Supplier selection for vendor-managed inventory in healthcare using fuzzy multi-criteria decision-making approach

被引:21
|
作者
Sumrit, Detcharat [1 ]
机构
[1] Mahidol Univ, Fac Engn, Cluster Logist & Rail Engn, Salaya, Nakhon Pathom, Thailand
关键词
Vendor managed inventory; Multi-criteria decision making; Fuzzy Delphi; Fuzzy SWARA; Fuzzy CORPRAS; INFORMATION-TECHNOLOGY; CHAIN MANAGEMENT; SUCCESS FACTORS; LOGISTICS; COPRAS; PERFORMANCE; MODEL; SWARA; VMI; SUSTAINABILITY;
D O I
10.5267/j.dsl.2019.10.002
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Vendor-managed inventory (VMI) is one of effective and crucial tools to alleviate the demand volatility of stocks problems, reduce time and operating cost in healthcare sector. VMI strategy becomes a necessity for both suppliers and hospitals to sustainably develop and to cope with stock availability and overall reliability process by sharing information. The process and management of VMI is a complicated work which needs substantial degrees of collaboration, expertise, and information sharing. This paper purposes a comprehensive multi-criteria decision making (MCDM) to select the best potential supplier for VMI collaboration in healthcare organization. The study developed MCDM framework consists of (i) Fuzzy Delphi approach to select the appropriate evaluation criteria for VMI supplier selection (ii) Fuzzy Step-wise Weight Assessment Ration Analysis (SWARA) method to determine the relative importance weight of evaluation criteria, (ii) Fuzzy Complex Proportional Assessment of Alternatives (COPRAS) to compare, rank and select the best appropriated supplier. An empirical case study was applied for a local famous public hospital and the best potential supplier was selected. The study reveals that the most evaluation criteria when selecting supplier for VMI in healthcare sector are institutional trust, information sharing and exchanging as well as information technology. (C) 2020 by the authors; licensee Growing Science, Canada.
引用
收藏
页码:233 / 256
页数:24
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