Development of Remanufacturing Readiness Index for MSMEs: A Comprehensive Framework

Abstract

Micro-, small-, and medium-sized enterprises (MSMEs) have the opportunity to increase resource efficiency, decrease waste, and promote sustainability by implementing remanufacturing techniques. However, determining whether MSMEs are prepared for the adoption of such techniques requires a methodical approach that considers several aspects of manufacturing readiness, such as core acquisition, the design of remanufacturing, and others. Therefore, this study proposes a framework to measure the readiness for the adoption of remanufacturing practices. Further, the remanufacturing readiness index (RRI) is proposed by combining remanufacturing indicators with a structural approach based on graph theory and matrices (GTM). Nine remanufacturing readiness attributes are identified through the literature and validated by an expert team. These nine attributes include core acquisition, reverse logistics availability, resource availability for remanufacturing initiatives, the design for remanufacturing, enterprise collaboration, remanufactured product positioning, performance measurement, labor skill and availability, and a flexible remanufacturing system. The finalized remanufacturing readiness attributes are modelled using GTM to explore their interdependencies, forming the basis for a quantitative index (RRI) that reflects MSMEs’ readiness for the adoption of remanufacturing. It is used to measure the possibility of MSMEs implementing remanufacturing processes. To illustrate the efficacy of the RRI, a case study of a remanufacturing facility was conducted. This RRI acts as a decision-support tool to help MSMEs, industry stakeholders, and governments identify priority areas for intervention, promote resource efficiency, and create sustainable growth. The results highlight the importance of readiness attributes as fundamental components in implementing remanufacturing practices at the MSME level.

1. Introduction

The significance of the environment and sustainable practices has grown significantly with population growth and industrialization across the globe [1,2]. Even though industrial growth makes life easier for people, it also causes issues like ecological damage and environmental degradation, which makes sustainable development challenging [3,4]. In order to address these challenges, countries are taking certain measures such as reducing resource usage [5], the end-of-life management of products [6,7], the adoption of circular practices [8,9], waste reduction [10], and more. End-of-life management practices such as product recovery have gained more attention in the past decades [11]. As a result, there has been a notable increase in product recovery initiatives such as reconditioning, repair, and remanufacturing [12,13]. Remanufacturing is considered one of the facilitators for end-of-life management, waste reduction, value recovery, and circular economy initiatives [14]. Remanufacturing is an industrial process that involves cleaning, dismantling, inspecting, and restoring used products to nearly new conditions [15]. It has gained popularity recently because of its intrinsic benefits to the environment, society, and the economy [16]. Remanufactured goods require only 60% of the energy and 70% of the resources compared to producing new items from virgin materials [2,17]. Remanufacturing procedures also emit fewer emissions than traditional manufacturing methods [18]. In addition, remanufacturing reduces the requirement for energy and virgin materials, which decreases the cost associated with them. These advantages push industries to adopt remanufacturing practices for their sustainable growth [19,20].

Remanufacturing is defined as “an industrial process whereby used products are restored to useful life” [21]. Due to increased consumer awareness, government regulations, and the environment, it has become increasingly important in recent years for both industries and consumers to adopt sustainable practices such as remanufacturing [22,23]. The adoption of remanufacturing practices is influenced by several factors such as core acquisition [21], reverse logistics availability [24], the design for remanufacturing [25], collaboration [24], and others. These factors measure the readiness of an enterprise to adopt remanufacturing practices, and it depends on the type of enterprise: large-, small-, or micro-sized enterprises. The nature of an enterprise can significantly influence its adoption and implementation of remanufacturing practices due to various factors such as resource availability, expertise, market dynamics, and strategic priorities [26]. For instance, large enterprises have greater financial capability to invest in sophisticated remanufacturing facilities and technologies and specialized R&D teams for remanufacturing techniques. Despite this, these enterprises also face some challenges such as higher initial setup costs and complexities involved in the integration of remanufacturing systems. On the other side, MSMEs can implement pilot remanufacturing projects due to simpler organizational structures, but they have limited financial resources and expertise. This suggests that the adoption of remanufacturing varies across enterprises [27].

