We develop, in this article, a sales model for movie and game products at Blockbuster. The model assumes that there are three sales components: the first is from consumers who have already committed to purchasing (or renting) a product (e.g., based on promotion of, or exposure to, the product prior to its launch); the second comes from consumers who are potential buyers of the product; and the third comes from either a networking effect on closely tied (as in a social group) potential buyers from previous buyers (in the case of movie rental and all retail products) or re‐rents (in the case of game rental). In addition, we explicitly formulate into our model dynamic interactions between these sales components, both within and across sales periods. This important feature is motivated by realism, and it significantly contributes to the accuracy of our model. The model is thoroughly tested against sales data for rental and retail products from Blockbuster. Our empirical results show that the model offers excellent fit to actual sales activity. We also demonstrate that the model is capable of delivering reasonable sales forecasts based solely on environmental data (e.g., theatrical sales, studio, genre, MPAA ratings, etc.) and actual first‐period sales. Accurate sales forecasts can lead to significant cost savings. In particular, it can improve the retail operations at Blockbuster by determining appropriate order quantities of products, which is critical in effective inventory management (i.e., it can reduce the extent of over‐stocking and under‐stocking). While our model is developed specifically for product sales at Blockbuster, we believe that with context‐dependent modifications, our modeling approach could also provide a reasonable basis for the study of sales for other short‐Life‐Cycle products.

Blockbuster Inc., a chain of VHS, DVD, Blu-ray, and video game rental stores, has developed a highly specialized distribution network. The company maintains a single distribution center in which it receives products from suppliers, and processes and packs them for shipping to stores across the United States. The volumes of particular products and the number of different products shipped in a week have significant week-to-week volatility. Short lead times are typical because of supplier manufacturing delays and strict in-store due-date requirements. At the distribution center, processing and packing are scheduled through multiple processing departments that compete for use of shared merge conveyors and shared sortation systems. Blockbuster's general processing and packing goal is on-time delivery of products to stores while controlling costs. In this paper, we describe the development and implementation of a mixed-integer programming model to schedule Blockbuster's short-range order-processing operations. Implemented beginning in January 2007, the model has helped Blockbuster to maintain timely shipping, reduce related labor and transportation costs, improve capacity utilization, and attain a better understanding of how to achieve further improvements. Blockbuster's structure, in which multiple processing departments compete for subsequent shared resources, such as merge conveyors and sortation systems, is common in other industries; therefore, we also discuss the relevance of this model to other organizations.

The online movie rental industry in the United States has grown at a rapid pace during the past decade. Netflix and Blockbuster are two prime examples of companies that operate in this arena, each with a large subscriber base. These two companies employ two standard methods for movie delivery: streaming and DVD-by-mail services. Although the industry is clearly undergoing a transition into streaming service, a substantial fraction of subscribers continue to prefer the more traditional DVD-by-mail service. We will study the problem of how to efficiently manage DVD-by-mail service. An essential task in solving our problem is a proper modeling of subscriber demand. We will therefore first develop a demand formulation based on data from Blockbuster. This formulation is then used to construct a nonlinear mathematical program for making the following decisions: (i) the initial order quantity of a new movie (DVD) title and (ii) the shipment size of the title for every period (i.e., day) after its release. By exploiting the structure of this mathematical program, we develop a heuristic algorithm based on a simple shipment policy. Through extensive computational experiments, we demonstrate that our algorithm provides optimal or close to optimal solutions for practically relevant problem instances. The proposed policy and solution are simple, easy to understand, and easily implementable in practice.

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