Framework for adaptable remanufacturing

ABSTRACT

Remanufacturing has received increased attention for its potential to reduce resource consumption and emissions in the manufacturing sector. Remanufacturing involves restoring and potentially upgrading end-of-life products to their original functionality and warranty. However, several challenges such as uncertainty about the timing and volume of returns, varying conditions and requirements for remanufacturing, reduced batch sizes, and increased workload variability hinder the efficiency of remanufacturing. Adaptable systems provide possibilities to manage these uncertainties and complexities. This paper aims to assess whether adaptability is beneficial for remanufacturing and to provide initial guidance on selecting appropriate implementation options. A framework is developed based on findings from a literature review, followed by analysis and systematization. This framework links remanufacturing influences and challenges to adaptability characteristics and their enablers, illustrating how these challenges can be addressed. The resulting framework demonstrates that some of the challenges in remanufacturing can be alleviated by increasing adaptability.

KEYWORDS:

• Remanufacturing
• adaptability
• circular economy
• framework

1. Introduction

The manufacturing sector has witnessed substantial growth over recent decades, leading to improved living standards and increasing emissions as an unintended consequence (UN Department of Economics and Social Affairs, 2022). This growth is accompanied by heightened resource consumption and escalated greenhouse gas emissions (European Environment Agency, 2011). In response, the principles of the Circular Economy have been suggested as a potential solution (Calzolari et al., 2021). A vital component of this approach is remanufacturing, which entails restoring end-of-life products to their original functionality and warranty, sometimes with enhancements (W. Ijomah, 2002), which could save energy and resources (Sundin & Bras, 2005) but faces several obstacles: the uncertain timing and quantity of returns, as well as individual conditions and restoration requirements, reduced batch sizes, and increased load variations, making efficient implementation challenging (Rizova et al., 2020). To address these obstacles, there is a growing recognition of the need for more adaptive production systems that can respond to the uncertain and variable nature of remanufacturing inputs. This adaptability is critical not only for enhancing cost-effectiveness but also for ensuring the successful integration of remanufacturing processes (Boucher et al., 2019; H.-P. Wiendahl et al., 2007). However, the existing research lacks a scientifically derived framework that connects the challenges of remanufacturing with the benefits of adaptability.

This publication seeks to fill this gap by developing a comprehensive framework based on extensive literature reviews and systematic analysis. The framework correlates the specific challenges of remanufacturing with adaptability characteristics and their enabling factors, providing guidance for implementing adaptable systems. This framework aims to demonstrate the potentials of an adaptability, showing how it can mitigate many of the remanufacturing-related difficulties currently hindering its implementation. Ultimately, this research will lay the groundwork for future methodologies, enabling practitioners to evaluate and implement strategies that enhance both the efficiency and sustainability of remanufacturing operations.

The publication is divided into the chapters State of the Art, Methodology, which explains the methods used within this research, Results, which shows how the framework is structured and its contents and Conclusion and Outlook.

2. State of the art

The following chapter provides a definition of remanufacturing and adaptability as well as an overview of the current research on structuring remanufacturing influences, remanufacturing challenges, and synergies between remanufacturing and adaptability. Based on this, the research gap is defined.

2.1. Remanufacturing

Remanufacturing is defined as the process of returning a used product – a core – to at least the original equipment manufacturer (OEM) performance specification from the customers’ perspective (W. Ijomah, 2002; Matsumoto & Ijomah, 2013). The restoration to at least original quality differentiates remanufacturing from refurbishing and repair approaches, alongside the option of upgrading the functionality beyond its original state (Kirchherr et al., 2017). The remanufacturing process typically involves the steps of core acquisition, disassembly, cleaning, testing, reprocessing, and reassembly. Not all must be included, and their order can vary (Sundin, 2004).

Remanufacturing offers multiple potential benefits: case studies report up to a 50% reduction in production cost (Ferrer & Ayres, 2000; Geyer & van Wassehove, 2000), a 60% reduction in energy usage (Jiang et al., 2016), and a 50% waste reduction (Kerr & Ryan, 2001). The widespread adoption of remanufacturing could save an estimated EU-wide 900 million Euros by 2030 (Sundin & Bras, 2005) while replacing material requirements with labor demand, creating new jobs (Ferrer & Ayres, 2000).

2.2. Adaptability

As defined in the literature, adaptation refers to the capacity to modify and adjust to new conditions, goals, or purposes (H. ElMaraghy et al., 2021). Within this context, various forms of adaptability are distinguished, each characterized by the level of the system they impact and the degree of adaptation required. For instance, when evaluating the adaptability of an entire organization, this concept is often termed agility. Transformability pertains to the foundational structures of a factory and its ability to smoothly transition to new product lines (H.-P. Wiendahl et al., 2007). Focusing further on individual production lines and workstations, concepts such as flexibility, reconfigurability, and changeover ability are applied. Here, flexibility is particularly notable. It is defined as the system’s capacity to alter its function without necessitating physical modifications in its configuration (H.-P. Wiendahl et al., 2007). This implies that the system can adapt to new requirements or tasks with minimal physical alterations, thereby saving on work and resources typically associated with such changes. This level of adaptability is essential for maintaining efficient operations in a dynamic manufacturing environment, where the ability to pivot quickly with minimal disruption is a significant competitive advantage.

