Tactical supply chain planning after mergers under uncertainty with an application in oil and gas. (May 2023)

• Record Type: Journal Article
• Authors: Alnaqbi, A.; Trochu, J.; Dweiri, F.; Chaabane, A.
• Is Part Of: Computers & industrial engineering. Volume 179(2023)
• Publication Date: 2023-05
• Subjects: Reverse logistics -- Supply chain -- Tactical planning -- Merger -- Mathematical modeling -- Optimization -- Uncertainty -- Stochastic optimization -- Oil and gas; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2023.109176
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Build a stochastic model for supply chain planning under uncertainty. The model handles multi-echelon Crude Oil Supply Chain (SC) after a merger. Uncertainty combines shared services costs and demand. We apply our model to a large real case study from a Middle East country. We demonstrate numerical results for the impact of uncertainties on SC synergy. Abstract: With today's rapidly changing supply chain environment, it is essential to include uncertainty in an explicit manner in supply chain planning models. Therefore, we propose a stochastic model for tactical planning of the Crude Oil Supply Chain (COSC) under cost and demand uncertainties. The mathematical model considers a multi-echelon supply chain with multi-products and a multi-period planning horizon. It integrates inventory and backorder penalties. A Sample Average Approximation (SAA) procedure with Multiple Replications Procedure (MRP) is developed to solve the stochastic model. We illustrate how our model directly applies to supply chain planning. We present numerical results that show the impact of cost uncertainty on supply chain planning decisions and synergy gains. We also measure the value of modeling uncertainty against deterministic planning and characterize the cost/bbl after a merger under shared services cost and demand uncertainty.