representative, as they have all undergone RL activities as one of their business operations.

Table 2.

Characteristics of the participating companies

Company Products Ownership Number of employees

X Engine, powertrain, hydraulic components, hydraulic cylinder, etc.

Domestic

investment 0 - 250 Y Engine, torque converter, transmission,

hydraulics components, etc.

Domestic

investment 250 - 500 Z Engine, transmission, hydraulic components,

pump, etc

Domestic

Investment > 500 1) RQ1: Which Dimensions and RL Indicators

Should Be Accommodated in the Performance Measurement of the Construction Machinery Remanufacturing Companies?

To obtain an overview of the RL implementation in the participating companies, the initial stage of this study was completed using

surveys and interviews. At the initial stage, we identified how the front-end, engine, and back-end dimensions were implemented in the company. The RL system mechanism implemented in the Indonesian construction machinery remanufacturing companies is illustrated in Figure 3. Remanufactured product Used products Remanufacturing Process

> Reuse -- Work in Process > Repair -- Work in Fabrication

> Replace -- Work in Order

Engine Back End

Front End

Collection Redistribution

Figure 3. RL system on Indonesian construction machinery remanufacturing companies

The RL mechanism starts at the front-end dimension, where the consumers of heavy equipment with faulty engines or components

will return it to the collection point. Consumers have two alternatives as collection points: third-party logistics (3PL) and workshops owned by

remanufacturing companies. Thereafter, product returns are delivered to the workshop for recovery. At the initial stage, the company sorts and inspects the product returns to arrive at the required decision.

Four process options are associated with the engine dimensions: reuse, repair, replace, and waste treatment. If the returned product could be reused immediately without any particular type of recovery process, it will be sent to and held in the work-in-process inventory. Whereas, if the faulty components of a returned product could be repaired, it will be sent to the fabrication for the repair process. If the faulty components of the returned product could be neither reused nor repaired but could still be replaced, it will be sent to the work-in-order inventory. Finally, if the returned product is irreparable, it will be removed for waste treatment. Choosing the most suitable recovery process is crucial to effectively restore the product function. In the final stage, remanufacturing companies will redistribute and resell the recovered products in the secondary market.

The framework of RL performance measurements consists of three dimensions: front-end, engine, and back-end, which have been identified previously from literature reviews, surveys, and interviews with experts and academics. At this point, ”experts” refer to practitioners who work in production, inventory control, supervisory roles and sales departments at the participating companies that carry out the RL processes. There are nine indicators to assess the dimensions of the front-end, 11 indicators to assess the engine dimensions, and five indicators to assess the back-end dimensions.

The validity test results showed that all 25 indicators were valid. The test was conducted using df = 8 and α = 0.95, resulting in the value of Rtable = 0.6319. The Rcount value for all

indicators is greater than the Rtable. Furthermore,

the test of reliability resulted in a Cronbach’s alpha value of 0.981, where a value greater than 0.6 indicates reliability [19]. These results indicate that the 25 indicators are reliable, meaning that all of them have a high level of consistency and can be relied upon as a set of performance measurements.

2) RQ2: How are the Performance Levels of Indonesian Construction Machinery

Remanufacturing Companies in Implementing the RL Operations?

The assessment of RL maturity level was carried out by distributing questionnaires to the person in charge of the RL-related departments of each participating company. Ten people were chosen as our respondents. Since we use the Likert scale as our rating scale, the results fall into the ordinal or qualitative data category. To facilitate data processing, the data was transformed into interval categories. The process was carried out using the method of successive intervals. The results of the questionnaires for the three participating companies along with the data transformation can be seen in Table 3.

Considering the interval values in Table 3, an assessment of the company's maturity level was carried out with the following categories:

1. 1.000 – 1.999 : Level 1 (Initial) 2. 2.000 – 2.999 : Level 2 (Aware) 3. 3.000 – 3.999 : Level 3 (Defined) 4. 4.000 – 4.999 : Level 4 (Managed) 5. 5.000 – 6.000 : Level 5 (Optimizing) Table 3.

