Remanufacturing Shoemaking Machine: Feasibility Study Using AHP and DEMATEL Approach

Abstract

This study investigates the feasibility of remanufacturing shoemaking machines for sustainability using a combined AHP and DEMATEL approach. The AHP prioritizes machine types for remanufacturing, while the DEMATEL analyzes the interdependencies of influencing factors. Results indicate sole-making equipment as the most suitable candidate, followed by surface and forming equipment. Furthermore, appropriate product design, modularity, and a complete recycling system emerge as crucial for successful strategies. The DEMATEL confirms the foundational role of these factors in influencing a positive corporate image and business model. This study offers valuable insights and recommendations for Taiwan-based shoemaking machine OEMs in India to formulate strategies that promote remanufacturing. The findings highlight the critical role of OEMs in raising customer awareness regarding the environmental and economic benefits of returning end-of-life products and utilizing remanufactured machinery. Effective strategies should emphasize the importance of design for remanufacturing principles, modular machine architectures, and the establishment of comprehensive recycling systems. By fostering customer engagement through such initiatives, OEMs can create a collaborative ecosystem that facilitates the successful implementation of remanufacturing practices within the Indian market. Overall, the study presents a compelling case for remanufacturing as a strategic approach for OEMs to promote a circular economy, resource efficiency, and a sustainable future for footwear production.

1. Introduction

The footwear industry is undergoing a crucial transformation driven by consumer demand for sustainable practices and a growing awareness of the environmental impact of traditional production methods. This shift necessitates a comprehensive approach to reducing the industry’s footprint. Environmental challenges of footwear production include high water consumption, extensive use of chemicals, large carbon footprint, and solid waste generation. Consumers are increasingly concerned about the environmental impact of the products they purchase. This leads to a growing demand for sustainable footwear options that minimize environmental damage throughout the product life cycle. Governments worldwide are implementing stricter environmental regulations to curb pollution and promote sustainable practices. Footwear companies face increasing pressure to comply with these regulations and demonstrate environmental responsibility. As concerns about resource depletion grow, the availability of essential materials becomes less secure. This necessitates a shift towards more sustainable practices that minimize resource dependency. The footwear industry faces a critical juncture. By acknowledging the environmental challenges of current practices and embracing sustainable solutions, the industry can navigate this transition and ensure its long-term viability. The imperative for sustainability transcends mere environmental responsibility; it has become a cornerstone for the footwear industry’s future success. Consumers increasingly prioritize eco-conscious practices throughout the product life cycle, while stricter regulations and resource scarcity necessitate a shift towards a more sustainable production model. As such, embracing sustainable practices is no longer optional but a strategic imperative for footwear companies to ensure their long-term viability.

For centuries, shoemaking has been a skilled trade relying on manual dexterity and specialized tools. The traditional process involved numerous labor-intensive steps, from leather cutting and stitching to shaping and finishing, all performed by experienced craftspeople. However, the 20th and 21st centuries have witnessed a significant transformation in this industry driven by advancements in technology. Modern shoemaking embraces a blend of tradition and innovation. While skilled workers remain crucial for design, quality control, and certain specialized tasks, automation and robotics have significantly altered the production landscape. The examples of modern technologies include Computerized Numeric Control (CNC) machines, automated sewing machines, robotic arms, etc. The integration of modern technologies has significantly transformed the shoemaking industry and brought in the benefits of increasing production efficiency, enhancing product quality, and improving working conditions. The future of shoemaking likely lies in a synergistic blend of traditional craftsmanship and cutting-edge technological advancements. As technology continues to evolve, we should expect further innovations that improve sustainability, efficiency, and the overall quality of footwear production.

The global footwear industry is facing increasing pressure to minimize its environmental footprint. Traditional production processes often generate significant waste through the disposal of machinery at the end of its lifecycle. Remanufacturing, the process of restoring a used machine to its original functionality through repair, replacement of components, and performance testing, presents a promising approach to promote sustainability within this sector.

This study investigates the feasibility of remanufacturing shoemaking machines using a combined AHP and DEMATEL approach. The AHP (Analytic Hierarchy Process) allows for us to prioritize different types of shoemaking machines (e.g., sole-making, surface production, forming) for remanufacturing based on key criteria determined by industry experts. The DEMATEL (Decision-Making Trial and Evaluation Laboratory) then helps us understand the interdependencies and causal relationships between these criteria, providing valuable insights for developing effective remanufacturing strategies.

By focusing on remanufacturing machines themselves, this study aims to extend their usable life, reduce waste generation, and promote resource efficiency within the shoemaking industry. The findings contribute valuable knowledge for Original Equipment Manufacturers (OEMs) considering the integration of remanufacturing into their business models, ultimately fostering a more sustainable future for footwear production.

The research offers valuable insights and recommendations for Taiwan-based shoemaking machine OEMs operating in India. The findings highlight the critical role these companies can play in promoting remanufacturing by raising customer awareness about its environmental and economic benefits. This includes emphasizing the importance of design for remanufacturing principles, modular machine architectures, and establishing comprehensive recycling systems. By fostering customer engagement through such initiatives, OEMs can create a collaborative ecosystem that facilitates the successful implementation of remanufacturing practices within the Indian market.

2. Literature Review

This section delves into the existing body of knowledge relevant to shoemaking machine remanufacturing. The first sub-section provides a comprehensive overview of international remanufacturing practices, highlighting key evaluation elements that influence the success of such initiatives. The second sub-section shifts the focus to the specific context of the Indian shoemaking industry, offering an analysis of the current remanufacturing landscape and relevant background information. By establishing a foundational understanding of both the global and Indian perspectives on remanufacturing, this review provides a robust framework for the subsequent analysis of shoemaking machine remanufacturing in India.

2.1. Remanufacturing

The footwear industry has witnessed a significant shift towards mass customization, moving away from traditional large-scale production models. This trend is driven by the growing consumer demand for personalized and tailored products. Recognizing the potential of this market segment, traditional shoe manufacturers are actively integrating modern equipment and methodologies into their production processes. These advancements are often facilitated by the adoption of information and communication technologies (ICTs). By leveraging ICTs, footwear companies can capture a broader spectrum of consumer preferences, enabling them to produce customized footwear in a more efficient and cost-effective manner [1]. Recognizing this trend, the European Commission has actively supported several industry projects aimed at facilitating this transformation. One such noteworthy project is the IDEA-Foot project, launched in 2010. The IDEA-Foot project proposes a novel methodology for integrated design and production of footwear. This approach leverages 3D CAD models to directly derive a significant portion of the production parameters. Additionally, the project introduces an innovative automated production plant characterized by a high level of integration between manipulators and other automated machines. This integrated approach has the potential to streamline the footwear production process and potentially enhance efficiency [2]. This initiative focuses on developing new methods for shoe standardization and streamlining the transfer of geometrical information from the design phase to the production process. The project aims to establish a digital standard data format for efficient data transfer, enhancing collaboration and facilitating mass customization within the footwear industry [3].

While handcrafted production remains prevalent in the fashion footwear industry, the growing demand for short production runs and mass customization necessitates exploring automation possibilities. However, the complexity of the manufacturing process and the critical importance of final product quality pose significant challenges to full automation. Recognizing these limitations, a consortium named ROBOFOOT was established in 2010. This collaborative effort brought together robotic solution providers, research institutes, and shoe manufacturers. The consortium’s primary objective was to promote the development and implementation of innovative solutions for automating tasks involving non-rigid materials commonly encountered in footwear production [4]. The ROBOFOOT project focuses on the project’s progress in three key areas: user requirements, targeted operations, and technical achievements. Particular emphasis is placed on the detailed description of the visual servoing solution developed for accurate shoe pose identification. This technology offers a significant advantage by enabling the integration of robotic solutions alongside existing production practices, minimizing the need for substantial modifications to current manufacturing infrastructure within footwear companies [5].