Conversely, reconfigurability changes a system’s behavior by changing its configuration and altering the physical state. Changeover ability describes ‘the operative ability of a single machine or workstation to perform particular operations on a known workpiece or subassembly at any desired moment with minimal effort and delay’ (H.-P. Wiendahl et al., 2007). Changeability encompasses all three characteristics, generally describing changes on a station level (Andersen, 2017; H.-P. Wiendahl et al., 2007). A diagram of the different adaptability aspects can be seen in Figure 1.

Figure 1. Adaptability classes with corresponding production system levels – based on Andersen (Andersen, 2017) and Wiendahl et al. (H.-P. Wiendahl et al., 2007).

2.3. Structuring remanufacturing influences

To address the gaps in current research methodologies and the scope of their analyses in remanufacturing, existing approaches for the identification and assessment of key influencing factors are examined. The field of remanufacturing often employs decision frameworks based on limited input sets, typically sourced from literature reviews (Priyono et al., 2016) or identified through expert interviews (Ansari et al., 2019). Some publications focusing on specific aspects of the remanufacturing process provide more comprehensive assessments of inputs and influences. Priyono et al. (Priyono et al., 2016) analyze the disassembly process, identifying 17 factors and providing their sources. Goodall et al. (Goodall et al., 2014) analyze tools for assessing remanufacturing feasibility at different stages in the process in their systematic review. The authors do not provide detailed lists of the considered influences; they highlight the degree to which economic, social, and environmental factors are included (Goodall et al., 2014). Sitcharangsie et al. (Sitcharangsie et al., 2019) review the literature regarding five significant decisions in the remanufacturing process and list the inputs of the analyzed papers. The closest to an overall remanufacturing assessment is their section on integrated decision-making, which analyzes publications looking at more than one of the five decisions. Ahlstedt and Sundin (Ahlstedt & Sundin, 2023) provide an overview of factors influencing the remanufacturability of an assembly tool. Other publications provide further sets of inputs, though since they face issues similar to those of the papers presented in this paragraph, they are not described here in detail (W. L. Ijomah, 2009; Jiang et al., 2011; Subramoniam et al., 2010). The existing literature does not provide a generally applicable and comprehensive list of remanufacturing influences.

2.4. Remanufacturing challenges

The adoption of remanufacturing still proves difficult. One challenge is the uncertainty regarding production requirements. Figure 2 shows the difference in expected production volumes between a traditional manufacturing lifecycle and one involving remanufacturing, based on the assumptions that the new and remanufactured products share a market (Brunoe et al., 2019). The production of new products remains consistent during the traditional scenario. In the remanufacturing case, on the other hand, restored products slowly start to cannibalize new sales, reducing the need for newly manufactured goods while requiring an increasing number of spare parts. The exact number of returns and needed spares are hard to predict, further complicating matters.

Figure 2. Production volumes over time for manufacturing and remanufacturing – based on Brunoe et al (Brunoe et al., 2019). and Matsumoto and Komatsu (Matsumoto & Komatsu, 2015).

The variation in core conditions introduces another area for improvement and requires individual processes to restore them, hindering efficient production plans. These factors deter many companies from investing in remanufacturing and explain the prevalence of low-cost manual solutions that further exacerbate the low efficiency of remanufacturing systems (Andersen et al., 2023; Bockholt et al., 2020; Kurilova-Palisaitiene et al., 2018). Issues unrelated to the production system itself include the hesitant acceptance of remanufactured products by consumers (Bras & McIntosh, 1999), the complex core collection (Matsumoto et al., 2016), and the competitive market preventing experimentation (Francalanza et al., 2017). All these factors increase planning difficulties further. The overall market can thus be uncertain and complex, necessitating systems capable of adjusting to these unpredictable conditions.

2.5. Synergies between remanufacturing and adaptability

Research exploring the synergies between remanufacturing and adaptability, while limited, has shown a notable increase in recent times (Andersen et al., 2023; Bockholt et al., 2020; Brunoe et al., 2019). The publication by Brunoe et al. (Brunoe et al., 2019) looks at the role of different adaptability concepts in various end-of-life options. They compare open-loop systems to refurbishing models and remanufacturing approaches. Based on assumed demand curves for new parts, spare parts, and remanufactured units, they reason which type of adaptability might benefit each scenario. The authors conclude that a refurbishing approach requires increased scalability compared to the classical linear concept due to restored products impacting the demand for new ones in hard-to-predict ways. This paper does not make the connection to suitable enablers nor consider the effects of different scenarios, like separate markets for new and remanufactured goods.

Andersen et al. (Andersen et al., 2023) and Bockholt et al (Bockholt et al., 2020). analyze a case company in Denmark, first highlighting the issues the company faces. They are divided into the four areas of disassembly, forecasting, product design, and quality, with three sub-categories each (Bockholt et al., 2020). While the publication by Bockholt et al (Bockholt et al., 2020). briefly describes adaptability as a possible solution, Andersen et al (Andersen et al., 2023). designs four systems with differing levels of adaptability and numerically validates them in simulations. According to the simulation results, adaptability approaches have been more profitable than highly automated dedicated lines or purely manual concepts. These results are promising, offering a first point of data that validates the hypothesis of synergies. The design process behind the four potential systems is not detailed.

