Benchmarking framework for sustainable manufacturing based MCDM techniques

Abstract

PurposeThe purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.Design/methodology/approachThe proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.FindingsThe results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.Research limitations/implicationsThe main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.Originality/valueThis paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

Investigating the barriers of blockchain technology integrated food supply chain: a BWM approach

“…BWM calculates relative importance weight by comparing the best/worst barriers and sub-barriers (in our case) with equivalent category-wise other barriers and sub-barriers. The primary advantage of BWM is over the other MCDM method is that it requires fewer pairwise comparisons (Ahmad et al, 2020;Hichem et al, 2021;Rezaei, 2015). Because of these features, this method saves a substantial amount of time for experts .…”

“…In order to prioritise these barriers, BWM is selected among the several available MCDM methods. The rationale behind the section of BWM is over the other MCDM method is that it requires fewer pairwise comparisons (Rezaei, 2015;Hichem et al, 2021;Khan, 2021; that reduces the amount of expert's time. Additionally, the obtained reliability and consistency of the acquired weights are competitively high.…”

“…In order to prioritise these barriers, BWM is selected among the several available MCDM methods. The rationale behind the section of BWM is over the other MCDM method is that it requires fewer pairwise comparisons (Rezaei, 2015; Hichem et al. , 2021; Khan, 2021; Khan et al.…”

Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing

“…The fourth level refers to supporting analysts in solving a considered problem, for instance, design optimization, energy optimization, and quality improvement. Data-driven approaches dedicated to the evaluation of sustainable manufacturing and product development are often based on methodologies referred to as an analytical hierarchy process [38][39][40][41] and a multi-criteria decision analysis [25,[42][43][44][45][46].…”

Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector

“…The extant literature shows a plethora of applications of MCDM models in solving complex real-life issues [22]. MCDM models are found useful in evaluation, preferential ordering, ranking, sorting and assessment problems and stand out as a structured benchmarking framework [23][24]. Accordingly, there has been a substantial growth in developing and applying a numerous number of new models [25].…”