RL performance assessment results of the three participating companies

Dimension Indicator

Ordinal data Interval data

Company X Company Y Company Z Company X Company Y Company Z Front-end F1 F2 F3 F4 F5 F6 F7 F8 F9 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4.000 4.000 4.000 4.000 4.000 4.000 3.000 4.000 4.000 4.000 4.000 4.000 4.000 4.000 4.000 4.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636 4.636 5.636 5.636 Engine E1 E2 E3 E4 E5 E6 E7 E8 E9 4 4 4 4 4 4 4 4 4 5 4 5 5 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4.000 4.000 4.000 4.000 4.000 4.000 4.000 4.000 4.000 5.636 4.000 5.636 5.636 4.000 4.000 4.000 5.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636 5.636

E10 E11 4 4 5 4 5 5 4.000 4.000 5.636 4.000 5.636 5.636 Back-end B1 B2 B3 B4 B5 4 4 3 4 4 5 5 5 5 5 5 5 5 4 5 4.000 4.000 4.000 3.000 4.000 5.636 5.636 5.636 5.182 5.636 5.636 5.636 5.636 5.636 5.636 Average 3.920 4.858 5.584

Based on the result in Table 3, we understand that company X is classified as “defined”, whereas Company Y and Z are classified at

“managed” and “optimizing”, respectively. The result can be seen in Table 4.

Table 4.

Maturity level classification result of the RL remanufacturing companies

Company Average interval value Maturity level classification

X 3.920 Defined

Y 4.858 Managed

Z 5.584 Optimizing

Figure 4. RL performance assessment on: (a) Front-end dimension, (b) Back-end dimension, and (c) Engine dimension of Company X, Y, and Z

3) RQ3: How are the Performance Level of RL Dimensions in Construction Machinery Remanufacturing Company? Are There Any Dimensions That Can Be Improved?

After identifying the maturity level of each company, an investigation was carried out to determine what dimensions which have the lowest performance. The company must strive to improve these dimensions. The RL performance evaluation was done based on the identified 25 indicators (Figure 4).

The performance evaluation of the three companies on the three dimensions shows that front-end dimension has an average score of 4.616, engine dimension has an average score of 4.843, and back-end dimension has an average score of 4.891. From these values, we can compare the three dimensions and find out that the front-end dimension has the lowest score. From these findings, we understand that performance of the front-end dimension is potential to be improved. Construction machinery

remanufacturing companies should be focusing more on the front-end dimension by conducting a better management of product returns collection.

B. Managerial Implications

In general, the three construction machinery remanufacturing companies participating in this study have been aware and understood the benefits of implementing an RL system, which is proven by the maturity level of the three companies.The company has been able to manage its resources to maximize its potential benefits, both economically, socially, and environmentally. In the front-end dimension, companies have developed strategies to get product returns. From the perspective of the success rate of implementing the RL system, the score for the front-end dimension is still not optimal. However, the company will improve coordination and communication with the parties involved in the supply chain. Effective relationships with 3PL and customers can support

the availability of cores as raw material in the related industry.

If we take a look at the score of the engine and front-end dimensions, we will find that the RL performance of the engine dimension is better than the front-end dimension. The equipment, infrastructure, and technology support the three companies to produce a better quality of recovered products. The company has developed green production by utilizing used products as raw materials. The system requires less energy and the process becomes more efficient. In addition, operators are reliable in mastering high-tech recovery processes. In this dimension, the target can be achieved if the company develops a waste management strategy that is more effective and efficient.

Finally, the back-end dimension has the highest performance compared to the other two dimensions, even though the maturity level does not reach the optimizing level. All of the three companies have subsidiaries that act as distributors who assist in remarketing the recovered products. This facilitates the construction machinery re-manufacturing company to resell its products. The company also provides warranty services for recovered products, even though the warranty scheme is different and not as much as the warranties offered for new products. An indicator that can be developed in this dimension is remarketing strategy. In addition to competing with fellow recovery products in the secondary market, the company also competes with new products in the primary market.

The proposed framework supports the implementation and management of RL by providing strategic guidance for sustainable reverse logistics systems. RL performance measurement system was defined based on the level of maturity and specifications. A validated set of indicators allows an integrated approach to measuring RL system performance.

