Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Abstract

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of 'if-then' decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the crossvalidation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision-making process.

A Novel Improvement Strategy of Competency for Education for Sustainable Development (ESD) of University Teachers Based on Data Mining

“…This replacement prevents the paradoxes that may be caused in the processing of sequential information by using the original RSA, which is based on equivalence relationships [38,39]. The expanded RSA has been applied in numerous fields of service, such as online open courses [37], pollution management strategy-making [40], airline performance evaluation [41], spare part classification [42], agricultural fund allocation [43], research and development project selection [44], financial performance evaluation [32], and pavement maintenance [31]. The definition and calculation procedures of this theory are as follows:…”

OR in spare parts management: A review

“…Among them, fourteen (Guvenir & Erel (1998), Flores & Whybark (1986, 1987, Partovi & Burton (1993), Partovi & Anandarajan (2002), Bhattacharya et al (2007) In Appendix A, most of the research papers applied classification approaches to common inventory classification, therefore we will not provide further details on them but interested readers can refer to them directly. We focus on the eight contributions (Guvenir & Erel (1998), Flores & Whybark (1986, 1987, Partovi & Burton (1993), Partovi & Anandarajan (2002), Chu et al (2008), Rezaei & Dowlatshahi (2010), Hu et al (2017)) that have carried out case studies applied to spare parts classification. Based on them, we divide the ABC classification criteria, which were employed in them,…”

An innovative decision rule approach to tyre pressure monitoring

“…The generated monitoring rules first need to be validated and they can then be used in practice. The authors in Hu et al (2017) distinguish three complementary techniques for decision rules validation: (i) direct analysis of the monitoring rules analysis; (ii) reclassification; and, (iii) cross-validation. In the direct analysis validation technique, the decision maker is asked to consider all the decision rules and indicate his/her level of agreement on a five-level Likert scale (strongly disagree to strongly agree).…”

“…The basic idea of DRSA is to replace the indiscernibility relation used in the classical RST with the dominance relation, which is more appropriate for multicriteria classification. The DRSA has been successfully applied in different real-world decision problems, including risk assessment , knowledge management , service improvement (Liou et al, 2011), bankruptcy risk evaluation (Greco et al, 2002), supply chain management (Chai et al, 2013), supplier selection, airport service quality (Liou et al, 2011), product mix (Greco et al, 2008), performance of cooperations and business values (Peters & Poon, 2011) and spare parts management (Hu et al, 2017). A brief overview of the DRSA is presented in this appendix.…”