Several studies focus on the implementation of remanufacturing from various aspects such as barriers [28], determinants [23], and drivers [29]. However, there are few studies that focus on readiness to adopt remanufacturing. Some studies focus on the initiatives that are required to start remanufacturing, such as [30], which explores the initiatives to start remanufacturing, and Ref. [24], which proposes a framework for initiating remanufacturing and identifies the prerequisite factors for remanufacturing. Ref. [31] proposed a maturity model to measure remanufacturing practices. It is evident from the literature that most studies focus on initiatives, prerequisites, and maturity levels for remanufacturing but do not address whether the manufacturing facilities is ready for operations or not?. Additionally, there is a lack of quantitative assessment methods to measure this readiness. Therefore, a significant gap exists in the literature regarding the quantitative evaluation of remanufacturing readiness. This study tries to fill this gap in the literature.

MSMEs face several challenges in preparing to adopt remanufacturing practices [32]. Financial constraints, lack of awareness and expertise, market challenges, and many other factors prevent MSMEs from being ready to adopt remanufacturing practices [33,34]. MSMEs also lack knowledge on how remanufacturing practices can be a game-changer and what factors need to be considered for their readiness to adopt remanufacturing practices [35]. This problem needs to be addressed to prepare MSMEs for the adoption of remanufacturing practices. To address this issue, this study proposes a framework to measure the readiness of an organization for the adoption of remanufacturing practices. Based on this framework, a quantitative measure (i.e., the RRI) is also proposed for measuring the readiness of an organization to adopt remanufacturing practices. This framework can also help assess the organization’s progress towards their readiness to adopt remanufacturing practices. Further, this can also be used to compare two organizations in terms of their readiness to use the RRI. The specific objectives of this study are as follows:

• To identify the remanufacturing readiness attributes for MSMEs;
• To propose a framework for measuring their readiness to adopt remanufacturing practices;
• To develop the RRI for MSMEs.

The first objective aims to systematically identify the major critical attributes that determine the readiness of MSMEs to adopt remanufacturing practices. It involves a comprehensive review of the existing literature and engagement with industry experts to capture the attributes that influence readiness. Further, the second objective focuses on designing a structured and quantitative framework to assess remanufacturing readiness. The framework will incorporate the identified attributes and establish a methodological approach for evaluating MSMEs’ readiness. It will provide a systematic tool for measuring the readiness levels of the current facility. The third objective involves constructing a quantitative index to measure the degree of readiness for a remanufacturing facility. The RRI will integrate the identified attributes and apply graph matrix theory to generate a comprehensive readiness score. The index will facilitate benchmarking, comparisons between facilities, and informed decision making to support the successful adoption of remanufacturing in MSMEs.

To fulfil the mentioned objectives, a comprehensive literature review was performed to identify the significant remanufacturing readiness attributes of MSMEs. These attributes were validated by an expert team. Further, GTM is applied to develop the interrelationship between the identified attributes. Based on this relationship, GTM is applied to develop the framework for measuring the readiness to adopt remanufacturing practices. This proposed framework is applied to determine the RRI and illustrated through the case study. This study has significant contributions to the existing literature, some of which are highlighted below.

Firstly, this study systematically identifies significant attributes that determine MSMEs’ readiness to adopt remanufacturing practices. By filling a literature gap in which no prior work has established these attributes clearly, the research attains a foundational understanding of the most significant attributes influencing remanufacturing readiness. Secondly, this study proposed a novel and structured framework to measure remanufacturing readiness. This framework encompasses identified attributes and assesses readiness to adopt remanufacturing facilities in the context of MSMEs. Thirdly, a significant contribution of this study is the development of the RRI, which quantitatively measures the readiness level of MSMEs. Using GMT, the RRI offers a robust and scalable tool for benchmarking readiness, facilitating objective comparisons between different manufacturing facilities. Finally, the proposed framework and the RRI have been applied to a real-life MSME case study to demonstrate how the RRI (quantitative index) can be utilized to measure readiness. This real-life validation provides decision makers with a concrete illustration of how to apply the framework and interpret results for strategic planning.

The remaining article is structured as follows: Section 2 identifies remanufacturing readiness attributes, Section 3 proposes the remanufacturing readiness assessment framework, Section 4 provides the readiness assessment of a remanufacturing facility, Section 5 demonstrates the application of the proposed framework, Section 6 highlights the implications of the study, and finally, Section 7 concludes the study and outlines its limitations and future research directions.

