Backroom space allocation in retail stores
Massachusetts Institute of Technology. Institute for Data, Systems, and Society.
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Space is one of the most scarce, expensive, and difficult to manage resources in urban retail establishments. A typical retail space broadly consists of two areas, the customer facing frontroom area and the backroom area, which is used for inventory storage and other support activities. While frontrooms have received considerable amount of attention from both academics and practitioners, backrooms are an often neglected area of retail space management and design. However, the allocation of space to the backroom and its management impact multiple operational aspects of retail establishments. These include in-store labor utilization, delivery schedules, product packaging, and inventory management. Therefore, the backroom area directly affects the performance of the store because it impacts stock-outs, customer service levels, and labor productivity. Moreover, extant literature suggests that backroom related operations contribute to a large fraction of the total retail supply chain costs. Thus, optimizing the management of backroom spaces is an important lever for store performance improvement. We address the gap in the extant literature related to space management of retail backrooms by investigating the following three questions: First, what is the effect of pack size on inventory levels and space needs in the backroom? Second, how can a given backroom space be efficiently utilized through optimal inventory control? Third, what is the optimal amount of space that should be allocated to the backroom in a given retail establishment? To address the first question, we evaluate the effect of two discrete pack sizes, order pack size (OPS) and storable pack size (SPS), on inventory levels and storage space requirements in the backrooms. While SPS drives the space needs for a given inventory level, OPS drives the amount of excess inventory and therefore, the space needs. Using inventory theory and probability theory, we quantify the amount of excess inventory and the expected stock-out probability for a given OPS in the case of a normally distributed demand. To address the second question, we discuss an inventory-theoretic approach to efficiently manage a given backroom space within a limited service restaurant. Specifically, we formulate a mathematical optimization model using mixed-integer linear programing with the objective of maximizing store profit. Applying this optimization model to real store data in collaboration with a major US retailer reveals cost implications related to constrained backroom space and the sensitivity of backroom space requirements to changes in OPS and SPS. The proposed model can serve as a decision support tool for various real-world use cases. For instance, the tool can help the retailers to identify (i) items whose contribution to the store profit does not justify their space needs in the backroom, and (ii) stores that are constrained in their profitability growth by backroom space limitations. To address the third question, we introduce the notion of interdependency between the frontroom and the backroom of a retail establishment. Such interdependencies yield nontrivial trade-offs inherent to the optimal retail space allocation. Demand can be lost due to unavailability of inventory (or inventory stock-out), which is a result of scarce amount of backroom space, or due to unavailability of sufficient frontroom space (or space stock-out). Furthermore, constrained backroom spaces increase in-store labor cost and the ordering costs incurred per unit of revenue generated in a retail establishment. The strategic decision model formulated in this chapter accounts for revenue, inventory cost, labor cost and ordering cost to determine the optimal amount of backroom space that should be allocated within a retail establishment. Sensitivity analyses with respect to the change in input parameters is used to connect the backroom space allocation and its impact on store profit to the different supply chain levers that can be managed by the retailers.
Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 168-171).
DepartmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society.
Massachusetts Institute of Technology
Institute for Data, Systems, and Society.