IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v18y1999i3p352-372.html
   My bibliography  Save this article

SilverScreener: A Modeling Approach to Movie Screens Management

Author

Listed:
  • Sanjeev Swami

    (Department of Industrial and Management Engineering, Indian Institute of Technology, Kanpur, India)

  • Jehoshua Eliashberg

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Charles B. Weinberg

    (Faculty of Commerce and Business Administration, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia V6T 1Z2, Canada)

Abstract

Managing the allocation of shelf space for new products is a problem of significant importance for retailers. The problem is particularly complex for exhibitors—the retailers in the motion picture supply chain—because they face dynamic challenges, given the short life cycles of movies, the changing level of demand over time, the scarcity of shelf space, and the complex revenue sharing contract between the exhibitor and the distributor. In the face of this complexity, the aim of current research is to provide a structure for analyzing management problems of exhibitors in the movie industry. Using a mathematical programming approach and a fast, but readily accessible algorithm, we propose a decision support model, SilverScreener, whose aim is to help exhibitors make effective and timely decisions regarding theater screens management. The major objective is to help select and schedule movies for a multiple-screens theater over a fixed planning horizon in such a way that the exhibitor's cumulative profit is maximized. By treating the multiple screens as and the movies as , we provide an analogy of the current problem to the parallel machine scheduling problem. We formulate the resulting problem as an integer program. We depart from the typical parallel machine scheduling problems by introducing the that is particularly useful for solving the current problem. An important distinction between the current problem and typical machine scheduling problems is that the present approach allows for the choice of which movies to play; typically, in machine scheduling, all jobs have to be scheduled. We provide various analyses of normative versus actual decision making, based on publicly available data. The developed model is readily implementable and appears to lead to improved profitability in different comparative cases. Through sensitivity analysis, we demonstrate that the above results are robust to variations in various parameters of the problem. The main findings and insights from the normative policy suggest the following: • Based on SilverScreener's recommendations, the exhibitor can achieve substantially higher cumulative profit. • The improvement over actual decisions in terms of profitability appears to result from a combination of better selection and scheduling of the movies. • The general structure of the exhibitor's normative decision is: . We propose a two-tier integrated application of the model to show how the model can be applied to realistic decision making. The first tier involves development of a to help the manager plan an entire season and bid for movies before the start of that season. An ex ante revenue prediction scheme is developed, based intuitively on a of the forthcoming movies with similar movies played in this theater previously. If the forthcoming season's scheduling plan can be visualized as a two-dimensional (week-by-screen) matrix, then that matrix contains only “empty cells” before the first tier. After a bid plan is developed, the exhibitor can “fill” some of those empty cells. The remaining empty cells represent slots, which can be decided during the season by either extending movies the exhibitor booked before the season or by scheduling other movies which may become available later in the season. This motivates the second tier——of the integrated approach. The second tier helps the exhibitor in weekly decision making during the season. This application involves “rolling,” and updating data, from one time window to another. The approaches followed in the two tiers of the integrated application are quite general in that they can incorporate a sophisticated demand prediction model, managerial judgments, or a combination of both. We also propose an alternative behavioral decision rule (heuristic), which exemplifies relationship dilemmas in the movie industry. This heuristic shows that the exhibitors need to be selective in their choice of movies and may suffer a substantial loss in profitability if they place too much emphasis on accommodating distributors.