IV. C

ONCLUSION

In this research, the Reverse Logistics Maturity Model was implemented in the case of the construction machinery re-manufacturing industry. Successful implementation was measured by incorporating the three dimensions of RL: front-end, engine, and back-end; five levels of model maturity: initial, aware, defined, managed, and optimizing; and 25 assessment indicators. From the result, we understand that Company Z has reached the highest level of maturity, which is the optimizing level, Company

X is at the defined level, and Company Y is at the managed level.

Further analysis discussed the performance evaluation of each dimension in the RL system. The result showed that the front-end dimension has the lowest achievement score, meaning that it requires more support and focus on development strategies compared to the other dimensions. F1, F2, F3, and up to F9 indicators contribute to the low maturity level of re-manufacturing companies. Control of these indicators provides further research opportunities. Further research can also increase the number of re-manufacturing companies used as samples of RL performance measurement, thus allowing the elaboration of indicators that have not been captured from the existing participating companies.

A

CKNOWLEDGMENT

Special gratitude for the Directorate General of Higher Education, Ministry of Research, Technology, and Higher Education of the Republic of Indonesia for funding this research.

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Appendix.

Maturity level assessment criteria of each indicator in RL dimensions RL dimension Indicator Maturity level 1 2 3 4 5 Front end Collection point

The company is not involved in the determination of collection points

The company is involved in the determination of collection points

The number of collection points does not cover all existing customer points

The number of collection points includes all existing customer points

The company has a structured reverse logistics network

Product return mechanism

The company does not have a product return mechanism

The company has a product return mechanism

The product return mechanism is clear and comprehensive

The product return mechanism is properly documented

The product return mechanism is clear, comprehensive, structured and properly documented Product return sorting

and classification

Product returns are not sorted

Product returns are not sorted, but the company has the desire to do so for efficiency

Product returns are sorted into two categories, functional and non-functional

Product returns have been better sorted and classified based on initial testing results

Product returns are sorted and classified according to company requirements

Refund policy The company does not have a refund policy

The company understands that the refund policy can increase the number of product returns

The company has a refund policy, but its implementation is still limited

The company has a refund policy along with its standard operating procedure

The company provides various types of refund policy that are attractive to consumers, which can increase product returns

Product return planning and control

The company does not conduct a product return planning and control

The company conducts planning and control of product returns, but without a specific strategy

The company has a product return planning and control strategy, which is

straightforward and limited

The company has a product return planning and control strategy that has been operated properly

The availability of product returns can be maintained, because the company has sustainable planning Information

regarding product returns

The company does not aware of the importance of product returns information

The company starts to aware of the importance of product returns information

The company has information about the quality of product returns

The company has information about the quality and quantity of product returns

The company has information about the quality, quantity, and timing of product return availability

Customer Environmental Awareness

Consumers are not concerned with environmental factors

Consumers begin to understand environmental factors and impacts

Consumers are concerned about environmental factors, but their concern is straightforward and limited

Consumers have a very good level of understanding and concern for environmental factors and impacts.

Consumers have a high level of concern for environmental factors and impacts in a sustainable manner. Coordination and communication between channels in the RL system There is no coordination and communication between channels in the RL system

Coordination and communication have not been done by all channels in the RL system, only a few channels coordinate

Coordination and communication has been carried out by all channels in the RL system, but it is still straightforward and limited

Coordination and

communication has been carried out by all channels in the RL system through a reliable IT system

Coordination and communication has been carried out continuously by all channels in the RL system

Compliance with legislations of remanufacturing

The company ignores the legislations

The company begins to study the legislations

The company has complied with part of the legislations

The company complies with legislations that are in line with the company's needs

The company complies with all legislations that apply to remanufacturing companies Engine Availability and reliability of technology for product recovery

Technology for product recovery is not yet available

Technology for product recovery is available, but not standard

Standard technology for product recovery is available, but has not been disseminated to the operator

Standard technology for product recovery is available and has been disseminated to operators

The standard technology for product recovery has been designed in line with the company's business strategy Availability and

application of IT

The IT facilities do not support the process

IT facilities are already installed and managed properly

IT facilities have been utilized to all departments in the company

IT facilities are developed for stakeholders and partnerships

IT facilities are developed in line with the company's vision

Availability and optimal use of infrastructure

Infrastructure for product recovery is not yet available

Infrastructure for product recovery is available, but the amount is still limited and has not been able to meet consumer demand