Two other relevant papers are the publications by Low and Ng (Low & Ng, 2018) and Yao et al (J. Yao et al., 0000). In their paper, Low and Ng (Low & Ng, 2018) provide a planning concept for remanufacturing systems consisting of six steps, with the last two being an uncertainty analysis and a flexibility analysis. While including these phases is a step towards adopting adaptability in remanufacturing planning approaches, the framework does not provide much guidance on how to perform these steps, citing the individual nature of flexibility as a reason (Low & Ng, 2018). Yao et al. (Bi et al., 2014) propose a structure to model flexible remanufacturing systems. They highlight different components and modules that should be included, such as flexible logistics systems. The assistance provided to companies is limited, as the concept remains conceptual.

The literature detailed so far frequently cites the publications by Bi et al. (Bi et al., 2014) and Huang et al. (Huang et al., 2018). when representing the available literature. Bi et al. (Bi et al., 2014) do not focus on remanufacturing itself. Instead, they apply a sustainability model to manufacturing resources, achieving sustainability and adaptability through reusing and reconfiguring discarded equipment. The idea has seen continued research (Napoleone et al., 2022), though the application towards the design of adaptable remanufacturing systems is limited. Huang et al (Huang et al., 2018). look at reconfigurable manufacturing systems and analyze ways of making them more sustainable. The researchers conclude that applying the strengths of these systems towards remanufacturing might prove beneficial but provide little detail on how to do so (Huang et al., 2018).

2.6. Research gap and scope

The concept of adaptability in remanufacturing is increasingly acknowledged for its potential to enhance sustainability and economic viability. However, while existing literature highlights the theoretical benefits of adaptability, actionable research that systematically applies these concepts to real-world remanufacturing scenarios remains limited. Most studies provide case-based insights or theoretical discussions, but often lack a detailed strategy for implementation across varied industrial settings. A significant research gap lies in the absence of a comprehensive framework that connects specific remanufacturing challenges with adaptability traits in a structured manner. Additionally, there is a dearth of studies that offer a holistic approach integrating adaptability and remanufacturing practices that is adjustable to different operational contexts.

This research seeks to address these gaps by proposing a framework that not only depicts the relationship between remanufacturing challenges and adaptability characteristics but also connects the potentials of adaptability to its enablers. The development of this framework is intended to facilitate a better understanding of the complexities involved in remanufacturing processes and to encourage the broader adoption of adaptability.

3. Methodology

The following chapter details the methods used within this publication. Based on the analysis of two different literature reviews, the results are systematized and analyzed, and a framework is built upon those. The choice of a literature review over expert interviews for identifying influencing factors in the context of adaptable remanufacturing was strategic, aiming to ensure a broad, unbiased collection of data and insights. Literature reviews allow for the aggregation of a wide range of published studies, theories, and findings, providing a comprehensive understanding of the field without the limitations of individual expert perspectives or experiences.

3.1. Building a framework structure

A framework for selecting suitable concepts and enablers is created to make the acquired findings applicable and illustrate adaptability’s potential. Two sets of information are incorporated into the framework: the first set is the influences on remanufacturing that serve as inputs for deciding which adaptability approach is appropriate. The barriers to adoption are derived from the influencing factors. The second set of information is all available data on adaptive remanufacturing. The enablers are drawn from the literature on adaptability characteristics and are to be adapted depending on the use case since the enablers are highly industry and context-dependent and, therefore not comprehensive. The framework aims at identifying key influences causing potential issues and determining effective adaptability strategies, and uses a four-layer structure, as shown in Figure 3. The diagram also highlights the points at which the collected information is inserted.

Figure 3. Framework structure and integration of knowledge gathered from the literature.

The term framework is deliberately chosen over alternatives such as guidelines. The current data only allows partial details of the planning process. Instead, a structure is proposed and examined in the following sections based on the currently available data.

Systematic literature reviews are chosen to gather this knowledge. Literature reviews are well suited to provide the required comprehensive foundation (Hatcher et al., 2013).

3.2. Review on remanufacturing influences

The influences on remanufacturing are obtained through a systematical assessment of the literature regarding remanufacturing and the creation of a list containing all distinct influences that impact remanufacturing. The following search string is used to search the SCOPUS database:

TITLE ((influence* OR affect* OR impact* OR factor*) AND (remanufactur* OR (closed-loop W/1 (manufactur* OR produc* OR assembl* OR disassembl*))))

In addition to the term remanufacturing, synonyms like closed-loop manufacturing and production are included in a generalized version as denoted by the asterisk. These are combined with different words for influencing factors: influence, affect, impact, and factor. A paper is deemed relevant if it discusses one or more influences on remanufacturing. The search is limited to searching the title of each publication.

The search was conducted in August 2022 and yielded 97 results, most of which were published after 2013. The papers are analyzed manually. Each mentioned influence is added to the list and categorized into groups based on the area of the company they affect. New areas are appended as necessary in a bottom-up approach. For example, product properties are sorted under Product, and aspects of the remanufacturing process are listed under Process. 125 influences are identified this way, sorted into seven main categories. Each category can contain up to two further levels of sub-categorization. These results are displayed in Table 1.

Table 1. Influence classification.

