Flexibility evaluation of multiechelon supply chains

Abstract

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.

A Decomposition Approach for the Two-Stage Stochastic Supply Network Planning in Light of the Rolling Horizon Practice

“…This section proposes a 2SSP formulation for S&OP adopting technology constraints to industries that faces uncertainty on product price and demand. The approach enhances the SC model to evaluate flexibility on tactical SC planning (Almeida et al, 2018) and model the planning process uncertainties in second-stage scenarios, such that the practice is responsible for implementing only the first-period results. At the end of every period, the newly available information updates the second-stage scenarios of the following periods on a rolling planning horizon basis.…”

“…Constraints ( 5) and ( 6) mean that the number of lots of raw materials purchased must respect their availability with suppliers in each period. Availability of raw materials for product p at location l in period t Scalar Non-delivery cost for each customer in the second-stage a Elements subscript 1 and 2 represents the first-stage [1] and the second-stage [2] .…”

“…3 Relax integrality constraints of MP [1] . 4 If GAP ≤ tol then Activate integrality constraints of SP [2] scenarios. Solve the SP [2] scenarios.…”

“…4 If GAP ≤ tol then Activate integrality constraints of SP [2] scenarios. Solve the SP [2] scenarios. Update variables of optimality cuts constraints.…”

Design and Optimization of the Dual-Channel Closed Loop Supply Chain with E-Commerce

“…The experimental computation manipulates four scenarios of parameter sensitivity analysis and their impact on the total supply chain cost. The baseline scenario is the current situation as presented in the previous section for all such analyses in this section using the same approach implemented in the literature [54][55][56] . A crucial question that the baseline can address relates to knowing that the model is valid [57].…”