2. Remanufacturing Readiness Attributes

The integration of a literature review and experts’ input is applied to identify the attributes for readiness to adopt remanufacturing practices. A systematic methodology was implemented to identify and validate remanufacturing readiness attributes specific to MSMEs using the PRISMA framework. The PRISMA framework consists of four steps: identifying the relevant literature, screening and selecting studies based on inclusion and exclusion criteria, extracting data from the chosen studies, and synthesizing and summarizing the findings [36]. To identify the relevant literature, this study initiated an extensive search in the Scopus database, employing Boolean operators to combine keywords such as “remanufacturing”, “value restore”, “refurbishing”, “readiness”, and “initiative”. These keywords are converted into search strings using the Boolean operator (AND/OR). The search was restricted to articles in English published post-2014 to ensure contemporary relevance. The articles were screened through a multi-stage process, starting with title and abstract reviews, followed by an independent evaluation conducted by two authors to minimize selection bias and enhance reliability. The adopted PRISMA framework is illustrated in

Table 1. Article selection criteria for identification of remanufacturing attributes.

Stage	Description	Number of Records
Identification	An extensive search in the Scopus database using Boolean operators and keywords such as “remanufacturing”, “value restore”, “refurbishing”, “readiness”, “initiative” (TITLE-ABS-KEY (“remanufacturing” OR “value restore” OR “refurbishing”) AND TITLE-ABS-KEY (“readiness” OR “initiative”)) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (LANGUAGE, “English”))	130
Screening	Articles were screened through a multi-stage process: initial title and abstract reviews.	47
Eligibility	Full-text articles were independently evaluated by two authors to minimize selection bias and enhance reliability.	44
Inclusion and Exclusion	Articles were included after meeting all inclusion criteria (English language, post-2014, and relevant to remanufacturing readiness for MSMEs). We excluded articles that were published as a book chapter, editorial, or book series.	21

.

Following a comprehensive review of the finalized articles, eleven key remanufacturing readiness attributes were identified. To ensure robustness, a validation exercise was conducted through an expert panel comprising ten members: eight industry professionals with over a decade of management-level experience in remanufacturing and sustainability, employed by organizations with a minimum of 18 years of operational history, and two academicians with substantial expertise in sustainability and remanufacturing systems. This integration of industry and academic perspectives provided a rigorous foundation for the validation and applicability of the identified attributes. The experts’ information is provided in

Table 2. Experts’ details.

Expert No.	Organisation Type	Designation	Experience (Year)	Educational Qualification	Expertise Area
1	Small	Manager	12	Postgraduate	Remanufacturing
2	Medium	Operations Manager	13	Graduate	Manufacturing
3	Medium	Procurement Manager	11	Postgraduate	Procurement
4	Micro	Supply chain manager	15	Postgraduate	Supply chain
5	Medium	R&D Head	10	PhD	Remanufacturing and sustainability
6	Micro	General Manager	13	Postgraduate	Remanufacturing
7	Small	Logistics Manager	14	Postgraduate	Logistics
8	Small	Design Engineer	12	PhD	Design
9	Academic Institution	Professor	19	PhD	Remanufacturing and closed-loop supply chains
10	Academic Institution	Associate Professor	15	PhD	Sustainability and circular economy

.

The identified list of eleven remanufacturing readiness attributes was presented to the expert panel for their feedback. The expert team, comprising industry professionals and academicians, reviewed the remanufacturing readiness attributes and suggested the removal of two attributes deemed no longer relevant in the current MSME context. Based on their recommendations, the two attributes were excluded from the list, and the remaining nine attributes were finalized for subsequent analysis. This iterative refinement process ensured that the finalized remanufacturing readiness attributes were both relevant and aligned with practical and theoretical insights. The finalized remanufacturing readiness attributes are shown in

Table 3. Remanufacturing readiness attributes.

Remanufacturing Readiness Attributes	Code	Description	References
Core acquisition	ReM1	Core acquisition refers to the process of collecting used, worn-out, or end-of-life products (called “cores”) that can be remanufactured to meet the original quality standards.	[37,38,39]
Reverse logistics availability	ReM2	Facilitates the collection, transportation, and management of cores across the supply chain, playing a critical role in remanufacturing.	[38,40,41]
Resource availability for remanufacturing initiatives	ReM3	Resource availability such as manpower, capital, and technology to initiate remanufacturing practices	[25,42,43]
Design for remanufacturing	ReM4	Design for remanufacturing focuses on optimizing product design to facilitate efficient disassembly, cleaning, repair, and reassembly, ensuring maximum value recovery.	[31,44,45,46]
Enterprise collaboration	ReM5	Facilitates the collection, transportation, and management of cores across the supply chain, playing a critical role in remanufacturing	[24,47,48]
Remanufactured product positioning	ReM6	It refers to the strategic placement of remanufactured products in the market to ensure they align with customer expectations and market demand.	[29,49,50]
Performance measurement	ReM7	Performance measurement systems facilitate the evaluation of efficiency and effectiveness of remanufacturing processes, including productivity, quality, environmental impact, and profitability.	[44,51,52]
Labor skill and availability	ReM8	The availability of a qualified workforce is critical for successful remanufacturing operations and maximizing value recovery. It involves ensuring that personnel possess the knowledge, technical skills, and expertise required for the complex processes involved in remanufacturing.	[31,53]
Flexible remanufacturing system	ReM9	A flexible remanufacturing system handles inconsistencies in core quality, fluctuating supply volumes, and variability in product designs, ensuring adaptability to changing demands.	[54,55,56]

