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Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process

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  • Bai, Chunguang
  • Sarkis, Joseph

Abstract

Globalization and greening are two major trends manufacturing. Each trend has increased organizational and supply chain risk and uncertainty. Advanced manufacturing technology (AMT) are resources that can competitively aid modern industry in this volatile and complex environment. Thus, the evaluation, selection and implementation of more environmentally conscious AMT is important for meeting global requirements, especially with respect to environmental sustainability. Although a wide variety of methods to support AMT selection and evaluation processes exist, important aspects including green flexibility performance and psychological characteristics of decision makers under risk and uncertainty are missing. This paper presents a novel method for general investment appraisal of AMT, but especially introducing the context of green flexibility within manufacturing organizations. This paper aims to (1) develop effective green flexibility measures for manufacturing firms, incorporating various economic and environmental flexibility types, (2) introduce a hybrid possibility multiple criteria decision model for AMT evaluation and ranking integrating neighborhood rough set theory and cumulative prospect theory based on the three-parameter interval grey number, and (3) investigate the application of the proposed method in an illustrative case example to help manufacturing practitioner and researchers understand how to investigate various AMTs in this decision environment. Various advantages and disadvantages of the methodology are introduced. The results are evaluated with theoretical, methodological and managerial implications identified. This paper sets the foundation for significant future research in green manufacturing flexibility in an AMT environment.

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  • Bai, Chunguang & Sarkis, Joseph, 2017. "Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process," International Journal of Production Economics, Elsevier, vol. 188(C), pages 86-104.
    • Handle: RePEc:eee:proeco:v:188:y:2017:i:c:p:86-104
    DOI: 10.1016/j.ijpe.2017.03.013
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