Process	General: Volume (Hatcher et al., 2013); Rate (Tombido et al., 2022); Capacity (Georgiadis et al., 2006) Process: Overall (Bansal et al., 2021; Behret & Korugan, 2005; Duncan et al., 2007; Goltsos et al., 2019; Mohamed et al., 2018; Vogt Duberg et al., 2020); Number of Operations (Arredondo-Soto et al., 2019); Number of different Stations (Lim & Noble, 2006); Individuality of Process (Mustajib et al., 2019); Disassembly Level (Hatcher et al., 2013; Singh & Sarkar, 2021; Singhal, Tripathy, et al., 2019); Disassembly Sequence (Singh & Sarkar, 2021); Inspection & Sorting Strategy (Ansari et al., 2019; Behret & Korugan, 2005; Korugan et al., 2013; Singh & Sarkar, 2021; Singhal, Tripathy, et al., 2019; Singhal et al., 2020; Vasanthakumar et al., 2016; H. P. Zhang, 2019); Flow & Lead Times (Lim & Noble, 2006; Vasanthakumar et al., 2016); Buffers (Behret & Korugan, 2005; Korugan et al., 2013); Process Performance (Arredondo-Soto et al., 2019); QM (Q. Deng et al., 2015; Mohamed et al., 2018; Singhal, Tripathy, et al., 2019; Yu & Peng, 0000); Vertical Integration; Waste Management & Disposal Cost (X. Zhang et al., 2021) Operation: Scheduling (Lim & Noble, 2006; Singhal, Tripathy, & Jena, 2018; Singhal, Tripathy, & Kumar Jena, 2018); Order Management (Singh & Sarkar, 2021); Inventory Management (Ata & Corum, 2023; Behret & Korugan, 2005; Corum et al., 2014; Samarghandi, 2017; Singhal et al., 2020; Singhal, Tripathy, et al., 2019); Inventory Levels (Georgiadis et al., 2006; Vasanthakumar et al., 2016); Controlling (Singhal, Tripathy, & Kumar Jena, 2018); Energy Consumption (Bansal et al., 2021; Ouyang et al., 0000; Singhal, Tripathy, & Jena, 2018); Shop load level (Lim & Noble, 2006); Part Tracking (Kumar et al., 2015; Singhal et al., 2020); Shop Floor Management (Singhal, Tripathy, et al., 2019; Vogt Duberg et al., 2020); WIP Requirements (Lim & Noble, 2006); MRP (Material Requirements Planning) (Singh & Sarkar, 2021) Resources: Facility Layout (Mohamed et al., 2018; Vogt Duberg et al., 2020); Facility Location (N. Li, 0000; Singhal et al., 2020; Subramoniam et al., 2009); Tools, Machinery & Technology (Ansari et al., 2019; Chakraborty et al., 2017; Chaowanapong et al., 2017; Hatcher et al., 2013; Kumar et al., 2015; Mohamed et al., 2018; Singh & Sarkar, 2021; Singhal, Tripathy, & Kumar Jena, 2018; Singhal, Tripathy, et al., 2019; Vasanthakumar et al., 2016); Worker Skill (Arredondo-Soto et al., 2019; Chakraborty et al., 2017; Chaowanapong et al., 2017; Hatcher et al., 2013; W. Liu et al., 2016; Mohamed et al., 2018; Singh & Sarkar, 2021; Singhal, Tripathy, & Kumar Jena, 2018; Singhal, Tripathy, et al., 2019; Singhal et al., 2020; Vasanthakumar et al., 2016; Vogt Duberg et al., 2020); Worker Safety (Ansari et al., 2019); IT- Infrastructure (Q. Deng et al., 2015); Data (Mohamed et al., 2018; Singh & Sarkar, 2021)
Product	Value (Singh & Sarkar, 2021) Design for Remanufacturing: Overall (Ansari et al., 2019; Bansal et al., 2021; Chakraborty et al., 2017; Q. Deng et al., 2015; Hatcher et al., 2013; Kim & Kim, 2020; Mohamed et al., 2018; Singh & Sarkar, 2021; Singhal et al., 2020; Subramoniam et al., 2009; Sundin & Bras, 2005; van Loon & van Wassenhove, 2018; Vasanthakumar et al., 2016; Vogt Duberg et al., 2020); Design for Inspection (Singhal et al., 2020); Design for Disassembly (Hatcher et al., 2013); Upgradability (Hatcher et al., 2013); Component Commonality (Kim & Kim, 2020) Design: Innovation (Ansari et al., 2019; Singh & Sarkar, 2021); Complexity (Lim & Noble, 2006); Number of Components (Singh & Sarkar, 2021); Number of Products and Variants (Kim & Kim, 2020); Materials (Singh & Sarkar, 2021; H. P. Zhang, 2019; X. Zhang et al., 2021); Geometry (H. P. Zhang, 2019); Durability (Hatcher et al., 2013) Return Properties: Life Cycle Length (Mustajib et al., 2019; Ortegon et al., 2014); Technical Relevance & Obsolescence (Mustajib et al., 2019; Ortegon et al., 2014; L. Zhou & Gupta, 2020); Return Quality (Arredondo-Soto et al., 2019; Behret & Korugan, 2005; Q. Deng et al., 2015; Inderfurth, 2005; Korugan et al., 2013; Mustajib et al., 2019; Ouyang et al., 0000; Singhal, Tripathy, & Jena, 2018; Subramoniam et al., 2009; Turki & Rezg, 2018; Yu & Peng, 0000; H. P. Zhang, 2019; X. Zhang et al., 2021) Remanufactured Properties: Remanufactured Quality (Kabel et al., 2021; Singhal, Jena, et al., 2019); Aesthetics (Mohamed et al., 2018)
Company & Business	Size (Singh & Sarkar, 2021) Strategy: Fear of Cannibalization (Chaowanapong et al., 2017; Singhal et al., 2020); Independent Remanufacturer Competition & Licensing (Ansari et al., 2019; J. Cao & Liu, 2014; Hatcher et al., 2013; Jin et al., 2017; Subramoniam et al., 2009; Xia & Zhang, 2019; Q. Zhou et al., 2022; Zou et al., 2019); Pricing (Ansari et al., 2019; Kabel et al., 2021; Singhal et al., 2020; Singhal, Jena, et al., 2019; Subramanian & Subramanyam, 2012; van Loon & van Wassenhove, 2018); Potential Cost Savings (Chaowanapong et al., 2017; Q. Deng et al., 2015; Y. Zhou et al., 2021; Zou et al., 2022); Ease of Quality Improvement (G. Li et al., 2018); Exchange Policy (Le et al., 0000; Z. Wang et al., 2020) Management: Commitment & Exemplary Role (Chakraborty et al., 2017; Hatcher et al., 2013; Mohamed et al., 2018; Singhal et al., 2020; Vasanthakumar et al., 2016); Long Term Vision (Singhal, Tripathy, et al., 2019; Vasanthakumar et al., 2016); Understanding of Product & Process (Vasanthakumar et al., 