.

3. Development of the Remanufacturing Readiness Assessment Framework

Remanufacturing attributes and their interdependence are necessary for the assessment of readiness to adopt remanufacturing practices. The identified remanufacturing attributes are modelled using the GTM approach to determine the interdependence among them. GTM allows for the structural modelling of these attributes [57,58]. The digraph model is well suited for understanding the linkages among remanufacturing attributes and the development of the RRI. GTM is an organized and consistent decision-making method that maintains the interdependence between attributes. It can simultaneously accommodate any number of quantitative and qualitative determinants [58]. Unlike conventional MCDM methods, GTM provides a visual representation of the problem, offering insights that other approaches may overlook [59]. Furthermore, GTM mathematically captures the interaction between a problem’s components, transforming complex network relationships into simple, comprehensible matrices for systematic and critical analysis [60]. In addition, the matrix representation of the problem is compatible with computer programs, enabling efficient processing and analysis.

A digraph has a limited number of vertices, or nodes, and directed edges that join them [61]. The edges show how they interact, whereas the nodes may stand for different system elements, states, variables, etc. [59]. This notion is utilized in the construction of the RRI and the modelling of remanufacturing attributes. The structure of the system is depicted using a digraph.

The nodes in a digraph represent the remanufacturing attributes, and the connecting edges (or arrow/directed edges) show the interdependence among them. If node i is connected to node j through an arrow from node i to node j then it shows the influence of remanufacturing readiness attributes i over j.

The remanufacturing readiness attribute digraph of the remanufacturing facility consists of nine nodes: core acquisition (ReM1), reverse logistics availability (ReM2), resource availability for remanufacturing initiatives (ReM3), the design for remanufacturing (ReM4), enterprise collaboration (ReM5), remanufactured product positioning (ReM6), performance measurement (ReM7), labor skill and availability (ReM8), and a flexible remanufacturing system (ReM9). The edges also display how these attributes interact with one another. The digraph of remanufacturing attributes is displayed in Figure 1.

The process of creating the digraph of remanufacturing attributes entails establishing the relationships between these attributes. An experienced team was assembled, including academicians and remanufacturing and circular economy professionals, such as managers, operations managers, and logistics managers. The experts were selected based on their managerial positions and years of professional experience, with a focus on those possessing over a decade of expertise in end-of-life management practices, remanufacturing, and sustainable manufacturing and those working at the managerial level. The expert panel included 10 members from the industry and academia, ensuring diverse perspectives and balanced opinions. This selection process ensures that the study benefits from the knowledge of experienced professionals with hands-on experience in remanufacturing and related fields. Table 2 contains the experts’ details such as their experience, education, and domain.

As per the experts’ input, each remanufacturing attribute impacts the other attributes. For instance, if ReM2 has an impact on ReM1, then an edge exists between nodes 1 and 2. This is depicted by a direct edge originating from node ReM2 and directed to node ReM1. Similarly, the expert team constructed the connections between the other remanufacturing readiness attributes, which can be seen in the digraph in Figure 1.

