Research On Shipping Quantity And Product Pricing In The Context Of Anticipatory Shipping

Online retail has been growing rapidly in recent years.However,compared with physical retail,online retail has been facing the disadvantage that customers cannot receive the products immediately after placing an order online.To alleviate this problem,Amazon has proposed the solution of " anticipatory shipping",which refers to shipping products from the order fulfillment center to a distribution center near the customer before the customer places an order online,thus reducing customer waiting time.At present,limited research on anticipatory shipping can be found.The shipping quantity(i.e.,anticipatory shipping quantity,referring to the quantity of products that an online retailer ships in advance to customers’ nearby distribution centers before they place orders online)under uncertain demand is one of the core problems of anticipatory shipping and needs to be further investigated.Moreover,there is a lack of relevant research on product pricing problem in the context of anticipatory shipping.Based on the above considerations,this study focuses on the problem of anticipatory shipping quantity and product pricing in the context of anticipatory shipping.Firstly,this study investigates the multiperiod and multiobjective anticipatory shipping quantity problem based on data-driven.Present studies on anticipatory shipping mainly consider a single period and ignore the waiting time saved for customers.This dissertation develops a data-driven framework to investigate the multiperiod anticipatory shipping quantity problem with the objectives of minimizing the cost of online retailers and maximizing the saved waiting time of customers.this study first proposes a dual-process sales forecasting framework to forecast online retailers’ daily sales.Then,this study builds a multiperiod and multiobjective integer programming model based on the forecasting sales and forecasting errors to explore the optimal quantity and time of products of anticipatory shipping.Finally,using TOPSIS,Shannon entropy,and LINMAP decision-making methods,the final optimal solution is selected from the Pareto set obtained by the NSGA-II algorithm.The case study results show that anticipatory shipping saves 5.96% cost for the online retailer and 1.69 days of waiting time on average for the customer compared with non-anticipatory shipping.Moreover,performing anticipatory shipping is especially beneficial in the case of high inventory holding cost,large distribution center capacity,and long transportation duration.Secondly,this study examines the problem of anticipatory shipping quantity and product pricing considering the repurchase value of anticipatory shipping.Existing research ignores the repurchase value of anticipatory shipping and lacks an investigation on the product pricing problem in the context of anticipatory shipping.Besides,existing research on anticipatory shipping have not examined the risk attitudes of decision makers.This study constructs a newsvendor model.This study considers online retailers to be risk-averse and adopt conditional value-at-risk(CVa R)as the risk measure criterion.For both multiplicative and additive demands,this study obtains optimal strategies and analyze the differences between optimal strategies for anticipatory shipping and non-anticipatory shipping cases,risk-neutral and risk-averse decision-makers.Moreover,this study analyzes the carbon emission variations of anticipatory shipping compared with non-anticipatory shipping.Numerical experiments analyze the effect of some parameters on the optimal strategies,profit and carbon emission variations of online retailers.The results show that the online retailer should reduce the product price in the case of anticipatory shipping compared with non-anticipatory shipping,and that the degree of risk aversion has little effect on the optimal price but has a significant effect on the anticipatory shipping quantity.Finally,this study further investigates the problem of anticipatory shipping quantity and product pricing under pricing-dependent and shipping-dependent demand.Existing research ignores the effect of anticipatory shipping on demand.However,anticipatory shipping helps to increase customer’s delivered value and thus affects customer demand.This dissertation explores the problem of anticipatory shipping and product pricing under price-dependent and shipping-dependent demand by constructing an extended newsvendor model.For both multiplicative and additive demand models,this study proves the existence and uniqueness of the optimal strategies,analyze the upper and lower bounds of optimal strategies,and explore the optimal strategies of risk-averse and risk-neutral online retailers and their differences,etc.Numerical experiments analyze the effect of some parameters on the optimal strategies and profit of online retailers.The results show that online retailers should raise the product price when considering price-dependent and shipping-dependent demand compared with non-anticipatory shipping.Moreover,this study identifies the key parameters that affect the optimal strategies and profits of online retailers.