Green production contributes to green competitive advantage, which in turn enhances sustainable firm performance and confirms the mediating effects of green production and green competitive advantage and the moderating effects of corporate reputation and supply chain innovativeness. Big Data Analytics (BDA) technology capability, Green Technological Innovation Capabilities (GTICs), and environmental orientation can contribute to sustainable firm performance, which in turn can positively influence a company’s reputation and image in the eyes of environmentally conscious consumers [6]. By integrating the sustainability of End-of-life (EOL) product in disassembly operations, a corporation can enhance its competitive advantage in the long term by aligning its business goals with environmental and social objectives and by creating a distinctive and reputable corporate image. Sustainable disassembly line balancing (DLB) can enhance a company’s corporate image. By minimizing waste and maximizing resource recovery, DLB demonstrates a commitment to environmental responsibility, resonating with environmentally conscious stakeholders. Additionally, the TBL (Triple Bottom Line)-based DLB model optimizes disassembly processes, potentially leading to cost savings and improved efficiency. Communicating these operational improvements can project a forward-thinking and efficient company image [7]. The incorporation of environmental factors into the supplier selection criteria enhances both the profitability and the reputation of the firms, which tend to improve the quality of the production process as a consequence of adopting environmentally friendly practices. Integrating environmental considerations into supplier selection enhances the corporate image. Companies selecting environmentally responsible suppliers demonstrate their own sustainability commitment, resonating with environmentally conscious stakeholders. Additionally, such practices may lead to improved production quality, further bolstering a company’s image as a provider of high-quality products [8]. The above three studies emphasize the importance of environmental responsibility for the corporate image. However, they differ slightly in the additional mechanisms identified, with some studies focusing on operational efficiency and quality improvements as contributing factors.

A notable global trend is emerging within Original Equipment Manufacturers (OEMs) characterized by a shift towards remanufacturing operations. This phenomenon is fueled by the growing prominence of circular economy and sustainability principles, further bolstered by environmental regulations and potential profit opportunities. Consequently, the development of effective business models specifically tailored for remanufacturing has become an essential endeavor [9]. Circular economy emphasizes both resource efficiency and closed-loop systems, maximizing product recovery, waste valorization, and minimizing waste disposal to achieve holistic circularity. The development or adaptation of robust business models becomes fundamental for a successful transition to circular economy. This necessitates reforming the existing models and strategically implementing novel technologies to leverage their full potential) [10]. Enhanced circular business models emerge from innovation mechanisms within the context of transitions towards circular economy [11].

Remanufacturing transcends mere maintenance or material recycling. It encompasses a comprehensive process of disassembly, cleaning, testing, repair, and reassembly, effectively restoring used parts to their original functionality and creating functionally equivalent products [12]. Recycling and remanufacturing stand as the foremost product recovery options, driving efficient and sustainable manufacturing designs [13]. Remanufacturing restores used products to “as-new” condition through disassembly, overhaul, and replacements. Functioning as a sociotechnical intermediary, the supply chain bridges technical and social systems to fulfill societal needs. OEMs operating in a competitive, profit-driven environment face brand protection challenges as the remanufactured product segment expands. However, both segments prioritize product functionality, gaining importance in circular economy transition [14].

To meet diverse customer demands, companies increasingly leverage product family design (PFD), offering component and module variations for different markets. PFD benefits include reduced development time, cost, and complexity, enhanced upgradeability, and improved disassemblability. Remanufacturing presents a trade-off, as it may not always be economical compared to producing new components. Additionally, remanufactured products can potentially cannibalize the market share of existing ones. Therefore, the design of remanufactured products requires careful consideration of both options to optimize economic and environmental impact [15]. Remanufacturing engineering hinges on capitalizing on a product’s design life redundancy to offer new life to used parts. However, challenges arise in adapting to customer demand fluctuations and managing fragmented part availability [16]. Remanufacturing extends product life by restoring used products to original quality and reintroducing them to the market. To improve resource efficiency and facilitate remanufacturing, design for remanufacturing principles can be integrated into new product development, particularly during the early stages, as highlighted by existing research [17].

Table 1. Summary of the literature review between remanufacturing and each key evaluation element.

Key Elements	Research Briefing	References
Corporate image	The study explored how Big Data Analytics (BDA) and Green Technological Innovation Capabilities (GTICs) affect the sustainability of the manufacturing industry. It found that green production, competitive advantage, corporate reputation, and supply chain innovativeness mediate and moderate the relationships.	[6]
	This study tackled the issue of e-waste by proposing a sustainable model for disassembly line balancing. The model considers environmental, social, and economic factors (Triple Bottom Line) and leverages a multi-criteria decision-making approach. The research suggests this sustainable approach can improve a company’s long-term competitive edge and image.	[7]
	The study examined how the inclusion of environmental factors in the supplier selection process affected the performance and reputation of firms. Using a structural equation model based on a survey of 206 purchasing managers, the study found that firms that considered environmental attributes of suppliers improved their profits, corporate image, and production quality.	[8]
Business model	This study explored transitioning to a circular economy through business models and waste valorization (finding value in waste). It analyzed existing research and a case study to provide practical recommendations for businesses. The research suggested that gradual technological advancements, collaboration, and government incentives can ease this transition and drive the development of new, circular business models.	[10]
	The paper proposed a framework that elucidates how innovation mechanisms within emerging circular economies enhance business models. A two-step approach was employed: (1) theoretical framing using transition studies insights and (2) systematic literature review to validate the proposed framework.	[11]
	The research focused on the global trend of OEMs shifting towards remanufacturing, driven by sustainability and profitability concerns. A holistic business model for automotive parts remanufacturing comprised nine key components and sought to empower investors and entrepreneurs in the automotive remanufacturing industry with data-driven insights for sound decision-making.	[9]
Recycling system	This study employed a system dynamics model to analyze the long-term economic impact of different used car engine recycling modes on remanufacturing costs, sales profits, and revenue. The framework offered valuable insights for selecting optimal recycling approaches and supporting the sustainable development of the remanufacturing industry.	[12]
	Rising interest in sustainable manufacturing highlights design challenges for end-of-life product recovery. This research explored the integration of two systems, RCMS (Reconfigurable Cellular Manufacturing Systems—flexible manufacturing) and HMRS (Hybrid Manufacturing Remanufacturing Systems—hybrid remanufacturing), to optimize design, analysis, and planning for sustainable manufacturing systems. RCMS embodies a holistic approach to sustainable manufacturing by integrating closed-loop supply chains, cellular manufacturing principles, remanufacturing capabilities, and reconfigurable manufacturing practices. HMRS utilizes a comprehensive mixed linear integer programming model to optimize various aspects of Sustainable Manufacturing Systems design, including the integration of recycling, remanufacturing, disposal options, reconfigurability, and cellular manufacturing principles.	[13]
	This study examined the under-researched connection between capital goods supply chains and sustainability. It analyzed a model where remanufacturing and recycling improve sustainability, but competition between new and remanufactured products creates hurdles for both economic and environmental benefits.	[14]
Product design	The footwear industry is undergoing a paradigm shift from mass production to mass customization, driven by consumer demand for personalized products. Traditional shoe manufacturers are embracing this trend by integrating modern equipment, methodologies, and information and communication technologies (ICT) into their production processes. ICT plays a crucial role in capturing a wider range of consumer preferences, facilitating the efficient and cost-effective production of customized footwear.	[1]
	The IDEA-Foot project (2010) proposed a novel integrated design and production methodology. This approach leverages 3D CAD models for production parameter generation and introduces an innovative automated production plant with high manipulator–machine integration, potentially streamlining the process and enhancing efficiency.	[2]
	To further support the transition towards mass customization in footwear, the IDEA-Foot project (2010) addressed key challenges by developing methods for shoe standardization and establishing a digital data format for transferring geometrical information between design and production. This streamlined data exchange fosters collaboration and empowers mass customization within the footwear industry.	[3]
	Despite the prevalence of handcrafted methods in fashion footwear, the rise of mass customization and short production runs necessitates exploring automation. However, the intricate nature of the manufacturing process and the paramount importance of quality present significant hurdles to complete automation. The ROBOFOOT consortium aims to develop innovative solutions for automating tasks involving the non-rigid materials characteristic of footwear production.	[4]
	The ROBOFOOT project addresses the integration of robotics in footwear manufacturing. It details user requirements, targeted operations, and technical advancements. Notably, the project presents a novel visual servoing solution for precise shoe pose identification. This technology facilitates the seamless integration of robots into existing production lines, minimizing disruptions to current footwear manufacturing infrastructure.	[5]
	Remanufacturing combats resource depletion and pollution by reviving used products. However, customer demand and fragmented parts pose challenges. This research proposed a new information model for remanufacturing that mathematically analyzes design parameters and uses algorithms to simulate production processes. This can help address design conflicts and optimize the remanufacturing process.	[16]
	Remanufacturing breathes new life into used products, but to be truly effective, it needs to be considered early in the design phase (DfR). This study explored how to integrate DfR into a company’s strategy using interviews and design management theory. The research revealed that current practices keep these processes separate, limiting remanufacturing’s potential. By aligning strategy and DfR, companies can unlock significant value, potentially benefiting similar organizations as well.	[17]
	This study examined a new model for optimizing product family design (PFD) used for diverse product lines. It aimed to minimize costs and maximize market share while ensuring reliability for both new and remanufactured products. The model utilizes advanced algorithms to find the best solution, demonstrating its effectiveness through case studies.	[15]

