Optimal pricing for build-to-order supply chain design under price-dependent stochastic demand

Build to order (BTO) is a supply chain disruption mitigation strategy. Whereas cost minimization is an operational objective, the goal of the BTO manufacturer is to maximize its profit by using pricing as its competitive decision-making strategy. In this paper, we study a BTO manufacturer who simultaneously determines its product prices and designs its supply chain network to maximize its expected profit under price-dependent stochastic demand. We propose an L-shaped decomposition with complete enumeration to solve for optimality and show that the expanded master problem remains convex programming, although the optimality cuts are quadratic inequalities. The computational results demonstrate that stocking up on differentiated components and allocating modules appropriately to meet realized demand is a resilient policy that sustains variations in demand. Furthermore, the pricing decision balances the expected revenue and expected operating cost with an increase in expected profit. The integration of pricing and operational planning results in a higher expected profit than by individual decisions. We also demonstrate that cost minimization may not provide the same level of profit if the manufacturer overestimates or underestimates its most profitable demand.