An examination of the impact of flexibility and agility on mitigating supply chain disruptions

This paper examines the effect of flexibility and agility on improving supply chain responsiveness. We examine a supply chain with multiple sites, multiple transportation channels, and multiple product planning, over multiple periods, under supply risk and demand risk. Using a numerical example, we...

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Bibliographic Details
Published in:International journal of production economics Vol. 220; p. 107438
Main Authors: Shekarian, Mansoor, Reza Nooraie, Seyed Vahid, Parast, Mahour Mellat
Format: Journal Article
Language:English
Published: Elsevier B.V 01.02.2020
Elsevier Science Publishers
Subjects:
Algorithms
Analysis
Augmented ε-constraint (AUGMECON) method
Disruption risk
Logistics
Multi-objective mixed integer programming (MOMIP)
Non-dominated sorting genetic algorithm (NSGA-Ⅱ)
Supply chain management
Weighted goal programming (WGP) method
Disruption risk
Supply chain management
Augmented ε-constraint (AUGMECON) method
Weighted goal programming (WGP) method
Multi-objective mixed integer programming (MOMIP)
Non-dominated sorting genetic algorithm (NSGA-Ⅱ)
ISSN:0925-5273, 1873-7579
Online Access:Get full text
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Summary:This paper examines the effect of flexibility and agility on improving supply chain responsiveness. We examine a supply chain with multiple sites, multiple transportation channels, and multiple product planning, over multiple periods, under supply risk and demand risk. Using a numerical example, we determine the relationship between three objective functions related to responsiveness, risk, and the cost of new and seasonal products, then discuss the impact of flexibility and agility on mitigating supply chain disruptions. To show a trade-off among objective functions in our numerical example, we solve the multi-objective mixed integer programming (MOMIP) model using three multi-objective optimization methods: the weighted goal programming (WGP) method, the augmented ε-constraint (AUGMECON) method, and the non-dominated sorting genetic algorithm (NSGA-Ⅱ). Our findings help decision makers with these two tasks: anticipate how much improvement in flexibility and agility will lead to an improvement in responsiveness; and create an investment plan to minimize the negative impact of supply chain disruptions by an examination of the trade-offs among responsiveness, risk, and cost. The results show that the NSGA-Ⅱ method outperforms the AUGMECON method in several metric indexes: the spacing metric, the diversity metric, and simple additive weighting.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2019.07.011