summarizes the literature review between remanufacturing and each key evaluation element.

2.2. Remanufacturing Scenario in India

India’s position as the world’s second most populous nation presents a significant opportunity for the development of a robust remanufacturing industry. This potential stems from the country’s large workforce with established technical skills and a competitive labor cost advantage. These factors position India as a potential hub for remanufacturing expertise. However, the current role of the Indian remanufacturing industry remains limited. This can be primarily attributed to negative perceptions held by end users regarding the quality and reliability of remanufactured products. Nevertheless, the recent entry of multinational players into the Indian market suggests a potential shift in this perception in the future [18].

Remanufacturing, a labor-intensive process involving product disassembly, refurbishment, and rigorous testing, presents a unique opportunity for India. Compared to recycling (5 to 10 per thousand tons of waste), remanufacturing creates significantly more jobs (8 to 20 per thousand tons of waste) and fosters the formalization of the informal waste management sector, potentially improving working conditions. This aligns perfectly with India’s national resource efficiency goals and the circular economy model. Furthermore, remanufacturing can “industrialize” traditional practices of recovering value from discarded products by designing for multiple lifecycles and ensuring remanufactured products meet or exceed performance standards of new ones. This collaboration between industry, government, and consumers can contribute significantly to India’s sustainable economic recovery [19].

India has a significant opportunity to leverage remanufacturing for several compelling reasons: (1) Economic growth potential: Remanufacturing presents a substantial economic opportunity for India, potentially reaching a USD 157.5 billion industry—a significant increase over current levels—without the environmental impact of traditional manufacturing; (2) Geopolitical shift and collaboration: Global disruptions highlight the need for diversified supply chains. Remanufacturing positions India as a collaborative partner in a climate-friendly, resource-efficient global economy, aligning with existing international partnerships focused on sustainability; (3) Alignment with sustainable development goals (SDGs): Remanufacturing directly contributes to achieving multiple SDGs, including decent work and economic growth, responsible consumption and production, and climate action; (4) Investment opportunities: The global focus on a circular economy is attracting significant investments through green bonds, sustainability-linked loans, and impact investing, providing financial resources for remanufacturing development; (5) Evolving consumer preferences: Indian consumers are increasingly prioritizing sustainability, creating a growing market for remanufactured products [20].

3. Methodology

By comprehensively applying the DEMATEL and the AHP, the problem of interrelated indicator analysis and indicator weight comparison is solved under the premise of acceptable subjectivity and operability when under conditions of limited data and unclear evaluation objectives [21]. A hybrid fuzzy AHP–DEMATEL method was designed to investigate the social sustainability of the supply chain. The framework is proposed to locate key metrics to evaluate the social sustainable supply chain (SSC) performance. The study analyzed the social and economic sustainability performance in terms of demand planning, innovation, manufacturing, finance, sales and customer relationship, distribution and delivery, and compliance [22]. An integrated methodology of fuzzy multi-criteria decision-making (MCDM) using fuzzy DEMATEL and fuzzy AHP is applied for modelling and prioritizing the enablers which were identified from the extensive literature and expert interview for supply chain responsiveness. The model revealed the most crucial responsiveness enablers for the supply chain [23].

3.1. Limitation

Remanufacturing, a cornerstone of circular economy, aims to eliminate waste and resource depletion through product recovery and reuse. However, challenges persist from both OEMs and end users. OEM challenges include:

• Design: Lack of remanufacturing considerations during initial design (e.g., focusing solely on functionality and cost) hinders remanufacturing potential.
• Process: Product variety complicates standardization of remanufacturing processes.
• Capability: Insufficient knowledge, skilled personnel, and capacity for remanufacturing within OEMs.
• Logistics: Increased costs associated with reverse logistics like product collection and transportation.
• Market cannibalization: Remanufactured products competing with new ones, potentially impacting overall revenue.
• Quality assurance: Lack of established and trusted quality certification systems for remanufactured products.

End-user challenges include:

• Price sensitivity: Customers expect significant price discounts for remanufactured products compared to new ones.
• Uncertainty about quality: Concerns regarding the quality and reliability of remanufactured products.
• Information asymmetry: Reluctance of OEMs to share remanufacturing knowledge and tools with third parties.
• Financing difficulties: Difficulty securing financing for remanufacturing projects due to perceived higher risk.

Figure 1 illustrates the linkage between OEM/end-user challenges and the key evaluation elements.

Table 2. Key barriers hindering shoemaking machine remanufacturing.

