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

Abstract

Lower oil prices are causing Oil and Gas (O&G) companies to re-evaluate their supply chain to see if they can improve efficiencies and reduce costs. As a result, the Crude Oil Supply Chain (COSC) management receives increased importance in business. There has been a growing interest in Supply Chain Management (SCM) and the use of mathematical programming models in the past five years. Mergers, if planned properly, can result in improved efficiencies and reduced costs due to the internal re-organization and transformations of the supply chain. However, mergers are complex, and the expected gains and success are highly dependent on different factors and drivers that affect supply chain efficiency. This thesis addresses the tactical planning of upstream COSC under uncertainties caused by demand and shared service cost after a horizontal merger. First, a Mixed-Integer Linear Programming (MILP) model is developed to aid decision-makers in understanding where effort should be concentrated to achieve the highest return during and post-merger considering the oil supply chain. The model was validated using a real case study from a Middle East country. Second, to address the tactical planning of upstream Crude Oil and Gas Supply Chain subject to demand and shared services cost uncertainties, the problem is formulated as a mixed-integer linear programming (MILP) model. The model is used to evaluate the extent to which the economy of scope and the economy of scale favorably impact potential mergers. Also, it determines the investment level and the efficient implementation of operational strategies at shared services and the production and processing of oil and gas. A real case example from the oil and gas industry in the Middle East region is used to validate the model. The experimental studies examine three scenarios: merger under economies of scale, merger under economies of scope, and merger under the joint economies of scope and scale. The results reveal the impact of different operational strategies on potential synergetic gains. For this case study, computational results show if only economies of scale are performed, the COSC fails to achieve the targeted cost/barrel (cost/bbl) after the merger. The joint performance of economies of scale and scope in reducing shared services costs at different supply chain echelons leads to a substantive synergy gain. It reduces the cost/bbl below the targeted value. Finally, with today's rapidly changing global marketplace, 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 COSC under cost uncertainty. The mathematical model considers a multi-echelon supply chain with multi-products and a multi-period planning horizon. It integrates inventory and backorder penalties. 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.

Date	19 Nov 2021
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Amin Chaabane (Supervisor) & Fikri Dweiri (Co-supervisor)