Development of Equivalent Matrix of the Digraph

This section explains how to create an equivalent matrix from the digraph of remanufacturing readiness attributes. The equivalent matrix is the matrix version of the digraph of remanufacturing readiness, and it is the asymmetric adjacency matrix. The adjacency matrix will produce a mathematical equation that shows the RRI. Equation (1) represents the remanufacturing readiness attribute matrix (ReM_I) that corresponds to the remanufacturing readiness attribute digraph shown in Figure 1.

$${\mathrm{ReM}}_{\mathrm{I}}=\left[\begin{array}{c}\begin{array}{ccc}\begin{array}{ccc}{M}_1& 0& m_{13}\\ m_{21}& M_2& m_{23}\\ m_{31}& m_{32}& M_3\end{array}& \begin{array}{ccc}0& 0& m_{16}\\ 0& m_{25}& 0\\ m_{34}& m_{35}& m_{36}\end{array}& \begin{array}{ccc}{m}_{17}& 0& 0\\ m_{27}& m_{28}& 0\\ 0& m_{38}& m_{39}\end{array}\\ \begin{array}{ccc}{m}_{41}& 0& m_{43}\\ m_{51}& m_{52}& m_{53}\\ m_{61}& 0& m_{63}\end{array}& \begin{array}{ccc}{M}_4& 0& 0\\ m_{54}& M_5& m_{56}\\ 0& m_{65}& M_6\end{array}& \begin{array}{ccc}{m}_{47}& m_{48}& m_{49}\\ 0& m_{58}& 0\\ 0& 0& 0\end{array}\\ \begin{array}{ccc}{m}_{71}& 0& 0\\ m_{81}& m_{82}& m_{83}\\ 0& m_{92}& m_{93}\end{array}& \begin{array}{ccc}{m}_{74}& 0& m_{76}\\ 0& m_{85}& 0\\ 0& 0& 0\end{array}& \begin{array}{ccc}{M}_7& 0& m_{79}\\ 0& M_8& 0\\ m_{97}& m_{98}& M_9\end{array}\end{array}\end{array}\right]$$

(1)

In Equation (1), ‘M_i’ represents the numerical score of a remanufacturing facility on attribute ‘Ai’, using the linguistic score as provided in

Table 4. Attribute assessment for remanufacturing facility.

Linguistic Scale	Crisp Scale
Very High	5
High	4
Medium	3
Low	2
Very Low	1
Unavailable	0

. Further, the off-diagonal element ‘m_ij’ represents the degree of influence of attributes i on j using the linguistic score provided in

Table 5. Degree of influence among attributes.

Linguistic Scale	Crisp Scale
High	4
Above average	3
Average	2
Minor	1
None	0

. The off-diagonal elements with a value of zero in Equation (1) show the absence of any linkages between the corresponding attributes. This matrix represents the remanufacturing readiness attribute matrix for the nine identified attributes; however, it is adaptable and can be extended to include a varying number of attributes based on specific needs as shown in Equation (2).

This matrix is utilized to determine the permanent function to assess the readiness of the facility to adopt remanufacturing. The permanent of the matrix, denoted as Per (ReM), represents the remanufacturing readiness function (RRF) of the remanufacturing facility. Like the determinant of the matrix, the permanent function has a positive sign rather than a negative one [62]. The permanent function was chosen by the researchers over the determined function since the determinant functions lose some information because of negative signs [63]. Several studies have used the permanent function to develop the indices for different measures such as the sustainability index and performance index. For instance, ref. [64] use the permanent function to develop the sustainability index, ref. [58] use it for the sustainability index for logistics, and ref. [57] use it for the circular economy performance index.

To formalize the derivation of a common expression for a remanufacturing readiness matrix (i.e., ReM), having N distinct attributes is critical to remanufacturing readiness. This is represented mathematically in Equation (2).

$${\mathrm{ReM}}_{\mathrm{N}}=\left[\begin{array}{ccc}\begin{array}{cc}{M}_1& m_{12}\\ m_{21}& M_1\end{array}& \cdots & \begin{array}{c}{m}_{1N}\\ m_{2N}\end{array}\\ ⋮& \ddots & ⋮\\ \begin{array}{cc}{m}_{N1}& m_{N2}\end{array}& \cdots & M_{NN}\end{array}\right]$$

(2)

The corresponding permanent function value of RRF, formulated based on the attributes within the ReM matrix, is expressed as Equation (3).

$$Per \left({ReM}_N\right)={\sum }_P{\prod }_{i=1}^N{m}_{ij}, P(i)$$

(3)

where the sum is calculated for all permutations P.

4. Readiness Assessment of a Remanufacturing Facility

The remanufacturing readiness assessment, incorporating elements M_i and m_ij, evaluates the overall readiness of a facility for remanufacturing practices. This quantitative assessment requires numerical values for each of the remanufacturing readiness matrix’s M_i and m_ij values. The steps involved in performing a quantitative analysis of the elements of the remanufacturing readiness evaluation matrix are outlined below and presented in Figure 2.

