PMID- 37112382
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230430
LR  - 20230501
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 8
DP  - 2023 Apr 17
TI  - Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid 
      AHP-PROMETHEE Algorithms.
LID - 10.3390/s23084041 [doi]
LID - 4041
AB  - In today's global environment, supplier selection is one of the critical 
      strategic decisions made by supply chain management. The supplier selection 
      process involves the evaluation of suppliers based on several criteria, including 
      their core capabilities, price offerings, lead times, geographical proximity, 
      data collection sensor networks, and associated risks. The ubiquitous presence of 
      internet of things (IoT) sensors at different levels of supply chains can result 
      in risks that cascade to the upstream end of the supply chain, making it 
      imperative to implement a systematic supplier selection methodology. This 
      research proposes a combinatorial approach for risk assessment in supplier 
      selection using the failure mode effect analysis (FMEA) with hybrid analytic 
      hierarchy process (AHP) and the preference ranking organization method for 
      enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes 
      based on a set of supplier criteria. The AHP is implemented to determine the 
      global weights for each criterion, and PROMETHEE is used to prioritize the 
      optimal supplier based on the lowest supply chain risk. The integration of 
      multicriteria decision making (MCDM) methods overcomes the shortcomings of the 
      traditional FMEA and enhances the precision of prioritizing the risk priority 
      numbers (RPN). A case study is presented to validate the combinatorial model. The 
      outcomes indicate that suppliers were evaluated more effectively based on company 
      chosen criteria to select a low-risk supplier over the traditional FMEA approach. 
      This research establishes a foundation for the application of multicriteria 
      decision-making methodology for unbiased prioritization of critical supplier 
      selection criteria and evaluation of different supply chain suppliers.
FAU - Altubaishe, Bandar
AU  - Altubaishe B
AD  - Department of Supply Chain Management, University of Business and Technology, 
      University of Business and Technology St, Jeddah 23847, Saudi Arabia.
FAU - Desai, Salil
AU  - Desai S
AUID- ORCID: 0000-0002-6116-2105
AD  - Department of Industrial and Systems Engineering, North Carolina Agricultural and 
      Technical State University, Greensboro, NC 27411, USA.
AD  - Center of Excellence in Product Design and Advanced Manufacturing, North Carolina 
      Agricultural and Technical State University, Greensboro, NC 27411, USA.
LA  - eng
GR  - CMMI/National Science Foundation/
GR  - Center of Excellence in Product Design and Advanced Manufacturing (CEPDAM)/North 
      Carolina A&T State University/
PT  - Journal Article
DEP - 20230417
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10146861
OTO - NOTNLM
OT  - analytical hierarchy process (AHP)
OT  - failure mode and effects analysis (FMEA)
OT  - internet of things (IoT) sensors
OT  - preference ranking organization method for enrichment evaluation (PROMETHEE)
OT  - supply chain risk management
COIS- The authors declare no conflict of interest. The funders had no role in the 
      design of the study, in the collection, analyses, or interpretation of data, in 
      the writing of the manuscript, or in the decision to publish the results.
EDAT- 2023/04/28 06:42
MHDA- 2023/04/28 06:43
PMCR- 2023/04/17
CRDT- 2023/04/28 01:51
PHST- 2023/03/08 00:00 [received]
PHST- 2023/04/08 00:00 [revised]
PHST- 2023/04/11 00:00 [accepted]
PHST- 2023/04/28 06:43 [medline]
PHST- 2023/04/28 06:42 [pubmed]
PHST- 2023/04/28 01:51 [entrez]
PHST- 2023/04/17 00:00 [pmc-release]
AID - s23084041 [pii]
AID - sensors-23-04041 [pii]
AID - 10.3390/s23084041 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Apr 17;23(8):4041. doi: 10.3390/s23084041.