Online marketing:When to offer a refund for advanced sales

Advance selling is a marketing strategy commonly used by online retailers to increase sales by exploiting consumer valuation uncertainty. Recently, some online retailers have started to allow refunds on products sold in advance. On the one hand this reduces the net advance sales, but on the other hand it allows a higher advance sales price. This research is the first to explore the overall effect of allowing a refund on profits from advance sales, identifying conditions where advance selling with or without refunds (or no advance selling at all) is best. We analytically compare the profits of three advance selling strategies: none, without refund, and with refund. We show that selling in advance and allowing a refund is optimal for products with a relatively small profit margin and small strategic market size, and that the added profit can be considerable. Our results guide managers in selecting the right advance selling strategy. To facilitate this, we graphically display, based on the two dimensions of regular profit margin and strategic market size, under what conditions the different strategies are optimal

Base-stock policies with reservations

All intensively studied and widely applied inventory control policies satisfy demand in accordance with the First-Come-First-Served (FCFS) rule, whether this demand is in backorder or not. Interestingly, this rule is sub-optimal when the fill-rate is constrained or when the backorder cost structure includes fixed costs per backorder and costs per backorder per unit time. In this paper we study the degree of sub-optimality of the FCFS rule for inventory systems controlled by the well-known base-stock policy. As an alternative to the FCFS rule, we propose and analyze a class of generalized base-stock policies that reserve some maximum number of items in stock for future demands, even if backorders exist. Our analytic results and numerical investigations show that such alternative stock reservation policies are indeed very simple and considerably improve either the fillrate or reduce the total cost, without having much effect on the backorder level

Advance Selling and Advertising:A Newsvendor Framework

Many firms offer consumers the opportunity to place advance orders at a discount when introducing a new product to the market. Doing so has two main advantages. First, it can increase total expected sales by exploiting valuation uncertainty of the consumers at the advance ordering stage. Second, total sales can be estimated more accurately based on the observed advance orders, reducing the need for safety stock and thereby obsolescence cost. In this research, we derive new insights into trading off these benefits against the loss in revenue from selling at a discount at the advance stage. In particular, we are the first to explore whether firms should advertise the advance ordering opportunity. We obtain several structural insights into the optimal policy, which we show is driven by two dimensions: the fraction of consumers who potentially buy in advance (i.e., strategic consumers) and the size of the discount needed to make them buy in advance. If the discount is below some threshold, then firms should sell in advance and they should advertise that option if the fraction of strategic consumers is sufficiently large. If the discount is above the threshold, then firms should not advertise and only sell in advance if the fraction of strategic consumers is sufficiently small. Graphical displays based on the two dimensions provide further insights

Corrigendum to “A risk-averse competitive newsvendor problem under the CVaR criterion” [Int. J. Prod. Econ. 156 (2014) 13–23]

The authors regret that the calculated equilibrium order quantities provided in Table 1 of Section 7.2 on Page 18 are incorrect. In the corrigendum, we provide the updated values to Table 1. The authors would like to apologise for any inconvenience caused

Taxi-hailing platforms:Inform or Assign drivers?

Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counterintuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system. (c) 2020 Elsevier Ltd. All rights reserved

Effects of introducing low-cost high-speed rail on air-rail competition:Modelling and numerical analysis for Paris-Marseille

Given the trend of railway liberalization in Europe and Asia, we explore the effects of introducing low-cost high-speed rail as an answer to the railway reform on air-rail competition. In particular, by proposing a vertically differentiated model, we first derive the optimal pricing policies as well as the corresponding profits and market shares for low-cost high-speed rail (LCR), full-service high-speed rail (FSR) and air transport (Air). We do so for two types of LCR entrants, namely the incumbent owned entrant (to the FSR company) and the independently owned entrants. For both situations, we prove analytically that introducing LCR leads to reduced FSR and Air fares as well as to reduced Air traffic. The fare and traffic reductions increase with the passenger's time value and with the LCR travel time, while they decrease with the Air unit seat cost. Moreover, all LCR effects are stronger for an independently operated LCR. We apply our model to the Paris-Marseille route, based on data collected from publicly available sources. It is found that introducing an independently owned (incumbent owned) LCR on this route leads to 39% (33%) less air traffic, 20% (14%) less FSR traffic and a 37% (29%) increase in total rail traffic. Furthermore, this comes with increases of 2% (8%) in combined railway profit and 6% (5%) in total social welfare. These results support the decision of French policy makers to have LCR and FSR operated by the same company, as it comes with much higher combined railway profits and almost the same welfare increase as independently owned LCR. Further sensitivity analyses suggest that most LCR passengers would otherwise have traveled by FSR or Air, although LCR also attracts new passengers. In addition, offering a low-cost alternative is more effective if passengers value time more highly. Implications in terms of methodology and industry are provided.</p