Key Barriers	Content	References
OEM challenges
Design	Remanufacturing offers undeniable benefits, but several hurdles remain including poor initial design, lack of leadership support and strategic integration, challenges in promoting, and creating market demand for remanufactured products. The study identifies “lack of resources for research and development (R&D) and information systems/data for product remanufacturing” as the most influential barrier. This highlights the need for increased investment in R&D specific to footwear machinery remanufacturing. Developing efficient disassembly processes, establishing standardized component lifecycles, and creating databases of remanufacturing data are crucial.	[24]
	This paper examines how design management can bridge the gap between the potential of DfR (Design for Remanufacturing) for a circular economy and its actual implementation. By emphasizing company-wide benefits and integrating DfR with early design stages, design management can unlock DfR adoption across material-intensive industries.	[25]
	Remanufacturing, a sustainable approach to product lifecycles, faces challenges due to a lack of knowledge in DfR (Design for Remanufacturing). This is particularly true for components not designed for disassembly or made from less durable materials like plastics and rubber, which significantly impact remanufacturing feasibility.	[26]
Process	Product variety creates complexity in remanufacturing due to the unpredictable quality of returned products. This complexity discourages multi-product remanufacturing and steers existing research towards standardization strategies like Product Family Design (PFD) and modularity for smoother circular supply chains.	[27]
	Several factors hinder DfR integration: poor communication in design teams, lack of DfR focus, and non-standard parts with limited availability and long lead times. These issues create delays and unnecessary operations during remanufacturing. Seven lean manufacturing principles are potential solutions to tackle these remanufacturing challenges and achieve shorter lead times: standard operation, continuous flow, Kanban, teamwork, factory layout for continuous flow, employee cross-training and learning through problem solving, and supplier partnership.	[28]
	High product variety, once praised for customers, is now seen as harming profits due to complexities in recycling, remanufacturing, and resale. Closed-loop supply chains, considering both production and end-of-life processes, are emerging to address this challenge.	[29]
Capability	This study highlights eight critical factors for successful OEM remanufacturing: acquiring cores, managing reverse logistics, securing skilled labor, developing appropriate facilities and processes, designing for remanufacturing, gathering market intelligence, and implementing effective planning and control.	[30]
	Focusing on Thailand’s auto parts industry, this study reveals business feasibility as the key driver for remanufacturing, with mature products being most suitable. While skilled labor and technical expertise are important, environmental factors have less influence. This emphasizes the importance of both a company’s capabilities and the product’s fit for remanufacturing.	[31]
	Respondents in this study were more optimistic about overcoming market and technology challenges (lack of knowledge, technology) within 5–10 years compared to core availability (volume, quality) issues. Additional concerns include increased management costs from mixed-brand cores, negative market influences, and competition from low-quality, uncertified repairs.	[32]
Logistics	This case study presented a model for optimizing reverse logistics for large appliances in the UAE. Prioritizing inspection centers and remanufacturing plants over in-house fleets maximizes profitability. The model can be particularly beneficial for companies with extensive transportation needs, potentially suggesting a gradual shift towards in-house systems as businesses grow.	[33]
	A study of the US firms reveals a significant challenge: the high cost of returns often exceeds product value. Despite the cost challenges, managers generally express satisfaction with their companies’ reverse logistics operations.	[34]
	This framework considers not just initial purchase costs, but also the often-overlooked impact of reverse logistics on the total cost of ownership (TCO). Reverse logistics, the process of returning products, includes costs like product returns and end-of-life management, and is crucial for a complete cost assessment.	[35]
Market cannibalization	Consumer preference for new products and the need for remanufactured goods to be demonstrably cheaper hinder their adoption. For remanufacturing to be truly sustainable, it must be both environmentally and economically sound, requiring enterprises to prioritize profitability throughout the process.	[36]
	This study explores strategies to overcome challenges in selling remanufactured products. Targeting new market segments with lower price points, full warranties, and rebate systems is suggested. Developing new sales channels and strategic partnerships with receptive clients and public entities are also seen as crucial for success.	[37]
	Remanufactured products challenge traditional demand management. Unlike products with distinct features, they compete directly with new versions, but are perceived as inferior. This “cannibalization” effect can lead to lost sales for both new and remanufactured goods.	[38]
Quality assurance	This research proposes a blockchain-based certification tool to address consumer concerns about refurbished products. The tool provides transparent and traceable information on a product’s life cycle and components, along with verifiable certificates. This enhanced visibility and trust can promote a more widespread circular economy.	[39]
	Consumer doubts about remanufactured product quality hinder market acceptance. Credible certifications set strict guidelines, mitigating these concerns and building trust, ultimately promoting wider adoption of remanufactured goods.	[40]
	While remanufacturing offers a solution, consumer perception often views remanufactured products as inferior. The study explores the potential of a comprehensive, quality-focused labelling scheme for remanufactured products in Sweden to address the challenge and promote method adoption.	[41]
End-user challenges
Price sensitivity	This study examines optimal remanufacturing strategies for modular products. It considers offering new, partially remanufactured (combining new and used modules), and fully remanufactured products. The best strategy depends on production costs for remanufactured options and the prevalence of environmentally conscious consumers in the market.	[42]
	The research focuses on optimizing pricing and remanufacturing decisions for manufacturers offering both new and remanufactured products, considering consumer perception of these products as distinct. The proposed model develops pricing strategies for both new and differentiated remanufactured products, addressing the challenge of demand cannibalization.	[43]
	This study investigates optimal pricing for new and remanufactured products. Surprisingly, the research suggests that OEMs should raise prices on new products when remanufactured options enter the market. This strategy, based on a consumer preference model, can help mitigate lost sales (cannibalization) and potentially increase profits, even in competitive environments.	[44]
Uncertainty about quality	This research introduces a novel model for evaluating remanufactured product quality. Inspired by Taguchi’s quality principles, it analyzes how defects in parts and assemblies affect overall function and consumer perception. The model even includes a neural network to adapt to evolving markets and technologies. This tool can empower businesses to enhance quality, address consumer doubts, and ultimately foster a thriving high-quality remanufacturing industry.	[45]
	This study highlights the importance of quality, reliability, maintenance, and warranty in reverse logistics to maximize the lifespan of recovered products. Reliability is analyzed through remaining useful life estimation, reusability, maintenance strategies, and cost minimization. Quality is assessed through factors like product acquisition, testing, reusability for remanufacturing, overall profitability, and ultimately the way it impacts reliability.	[46]
	Remanufacturing offers an efficient way to extend product life and conserve resources. Despite its widespread adoption across various industries, a consistent international standard for remanufactured product quality is lacking. The study compares the quality management and certification systems of major countries, with a focus on Korea and China.	[47]
Information asymmetry	This study examines a challenge in outsourcing used product collection for remanufacturing. When OEMs (Original Equipment Manufacturers) are unsure of a third-party collector’s (3PR) efficiency (how many products they collect), an information gap arises. This can lead to lower collection volumes and potentially force OEMs to reduce new product production to avoid remanufacturing supply shortage.	[48]
	This research tackles the issue of potential cost misreporting by third-party remanufacturers (3PRs) in an outsourced collection system. It uses an evolutionary game approach to analyze outsourcing strategies and find the most sustainable option in the long run. Interestingly, the study suggests that information sharing strategies can lead to positive economic, social, and environmental outcomes, but only under specific conditions.	[49]
	This study explores competition between manufacturers (OEMs) and third-party remanufacturers (TPRs) when remanufacturing costs are unclear. Due to this uncertainty, OEMs cannot verify TPR costs. The research analyzes scenarios where TPRs share cost information, revealing that under specific conditions, this transparency benefits TPRs by enabling better production planning and avoiding over- or underproduction.	[50]
Financing difficulties	Despite government subsidies, operating Closed-Loop Supply Chains (CLSCs) requires substantial investments. The recent economic downturn further intensifies funding challenges. Understanding effective CLSC financing strategies is crucial for ensuring their long-term viability.	[51]
	The study investigates financing options for remanufacturers facing financial constraints in a Trade-Old-for-Remanufactured (TOR) supply chain. Two financing methods are explored: bank financing (BF) and retailer financing (RF). Under the same level of government subsidy, the remanufacturer achieves higher profits with RF compared to BF.	[52]
	To accurately assess the viability of remanufacturing, decision-makers must understand its economic implications, including analyzing factors influencing operational costs. Leveraging existing resources proves crucial for cost reduction, as investments in new facilities, equipment, and personnel can significantly increase costs and potentially render remanufacturing unappealing.	[53]

summarizes the key barriers hindering shoemaking machine remanufacturing.

Overcoming this limitation requires a collaborative effort from both OEMs and end users. OEMs must invest in developing robust remanufacturing processes and fostering trust in the quality of remanufactured machinery. Meanwhile, end users need to be open to adopting remanufactured options and incentivized through factors like cost savings and environmental benefits.

