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Demand Management Opportunities in E-fulfillment: What Internet Retailers Can Learn from Revenue Management
25 Apr 2008-ERIM report series research in management Erasmus Research Institute of Management (Erasmus Research Institute of Management)-
TL;DR: In this paper, the authors explain how Internet retailers can learn from proven revenue management concepts and use them to reduce costs and enhance service, focusing on attended deliveries as these provide the greatest opportunities and challenges.
Abstract: textIn this paper, we explain how Internet retailers can learn from proven revenue management concepts and use them to reduce costs and enhance service. We focus on attended deliveries as these provide the greatest opportunities and challenges. The key driver is service differentiation. Revenue management has shown that companies can do much better than a one-size-fits-all first-come-first-serve strategy when selling scarce capacity to a heterogeneous market. Internet retailers have strong levers at their disposal for actively steering demand, notably the offered delivery time windows and their associated prices. Unlike traditional revenue management, these demand management decisions affect both revenues and costs. This calls for a closer coordination of marketing and operations than current common practice.
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TL;DR: This work estimates a multinomial logit customer choice model from historic booking data and proposes dynamic pricing policies based on this choice model to determine which and how much incentive to offer for each time slot at the time a customer intends to make a booking.
Abstract: Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared with current industry practice.
122 citations
TL;DR: This research shows how to use historical delivery data to predict future delivery results by applying address intelligence and shows that pre-delivery contact with the customer about the delivery time window seems to be the most promising concept to guarantee efficient delivery.
Abstract: This research shows how to use historical delivery data to predict future delivery results by applying address intelligence. The application of multiple linear regression techniques supports the development of address intelligence identifying and predicting the improvement potential (rework) for other zip code areas. The research has been successfully applied for a logistics parcel service company. In our case the application of address intelligence has shown that pre-delivery contact with the customer about the delivery time window seems to be the most promising concept to guarantee efficient delivery.
87 citations
01 Dec 2008
TL;DR: The objective of this chapter is to highlight and illustrate issues arising in attended home delivery related to these time slots and to present and discuss promising approaches for addressing some of them.
Abstract: In this chapter, we focus on home delivery, and, more specifically, on attended home delivery, where the consumer must be present for the delivery To provide a high service level and to avoid delivery failures as much as possible, it is customary in attended home delivery services for the company to offer the customer a choice of narrow delivery time slots The objective of this chapter is to highlight and illustrate issues arising in attended home delivery related to these time slots and to present and discuss promising approaches for addressing some of them We will use Peapod, one of the more successful e-grocers, as an illustrative example
65 citations
TL;DR: This study considers a tactical problem where a time slot schedule for delivery service over a given planning horizon must be selected in each zone of a geographical area, and proposes two heuristics to address the tactical problem.
42 citations
01 Jan 2011
TL;DR: There is a huge body of literature devoted to quantity control policies and dynamic pricing in revenue management, see Chiang et al. as mentioned in this paper for a review, see Table 1 for a survey.
Abstract: Demand management, or revenue management, is commonplace in many industries. In order to enhance revenues, revenue management aims to exploit differences in product preferences between customers by prioritizing service to customers with a higher willingness-to-pay. The key lesson from revenue management is that a company should try to use its scarce capacity for its most valuable customers. Instead of accepting demand first-come, first-serve, it may be beneficial to reserve capacity for more valuable customers that may still arrive in the future. There is a huge body of literature devoted to quantity control policies and dynamic pricing in revenue management (for a review, see Chiang et al. 2007), Talluri and Van Ryzin (2004) and Boyd and Bilegan (2003). Quantity-based control manages the availability of product classes, while price-based control is concerned with the price of products. Note that pricing refers to the whole range of incentives that are available to manage customer demand, e.g. financial rewards for peak travel avoidance (Knockaert et al., Chap. 6). Airlines, hotels and rental car industries represent the most successful applications of revenue management (Chiang et al. 2007). These industries all share some similar business characteristics. In particular, their products are perishable, the availability of capacity is relatively inflexible in the short run, and the variable costs operating costs are small, relative to the fixed costs.
