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A Modeling Framework for Coordinating Promotion and Production Decisions within a Firm

Author

Listed:
  • Aram G. Sogomonian

    (Anderson Graduate School of Management, University of California, Los Angeles, California 90024)

  • Christopher S. Tang

    (Anderson Graduate School of Management, University of California, Los Angeles, California 90024)

Abstract

This paper presents a modeling framework for evaluating the benefits of coordinating promotion and production decisions over a finite planning horizon within a firm. These decisions include the timing and level of promotion, and the level of production. The modeling framework consists of the development of a baseline model and an integrated model. In the baseline (integrated) model, the firm would consider those promotion and production decisions separately (jointly). For each of the models, we formulate the problem that determines the optimal promotion and production decisions so that the total net profit is maximized as a mixed integer program. By exploiting the structure of the problem, we show that each problem can be reformulated as a "longest path" problem over a network. Hence, each problem can be solved efficiently. This solution approach enables us to evaluate the benefits of coordinating promotion and production decisions by comparing the total net profit generated by the baseline model to that of the integrated model.

Suggested Citation

  • Aram G. Sogomonian & Christopher S. Tang, 1993. "A Modeling Framework for Coordinating Promotion and Production Decisions within a Firm," Management Science, INFORMS, vol. 39(2), pages 191-203, February.
    • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:2:p:191-203
    DOI: 10.1287/mnsc.39.2.191
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    Citations

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    Cited by:

    1. Tang, Christopher S., 2010. "A review of marketing-operations interface models: From co-existence to coordination and collaboration," International Journal of Production Economics, Elsevier, vol. 125(1), pages 22-40, May.
    2. Kurata, Hisashi & Liu, John J., 2007. "Optimal promotion planning--depth and frequency--for a two-stage supply chain under Markov switching demand," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1026-1043, March.
    3. van den Heuvel, Wilco & Wagelmans, Albert P.M., 2006. "A polynomial time algorithm for a deterministic joint pricing and inventory model," European Journal of Operational Research, Elsevier, vol. 170(2), pages 463-480, April.
    4. Dengpan Liu & Subodha Kumar & Vijay S. Mookerjee, 2012. "Advertising Strategies in Electronic Retailing: A Differential Games Approach," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 903-917, September.
    5. Rachel Croson & Karen Donohue, 2006. "Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information," Management Science, INFORMS, vol. 52(3), pages 323-336, March.
    6. Erickson, Gary M., 2011. "A differential game model of the marketing-operations interface," European Journal of Operational Research, Elsevier, vol. 211(2), pages 394-402, June.
    7. Yong Tan & Vijay S. Mookerjee, 2005. "Allocating Spending Between Advertising and Information Technology in Electronic Retailing," Management Science, INFORMS, vol. 51(8), pages 1236-1249, August.
    8. Fleischmann, M. & Hall, J.M. & Pyke, D.F., 2005. "A Dynamic Pricing Model for Coordinated Sales and Operations," ERIM Report Series Research in Management ERS-2005-074-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. E J Levin & Y Ma & R E Wright, 2004. "Profit maximization in a multi-product firm with impatient customers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(3), pages 211-218, March.
    10. Tunuguntla, Vaishnavi & Basu, Preetam & Rakshit, Krishanu & Ghosh, Debabrata, 2019. "Sponsored search advertising and dynamic pricing for perishable products under inventory-linked customer willingness to pay," European Journal of Operational Research, Elsevier, vol. 276(1), pages 119-132.
    11. Yiqiang Su & Joseph Geunes, 2013. "Multi-period price promotions in a single-supplier, multi-retailer supply chain under asymmetric demand information," Annals of Operations Research, Springer, vol. 211(1), pages 447-472, December.
    12. Feng Cheng & Suresh P. Sethi, 1999. "A Periodic Review Inventory Model with Demand Influenced by Promotion Decisions," Management Science, INFORMS, vol. 45(11), pages 1510-1523, November.
    13. Zhang, Zelin & Dong, Xiaodan & Mantrala, Murali & Zhang, Yihao, 2018. "Optimal depth and timing of price promotions in a vertically differentiated product line," Journal of Business Research, Elsevier, vol. 83(C), pages 215-228.
    14. Ghose, Sanjoy & Liu, John J. & Bhatnagar, Amit & Kurata, Hisashi, 2005. "Modeling the role of retail price formats, and retailer competition types on production schedule strategy," European Journal of Operational Research, Elsevier, vol. 164(1), pages 173-184, July.
    15. Rachel Croson & Karen Donohue, 2002. "Experimental Economics and Supply-Chain Management," Interfaces, INFORMS, vol. 32(5), pages 74-82, October.
    16. Zhang, Ju-Liang & Chen, Jian & Lee, Chung-Yee, 2008. "Joint optimization on pricing, promotion and inventory control with stochastic demand," International Journal of Production Economics, Elsevier, vol. 116(2), pages 190-198, December.
    17. Hemant K. Bhargava & Daewon Sun & Susan H. Xu, 2006. "Stockout Compensation: Joint Inventory and Price Optimization in Electronic Retailing," INFORMS Journal on Computing, INFORMS, vol. 18(2), pages 255-266, May.
    18. Abhijit Upasani & Reha Uzsoy, 2008. "Incorporating manufacturing lead times in joint production-marketing models: A review and some future directions," Annals of Operations Research, Springer, vol. 161(1), pages 171-188, July.

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