3.2. Analytic Hierarchy Process (AHP)

This study explores the feasibility of remanufacturing shoemaking machines within a closed-loop supply chain under the growing pressure towards net-zero carbon emissions. Remanufacturing offers a sustainable alternative to traditional end-of-life (EOL) practices like recycling or reuse by rebuilding products to original specifications through a combination of reused, repaired, and new parts. This approach reduces waste generation and embodied energy compared to new machine production.

AHP serves as a valuable tool for evaluating the feasibility of remanufacturing shoemaking machines in this context. AHP incorporates expert judgment and data to rank options and predict outcomes, aiding decision-makers in selecting the most suitable approach based on their specific goals and challenges. The AHP, developed by Thomas Saaty, is a widely used method for tackling complex decision-making problems with multiple criteria. It helps rank and select the best option when faced with competing objectives [54]. Using AHP, the model prioritizes the criteria, enabling alternatives selection and potentially enhancing a company’s commercial operations [55].

Two key features distinguish AHP from other decision-making techniques:

• Structured integration of rational and subjective judgment: AHP provides a framework for incorporating both logical and subjective factors into the decision-making process.
• Consistency evaluation: AHP allows the decision-maker to assess the consistency of their judgments throughout the process.

The AHP method involves the following steps [56].

• Problem definition: Clearly identify the decision problem.
• Objective expansion: Consider all stakeholders, objectives, and potential outcomes.
• Criteria identification: Define the relevant criteria for evaluating options.
• Hierarchical structuring: Organize the problem into a hierarchy, including the overall goal, criteria, sub-criteria, and alternative options.
• Pairwise comparison matrices: Create comparison matrices to assess the relative importance of criteria and alternatives.
• Calculations: Compute the maximum eigenvalue, consistency index, consistency ratio (CR), and normalized weights for criteria and alternatives.
• Decision-making: If the CR is less than 0.1, indicating acceptable consistency, use the normalized weights to make a final decision.

In group decision-making scenarios, AHP employs two primary approaches: aggregating individual judgments and aggregating individual priorities [57]. By using the row geometric mean method (RGMM) prioritization procedure, the group priorities obtained through the aggregation of the individual priorities verify the requirement of consistency proposed in AHP methodology if the individual priorities also verify this requirement [58]. The AHP addresses the challenge of incorporating subjective factors into decision-making. It utilizes expert judgment to establish priority scales for both tangible and intangible criteria through pairwise comparisons. AHP assesses the consistency of these judgments and refines them when necessary. The derived priority scales are then synthesized to arrive at an overall decision that reflects both quantitative and qualitative factors [59]. This study specifically utilizes the weighted geometric mean method to aggregate individual judgments.

Step 1: A hierarchical framework for identifying critical factors and sub-factors is proposed to support the development of supply chains.

Step 2: A pairwise comparison of the identified factors is conducted using a nine-point scale, resulting in a comparison matrix [60].

Table 3. The scales in pairwise comparisons.

Importance Ratio
	Absolutely Important	Extremely Important	Quite Important	Slightly Important	Equally Important	Slightly Important	Quite Important	Extremely Important	Absolutely Important
Evaluation items	5:1	4:1	3:1	2:1	1:1	1:2	1:3	1:4	1:5	Evaluation items
	1	2	3	4	5	6	7	8	9

shows the scales in pairwise comparisons.

Step 3: The normalized weights for the identified factors are calculated to form a weight vector. This vector is then used to rank the factors based on their relative importance.

Step 4: To ensure the reliability of the judgments, the consistency of the pairwise comparisons is evaluated. This involves calculating the maximum eigenvalue (${\lambda }_{max}$) and the Consistency Index (CI) using Equation (1). The Consistency Ratio (CR) is then determined using Equation (2), which compares the CI to a Random Consistency Index (RI) based on the matrix size. RI depends on the size of the comparison matrix in

Table 4. Random consistency index.

Order to Matrix (N)	1	2	3	4	5	6	7	8	9	10	11
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49	1.53

. A CR value of less than 0.1 indicates acceptable consistency in the pairwise comparisons.

$$\mathrm{CI}=\frac{{\lambda }_{max}-N}{N-1},$$

(1)

where N = Order of matrix.

The CR is calculated using formula

$$\mathrm{C}\mathrm{R}=\frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}$$

(2)

The AHP framework is tailored to the specific conditions of the footwear industry by

• Criteria selection: Focusing on criteria directly relevant to shoemaking machine remanufacturing, such as machine complexity, availability of spare parts, and specific material considerations for footwear production processes.
• Indicator selection: Choosing indicators that accurately reflect the practical application of each criterion within the footwear context. For instance, the technical criterion might consider “compatibility of existing shoemaking machine components with remanufactured parts” or “availability of skilled technicians for specific shoemaking machine models.”

To evaluate the feasibility of remanufacturing on a shoemaking machine, the research employs a four-level hierarchical structure. The highest level (Level 1) represents the overall goal, which is to assess the feasibility of remanufacturing for shoemaking machines. Level 2 comprises the key evaluation clusters, which are the main categories of criteria influencing this feasibility. Level 3 delves deeper into these clusters, containing the specific evaluation nodes, which are the individual sub-criteria to be considered. Finally, Level 4 allows for the introduction of alternatives, representing different remanufacturing approaches or configurations on the shoemaking machine (Figure 2).

The research is motivated by the global trend towards achieving net-zero carbon emissions. Shoemaking machine manufacturers currently employ various countermeasures to reduce their environmental footprint, including purchasing green energy, installing on-site power generation systems, participating in carbon trading schemes, and implementing energy-saving measures on their machines. However, remanufacturing offers significant economic and environmental benefits, potentially presenting an additional solution for the industry. The study aims to explore the feasibility of integrating remanufacturing principles into the next generation of shoemaking machine business models, fostering a greener and more circular economy.

To achieve this objective, an expert questionnaire was developed to capture professional insights from various relevant fields. This approach enhances the research’s objectivity and professionalism by incorporating the diverse perspectives of these experts. By analyzing the data collected through the questionnaire, this study aims to systematically evaluate the feasibility of remanufacturing shoemaking machines, providing valuable reference points for the industry. The AHP methodology employs pairwise comparisons to determine the relative importance of each criterion and sub-criterion within the hierarchy. Experts involved in the shoemaking machine industry provide their judgments on these comparisons using a well-defined scale (e.g., 1–9 scale). This process allows for the calculation of weights for each factor, reflecting their relative significance in the overall feasibility assessment. The AHP framework incorporates Consistency Ratio (CR) to evaluate the internal consistency of pairwise comparisons. This ensures the reliability of the resulting weights and strengthens the overall validity of the AHP model.

Following data collection, a total of 27 valid questionnaire responses were obtained. The Consistency Index (CI) was applied to evaluate the internal consistency of these responses, ensuring data quality.

Table 5. Respondent characteristics and response rate.

Respondents	Position Level	No. of Responses	Response Rate
Supplier	Senior executives (chairman, general manager, president, etc.)	1	41%
	Middle-level supervisors (managers, team leaders, etc.)	5
	Basic-level staff (commissioner, staff, engineer, etc.)	5
Green energy/carbon reduction consultant	Senior executives (chairman, general manager, president, etc.)	3	22%
	Middle-level supervisors (managers, team leaders, etc.)	1
	Basic-level staff (commissioner, staff, engineer, etc.)	2
Research institute	Middle-level supervisors (executive researcher, research lead, etc.)	2	15%
	Basic-level staff (researcher, engineer, etc.)	2
Shoe manufacturer (equipment user)	Middle-level supervisors (managers, team leaders, etc.)	3	11%
Media/journalist	Senior executives (editor, etc.)	1	7%
	Basic-level staff (journalist, etc.)	1
Distributor/agent	Basic-level staff (commissioner, staff, engineer, etc.)	1	4%
	Total	27	100%

summarizes the characteristics of the respondents.

