Application of Revenue Management in Induction Capacity Allocation of Postal Services

Abstract

Online shopping has been growing as an essential part of customer behavior over the past 15 years. The number of companies that offer online shopping opportunities has increased while customers have become enthusiastic online shoppers. The parcel volumes are collected from different online retailers (e.g., Amazon, eBay, Shopify) and delivered through the logistics networks of delivery companies and postal services. The fast-growing parcel volume, specifically e-commerce parcel volume, has turned induction capacity management into a serious challenge for postal organizations. Induction is the first step of the processing where the parcel volume gets inside the processing facility and starts its flow through the postal services supply chain. Postal services should allocate the existing capacity to the dynamic and increasing parcel arrival patterns of online retailers. The literature suggests overcoming this challenge by increasing the efficiency of the sorting equipment (i.e., optimized sort schematic and machine scheduling) or by increasing the productivity of the transportation network (i.e., higher truck utilization, optimized routing). However, these solutions, which are mainly considered as a part of the traditional supply chain, are cost-sensitive, temporary, and sometimes not feasible for postal services. Hence, most of the developed models focus on improving the operations performance indicators but not the revenue. Revenue Management (RM) has been applied in many industries (e.g., airlines, hotels, air cargo, rail freight) that have fixed and perishable capacity, and it should be utilized over a finite period of time. Successful applications of the RM allow many companies to generate higher revenue and increase their business profitability through overbooking, forecasting, and capacity allocation. The postal industry has not implemented RM approaches to address the capacity allocation problem. This research fills this gap and explores the application of revenue management techniques for allocating limited induction capacity among different classes of customers and develops a model to compute an optimum allocated/protected capacity. The developed induction capacity allocation model allows maximizing the expected revenue of postal services. This thesis contributes by developing an innovative solution for induction capacity allocation in postal services. The model implements the dynamic arrival patterns of demand from multiple classes of customers. The RM Induction Capacity Allocation Model (ICAM) was validated through simulation and optimization analyses considering all possible scenarios and a number of classes of customers.