Flexibility evaluation of multiechelon supply chains

• Published in: PloS one 2018-03, Vol.13 (3), p.e0194050-e0194050
• Main Authors: Almeida, João Flávio de Freitas, Conceição, Samuel Vieira, Pinto, Luiz Ricardo, de Camargo, Ricardo Saraiva, Júnior, Gilberto de Miranda
• Format: Article
• Language: English
• Subjects: Demand analysis; Engineering and Technology; Flexibility; Logistics; Management; Manufacturing; Mathematical models; Mathematical programming; Parameters; Physical Sciences; Procurement; Research and Analysis Methods; Robustness (mathematics); Social responsibility; Social Sciences; Stochasticity; Suppliers; Supply chains; Uncertainty
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0194050

Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.