Abstract
In this study, we examine the issue of strategic supplier selection in uncertain decision environments. In contrast to conventional supplier selection, strategic supplier selection comprehensively considers various influencing factors, such as supplier quality and offered price, as well as company strategies, uncertain environments, and human factors. Using a detailed literature review, we investigate the employment of human preference in supplier selection, including preference representation, modeling, and computation. To achieve successful strategic supplier selection, we propose a soft decision model involving multiple stakeholders and multiple perspectives. Founded on interval and hesitant fuzzy methodology, this novel model shows significant capabilities in handling ambiguous judgments of stakeholders and unbiased value preservation of conflicting opinions. The settings of this model guarantee that the selection process strictly conforms to the diverse strategies of the company and is applicable for a flexible number of stakeholders. We also provide numerical illustrations via a case study. To the best of our knowledge, this study is the first to perform theoretical decision modeling using interval and hesitant fuzzy methodology for strategic supplier selection. In addition, the proposed research framework and systematic investigations on preference elicitation theoretically facilitate knowledge accumulation and necessarily supplement previous studies on strategic supplier selection by providing research trends.
Original language | English |
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Pages (from-to) | 215-225 |
Number of pages | 11 |
Journal | International Journal of Production Economics |
Volume | 166 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
Keywords
- Hesitant fuzzy sets
- Preference analysis
- Strategic decision making
- Supplier selection
ASJC Scopus subject areas
- General Business,Management and Accounting
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering