Title

A simulation-based fuzzy analytic network process approach in developing sustainable manufacturing strategy

College

Ramon V. Del Rosario College of Business

Department/Unit

Management and Organization Department

Document Type

Article

Source Title

Management and Production Engineering Review

Volume

6

Issue

2

First Page

32

Last Page

39

Publication Date

6-1-2015

Abstract

This paper adopts a probabilistic fuzzy analytic network process (PROFUZANP) approach in developing a sustainable manufacturing strategy. In this hybrid method, analytic network process handles the complexity of the problem structure under consideration, fuzzy set theory is used to describe vagueness in individual decision-making and probability theory is used to handle randomness in group decision-making. This holistic methodological approach addresses complexity and uncertainty both in individual and group decision-making which is useful in modeling group decisions such as developing a sustainable manufacturing strategy. In this work, an inclusive approach of integrating traditional manufacturing strategy concepts and sustainable manufacturing principles is proposed as a unifying decision model. The proposed decision structure adopts the hierarchical structure of manufacturing strategy and explores the presence of strategic responses and stakeholders' interests as significant components of sustainability. Using PROFUZANP, the decision model identifies the content policies of sustainable manufacturing strategy. This content strategy is expected to address both competitiveness and sustainability of manufacturing firms. Results are reported in this paper along with insights and future work. The contribution of this work is the integration of manufacturing strategy and sustainability into a coherent decision framework that requires the use of PROFUZANP in dealing with complex and uncertain group decision-making problem.

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Digitial Object Identifier (DOI)

10.1515/mper-2015-0014

Disciplines

Business Administration, Management, and Operations

Keywords

Sustainability; Industrial ecology; Multiple criteria decision making

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