doi:10.1016/j.ejor.2006.03.043
Copyright © 2006 Elsevier B.V. All rights reserved.
Interfaces with Other Disciplines
Service and cost benefits through clicks-and-mortar integration: Implications for the centralization/decentralization debate
Elliot Bendolya,
,
, Doug Blocherb, Kurt M. Bretthauerb and M.A. Venkataramananb
aDepartment of Decision and Information Analysis, Goizueta Business School, Emory University, Atlanta, GA 30322, United States
bDepartment of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, IN 47405, United States
Received 10 November 2004;
accepted 14 March 2006.
Available online 26 May 2006.
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Abstract
Traditional “Brick-and-Mortar” operations face the challenge of adapting to a new set of competitive rules made necessary by consumers who want the option of ordering electronically via the Internet. To satisfy these customers, firms must develop strategies that integrate their standard retail in-store channel with this relatively new on-line channel. Therefore, this research is designed to provide insights into supply chain inventory management strategies relevant to “Clicks-and-Mortar” firms trying to satisfy both on-line and in-store sales. Specifically, this work considers the total cost implications of various inventory allocation strategies while maintaining target customer service levels. Analysis focuses on the development of models capable of handling new operating strategies made possible by electronic commerce. The implications of inventory risk pooling are considered in depth, revealing the existence of characteristics that determine whether completely centralized or decentralized policies are preferable.
Keywords: Internet; Inventory; Supply chain management; Optimization
Fig. 1. Network configurations for on-line fulfillment.
Fig. 2. Network configurations for on-line fulfillment in scenario 1 extremes.
Fig. 3. Minimal costs for various fixed cost scenarios (Model 1).
Fig. 4. Minimal costs for various shipping and handling scenarios (Model 1).
Fig. 5. Minimal costs for various inventory holding cost scenarios (Model 1).
Fig. 6. Minimal inventory cost for various facility scenarios (Model 1).
Fig. 7. Threshold pE levels for the choice between centralized vs. decentralized on-line storage (Model 1).
Fig. 8. Relationship between pE thresholds and variations in mean demand among satellites.
Fig. 9. Threshold pE levels for the optimality of centralized vs. decentralized on-line storage (Model 2).
Fig. 10. Scenario 3 – Central warehouse handles on-line inventory and deliveries directly.
Table 1.
A summary of the means and standard deviations of demand

Table 2.
System parameter notation

Table 3.
Cost parameters utilized in mathematical modeling

Table 4.
Additional modeling notation

Table 5.
Threshold pE values for special cases

Table 6.
Experimental design for comparing Model 1 with simulation

Table 7.
Comparisons of mathematically estimated and simulated performance measures for Model 1

Table 8.
Comparisons of mathematically estimated and simulated performance measures for Model 2