Suggested Citation

  • Sanjeev Swami & Jehoshua Eliashberg & Charles B. Weinberg, 1999. "SilverScreener: A Modeling Approach to Movie Screens Management," Marketing Science, INFORMS, vol. 18(3), pages 352-372.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:352-372
    DOI: 10.1287/mksc.18.3.352
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.18.3.352
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.18.3.352?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charles B. Weinberg, 1986. "Arts Plan: Implementation, Evolution, and Usage," Marketing Science, INFORMS, vol. 5(2), pages 143-158.
    2. Sawik, Tadeusz J., 1982. "Scheduling multi-operational tasks on nonidentical machines as a time-optimal control problem," European Journal of Operational Research, Elsevier, vol. 10(2), pages 173-181, June.
    3. Magid M. Abraham & Leonard M. Lodish, 1993. "An Implemented System for Improving Promotion Productivity Using Store Scanner Data," Marketing Science, INFORMS, vol. 12(3), pages 248-269.
    4. SOUSA, Jorge P. & WOLSEY, Laurence A., 1992. "A time indexed formulation of non-preemptive single machine scheduling problems," LIDAM Reprints CORE 984, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    6. Holbrook, Morris B & Hirschman, Elizabeth C, 1982. "The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun," Journal of Consumer Research, Oxford University Press, vol. 9(2), pages 132-140, September.
    7. Leonard M. Lodish, 1971. "Callplan: An Interactive Salesman's Call Planning System," Management Science, INFORMS, vol. 18(4-Part-II), pages 25-40, December.
    8. Charles B. Weinberg & Kenneth M. Shachmut, 1978. "ARTS PLAN: A Model Based System for Use in Planning a Performing Arts Series," Management Science, INFORMS, vol. 24(6), pages 654-664, February.
    9. Jehoshua Eliashberg & Mohanbir S. Sawhney, 1994. "Modeling Goes to Hollywood: Predicting Individual Differences in Movie Enjoyment," Management Science, INFORMS, vol. 40(9), pages 1151-1173, September.
    10. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
    11. Pinedo, Michael, 1984. "A note on the flow time and the number of tardy jobs in stochastic open shops," European Journal of Operational Research, Elsevier, vol. 18(1), pages 81-85, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sanjeev Swami & Martin L. Puterman & Charles B. Weinberg, 2001. "Play It Again, Sam? Optimal Replacement Policies for a Motion Picture Exhibitor," Manufacturing & Service Operations Management, INFORMS, vol. 3(4), pages 369-386, July.
    2. Sanjeev Swami, 2006. "—Research Perspectives at the Interface of Marketing and Operations: Applications to the Motion Picture Industry," Marketing Science, INFORMS, vol. 25(6), pages 670-673, 11-12.
    3. Michel Clement & Anke Hille & Bernd Lucke & Christina Schmidt-Stölting & Frank Sambeth, 2008. "Der Einfluss von Rankings auf den Absatz — Eine empirische Analyse der Wirkung von Bestsellerlisten und Rangpositionen auf den Erfolg von Büchern," Schmalenbach Journal of Business Research, Springer, vol. 60(8), pages 746-777, December.
    4. Ana Suárez-Vázquez, 2011. "Critic power or star power? The influence of hallmarks of quality of motion pictures: an experimental approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 35(2), pages 119-135, May.
    5. Prasad Ashutosh & Bronnenberg Bart & Mahajan Vijay, 2004. "Product Entry Timing in Dual Distribution Channels: The Case of the Movie Industry," Review of Marketing Science, De Gruyter, vol. 2(1), pages 1-20, March.
    6. Gazley, Aaron & Clark, Gemma & Sinha, Ashish, 2011. "Understanding preferences for motion pictures," Journal of Business Research, Elsevier, vol. 64(8), pages 854-861, August.
    7. Palsule-Desai, Omkar D., 2013. "Supply chain coordination using revenue-dependent revenue sharing contracts," Omega, Elsevier, vol. 41(4), pages 780-796.
    8. Jehoshua Eliashberg & Sanjeev Swami & Charles B. Weinberg & Berend Wierenga, 2001. "Implementing and Evaluating SilverScreener: A Marketing Management Support System for Movie Exhibitors," Interfaces, INFORMS, vol. 31(3_supplem), pages 108-127, June.
    9. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    10. Natalia Gmerek, 2015. "The determinants of Polish movies’ box office performance in Poland," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(1), pages 15-35.
    11. Krishnan Jeesha & Sumod S D & Prashant Premkumar & Shovan Chowdhury, 2018. "Does Story Really Matter In The Movie Industry? : PreProduction Stage Predictive Models," Working papers 284, Indian Institute of Management Kozhikode.
    12. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    13. Fei Peng & Lili Kang & Sajid Anwar & Xue Li, 2019. "Star power and box office revenues: evidence from China," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(2), pages 247-278, June.
    14. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    15. Baranchuk Nina & Seetharaman Seethu & Strijnev Andrei, 2019. "Revenue Sharing Vertical Contracts in the Movie Industry: A Theoretical Analysis," Review of Marketing Science, De Gruyter, vol. 17(1), pages 81-116, June.
    16. Brianna JeeWon Paulich & V. Kumar, 2021. "Relating entertainment features in screenplays to movie performance: an empirical investigation," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1222-1242, November.
    17. Jason M. T. Roos & Ron Shachar, 2014. "When Kerry Met Sally: Politics and Perceptions in the Demand for Movies," Management Science, INFORMS, vol. 60(7), pages 1617-1631, July.
    18. Thomas S. Gruca & Joyce Berg & Michael Cipriano, 2003. "The Effect of Electronic Markets on Forecasts of New Product Success," Information Systems Frontiers, Springer, vol. 5(1), pages 95-105, January.
    19. John D. C. Little, 2004. "Comments on ÜModels and Managers: The Concept of a Decision CalculusÝ," Management Science, INFORMS, vol. 50(12_supple), pages 1854-1860, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:352-372. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.