Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

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.

Automated summary

In this study, we propose a stochastic model for tactical planning of the Crude Oil Supply Chain (COSC) considering cost and demand uncertainties. The model takes into account a multi-echelon supply chain with multi-products and a multi-period planning horizon, as well as inventory and backorder penalties. We use a Sample Average Approximation (SAA) procedure with Multiple Replications Procedure (MRP) to solve the stochastic model and demonstrate its application in supply chain planning. We provide numerical results that illustrate the impact of cost uncertainty on planning decisions and synergy gains, and evaluate the value of modeling uncertainty compared to deterministic planning.