Improving order prioritization for the allocation of constrained supply

Abstract

For top wholesale retail companies, the demand for products from US-based customers (wholesale, digital, and direct to consumer) is extremely high. However, the available supply of a product is contingent upon the success of long-term forecasting, manufacturers across the globe, and intercontinental transportation. Therefore, there is not always enough supply to meet demand. In these situations, wholesale retailers must decide which orders to prioritize in the allocation of available supply. This thesis presents a method for improving order prioritization by utilizing readily available data to wholesale retail companies and a method for predicting the effectiveness of the new prioritization methodology utilizing historical data. By prioritizing orders that meet certain characteristics deemed to be in-line with company strategy and simulating multiple conditions, it is possible to deliver improved service on a specific set of orders. The impact of this work has been verified through a simulation model. The model was used to simulate three months of supply and demand and indicated a possible increase of 10-90% in the number of units made available to ship to specific marketplace segments.