The inconsistency test is used in the Super Decision software (v3.2). The software utilizes the Inconsistency Test (InCI), which operates under the same threshold criterion as the Consistency Index (CI). A value of InCI (or CI) of less than 0.1 indicates that the respondents’ judgments exhibit acceptable consistency, meaning their earlier and later responses fall within a tolerable range of error (

Table 6. The inconsistency report for the 27 responses. Source: Researcher extract from Super Decision software (v3.2).

Row	Column	Inconsistency
2.1 High corporate image	2.4 Appropriate product design	0.032097
2.1 High corporate image	2.2 Good business model	0.032992
2.3 Complete recycle system	2.4 Appropriate product design	0.068964
2.2 Good business model	2.3 Complete recycle system	0.069553
2.2 Good business model	2.4 Appropriate product design	0.090402
2.1 High corporate image	2.3 Complete recycle system	0.098408

).

While the AHP is typically employed to identify the optimal solution for shoemaking machine remanufacturing, resource constraints faced by OEMs necessitate a more nuanced approach. By applying sensitivity analysis, OEMs can explore how variations in key factors impacting remanufacturing feasibility might affect the ranking of potential solutions. The insights gleaned from the analysis can serve as a foundation for resource allocation decisions, enabling OEMs to strategically invest their limited resources in the most promising remanufacturing options.

Table 7. The priority analysis of each cluster and node. Source: Researcher extract from Super Decision software (v3.2).

Clusters	Nodes	Graphic	Normals	Limiting
1. Goal	Shoemaking machine remanufacture		0.00000	0.0000000
2. Criteria	2.1 High corporate image		0.03535	0.014139
	2.2 Good business model		0.13760	0.055042
	2.3 Complete recycle system		0.27015	0.108060
	2.4 Appropriate product design		0.55690	0.222759
3. Sub-criteria	3.1 High market share		0.00589	0.001178
	3.2 Trusted certification		0.02946	0.005891
	3.3 Attractive price		0.02293	0.004587
	3.4 High system share		0.11467	0.022934
	3.5 High reverse logistics		0.04503	0.009005
	3.6 Short downtime		0.22512	0.450250
	3.7 Easy disassembly material		0.13922	0.027845
	3.8 High modular components		0.41767	0.083535
4. Alternatives	4.1 Surface production equipment		0.30204	0.120816
	4.2 Sole making equipment		0.53869	0.215474
	4.2 Forming equipment		0.15928	0.063710

shows the priority of each cluster and node. For the alternatives, sole-making equipment (internal mixer and mixing machine, vulcanization molding machine, PU/PVC/TPR/ABS/Nylon, and other sole injection machinery, other ancillary equipment, etc.) is the most likely to be applied in remanufacturing according to the type and scope of the shoemaking machine, followed by surface production equipment (cutting, sewing machines, other ancillary equipment, etc.) and forming equipment (self-gluing, vulcanizing, heating, freezing, forming, and other bottom-mounted equipment, knotting machinery, etc.). When evaluating the criteria, appropriate product design is the most important to consider for developing strategies, followed by the complete recycle system, good business model, and high corporate image. When redesigning the product, the use of high modular components is more important than finding easy disassembly materials.

The Analytic Network Process (ANP) extends the widely used AHP by enabling the analysis of decision problems with interdependent elements. Unlike AHP’s strictly hierarchical structure (goal, criteria, alternatives), the ANP allows for feedback and dependence relationships between decision elements (nodes). This makes the ANP particularly useful for complex scenarios with interconnected factors. Recent applications of the ANP have focused on identifying common contingency factors, current trends, and potential areas for further research in economics, finance, and management. Notably, the ANP has become a prevalent tool in sustainable supply chain management and business evaluation frameworks. Additionally, a growing trend involves incorporating stakeholder input into the decision-making process facilitated by the ANP. While the ANP can be employed independently, it is most used as part of a multicriteria or integrated decision-making approach. This often involves combining the ANP with other methods, such as the AHP and the DEMATEL, to leverage the strengths of each technique. The flexibility of the ANP, particularly in facilitating stakeholder participation, makes it a valuable tool for evaluating complex, sustainable projects [61].

In Figure 3, the x axis represents the weight ratio of the variables selected for the criterion. The y axis represents the weight ratio of priority decisions. According to the AHP and ANP sensitivity analysis, sole-making equipment is also the most likely to be applied in remanufacturing, followed by surface production equipment and forming equipment.

The findings reveal that sole-making equipment presents the most favorable opportunity for remanufacturing within the shoemaking machinery sector, followed by surface production and forming equipment. Furthermore, the study highlights the paramount importance of appropriate product design for developing effective remanufacturing strategies. Easy disassembly and the use of high modular components emerged as crucial design considerations. A complete recycling system also plays a significant role, fostering a more sustainable approach.

3.3. DEMATEL Method

Once the AHP prioritizes these sustainability criteria, sub-criteria, and alternatives, the next step is to leverage the DEMATEL analyzing the interdependencies between them. This involves identifying pairwise relationships (e.g., experts assess the causal influence of one criterion on another), constructing a matrix which captures these pairwise relationships indicating the degree of influence between criteria, and categorizing the criteria into cause groups (with high influence on others) and effect groups (highly influenced by others). Visualizing these relationships aids in understanding the complex interplay between sustainability factors in the shoemaking machine industry. Unlike the AHP, the DEMATEL does not explicitly assign weights to individual criteria. Instead, it focuses on identifying the overall influence structure within the system. However, the AHP-derived priorities provide a foundation for interpreting the DEMATEL results, as high-ranking criteria from the AHP likely hold significant influence within the DEMATEL analysis.

The DEMATEL method, developed by the Battelle Memorial Institute, tackles complex interrelated problems. It goes beyond assuming independent elements like the AHP, and instead uses structural modeling to identify interdependencies within a system. This method helps to understand the problem structure, identify intertwined issues, and ultimately find workable solutions through a hierarchical approach [62]. Small and medium-sized enterprises (SMEs) often face challenges in selecting a management system due to limited resources. The DEMATEL method can identify interdependencies between selection criteria. This approach offers a valuable tool for SMEs to optimize management system selection within limited resource constraints [63]. Recognizing the limitations of traditional Multi-Criteria Decision-Making (MCDM) models in handling both interdependent criteria and subjective evaluations, a novel hybrid model leverages factor analysis to address independent relationships between criteria while utilizing the DEMATEL to capture their interdependencies. This combined approach offers a more comprehensive framework for complex decision-making scenarios [64]. The DEMATEL offers a valuable tool for analyzing complex decision-making problems. It goes beyond simply identifying cause-and-effect relationships between criteria. DEMATEL’s capability lies in its ability to unveil the inherent interdependencies within a set of criteria, allowing for a more nuanced understanding of their interactions and influence on the overall decision [65]. The application of the DEMATEL is combined with fuzzy logic to assist global managers in navigating the complexities within challenging business environments [66].

Addressing complex real-world problems often necessitates the synergistic implementation of multiple strategies, rather than relying on a single, isolated approach. Therefore, a crucial step involves prioritizing these strategies and assessing their direct and indirect interdependencies. The DEMATEL method offers a valuable tool for this purpose. It employs pairwise comparisons of various parameters within a complex system to analyze their causal relationships and the degree of influence exerted by one parameter on another. The resulting structural matrix or diagraph visually depicts these causal relationships, further categorizing parameters into cause-and-effect groups. The diagraph representation not only reveals the most influential parameter with the greatest potential impact, but also serves as a valuable aid in the decision-making process.

Step 1: Generate the direct relation matrix

In DEMATEL analysis, Matrix X, also known as the direct-relation matrix, plays a crucial role in capturing the perceived influence between different factors (criteria) within a complex system. It is an n × n matrix, where n represents the total number of factors being analyzed.

