Skip to main content

A Proactive Demand Management Model for Controlling E-Retailer Inventory

  • Chapter
  • 1032 Accesses

Part of the book series: Annals of Information Systems ((AOIS,volume 1))

Abstract

The web-based buying process requires the buyer to navigate through a series of web pages. Moreover, the web pages for a particular buyer can be customized based on his general profile and previous purchasing behavior. Hence, as compared to a traditional retailer, the e-retailer (who sells products directly to customers through the Internet) is able to exercise a much greater influence over the demand level for its products. This allows an e-retailer to proactively dampen the demand for a niche product and guide the customers to generic substitutes when the on-hand inventory level is low relative to the sales rate. Use of proactive demand management (PDM) can reduce inventory related costs and improve the overall profits.

In this paper, we develop a model for e-retailers exercising PDM by adjusting the display prominence of a product to control its supply chain inventory. The demand realized for the product is deterministic but dependant on the level of prominence with which the product is displayed on the web pages. The model considers two levels of prominence for the product’s display. When the product is displayed prominently, all the potential demand is captured. When the product is displayed less prominently, part of the demand for the product is guided to a generic product which has better economies of scale. The price of the product is fixed and there are standard inventory costs such as holding cost, penalty cost and ordering cost. The objective is to maximize the long-run average profits per year. We derive closed form equations for the optimal parameter values for implementing PDM.

A numerical analysis is performed to estimate the benefits of PDM. The numerical analysis reveals that PDM can increase profits by as much as 12%. PDM is more beneficial when a larger proportion of the potential demand for the product can be guided to a generic substitute. PDM is a beneficial strategy to adopt when the inventory related costs per unit of demand are significant compared to the profit margin.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Anderson, E. E., & Amato, H. N. (1974) A Mathematical Model for Simultaneously Determining the Optimal Brand-Collection and Display Area Allocation. Operations Research, 22, 13–21.

    Google Scholar 

  • Baker, R. C., & Urban, T. L. (1988a) A Deterministic Inventory System with an Inventory-Level-Dependent Demand Rate. Journal of the Operational Research Society, 39, 823–831.

    Article  Google Scholar 

  • Baker, R. C., & Urban, T. L. (1988b) Single Period Inventory Dependent Demand Models. Omega, 16, 605–607.

    Article  Google Scholar 

  • Bassok, Y., Anupindi, R., & Akella, R. (1999) Single-period Multi-product Inventory Models with Substitution. Operations Research, 47, 632–642.

    Google Scholar 

  • Bharagava, H., Sun, D., & Xu, S. (2006) Stockout compensation: Joint Inventory and Price Optimization in Electronic Retailing. INFORMS Journal of Computing, Forthcoming.

    Google Scholar 

  • Borin, N., & Farris, P. (1995) A Sensitivity Analysis of Retailer Shelf Management Models. Journal of Retailing, 71, 153–171.

    Article  Google Scholar 

  • Borin, N., Farris, P., & Freeland, J.R. (1994) A Model for Determining Retail Product Category Assortment and Shelf Space Allocation. Decision Sciences, 25, 359–384.

    Google Scholar 

  • Bultez, A. & Naert, P. (1988) SH.A.R.P.: Shelf Allocation for Retailers’ Profit. Marketing Science, 7, 211–231.

    Google Scholar 

  • Bultez, A., Naert, P., Gijsbrechts, E. & Abelle, P.V. (1989) Asymmetric Cannibalism in Retail Assortments. Journal of Retailing, 65, 153–192.

    Google Scholar 

  • Cairns, J. P. (1962) Suppliers, Retailers and Shelf Space. Journal of Marketing, 26, 34–36.

    Article  Google Scholar 

  • Cairns, J. P. (1963) Allocate Space for Maximum Profits. Journal of Retailing, 39, 43–55.

    Google Scholar 

  • Corstjens, M., & Doyle, P. (1981) A Model for Optimizing Retail Space Allocations. Management Science, 27, 822–833.