4.1. Diagonal Element Quantification

The diagonal elements of the remanufacturing readiness assessment matrix represent the readiness attributes of the remanufacturing facility. These attributes are shown in Table 1, and the value of these attributes is decided as per the state of the remanufacturing facility. The value of these attributes is assessed by the expert team for the selected remanufacturing facility using the linguistic scale provided in Table 4.

4.2. Off-Diagonal Element Quantification

After the quantification of the diagonal elements, the off-diagonal elements are also quantified. The quantification of non-diagonal elements entails the allocation of numerical values to each m_ij. In this regard, it is preferable to assign values using a numeric scale from high (4) to none (0). Additional levels are also included as above average (3), medium (2), and minor (1). The degree of influence among a facility’s remanufacturing readiness attributes is displayed in

Table 6. Remanufacturing readiness assessment matrix.

Remanufacturing Readiness Attributes	Core Acquisition	Reverse Logistics Availability	Resource Availability for Remanufacturing Initiatives	Design for Remanufacturing	Enterprise Collaboration	Remanufactured Product Positioning	Performance Measurement	Labor Skill and Availability	Flexible Remanufacturing System
Core acquisition		0	2	0	0	1	3	0	0
Reverse logistics availability	4		1	0	2	0	2	2	0
Resource availability for remanufacturing initiatives	3	2		4	3	1	0	2	1
Design for remanufacturing	3	0	2		0	0	1	1	2
Enterprise collaboration	3	2	2	1		2	0	1	0
Remanufactured product positioning	1	0	2	0	1		0	0	0
Performance measurement	2	0	0	2	0	3		0	1
Labor skill and availability	3	2	4	0	1	0	0		0
Flexible remanufacturing system	0	1	2	0	0	0	2	1

.

4.3. Remanufacturing Readiness Index

The RRI is a quantitative measure of the readiness of a facility for remanufacturing practices. This is derived from the remanufacturing readiness assessment by assigning numerical values to “M_i” and m_ij. A higher index value indicates a greater readiness to adopt remanufacturing practices and vice versa. The RRI is the ratio of the permanent function (Per(ReM_K)) value of the remanufacturing readiness assessment matrix to the permanent function (Per(ReM_Ideal)) of the ideal remanufacturing readiness assessment matrix. The ideal remanufacturing readiness assessment matrix refers to the most desirable and exceptional remanufacturing facility. In the case of the ideal remanufacturing facility, the value of each remanufacturing readiness attribute is the maximum (i.e., four) and the value of m_ij is the same as in Table 6. The RRI_k is the remanufacturing readiness index for ‘facility k’ and is mathematically expressed as follows:

$${\mathrm{RRI}}_{\mathrm{k}}={\displaystyle \frac{{Per \left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_k}{{Per \left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_{ideal}}}$$

(4)

$$\mathrm{Where}, {\left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_{ideal}=\left[\begin{array}{ccccccccc}4& 0& 2& 0& 0& 1& 3& 0& 0\\ 4& 4& 1& 0& 2& 0& 2& 2& 0\\ 3& 2& 4& 4& 3& 1& 0& 2& 1\\ 3& 0& 2& 4& 0& 0& 1& 1& 2\\ 3& 2& 2& 1& 4& 2& 0& 1& 0\\ 1& 0& 2& 0& 1& 4& 0& 0& 0\\ 2& 0& 0& 2& 0& 3& 4& 0& 1\\ 3& 2& 4& 0& 1& 0& 0& 4& 0\\ 0& 1& 2& 0& 0& 0& 2& 1& 4\end{array}\right]$$

(5)

The value of the RRI lies between zero and one, and a value of the RRI that is close to one represents that the facility is ready for remanufacturing practices. The ideal case value of the RRI is one.

5. A Case Study for RRI Measurement

A case study has been performed to demonstrate the implementation of the proposed RRI. A remanufacturing facility (ABC) located in the National Capital Region (NCR), India is considered for the assessment of the RRI. A case study from India offers an ideal background for the remanufacturing study with its large MSME sector, which can benefit from sustainable and cost-reduction approaches. Government policy support measures such as the National Resource Efficiency Policy and Extended Producer Responsibility promote the use of a circular economy and sustainable manufacturing, thus facilitating remanufacturing practices. Environmental concerns in India and growing market demand further encourage the implementation of remanufacturing to reduce waste, conserve resources, and address a research gap in the developing economy setting. The ABC facility is in the finalization phase and planning to start its operations in the next year (i.e., mid-2025). We have contacted its senior members of management to obtain their input regarding the facility on the identified nine readiness attributes using the linguistic scale as provided in Table 4. As a result, they have provided the input for their remanufacturing facility (ReM_ABC) and the response is shown in

Table 7. Assessment of remanufacturing readiness attributes for ABC facility.