2016); Organizational Structure (Chaowanapong et al., 2017; Mohamed et al., 2018; Subramoniam et al., 2009); Business Model (Ansari et al., 2019; Hatcher et al., 2013; Le et al., 0000); Sustainability in Decision-Making (Ansari et al., 2019); Lean & Kaizen (Ansari et al., 2019); Employee Involvement (Ansari et al., 2019); Benchmarking (Ansari et al., 2019); Marketing: Support Infrastructure (Kabel et al., 2021; Mohamed et al., 2018); Public Awareness (Ansari et al., 2019); Trust & Warranty (Ansari et al., 2019; Mohamed et al., 2018; Singhal, Jena, et al., 2019; Subramanian & Subramanyam, 2012; X. Zhang et al., 2022; Zhu & Yu, 2019); (Re-) Manufacturer Image (Ansari et al., 2019; Chaowanapong et al., 2017; Q. Deng et al., 2015; Han & Yan, 2022; Kabel et al., 2021; Singhal, Jena, et al., 2019; Singhal et al., 2020; Subramoniam et al., 2009); Retailer Image (Singhal et al., 2020; Subramanian & Subramanyam, 2012; Xu et al., 2017) Control: Monitoring (Ansari et al., 2019; Goltsos et al., 2019; Vasanthakumar et al., 2016); Information Transparency (Ansari et al., 2019; Vasanthakumar et al., 2016) Investments: Overall (Ansari et al., 2019; Chakraborty et al., 2017; W. Liu et al., 2016); Machines (Ansari et al., 2019; Chakraborty et al., 2017; Q. Deng et al., 2015; Singh & Sarkar, 2021; Vasanthakumar et al., 2016); R&D (Ansari et al., 2019; Q. Deng et al., 2015; W. Liu et al., 2016) Know-How: Patents (W. Liu et al., 2016); Academic Connections (W. Liu et al., 2016); Patents (Vogt Duberg et al., 2020)
Reverse Logistics & Supply Chain	Core Supply: Total Supply (Behret & Korugan, 2005; Q. Deng et al., 2015; Inderfurth, 2005; Lechner & Reimann, 2014; Subramoniam et al., 2009; Vasanthakumar et al., 2016); Supply Fluctuations (Lechner & Reimann, 2014; Subramoniam et al., 2009; Vasanthakumar et al., 2016); Uncertainty (Goltsos et al., 2019; Inderfurth, 2005) Core Acquisition: Collection Strategy (Bansal et al., 2021; Q. Deng et al., 2015; Singhal, Tripathy, & Kumar Jena, 2018; Singhal, Tripathy, et al., 2019; Singhal et al., 2020; Subramoniam et al., 2009; Vogt Duberg et al., 2020); Consumer Assistance (Singhal et al., 2020; Singhal, Tripathy, & Jena, 2018; Singhal, Tripathy, & Kumar Jena, 2018; Singhal, Tripathy, et al., 2019); Collection Cost (J. Cao & Liu, 2014) Collaboration (across the Supply Chain): Overall (Ansari et al., 2019; Chakraborty et al., 2017; Guidat et al., 2015; Hatcher et al., 2013; Singh & Sarkar, 2021; Vasanthakumar et al., 2016); Joint Certification (Ansari et al., 2019); Information Transparency (Q. Deng et al., 2015; Singh & Sarkar, 2021; Tang & Naim, 2004; Tsai et al., 2007; Vasanthakumar et al., 2016); OEM Buybacks & Incentives (Kleber et al., 2011; Singhal et al., 2021) Infrastructure (Ansari et al., 2019) Network Set-Up: Overall (Inderfurth, 2005; Mustajib et al., 2019); De- & Centralized (Jing et al., 2016; Mann et al., 2022) Transport: Cost (Corum et al., 2014; Inderfurth, 2005; Mustajib et al., 2019); Time (Chaowanapong et al., 2017; Georgiadis et al., 2006; Inderfurth, 2005)
Market	Overall: Size (Hatcher et al., 2013; Mustajib et al., 2019; Subramoniam et al., 2009); Overlap & Cannibalization (Chakraborty et al., 2017; Inderfurth, 2005; Lv et al., 2021; Singhal, Tripathy, & Jena, 2018; Sun et al., 2018; Tombido & Baihaqi, 2020); Demand Uncertainty (Goltsos et al., 2019; Inderfurth, 2005; Lim & Noble, 2006); Competition (Q. Deng et al., 2015; Singhal, Tripathy, & Jena, 2018); Internationality (Chaowanapong et al., 2017) Consumer: Attitude (Kabel et al., 2021; Singhal, Jena, et al., 2019; Singhal, Tripathy, & Jena, 2018; Singhal, Tripathy, et al., 2019); Awareness (Kabel et al., 2021; Singhal et al., 2020; Singhal, Jena, et al., 2019; Singhal, Tripathy, et al., 2019; Subramoniam et al., 2009; Sun et al., 2018; Vasanthakumar et al., 2016); Remanufacturing Acceptance (Singhal, Tripathy, & Kumar Jena, 2018; Vasanthakumar et al., 2016); Consumer Homogeneity (Kleber et al., 2018); Risk Aversion (W. Deng, 2020; Kabel et al., 2021; Kleber et al., 2018; Singhal, Jena, et al., 2019)
Policy	Assistance & Guidelines (Ansari et al., 2019; Q. Deng et al., 2015) Regulation: General (Chaowanapong et al., 2017; Q. Deng et al., 2015; Hatcher et al., 2013; Mustajib et al., 2019; Qiu & Jin, 2022; Singhal et al., 2020; Subramoniam et al., 2009; Wen et al., 2019); Legal Protection (Chaowanapong et al., 2017); Standardization (Q. Deng et al., 2015); Tariffs (W. Li et al., 2023); Take-Back-Mandates (Ansari et al., 2019; H. Cheng et al., 2022; Esenduran et al., 2012, 2017; Webster & Mitra, 2007); Waste Disposal Regulation (Ansari et al., 2019) Financial Incentives: Carbon Tax (Y. Cheng & Luo, 0000a; Esenduran et al., 2012; Guo et al., 2020; Kundu & Chakrabarti, 2018; Z. Li, Zhang, et al., 2019; Z. Li, Zheng, et al., 2019; B. Li et al., 2021; W. Li et al., 2023; Luo et al., 2022; Singhal, Tripathy, & Kumar Jena, 2018; Zhao et al., 2022); Green Subsidies (Ansari et al., 2019; Y. Cheng & Luo, 0000b; H. Cheng et al., 2022; Z. Li, Zhang, et al., 2019; Z. Li, Zheng, et al., 2019; B. Li et al., 2021; Nie et al., 2021; Qiao & Su, 2021; Qiu & Jin, 2022; Singhal, Tripathy, et al., 2019; Y. Wang et al., 2014; L. Zhang & Zhang, 2022; Y. Zhang et al., 2021)
Environment	Emission (Q. Deng et al., 2015; Hatcher et al., 2013); Resource Consumption (Q. Deng et al., 2015); Waste Generation (Q. Deng et al., 2015; Georgiadis et al., 2006); Social Impact (Y. Zhang et al., 2021)

