A holistic framework for evaluation and selection of remanufacturing operations: an approach

Abstract

Lack of efficient evaluation methods to help decision makers in making informed decision regarding in-house technology investments, outsourcing technology, and manual activities in the remanufacturing industry has raised serious concerns in the selection process of technologies based on company size and resources. This has resulted in the inability of decision makers to broadly assess the scope of alternative technologies against remanufacturing processes. Thus, it has resulted in the choice of a common solution based on inconsistencies and uncertainties. This paper proposes a holistic framework that can assist decision makers in evaluating the remanufacturing operations by offering better understanding of the performance and potential capabilities in the selection of appropriate technology. The salient feature of the proposed framework is its utilization of the fuzzy logic method as guiding tool to analyze criteria for technology selection in order to make informed decisions on whether to invest or outsource a technology or use labor-intensive operations to the specific process. The paper also highlights importance of the technology assessment framework in aligning and consolidating the dedicated thinking for the life cycle of the particular technology. The major contribution of the framework is its integration of obsolete and disposal phases with the acquisition and adoption phases. Therefore, during technology assessment, decision makers are guided by a step-by-step iterative process on technology requirement verification.

Decommissioning vs. repowering of offshore wind farms—a techno-economic assessment

“…As mentioned above, to calculate LCoE, the discounted electricity output has to be estimated based on Eq. (3).…”

“…The current academic literature about EoL scenarios is limited, forcing operators of wind farms to adopt their own practices when supporting relevant decisions. While in other industries systematic approaches have been established to support EoL decisions, this is not currently the case for OWFs [3][4][5][6]. Luengo and Kolios [7] have reviewed in detail the risks involved on service life extension based on a detailed failure mode identification and with a view to qualifying which are the key components to drive such decisions.…”

Remanufacturing supply chain: an analysis of performance indicator areas

“…Further, several studies have applied multi-criteria decision-making (MCDM) techniques to RSC problems for efficient decisionmaking. However, these studies intend to obtain a solution for a definite remanufacturing problem such as the selection of best-remanufacturing technology (Kafuku et al, 2016); analysis of barriers (Govindan et al, 2016a) (for details refer Sub-Section 2.2).…”

“…Meng et al (2016) propose a hybrid approach (Fuzzy inference model-PROMETHEE-Goal programming) to evaluate the factors that influence the products EOL performance and decision involved in the recovery of used products. Most recently, Kafuku et al (2016) develop a framework for assessing appropriate remanufacturing technology and further ranks using fuzzy logic to select the best from the available technology. Ilgin (2017) focused on selecting the most appropriate used product to be collected by TPRLP for remanufacturing.…”

“…Undoubtedly, these strategies best support sustainability but in the last few years, remanufacturing is one such strategy that is finding increased attention in the research community (Goepp et al, 2014). Remanufacturing is a multi-process that involves product recovery after its end-use, inspection, disassembly, cleaning, refurbishing, reconditioning, upgrading and reassembly to transform the used product to a "like-new" product (Sutherland et al, 2008;Ke et al, 2011;Kafuku et al, 2016). Remanufacturing results into several advantages such as the significant recovery of the asset (Nasr and Thurston 2006), improved profit margins (Wei and Zhao, 2015;Diaz and Marsillac, 2017) and reduced resource utilization along with the reduction of emissions to the environment (Yang et al, 2015).…”