Infrastructure for product recovery is available in sufficient quantities to meet consumer demand, but does not yet function properly

Infrastructure for product recovery is available in sufficient quantities to meet consumer demand and can function properly, both technically and economically

The company provides

considerable amounts of money to plan and invest in infrastructure for product recovery, including sustainable development plans

Specialization of workers

Workers do not have job specializations

Only few workers have job specializations

All workers have job specialization, but no workers have specialization in product recovery

Few workers have job specializations in product recovery

All workers have job

specializations in product recovery

Inventory control & management system

The company does not manage product returns inventory

The company manages product returns inventory

The company predicts product returns through forecasting

The company has a monitoring and performance assessment system for product return inventory

Inventory control and management of product returns are carried out in all collection points

Product return testing mechanism

The company does not conduct product return testing

The company begins to learn about the mechanism in product return testing

The company uses basic equipment in product return testing

The company uses modern equipment and structured mechanisms in product return testing

The company uses standardized equipment and mechanisms in product return testing

Quality of product recovery

The company is still struggling to produce a good quality product recovery

The company is able to produce product recovery with good quality but cannot yet function as good as new one

The company is able to produce product recovery as good as new one, but has not been able to meet the standards set by the company.

The company is able to produce product recovery as good as new one and can meet the standards set by the company.

Product recovery quality is designed to meet consumer needs

Product recovery mechanism

The company does not have a standard mechanism for product recovery

The company has a standard product recovery

mechanism, but its implementation is not optimal

The company has implemented the optimal standard

mechanism for product recovery, but it has not followed the national / international standards

The company has a national / international standard product recovery mechanism and has been implemented optimally

Product recovery mechanism is one of the company's competitive advantages

Recovery process management

The company does not have a recovery process management

The company began to study the management of recovery process

The company has a basic recovery process management

The company has a measurable recovery process management

The company has a measurable, structured and sustainable recovery process management

Engine

Utilization of used products as production material

Used products are not utilized as production materials

Recovery products utilize only a small portion of used material

Recovery product utilizes more than 50% of used material

Recovery product utilizes more than 80% of used material

Recovery product utilizes 100% of used material

Waste management The company has no waste management

The company began to study waste management

The company implements limited process of waste management

The company has an optimal process of waste management

The company has an optimal, structured, and sustainable process of waste management

Back end

Remarketing strategy

The company does not conduct a remarketing process

The company has carried out the remarketing process without a certain strategy

The company has a remarketing strategy, which is

straightforward and limited

The company has a remarketing strategy that has been

implemented properly

The company has a remarketing strategy, which is carried out both online and offline

Product recovery pricing strategies

The company determines the pricing of recovery products without a certain basis

The company began to learn about the pricing strategy for recovery products

The company has a product recovery pricing strategy based on the variable costs involved in the production process

The company has a product recovery pricing strategy based on all costs involved in a series of production to distribution processes

The pricing strategy for recovery products has been automated using a reliable IT system.

Consumer’s willingness-to-pay for recovery products

The company does not understand consumers' Willingness-To-Pay for recovery products.

The company investigates and studies consumers’ behavior and willingness-to-pay for recovery products

The company understands consumers’ behavior regarding their willingness-to-pay for recovery products

The company understands the underlying indicators that determine consumer's Willingness-To-Pay for recovery products

The company understands consumer’s willingness-to-pay for recovery product thoroughly and the company also begins to design recovery products according to consumer’s needs and requirements. Secondary market

competitors

The company is not aware of secondary market competitors

The company carries out data tracking on secondary market competitors

The company knows and understands its secondary market competitors

Secondary market competitors have been segmented

The company understands the performance of secondary market competitors to be sustainable

Warranties and after sales services

The company does not provide warranties for remanufactured products

The company does not provide warranties for remanufactured products, but consumers are given the opportunity to try it out within a few days.

The company provides warranties for remanufactured products according to its quality

Warranties and after sales service for remanufactured products are well documented.

Remanufactured products have the same warranties as new products.