The table highlights variations in research depth across identified sub-classifications. For instance, carbon taxes have been studied in over ten publications, whereas the impact of information transparency is explored in only two. The environmental section comprises only four influences, reflecting the usual impact of remanufacturing on the environment, with the reverse being less common.

3.3. Review on adaptable remanufacturing

The knowledge on adaptable remanufacturing is gathered through a systematic review based on the following search string:

TITLE-ABS-KEY ((remanufactur* OR (closed-loop W/1 (manufactur* OR produc* OR assembl* OR disassembl*))) AND (adaptab* OR changeab* OR reconfigurab* OR flexib* OR agil*))

Remanufacturing is included in the first part, as well as alternative terms like closed-loop manufacturing, production, assembly, and disassembly. Phrases like closed-loop supply chain are excluded due to their low applicability to the production system. All terms are included in their general forms as denoted by the asterisk. The second major bracket contains all relevant terms for adaptability introduced in the fundamentals section. The search was executed in March of 2023 and yielded 385 results, 362 of which are eligible scientific contributions. They are iteratively searched, manually judging the relevance of the topic based on the title, abstract, conclusion, and the entire text. Only papers that include remanufacturing systems and adaptability are included, while other research, like publications focusing on the supply chain or the automation of specific tasks, are eliminated. After reviewing the available results, 22 publications are deemed relevant.

The publications are grouped into frameworks, literature reviews, and case studies based on their content type, as displayed in Table 2. Some papers are split in two if they perform a literature review and a case study.

Table 2. Results of the systematic search for adaptable remanufacturing literature.