Remanufacturing Readiness Attributes	Mi for ABC
Core acquisition	3
Reverse logistics availability	2
Resource availability for remanufacturing initiatives	3
Design for remanufacturing	2
Enterprise collaboration	4
Remanufactured product positioning	2
Performance measurement	3
Labor skill and availability	3
Flexible remanufacturing system	4

.

Based on the above response, the remanufacturing readiness assessment matrix is developed for the ABC facility by substituting the value of M_i in the diagonal element of ReM_I. The off-diagonal element of ReM_ABC is the same as in Table 5. The remanufacturing readiness assessment matrix for ABC (ReM_ABC) is as follows:

$${\mathrm{R}\mathrm{e}\mathrm{M}}_{ABC}=\left[\begin{array}{ccccccccc}3& 0& 2& 0& 0& 1& 3& 0& 0\\ 4& 2& 1& 0& 2& 0& 2& 2& 0\\ 3& 2& 3& 4& 3& 1& 0& 2& 1\\ 3& 0& 2& 2& 0& 0& 1& 1& 2\\ 3& 2& 2& 1& 4& 2& 0& 1& 0\\ 1& 0& 2& 0& 1& 2& 0& 0& 0\\ 2& 0& 0& 2& 0& 3& 3& 0& 1\\ 3& 2& 4& 0& 1& 0& 0& 3& 0\\ 0& 1& 2& 0& 0& 0& 2& 1& 4\end{array}\right]$$

(6)

The RRF for the remanufacturing facility ABC is developed from the permanent function of the remanufacturing readiness matrix. ${\mathrm{R}\mathrm{e}\mathrm{M}}_{ABC}$ is determined as per Equation (3). In order to determine the value of the permanent function, a computer program has been developed in the R package R 4.2.1. With the help of this program, the value of the permanent function of the remanufacturing facility is determined as follows:

$$Per{\left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_{ABC}=\mathrm{2,751,357}$$

To evaluate the RRI, the permanent function of ideal remanufacturing readiness must be obtained. To do so, the permanent function value is calculated for the ideal matrix that is represented as an equation. The value of the permanent function is as follows:

$$Per{\left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_{Ideal}=\mathrm{9,581,660}$$

Now, the RRI for the ABC remanufacturing facility is calculated as per Expression (4).

$$\begin{array}{l}{\mathrm{RRI}}_{\mathrm{ABC}}={\displaystyle \frac{Per\left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}{{Per\left(\mathrm{R}\mathrm{e}\mathrm{M}\right)}_{ideal}}}\\ ={\displaystyle \frac{\mathrm{2,751,357}}{\mathrm{9,581,660}}}\\ =0.287148\end{array}$$

The RRI for the remanufacturing facility ABC has been successfully calculated, demonstrating that the RRI value can serve as a reliable metric for decision making in remanufacturing operations. The case result shows that the RRI for the facility is low and it needs certain improvements before operationalizing. This remanufacturing facility can be improved by focusing on the attributes that have low values, such as ‘reverse logistics availability’, ‘design for remanufacturing’, and ‘remanufactured product positioning’. The framework and RRI can provide such insights to managers before remanufacturing operations start.

6. Implications of Study

This study has several implications for managers, practitioners, and policymakers. The significant implications are outlined below.

6.1. Managerial Implications

The findings of this study provide actionable insights for MSME managers, industry stakeholders, and policymakers to facilitate the adoption of remanufacturing practices, thereby fostering resource efficiency, sustainability, and a competitive advantage. The suggested framework may be used by managers to evaluate how prepared their company is to implement remanufacturing practices. The calculated RRI serves as a measure to assess the extent to which a facility is prepared for remanufacturing operations. Additionally, this index provides decision makers with a valuable tool to compare two or more facilities in terms of their readiness to adopt remanufacturing practices. This comparative analysis can aid in identifying strengths, weaknesses, and areas requiring improvement across different facilities. The findings also suggest that MSMEs may optimize the effectiveness of remanufacturing activities by focusing expenditures on high-impact elements, such as labor skill development, resource availability, and reverse logistics development. Furthermore, the proposed RRI can be utilized to measure a remanufacturing facility’s progress towards the adoption of remanufacturing practices by systematically comparing the previous RRI with the current RRI. This quantified measure can also facilitate stakeholders, such as investors and regulatory bodies, in assessing the status of the remanufacturing facility and making informed decisions. In addition, the RRI can also be used to compare the different remanufacturing facilities and set benchmarks for the adoption of remanufacturing.

