IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v19yi1p36-42.html
   My bibliography  Save this article

Case Article—Business Value in Integrating Predictive and Prescriptive Analytics Models

Author

Listed:
  • David Kopcso

    (Division of Mathematics and Science, Babson College, Wellesley, Massachusetts 02457)

  • Dessislava Pachamanova

    (Division of Mathematics and Science, Babson College, Wellesley, Massachusetts 02457)

Abstract

This article suggests ways to frame classroom discussion around the business value of models in data science, predictive analytics, and management science classes. We consider an example in which predictive analytics is used to determine the inputs to prescriptive models for customer service, and illustrate how calculations of business value enter the process of creating recommendations for business stakeholders. A review of predictive and prescriptive techniques and how they map to business problems is provided to explain the context for the exercise, and the level of analytics maturity of organizations is discussed in connection with the use of predictive and prescriptive analytics. This example presents a unified view of concepts from traditionally disparate areas of analytics, making it suitable as a capstone or an ongoing project in a data science or business analytics course.

Suggested Citation

  • David Kopcso & Dessislava Pachamanova, 2018. "Case Article—Business Value in Integrating Predictive and Prescriptive Analytics Models," INFORMS Transactions on Education, INFORMS, vol. 19(1), pages 36-42, September.
    • Handle: RePEc:inm:orited:v:19:y::i:1:p:36-42
    DOI: 10.1287/ited.2017.0186ca
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ited.2017.0186ca
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ited.2017.0186ca?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. Ali, Abdul & Mancha, Ruben & Pachamanova, Dessislava, 2018. "Correcting analytics maturity myopia," Business Horizons, Elsevier, vol. 61(2), pages 211-219.
    2. Thomas A. Grossman & Judith Shaul Norback & Jill R. Hardin & Garlie A. Forehand, 2008. "Managerial Communication of Analytical Work," INFORMS Transactions on Education, INFORMS, vol. 8(3), pages 125-138, May.
    3. Thomas A. Grossman, 2001. "Causes of the Decline of the Business School Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 1(2), pages 51-61, January.
    4. Peter P. Belobaba, 1989. "OR Practice—Application of a Probabilistic Decision Model to Airline Seat Inventory Control," Operations Research, INFORMS, vol. 37(2), pages 183-197, April.
    5. Garrett van Ryzin & Jeff McGill, 2000. "Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels," Management Science, INFORMS, vol. 46(6), pages 760-775, June.
    6. Serguei Netessine & Robert Shumsky, 2002. "Introduction to the Theory and Practice of Yield Management," INFORMS Transactions on Education, INFORMS, vol. 3(1), pages 34-44, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas C. Sharkey & Steve Bublak & Lisa Disselkamp & Brittney Shkil, 2020. "Workforce Scheduling for Airport Immigration on the Island of Tropical Paradise," INFORMS Transactions on Education, INFORMS, vol. 20(2), pages 85-89, January.
    2. Matthew J. Drake, 2024. "Case Article—Creating a Brick Empire Through Data Visualization and Analytics," INFORMS Transactions on Education, INFORMS, vol. 24(3), pages 271-277, May.
    3. Dessislava Pachamanova & Vera Tilson & Keely Dwyer-Matzky, 2022. "Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations," INFORMS Transactions on Education, INFORMS, vol. 22(3), pages 178-187, May.
    4. Vahid Roshanaei & Bahman Naderi & Opher Baron & Dmitry Krass, 2024. "An Interactive Spreadsheet Model for Teaching Classification Using Logistic Regression," INFORMS Transactions on Education, INFORMS, vol. 25(1), pages 55-80, September.

    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. David Kopcso & Dessislava Pachamanova, 2018. "Case Article—Managing Staffing Inefficiencies Using Analytics (B): Business Value in Predictive and Prescriptive Analytics Models," INFORMS Transactions on Education, INFORMS, vol. 19(1), pages 43-47, September.
    2. Yingjie Lan & Michael O. Ball & Itir Z. Karaesmen, 2011. "Regret in Overbooking and Fare-Class Allocation for Single Leg," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 194-208, December.
    3. Becher, Michael, 2009. "Simultaneous capacity and price control based on fuzzy controllers," International Journal of Production Economics, Elsevier, vol. 121(2), pages 365-382, October.
    4. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    5. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2006. "Models of the Spiral-Down Effect in Revenue Management," Operations Research, INFORMS, vol. 54(5), pages 968-987, October.
    6. Gönsch, Jochen, 2017. "A survey on risk-averse and robust revenue management," European Journal of Operational Research, Elsevier, vol. 263(2), pages 337-348.
    7. Felix Papier, 2016. "Supply Allocation Under Sequential Advance Demand Information," Operations Research, INFORMS, vol. 64(2), pages 341-361, April.
    8. Michael O. Ball & Maurice Queyranne, 2009. "Toward Robust Revenue Management: Competitive Analysis of Online Booking," Operations Research, INFORMS, vol. 57(4), pages 950-963, August.
    9. Nurşen Aydın & Ş. İlker Birbil & J. B. G. Frenk & Nilay Noyan, 2013. "Single-Leg Airline Revenue Management with Overbooking," Transportation Science, INFORMS, vol. 47(4), pages 560-583, November.
    10. Yingjie Lan & Huina Gao & Michael O. Ball & Itir Karaesmen, 2008. "Revenue Management with Limited Demand Information," Management Science, INFORMS, vol. 54(9), pages 1594-1609, September.
    11. Thomas A. Grossman & Vijay Mehrotra & Mouwafac Sidaoui, 2016. "A Student-Centered Approach to the Business School Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 16(2), pages 42-53, January.
    12. Rainer Kolisch & Danilo Zatta, 2009. "Stand und Perspektiven des Einsatzes von Revenue Management in der Prozessindustrie," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 20(2), pages 197-214, October.
    13. Ezgi C. Eren & Zhaoyang Zhang & Jonas Rauch & Ravi Kumar & Royce Kallesen, 2024. "Revenue management without demand forecasting: a data-driven approach for bid price generation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(6), pages 499-516, December.
    14. William L. Cooper & Diwakar Gupta, 2006. "Stochastic Comparisons in Airline Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 8(3), pages 221-234, February.
    15. Dimitris Bertsimas & Sanne de Boer, 2005. "Simulation-Based Booking Limits for Airline Revenue Management," Operations Research, INFORMS, vol. 53(1), pages 90-106, February.
    16. Diego Escobari & Jim Lee, 2014. "Demand uncertainty and capacity utilization in airlines," Empirical Economics, Springer, vol. 47(1), pages 1-19, August.
    17. Maddah, Bacel & Moussawi-Haidar, Lama & El-Taha, Muhammad & Rida, Hussein, 2010. "Dynamic cruise ship revenue management," European Journal of Operational Research, Elsevier, vol. 207(1), pages 445-455, November.
    18. Chatwin, Richard E., 2000. "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices," European Journal of Operational Research, Elsevier, vol. 125(1), pages 149-174, August.
    19. Daniel Y. Mo & Stephen C. H. Ng & David Tai, 2019. "Revamping NetApp’s Service Parts Operations by Process Optimization," Service Science, INFORMS, vol. 49(6), pages 407-421, November.
    20. Xi Chen & Zachary Owen & Clark Pixton & David Simchi-Levi, 2022. "A Statistical Learning Approach to Personalization in Revenue Management," Management Science, INFORMS, vol. 68(3), pages 1923-1937, March.

    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:orited:v:19:y::i:1:p:36-42. 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.