Type of Paper	Reference and Short Description
Literature Reviews	Andersen et al. (Andersen et al., 2023) Part I and Bockholt et al. (Bockholt et al., 2020) Part I: Remanufacturing literature review focused on manufacturing systems Liu et al. (C. Liu et al., 2019): Uncertainty in assembly necessitates intelligent systems Tolio et al. (Tolio et al., 2017) Analysis of most remanufacturing literature → Adaptability one major requirement – However, little guidance on system planning or implementation
Frameworks & Methodologies	Kafuku et al. (Kafuku et al., 2019): Methodology for selecting cleaning technology based on expert opinion – Flexibility is one criterion Kimita et al. (Kimita et al., 2021): Create Organization based on dependencies for achieving Adaptability Koren et al. (Koren et al., 2018): Framework for evaluating the environmental characteristics of RMS – Notes applicability of adaptability towards remanufacturing Low and Ng (Low & Ng, 2018): 6-step Manufacturing System Design Framework including Flexibility Analysis Jukun et al. (Jukun et al., 0000; J. Yao et al., 0000): Purely conceptual Framework for Manufacturing System setup
Data Analysis & Surveys	Bag et al. (Bag et al., 2023): Analysis of I4.0 impact on sustainability → Flexible Orientation improves Dynamic Remanufacturing Capabilities
Case studies	Bockholt et al. (Bockholt et al., 2020): Issues of a Case Company Andersen et al. (Andersen et al., 2023): Demonstration of scalability potential through Simulation Bi et al. (Bi et al., 2014): Repurpose of old machines creates adaptability, even in existing systems Hunter and Black (Hunter & Black, 2007): Case study on lean Remanufacturing → Flexibility through small batches and a cellular layout
Thought experiment	Brunoe et al. (Brunoe et al., 2019): Thought experiment on adaptability benefits for different EoL options → Adaptability helps in coping with changes
Numerical Planning	Aljuneidi and Bulgak (Aljuneidi & Bulgak, 2016, 2017) and Hooshyar Telegraphi and Bulgak (Hooshyar Telegraphi & Bulgak, 2021): Numerically optimize reconfigurable systems across time Georgiadis and Athanasiou (Georgiadis & Athanasiou, 2013): Comparison of capacity planning strategies

4. Results

After the comprehensive literature review to investigate influences on remanufacturing and adaptable remanufacturing practices, a conceptual framework was developed to enhance adaptability in the context of remanufacturing operations. The structure of the framework is shown in Figure 4.

Figure 4. Overall structure of the resulting framework.

The first layer of the framework contains remanufacturing influences, which serve as input for the rest of the methodology. They provide practitioners with a list of aspects to consider when judging their businesses’ suitability for remanufacturing and when planning its implementation. The second layer is called barriers to the adoption of remanufacturing. Here, influences are combined into a list of hurdles to an efficient remanufacturing realization that must be addressed for a more widespread and cost-effective adoption. The third layer, adaptability characteristics, contains the different types of adaptability and their sub-types based on the fundamentals presented before. These are linked to the barriers in the previous layer, illustrating which of the challenges facing remanufacturing can be addressed through which adaptable concepts. The last layer serves as initial guidance for implementing the abstract paradigms contained in layer three and offers an exemplary list of enablers. The enablers are not connected directly to the challenges but are indirectly linked through adaptability paradigms because the context depends on the nature of the subject. Detailed technical solutions must be chosen and validated individually, whereas the more abstract paradigms serve as a broadly applicable lens to view the enablers.

4.1. Remanufacturing influences

The identified list of remanufacturing influences can be used in multiple ways. It can serve as the basis for assessing companies and provide guidance in industrial contexts and research purposes regarding aspects to evaluate. This assistance is relevant in many lifecycle stages, like the initial planning phase, the realization, and the slow transition to larger remanufacturing shares. It can additionally serve as a basis for future remanufacturing planning methodologies. Such methodologies require the development of rules based on a set of inputs, which this list could facilitate. An overview of the areas described in the research papers can be seen in Figure 5. The inner ring shows the main categories, and the outer ring shows the subcategories. The width of the columns is based on the number of publications found.

Figure 5. Overview of research topic shares.

As illustrated in Figure 6, the first layer encompasses the array of remanufacturing influences identified through the systematic literature review. The major thematic areas are presented on the left side of the figure, while the right side provides a detailed view of ‘Production,’ serving as a representative category. Due to its extensive nature, the complete list, comprising 125 influences, is not fully included here, but is shown in Table 1. This comprehensive compilation of influences lays the groundwork for an in-depth evaluation of potential obstacles to the adoption of remanufacturing in subsequent analyses.

Figure 6. Remanufacturing influences inserted in the framework.

4.2. Barriers to adoption

To understand how adaptability can aid remanufacturing, it is essential to identify key areas requiring change or intervention. Directly correlating individual influences on adaptability characteristics is too simplistic, as often several factors collectively contribute to a particular challenge. For example, product design aspects like variant diversity and return quality collectively demand highly individualized processes, which adaptability can address. A layer of barriers is created to simplify this complexity, summarizing numerous influences into fewer, more significant obstacles. These barriers are further organized into two sub-layers, a necessary step to effectively illustrate their causal relationships. A single layer would not adequately convey the complexities involved. The framework limits itself to two sub-layers for clarity. Figure 7 visually presents these barriers, showing the connections between the two sub-layers and providing a clearer picture of the challenges in integrating adaptability into remanufacturing. The different arrow styles serve to increase readability.

Figure 7. Excerpt of the framework: Connections between primary and secondary barriers.

4.3. Adaptability characteristics

The third layer of the framework focuses on various types of adaptability and their characteristics. The framework is tailored to assist in designing a remanufacturing system, and therefore, it selectively excludes elements that pertain solely to broader, large-scale design decisions. In the context of adaptability, this exclusion particularly applies to agility and transformability, as they are more aligned with long-term strategic considerations, and their relevance to the remanufacturing system overlaps significantly with Reconfigurability. Consequently, the framework primarily incorporates its key characteristics of flexibility, changeover ability, and reconfigurability.