6.2. Academic Implications

The current study offers a comprehensive addition to the limited studies on remanufacturing practices in MSMEs and offers guidance on how to evaluate readiness. It also quantified a remanufacturing facility’s readiness towards the adoption of remanufacturing to embrace sustainability. This study used GTM to outline the relationships between all of the remanufacturing readiness attributes that were found. Further, this study also demonstrates how GTM can be used to develop the index for remanufacturing readiness, and it can be extended for other indices such as the remanufacturing performance index. The proposed framework can be used to measure the readiness to adopt remanufacturing practices. This framework can serve as a foundational reference for future studies exploring readiness assessment in other sectors or geographical contexts. As per the requirements, the proposed framework can be utilized by various researchers in different settings, such as the manufacturing of automobile parts, electronic equipment, and wood furniture. Moreover, the RRI acts as a quantitative decision support tool that provides measurable insights into MSMEs’ readiness to adopt remanufacturing practices. Future academic studies can leverage this tool to evaluate readiness across different industries, regions, or levels of business maturity.

7. Conclusions

This study proposes a framework to assess readiness to adopt remanufacturing practices in the context of MSMEs. This study can be considered a step toward fostering sustainable manufacturing practices in MSMEs. Expert feedback and a review of the literature were used to determine the critical readiness attributes for the implementation of remanufacturing practices. Nine readiness attributes for remanufacturing adoption were finalized, including ‘core acquisition’, ‘reverse logistics availability’, ‘resource availability for remanufacturing initiatives’, ‘design for remanufacturing’, ‘enterprise collaboration’, ‘remanufactured product positioning’, ‘performance measurement’, ‘labor skill and availability’, and ‘flexible remanufacturing system’. Based on these readiness attributes, a framework was established using the GTM methodology to assess readiness to adopt remanufacturing practices. This approach facilitated the measurement of remanufacturing readiness, encapsulated in the form of the RRI, serving as a robust metric for evaluating and benchmarking the readiness of the facility for the adoption of remanufacturing practices. This systematic framework offers a comprehensive tool for MSMEs to evaluate and enhance their readiness to adopt remanufacturing practices. By leveraging this index, MSMEs can systematically identify gaps, prioritize resource allocation, and develop targeted strategies to overcome barriers to the adoption of remanufacturing. To illustrate the proposed framework, a case study was performed, and the result shows that the RRI value for the adopted remanufacturing facility is low. This helps the manufacturing facility to make an informed decision before starting operations. The evaluation of the RRI provides the remanufacturing facility with valuable insights regarding its readiness. Given that the RRI not only encompasses technical aspects but also has managerial implications, manufacturing facilities can leverage the RRI to identify specific areas of deficiency within their operations. This enables informed decision making, allowing managers to implement targeted strategies for improving their readiness towards the adoption of remanufacturing practices.

Certain limitations are also associated with this study. This study is based on a literature review and experts’ input; therefore, it has some limitations. One limitation of this study is that the readiness factors were identified through a literature review, and there is a chance some important attributes were not included. In order to eliminate this limitation, a comprehensive review can be performed by broadening the search string and using other databases, such as Web of Science. Further, the interrelationships were identified through the experts’ input and their opinions might have been biased. This bias can be reduced in future studies using the fuzzy and grey theories. In addition, this proposed framework was developed for MSMEs, and it can be extended to large-sized enterprises in future studies.

Future research could explore the application of the RRI across diverse industries and geographic regions, refining the framework to account for contextual variations. The proposed framework can be applied to product-specific remanufacturing facilities such as electronic, automobile, or electrical equipment. Future studies need to validate the RRI framework in different sectors and geographical regions to account for industry-specific and cultural variations. Comparative and longitudinal analyses are needed to assess the time adaptability of the framework and its performance compared to other maturity models. The findings can be validated through multiple case studies in future work. Further, the importance of the identified attributes is also considered in the determination of enterprises’ readiness to adopt remanufacturing attributes. In addition, the proposed framework can be studied in the context of the Sustainable Development Goals (SDGs). One can explore how remanufacturing readiness helps to achieve specific SDGs in future studies.