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ADVANCES IN INDUSTRIAL ENGINEERING AND MANAGEMENT
ISSN:2222-7059 (Print);EISSN: 2222-7067 (Online)
Copyright © 2000- American Scientific Publishers. All Rights Reserved.


Title : A Unified Framework for Manufacturing Strategy Decision to Gain Competitive Advantages
Author(s) : Manoj Kumar*
Author affiliation : Echelon Institute of Technology Faridabad, YMCA University of Science & Technology Faridabad, Haryan, India
Corresponding author img Corresponding author at : Corresponding author img  

Abstract:
Manufacturing strategy research aims at providing a structured decision making approach to improve the economics of manufacturing and to make companies more competitive. The overall objective of this paper is to investigate how manufacturing companies make use of different manufacturing practices or bundles of manufacturing practices to develop certain sets of capabilities, with the ultimate goal of supporting the market requirements. We propose a technique that can effectively take managerial preferences and subjective data into consideration, along with quantitative factors. The tool that is proposed here relies on the use of a more effective version of the Analytical Hierarchy Process (AHP) called the Analytical Network Process (ANP) to help integrate managerial evaluations into a more quantitatively based decision tool, data envelopment analysis (DEA). In this paper, these two techniques, when used together, can provide subjective and objective evaluations for manufacturing strategy decision makers. An illustrative example provides some insights into the application of this methodology. The research contributes to several insights to the research area of manufacturing strategy and to practitioners in manufacturing operations. A model that investigates process improvement investments, assuming that alternative process improvement initiatives exist, is then presented.

Key words:Analytical Hierarchy Process; Analytical Network Process; Manufacturing Strategy Decision; Data Envelopment Analysis.

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Full Text : PDF(size: 614.02 kB, pp. 47-66, Download times:34)

DOI : 10.7508/aiem.2019.01.003

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