Evaluation of recovery configuration options by product utilisation value

Abstract

Purpose The purpose of this paper is to propose an approach to evaluate product performance of returned products, using four key performance attributes as the basis for improving sustainability through product recovery. Design/methodology/approach A fuzzy logic approach is developed to account a trade-off scenario for a manufactured product with recovery options. This approach is demonstrated using a numerical example and is validated using a case study in the automotive parts and components industry. Findings Product utilisation value (PUV) is found to be a useful index that manufacturers can use to assess product recovery options, as it brings together a number of conflicting parameters into a rationalised value for decision making. In addition, PUV provides a rationalised approach for comparing and selecting the most appropriate recovery configuration option. Research limitations/implications The authors only utilise four key performance measures to derive PUV. Further research is needed to modify and incorporate other measures that are important to decision makers to improve sustainability in manufacturing supply chains. Practical implications The proposed approach may motivate decision makers to consider sustainable recovery options by comparing PUVs of products for primary and secondary markets. The case study demonstrated the conflict and complexity organisations face in a global supply chain of a competitive industry. Originality/value The authors propose an approach to optimise trade-off considerations of selected performance attributes through PUV. This PUV as a benchmark can help improve recovery of the returned products and reduce landfill.

Sustainable Supply Chain Management in the Automotive Industry: A Process-Oriented Review

“…In line with this type of study, some research works were performed to aid in ELV recovery systems and management. In this context, Đorđević and Kokić [108], Ahmed et al [109], Keivanpour et al [110], and Kuik et al [111] developed decision-making models for selecting recovery options including reuse, remanufacturing, recycling, and energy recovery. Subramoniam et al [112] and Ahmed et al [109] addressed strategic issues concerning the remanufacturing of auto parts.…”

Prioritization of sustainable supply chain practices with triple bottom line and organizational theories: industry and academic perspectives

“…Sustainable manufacturing is the foundation for sustainability as it provides increased collaboration between product design/development and product recovery (Govindan, Jha, and Garg 2016;Badurdeen et al 2009). In today's competitive environment, manufacturers need to adopt sustainable manufacturing activities (Nagalingam, Kuik, and Amer 2013) through greater utilisation of products with many recoverable components in order to reduce landfill (Kuik et al 2017). Sustainable manufacturing aligns with the "Make" management process of SCOR.…”

“…However, assessing sustainability within the context of TBL is a challenging issue because it is difficult to attribute performance to particular SSCM practices. In addition, lack of a systematic and integrated performance measurement framework hinders effective deployment of SSCM practices (Kuik et al 2017;Ahi and Searcy 2015). Hence, this study is based on three research questions: (a) What is the relative ranking and importance of the three TBL measures when organisations decide to implement SSCM strategy?…”

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

“…The ability for a firm to make changes in production planning and adapt to technology can give them the significant advantage over the competition (Yu et al , 2018; Leng and Jiang, 2018). To remain competitive, numerous firms have been considered to possibly adopt reliable and latest technologies and approaches to everyday business tasks for collaborative activities, such as procurement, manufacturing, quality assurance, warranty services, marketing, returns and recovery and logistics (Seuring, 2004; Kain and Verma, 2018; Kuik et al , 2016, 2017). An emerging technology is often utilising big data analytics to improve the effectiveness and efficiency of manufacturing industry and minimise used resources of producing different manufactured products (Kain and Verma, 2018; Seuring, 2004; Kokuryo et al , 2017; Kuik et al , 2017).…”

“…To remain competitive, numerous firms have been considered to possibly adopt reliable and latest technologies and approaches to everyday business tasks for collaborative activities, such as procurement, manufacturing, quality assurance, warranty services, marketing, returns and recovery and logistics (Seuring, 2004; Kain and Verma, 2018; Kuik et al , 2016, 2017). An emerging technology is often utilising big data analytics to improve the effectiveness and efficiency of manufacturing industry and minimise used resources of producing different manufactured products (Kain and Verma, 2018; Seuring, 2004; Kokuryo et al , 2017; Kuik et al , 2017). In dealing with planned obsolescence and production, there is a need to develop a DSS and promote computerised support for collaborative manufacturing.…”