1 citations
References
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17 Jun 2004
TL;DR: In this article, the authors present the economics of RM, including single-resource capacity control, network capacity control and overbooking, as well as dynamic pricing and auctioning.
Abstract: Quantity-Based RM.- Single-Resource Capacity Control.- Network Capacity Control.- Overbooking.- Price-based RM.- Dynamic Pricing.- Auctions.- Common Elements.- Customer-Behavior and Market-Response Models.- The Economics of RM.- Estimation and Forecasting.- Industry Profiles.- Implementation.
2,053 citations
TL;DR: In this paper, the authors identify research issues and gaps in existing knowledge on buyers' perceptions of price fairness, and conclude with guidelines for managerial practice for price fairness in the context of pricing.
Abstract: Recent news coverage on pricing portrays the importance of price fairness. This article conceptually integrates the theoretical foundations of fairness perceptions and summarizes empirical findings on price fairness. The authors identify research issues and gaps in existing knowledge on buyers’ perceptions of price fairness. The article concludes with guidelines for managerial practice.
1,217 citations
TL;DR: It is traced the history of revenue management in an effort to illustrate a successful e-commerce model of dynamic, automated sales, and how revenue management is practiced outside of the airline industry, its relationship to dynamic pricing, and future directions for the discipline.
Abstract: We trace the history of revenue management in an effort to illustrate a successful e-commerce model of dynamic, automated sales. Our discourse begins with a brief overview of electronic distribution as practiced in the airline industry, emphasizing the fundamental role of central reservation and revenue management systems. Methods for controlling the sale of inventory are then introduced along with related techniques for optimization and forecasting. Research contributions and areas of significant research potential are given special attention. We conclude by looking at how revenue management is practiced outside of the airline industry, its relationship to dynamic pricing, and future directions for the discipline.
231 citations
TL;DR: In this paper, two main approaches to unattended delivery are the reception box concept and the delivery box concept, and the authors assesses these two different concepts using simulation and compare the operational cost savings are compared to the respective investments required to calculate the payback period.
Abstract: One of the biggest challenges in B2C e‐commerce is the so‐called “last mile”, the home delivery service for the customer. Particularly in electronic grocery shopping it is difficult to combine profitability and high service level. The authors’ simulations suggest that the unattended reception of goods reduce home delivery costs considerably, by up to 60 percent. Unattended delivery has not been widely used because it requires investments and commitment from the customer. The two main approaches to unattended delivery are the reception box concept and the delivery box concept. The reception box is a refrigerated, customer‐specific reception box installed at the customer’s garage or home yard. The delivery box is an insulated secured box equipped with a docking mechanism. The reception box concept results in more effective home delivery transportation and the delivery box concept in smaller investment to achieve unattended receipt. This article assesses these two different concepts. The cost savings in transportation are analysed using simulation. The operational cost savings are compared to the respective investments required to calculate the payback period. Both concepts proved to be feasible but which one works better is not only a question of financial justification. The possible additional value to customers and overall suitability to the market must also be considered.
224 citations
TL;DR: This article defines routing and scheduling problems that incorporate important features of this emerging business model and proposes algorithms, based on insertion heuristics, for their solution.
Abstract: Many companies with consumer direct service models, especially grocery delivery services, have found that home delivery poses an enormous logistical challenge due to the unpredictability of demand coupled with strict delivery windows and low profit margin products. These systems have proven difficult to manage effectively and could benefit from new technology, particularly to manage the interaction between order capture and order delivery. In this article, we define routing and scheduling problems that incorporate important features of this emerging business model and propose algorithms, based on insertion heuristics, for their solution. In the proposed home delivery problem, the company decides which deliveries to accept or reject as well as the time slot for the accepted deliveries so as to maximize expected profits. Computational experiments reveal the importance of an approach that integrates order capture with order delivery and demonstrates the quality and value of the proposed algorithms.
193 citations