To identify the model of relations among the n criteria, an n × n matrix is first generated. The effect of the element in each row is exerted on the element of each column of this matrix. If multiple experts’ opinions are used, all experts must complete the matrix. The arithmetic mean of all the experts’ opinions is used and then a direct relation matrix X is generated, like in Equation (3). Each element $X_{ij}$ in the matrix represents the direct influence of factor $i$ (located in row $i$) on factor $j$ (located in column $j$). These influences are typically measured on an ordinal scale, with values ranging from 0 (no influence) to a pre-defined maximum value (very strong influence). A panel of experts familiar with the system under study provides their assessment of the influence each factor has on other factors. This is achieved through questionnaires. The individual matrices from each expert are then aggregated by calculating the arithmetic mean of the corresponding elements across all experts’ matrices.

$$X=\left[\begin{array}{ccc}0& \dots & X_{n1}\\ ⋮& \ddots & ⋮\\ X_{1n}& \dots & 0\end{array}\right]$$

(3)

Table 8. Direct relation matrix. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates).

	2.1 High Corporate Image	2.2 Good Business Model	2.3 Complete Recycle System	2.4 Appropriate Product Design
2.1 High corporate image	0	1	1	1
2.2 Good business model	2	0	2	2
2.3 Complete recycle system	3	3	0	3
2.4 Appropriate product design	4	4	4	0

shows the direct relation matrix, which is the same as the pairwise comparison matrix of the experts.

Step 2: Compute the normalized direct-relation matrix

In the normalization process of the direct-relation matrix (DRM) for DEMATEL analysis, the value of $k$ represents the greatest sum of either all the row sums or all the column sums in the matrix. It acts as a normalization factor to ensure all the elements within the normalized DRM fall between 0 and 1. Below are breakdowns of its significance and calculation:

• Significance: Dividing each element in the DRM by k scales the entire matrix to a range of 0 to 1. This allows for easier comparison between elements within the matrix and across different matrices, even if the original values were measured on different scales. After normalization, the values in the DRM represent the relative influence one factor has on another, on a scale of 0 (no influence) to 1 (highest possible influence).
• Calculation: The values in each row of the DRM are summed to obtain a row vector representing the total influence each factor has on others. The values are summed in each column of the DRM to obtain a column vector representing the total influence each factor receives from others. The highest value among all the elements in both the row sum vector and the column sum vector are identified. This maximum value represents $k$. Each element in the original DRM is divided by k. The resulting matrix is the normalized DRM.

Each element in the original DRM is divided by $k$. The resulting matrix is the normalized DRM. By using $k$, the normalization process essentially shrinks or expands the entire matrix while maintaining the relative influence relationships between the factors. This allows for a more consistent and interpretable analysis of the interdependencies within the system being studied using the DEMATEL.

To normalize, the sum of all rows and columns of the matrix is calculated directly. The largest number of the row and column sums can be represented by $k$. To normalize, it is necessary that each element of the direct-relation matrix is divided by $k$, like in Equations (4) and (5), and

Table 9. The normalized direct-relation matrix. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates).

	2.1 High Corporate Image	2.2 Good Business Model	2.3 Complete Recycle System	2.4 Appropriate Product Design
2.1 High corporate image	0	0.083	0.083	0.083
2.2 Good business model	0.167	0	0.167	0.167
2.3 Complete recycle system	0.25	0.25	0	0.25
2.4 Appropriate product design	0.333	0.333	0.333	0

.

$$k=max\left\{max{\sum }_{j=1}^n{X}_{ij},{\sum }_{i=1}^n{X}_{ij}\right\}$$

(4)

$$N=\frac{1}{K}\times X$$

(5)

Step 3: Compute the total relation matrix

After calculating the normalized matrix, the fuzzy total-relation matrix can be computed as follows, in Equation (6):

$$T=\underset{k\to +\infty }{\lim }(N^1+N^2+\dots +N^k)$$

(6)

In other words, an $n$ × $n$ identity matrix is first generated; then, this identity matrix is subtracted from the normalized matrix, and the resulting matrix is reversed. The normalized matrix is multiplied by the resulting matrix to obtain the total relation matrix, like in Equation (7) and

Table 10. The total relation matrix. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates).

	2.1 High Corporate Image	2.2 Good Business Model	2.3 Complete Recycle System	2.4 Appropriate Product Design
2.1 High corporate image	0.138	0.2	0.186	0.175
2.2 Good business model	0.399	0.228	0.346	0.324
2.3 Complete recycle system	0.559	0.519	0.285	0.454
2.4 Appropriate product design	0.699	0.649	0.606	0.318

.

$$T=N\times {(I-N)}^{-1}$$

(7)

Step 4: Set the threshold value

The threshold value must be obtained to calculate the internal relations matrix. Accordingly, partial relations are neglected and the network relationship map (NRM) is plotted. Only relations whose values in matrix T is greater than the threshold value are depicted in the NRM. To compute the threshold value for relations, it is sufficient to calculate the average values of matrix T. After the threshold intensity is determined, all values in matrix T which are smaller than the threshold value are set to zero, that is, the causal relation mentioned above is not considered. In the study, the threshold value is equal to 0.38. The threshold value is the average value of all elements in matrix T. Values below the average are considered weak and set to zero.

The threshold value of 0.38 in matrix T serves two key purposes:

• Filtering out weak relationships: The DEMATEL method aims to identify the most significant causal relationships within a complex system. The total relationship matrix T captures the direct and indirect influences between different factors. However, not all these influences might be equally important. Setting a threshold value helps filter out weak or negligible relationships. By setting all values in T below 0.38 to zero, the analysis focuses on the most impactful causal relationships, providing a clearer picture of the system’s underlying structure.
• Simplifying the network relationship map (NRM): The NRM is a visual representation of the relationships between factors derived from the total relationship matrix. By setting a threshold and eliminating weak relationships, the NRM becomes more streamlined and easier to understand. It allows for focusing on the most significant connections within the system.

All the values in matrix T which are smaller than 0.38 are set to zero, that is, the causal relation mentioned above is not considered. The model of significant relations is presented in the following

Table 11. Total-relationships matrix after threshold application. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates).

	2.1 High Corporate Image	2.2 Good Business Model	2.3 Complete Recycle System	2.4 Appropriate Product Design
2.1 High corporate image	0	0	0	0
2.2 Good business model	0.399	0	0	0
2.3 Complete recycle system	0.559	0.519	0	0.454
2.4 Appropriate product design	0.699	0.649	0.606	0

.

The threshold value in DEMATEL analysis acts as a filter, eliminating weak relationships from the final analysis. Only relationships with values in matrix T exceeding 0.38 are considered significant and depicted in the Network Relationship Map (NRM). The value in the T matrix corresponding to the influence of “appropriate product design” on “high corporate image” is the largest value and greater than the threshold, which suggests a strongest and significant positive influence, while “appropriate product design” on the “good business model” is the second largest value. This implies that implementing an appropriate product design is likely perceived as a major contributor to achieving a high corporate image and then a good business model. The same speculation to plan a complete recycle system will also play a foundational role in influencing a positive corporate image and a good business model.

Step 5: Final output and a causal diagram

The next step is to find out the sum of each row and each column of T (in Step 3). The sum of rows (D) in Equation (8) and columns (R) in Equation (9) can be calculated as follows:

$$D={\sum }_{j=1}^n{T}_{ij}$$

(8)

$$R={\sum }_{i=1}^n{T}_{ij}$$

(9)

Then, the values of D + R and D − R can be calculated by D and R, where D + R represent the degree of importance of factor $i$ in the entire system and D − R represent the net effects that factor $i$ contributes to the system.

