The design of robust value-creating supply chain networks

• Published in: OR Spectrum Vol. 35; no. 4; pp. 867 - 903
• Main Authors: Klibi, Walid, Martel, Alain
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2013; Springer Nature B.V
• Subjects: Approximation; Business and Management; Calculus of Variations and Optimal Control; Optimization; Character recognition; Design; Expected values; Horizon; Inventory; Mathematical analysis; Mathematical programming; Methodology; Networks; Operations management; Operations Research/Decision Theory; Optimization; Regular Article; Studies; Supply chain management; Supply chains; Uncertainty; Value creation; United States > US; Uncertainty; Supply chain network design; Scenario planning; Multi-hazards; Robustness; Anticipation; Network disruptions; Stochastic programming
• ISSN: 0171-6468; 1436-6304
• DOI: 10.1007/s00291-013-0327-6

This paper provides a methodology for supply chain network (SCN) design under uncertainty. The problem is initially casted as a two-level organizational decision process: the design decisions must be made here and now, but the reengineered SCN can be used for daily operations only after an implementation period. The network structure can also be adapted during the planning horizon considered. When making the design decisions, the operational response and structural adaptation decisions taking place during the planning horizon must be anticipated. The methodology recognizes three event types to characterize the future SCN environment: random, hazardous and deep uncertainty events. At the design time, plausible futures are anticipated through a scenario planning approach. Several Monte Carlo scenario samples are generated and corresponding sample average approximation programs are solved in order to produce a set of alternative designs. A multi-criteria design evaluation approach is then applied to select the most effective and robust design among candidate solutions. An illustrative case, based on the location–transportation problem, is finally introduced to illustrate the approach, and computational experiments are performed to demonstrate its feasibility.