In terms of sub-categories, the goal is to provide detailed insights into types of adaptability while avoiding undue complexity. Various categorization methods were evaluated, including those by Adam & Eversheim (Dietrich, 1993) and ElMaraghy (H. A. ElMaraghy, 2005). However, the approach outlined in the Fundamentals in Chapter 2.3.2 was deemed most effective in balancing informative content with visual clarity. Thus, the framework adopts the reconfigurability characteristics from Koren (Koren, 2010), supplemented by those from Rösiö & Säfsten (Rösiö & Säfsten, 2013). While changeover ability is not elaborated upon, flexibility is broken down into the dimensions proposed by Terkaj et al. (Terkaj et al., 2009). Figure 8 shows an excerpt of the framework in which the connections between barriers and adaptability are shown.

Figure 8. Excerpt of the framework: Links between the barriers to remanufacturing and the classes adaptability classes, indicating beneficial impacts.

4.4. Enablers

The fourth and final layer of the framework, dedicated to enablers, is essential for translating abstract adaptability concepts into practical applications in real production systems. As emphasized by Andersen (Andersen, 2017), enablers are critical tools, machines, and software solutions that facilitate the realization of adaptability. These diverse and highly context-dependent enablers require an organized categorization for effective application.

Enablers can be classified in several ways, with the most common criteria being the production level at which they are implemented (H.-P. Wiendahl et al., 2007). For example, a versatile tool that increases the adaptability of a particular station is one such enabler. Furthermore, enablers can be differentiated by their nature as either soft (or logical) or hard (or physical) (Andersen et al., 2018). Soft enablers, such as rerouting capabilities, influence processes and execution patterns, while hard enablers, such as moving machines, help change the system’s physical state. Another classification approach is based on the type of adaptability they enable. For example, a modular and expandable factory structure influences scalability (Andersen et al., 2018).

This framework presents enablers in a single layer for clarity, organized by the system level they impact and distinguished by their nature. While sorting by adaptability type was considered, it was not pursued due to many enablers’ overlapping nature. As shown in Figure 9, the framework provides a concise yet comprehensive view of these enablers and illustrates their connections to adaptability in production systems. This layer is not exhaustive but is a starting point that is adaptable to different industry contexts and open to future expansion.

Figure 9. Excerpt of the framework: Exemplary enablers linked to the corresponding adaptability classes.

4.5. Final framework

The completed framework, shown in Figure 10, links the influences across the barriers and adaptability characteristics to the technical enablers. Figure 10 includes a simplified representation of Figures 6 – 9, which show the different sections in detail. The intended application of this framework is multifaceted. Companies considering remanufacturing can begin by assessing relevant factors from the listed influences, progressing to anticipate potential challenges and barriers. This progression aids in identifying applicable adaptability paradigms and practical implementation methods. For companies already engaged in remanufacturing, the framework helps analyze the root causes of their challenges and identify effective solutions.

Figure 10. Excerpt of the finished framework.

5. Discussion and outlook

Exploring potential improvements and future applications of the identified list of remanufacturing influences requires a comprehensive approach. This includes strategies to quantify these influences for real-world applicability and to develop a robust framework to guide remanufacturing practitioners. A significant part of this approach involves assigning numerical values to the influences, prioritizing them based on their importance, and using the list to create questionnaires for companies engaged in remanufacturing to enhance usability. In addition, it is essential to validate the thoroughness of the list through practical applications since it is based on literature.

The resulting framework is critical in assessing the impact of adaptability on remanufacturing feasibility. It shows how adaptability influences various barriers, particularly in the manufacturing sector, and suggests that adaptability can effectively address many challenges. The framework also serves as a valuable tool for remanufacturing professionals, especially within remanufacturing strategy and planning, highlighting potential challenges, suggesting solutions, and helping to realize cost and environmental benefits. Within companies, the framework primarily aids strategic decision-makers and policy developers. It provides executives and sustainability officers with insights into integrating adaptability into remanufacturing strategies, aligning with broader corporate sustainability and circular economy goals. Additionally, it can guide policy developers in creating internal guidelines that promote adaptability and efficiency in remanufacturing operations, ensuring the company’s practices are both environmentally sustainable and economically viable. However, there are areas for improvement, including specificity regarding target companies, addressing challenges for third-party remanufacturers without prior data, refining the definition and assessment of barriers, and differentiating enablers for manufacturing versus remanufacturing. The framework’s complexity may deter some companies, indicating a potential need for simplification without significant loss of information. Going forward, it is important to incorporate a quantification system into the framework that allows users to prioritize significant relationships. The framework could also be enhanced by collecting industry data through questionnaires based on the influences and their interrelationships. Tailoring the framework to specific use cases is another future direction, increasing its relevance and allowing for more detailed quantification for a focused set of scenarios.

In conclusion, while the framework’s usefulness is acknowledged, the identified findings highlight the need for refinement and further development. The framework’s comprehensive structure, ranging from influences to technical enablers, provides a basic guide for companies at different stages of remanufacturing adoption. This underscores its importance in enhancing adaptability and effectively addressing challenges within the remanufacturing sector.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the German Federal Ministry of Education and Research within the “SME – Innovative: Research for Production” Funding Action [02K20K103] and implemented by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.