    Google Scholar 

  • Curhan, R. C. (1972) The Relationship between Shelf Space and Unit Sales in Supermarkets. Journal of Marketing Research, 9, 406–412.

    Article  Google Scholar 

  • Curhan, R. C. (1973) Shelf Space Allocation and Profit Maximization in Mass Retailing. Journal of Marketing, 37, 54–60.

    Article  Google Scholar 

  • Drezner, Z., Gurnani, H., & Pasternack, B.A (1995) An EOQ Model with Substitution Between Products. Journal of the Operational Research Society, 46, 887–891.

    Article  Google Scholar 

  • Fershtman, C., & Spiegel, U. (1986) Learning by Doing, Inventory and Optimal Pricing Policy. Journal of Business and Economics, 38, 19–27.

    Article  Google Scholar 

  • Gurnani, H. and Z. Drezner, Z. (2000) Deterministic Hierarchical Substitution Inventory Models. Journal of the Operational Research Society, 51, 129–133.

    Article  Google Scholar 

  • Ignall, E., & Veinott, A.F. Jr. (1969) Optimality of Myopic Inventory Policies for Several Substitutable Products. Management Science, 15, 284–304.

    Google Scholar 

  • Kunreuther, H., & Schrage, L. (1973) Joint Pricing and Inventory Decisions for Constant Priced Items. Management Science, 19, 732–738.

    Google Scholar 

  • Mahajan, S., & van Ryzin, G. (2001) Stocking retail assortments under dynamic consumer substitution. Operations Research, 49, 334–351.

    Article  Google Scholar 

  • McGillivray, A., & Silver, E.A. (1978) Some Concepts for Inventory Control Under Substitutable Demands. INFOR, 16, 47–63.

    Article  Google Scholar 

  • Parlar, M., & Goyal, S. (1984) Optimal Ordering Decisions for Two Substitutable Products with Stochastic Demands. OPSEARCH, 21, 1–15.

    Google Scholar 

  • Pasternack, B., & Drezner, Z. (1991) Optimal Inventory Policies for Substitutable Commodities with Stochastic Demand. Naval Research Logistics, 38, 221–240.

    Article  Google Scholar 

  • Shugan, S.M. (1989) Product Assortment in a Triopoly. Management Science, 15, 304–320.

    Google Scholar 

  • Smith, S., & Agrawal, N. (2000) Management of Multi-item Retail Inventory Systems with Demand Substitution. Operations Research, 48, 50–64.

    Article  Google Scholar 

  • Urban, G.L. (1969) A Mathematical Modeling Approach to Product Line Decisions. Journal of Marketing Research, 6, 40–47.

    Article  Google Scholar 

  • Urban, T.L. (1992) An Inventory Model with an Inventory-Level-Dependent Demand Rate and Relaxed Terminal Conditions. Journal of the Operational Research Society, 43, 721–724.

    Article  Google Scholar 

  • Urban, T.L. (1998) An Inventory-Theoretic Approach to Product Assortment and Shelf-Space Allocation. Journal of Retailing, 74, 15–35.

    Article  Google Scholar 

  • Veinott, A. Jr. (1965) Optimal Policy for a Multi-Product, Dynamic, Non-Stationary Inventory Problem. Management Science, 12, 206–222.

    Article  Google Scholar 

  • Whitin, T. (1955) Inventory Control and Price Theory. Management Science, 2, 61–68.

    Google Scholar 

  • Zufryden, F.S. (1986) A Dynamic Programming Approach for Product Selection and Supermarket Shelf Space Allocation. Journal of the Operational Research Society, 37, 413–422.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Apte, U.M., Viswanathan, S. (2007). A Proactive Demand Management Model for Controlling E-Retailer Inventory. In: Apte, U., Karmarkar, U. (eds) Managing in the Information Economy. Annals of Information Systems, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36892-4_15

Download citation

Publish with us

Policies and ethics