Ordering quantity decisions considering uncertainty in supply-chain logistics operations

This research seeks to determine the optimal order amount for a retailer given uncertainty in a supply-chain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching distribution centers (DCs) and retail stores poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies; this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of a DC's operation policies and model assumptions on a retailer's product-ordering plan and resulting sales profit.