Table 12. The final output. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates).

	R	D	D + R	D − R
2.1 High corporate image	1.795	0.699	2.494	−1.097
2.2 Good business model	1.596	1.298	2.894	−0.298
2.3 Complete recycle system	1.423	1.817	3.24	0.394
2.4 Appropriate product design	1.271	2.271	3.542	1

shows the final output.

For D + R and D − R values, the breakdown of their meaning and significance is presented:

• D + R (Degree of Importance): D + R represents the sum of the row value (D) and the column value (R) for a specific factor ($i$) in the T matrix. D (row sum) signifies the total influence factor $i$ exerts on all other factors in the system (its outgoing influence). R (column sum) signifies the total influence factor $i$ receives from all other factors (its incoming influence). Therefore, D + R provides a combined measure of a factor’s overall influence within the system, considering both the influence it exerts and the influence it receives. A high D + R value indicates a factor with a strong overall influence on the system’s dynamics. These factors are often considered critical or driving forces.
• D − R (Net Effects): D − R represents the difference between the row value (D) and the column value (R) for a specific factor ($i$) in the T matrix. D − R helps to determine a factor’s net contribution to the system’s causal relationships. A D − R value close to zero suggests the factor has a relatively balanced influence, both affecting and being affected to a similar degree.

The following figure shows the model of significant relations. The model can be represented as a diagram in which the values of (D + R) are placed on the horizontal axis and the values of (D − R) on the vertical axis. The position and interaction of each factor with a point in the coordinates (D + R, D − R) are determined by a coordinate system in Figure 4.

Step 6: Interpret the results

According to the diagram and table above, each factor can be assessed based on the following aspects:

• The horizontal vector (D + R) reflects the overall influence each factor exerts within the system. In other words, (D + R) indicates both factor i’s impact on the whole system and other system factors’ impact on the factor. In terms of degree of importance, “appropriate product design” is ranked in first place, followed by “complete recycle system”, “good business model”, and “high corporate image”.
• The vertical vector (D − R) represents the degree of a factor’s influence on the system. In general, the positive value of (D − R) represents a causal variable, and the negative value of D − R represents an effect. In the study, “complete recycle system” and “appropriate product design” are causal variables, while “high corporate image” and “good business model” are regarded as effects.

A high D + R value for “appropriate product design” in DEMATEL analysis indicates its overall influence within the system of shoe machinery remanufacturing. This value can be useful in decision-making related to remanufacturing in several ways:

• Prioritizing Design for Remanufacturing (DfR): A high D + R suggests that designing machinery with remanufacturing in mind is a critical factor for success. This emphasizes the importance of investing in DfR principles, such as modularity, use of readily remanufactureable materials, and easy disassembly. By incorporating DfR principles early in the design phase, manufacturers can create machinery that is more cost-effective and environmentally friendly to remanufacture at its end of life.
• Optimizing remanufacturing processes: Analyzing the D + R value alongside the D − R values of other factors can help identify bottlenecks or areas for improvement in the remanufacturing process itself.
• Cost–Benefit Analysis: When making decisions about remanufacturing specific machinery models, the D + R value of “appropriate product design” can be factored into the cost–benefit analysis.

4. SWOT to TOWS Matrix Analysis

The Indian shoemaking industry presents a unique opportunity for remanufacturing. However, navigating this complex business environment requires a strategic approach that considers both internal factors (strengths and weaknesses) and external forces (opportunities and threats). The SWOT to TOWS Matrix framework offers a valuable tool for analyzing these elements and formulating effective strategies (

Table 13. SWOT to TOWS matrix analysis.

		Playing to Strengths	Mitigating Weaknesses
	TOWS	Strengths (internal) Skilled workforce with expertise in machine repair. The potential cost-effectiveness of remanufactured machinery compared to new equipment.	Weaknesses (internal)Negative consumer perceptions regarding the quality of remanufactured products.The lack of a well-established remanufacturing infrastructure in India.
Embrace opportunities	Opportunities (external) Government support for resource efficiency and circular economy. Rising environmental awareness among consumers. Advancements in automation that could improve remanufacturing efficiency.	Strengths/Opportunities Leveraging India’s skilled workforce to develop a robust remanufacturing infrastructure. Addressing quality concerns and building consumer trust in remanufactured shoemaking making equipment.	Weaknesses/Opportunities Government support for remanufacturing can incentivize the development of standardized processes to ensure consistent quality and overcome negative perceptions.
Deal with threats	Threats (external) Competition from low-cost new machinery imports. The emergence of counterfeit remanufactured products. Evolving regulations that could increase costs and complexity.	Strengths/Threats The cost-effectiveness of remanufactured machinery can help mitigate the threat of competition from low-cost new machine imports.	Weaknesses/Threats The limited availability of high-quality used machinery can be exacerbated by counterfeiters. Implementing a robust system for identifying and removing counterfeit remanufactured products from the market is crucial.

).

5. Conclusions

The study investigates the feasibility and prioritization of remanufacturing strategies for shoemaking machines considering resource constraints faced by OEMs. The AHP and ANP with sensitivity analysis are employed to identify the most promising remanufacturing options and the key criteria for developing successful strategies. Additionally, DEMATEL analysis provides insights into the causal relationships between these criteria.

The findings reveal that sole-making equipment presents the most favorable opportunity for remanufacturing within the shoemaking machinery sector, followed by surface production and forming equipment. This prioritization aligns with the potential economic benefits of extending the lifespan of complex, specialized machines used in sole production.

Furthermore, the study highlights the paramount importance of appropriate product design for developing effective remanufacturing strategies. Easy disassembly and the use of high modular components emerge as crucial design considerations. A complete recycling system also plays a significant role, fostering a more sustainable approach. The results can be tailored to the most critical criteria identified in the AHP/ANP analysis and include: (1) Auditing production processes: It is recommended to conduct audits to assess the current production processes for opportunities to integrate DfR (Design for Remanufacturing) principles. This could involve evaluating factors like material selection, component modularity, and ease of disassembly; (2) Modular design integration: This might include recommendations for standardized component interfaces, readily available remanufactured parts, and clear disassembly instructions which should integrate modular design principles into new machinery development; (3) Sustainable design training: It is important to train engineers in sustainable design practices. This could involve incorporating DfR principles into engineering curricula, conducting workshops on remanufacturing feasibility assessments, and promoting collaboration between design and remanufacturing teams. Interestingly, DEMATEL analysis positions both “appropriate product design” and “complete recycling system” as causal variables, emphasizing their foundational role in influencing a good business model and positive corporate image.

One limitation of this study is that it does not explicitly address the potential knowledge gap between OEMs and third-party remanufacturers. OEMs possess a deeper understanding of the original design and functionalities of their machines. Conversely, third-party remanufacturers may need to rely on reverse engineering to gain this knowledge, potentially impacting the efficiency and cost-effectiveness of the remanufacturing process. Future research could explore strategies to bridge this knowledge gap, such as developing standardized information exchange protocols or collaboration models between OEMs and remanufacturers.

By employing the SWOT to TOWS Matrix framework and focusing on strategic design for remanufacturing, stakeholders in the Indian shoemaking industry can exploit opportunities, mitigate weaknesses, and navigate threats to establish a thriving remanufactured shoemaking machinery sector, particularly for economically advantageous segments like sole-making equipment.

In conclusion, this study presents a compelling case for OEMs to embrace remanufacturing as a strategic approach to sustainability and resource optimization within the shoemaking industry. By prioritizing remanufacturing of sole-making equipment and focusing on product design for disassembly and modularity, coupled with the implementation of a comprehensive recycling system, OEMs can achieve significant environmental and economic benefits. This approach not only fosters a more circular economy, but also enhances its corporate image and strengthens business models in the long run.