A circularity-based quality assessment tool to classify the core for recovery businesses

Abstract

To realise circularity in the management of used materials and associated recovery practices for value creation, a circularity-based quality assessment tool (CQAT) is proposed in this study. The issue is addressed with core classification based on quality factors assessing circularity at the product level. A circularity-based quality indicator (QI) is developed that classifies the core into reusable, remanufacturable, and recyclable (3R) at the acquisition phases of the core retrieval system. For the validation of CQAT, four real cases of small-medium-sized dismantling firms dealing with engines, motor gearboxes, motor pumps, and refrigerators involved in remanufacturing activities are analysed. The study employs an integrative coefficient correlation maximisation approach (CCMA) and whitenization weight functions (WWF) clustering model that estimates the priorities of regenerative and restorative actions. The result reveals that the severe damage with high disassembly processing time due to destructive disassembly in the post-acquisition phase is highly critical to the circularity of the retrieval system. It exhibits that damage, processing time, value retention classify core into lowest grade at acquisition phases. Since possibilities of value recovery are lost, firms are least concerned with social and environmental considerations. Additionally, the design and functionality dimensions in the inspection majorly classify cores into remanufacturable.

KEYWORDS:

• Quality indicator
• circularity
• core classification
• sustainability
• remanufacturing

Disclosure statement

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

Additional information

Notes on contributors

Ashutosh Mishra

Ashutosh Mishra is pursuing PhD in the area of Operations and Supply Chain Management area of the  National Institute of Industrial Engineering (NITIE), Mumbai. He received MTech in Industrial Design from Maulana Azad National Institute of Technology (MANIT), Bhopal, India, in 2015 and BE in Mechanical Engineering from Oriental Institute of Science and Technology, Bhopal, India, in 2012. His research interests are recovery-oriented supply chain network, remanufacturing systems, optimisation algorithms, and machine learning techniques.

Priyanka Verma

Priyanka Verma is a Faculty Member in Operations and Supply Chain Management area of the National Institute of Industrial Engineering (NITIE), Mumbai. Her teaching and research interest includes Logistics and Supply Chain Management, Optimization, Facility Planning, Project Resilience and Manufacturing Strategy modelling. She has published in journals of repute like International Journal of Production Economics, International Journal of Production Research, Computers and Industrial Engineering, International Journal of Physical Distribution & Logistics Management, Production Planning and Control, to name a few. She has presented her research in National and International conferences organized by PMI, POMS, IEEE, ISDSI, IEOM, ORSI, SOM etc.

Manoj Kumar Tiwari

Manoj Kumar Tiwari (FNAE, FNASc, FIISE) is Director, National Institute of Industrial Engineering (NITIE), Mumbai, India. He is also a Professor in the Department of Industrial and Systems Engineering at the Indian Institute of Technology, Kharagpur, India. He received BE in Mechanical Engineering from Visvesvaraya National Institute of Technology, Nagpur, India, in 1986, MTech in Production Engineering from Motilal Nehru National Institute of Technology, Prayagraj, India, in 1990, and Ph.D. in the Department of Production Engineering from University of Jadavpur. He is actively involved in research relevant to the applications of optimisation, modelling, decision support systems, and data mining in the domain of logistics, supply chain management, and manufacturing systems.