Effect of replenishment and backroom on retail shelf-space planning

Abstract

Shelf-space optimization models support retailers in making optimal shelf-space decisions. They determine the number of facings for each item included in an assortment. One common characteristic of these models is that they do not account for in-store replenishment processes. However, the two areas of shelf-space planning and in-store replenishment are strongly interrelated. Keeping more shelf stock of an item increases the demand for it due to higher visibility, permits decreased replenishment frequencies and increases inventory holding costs. However, because space is limited, it also requires the reduction of shelf space for other items, which then deplete faster and must be reordered and replenished more often. Furthermore, the possibility of keeping stock of certain items in the backroom instead of the showroom allows for more showroom shelf space for other items, but also generates additional replenishment costs for the items kept in the backroom. The joint optimization of both shelf-space decisions and replenishment processes has not been sufficiently addressed in the existing literature. To quantify the cost associated with the relevant in-store replenishment processes, we conducted a time and motion study for a German grocery retailer. Based on these insights, we propose an optimization model that addresses the mutual dependence of shelf-space decisions and replenishment processes. The model optimizes retail profits by determining the optimum number of facings, the optimum display orientation of items, and the optimum order frequencies, while accounting for space-elasticity effects as well as limited shelf and backroom space. Applying our model to the grocery retailer's canned foods category, we found a profit potential of about 29%. We further apply our model to randomly generated data and show that it can be solved to optimality within very short run times, even for large-scale problem instances. 123Business Research (2017) 10:123-156 DOI 10.1007 replenishment cost on retail profits and solution structures. Based on the insights gained from the application of our model, the grocery retailer has decided to change its current approach to shelf-space decisions and in-store replenishment planning.

A decision support system for retail assortment planning

“…Hübner, 2011;Irion et al, 2012), some assume a deterministic demand (e.g. Yücel et al, 2009;Hübner and Schaal, 2017), some consider limited shelf space (e.g. Kök et al, 2015), different costs and margin components and so forth.…”

“…Their approach provides only near-optimal solutions for the shelf-assignment problem with a predefined assortment. Hübner and Schaal (2017) integrate assortment planning into shelf-space and replenishment planning. They develop an iterative solution approach.…”

Merchandising rules for shelf space allocation with horizontal and vertical positions

“…Products shipped already priced in cage pallets and ready for display would need to be placed near the entrances so that they can be wheeled in with minimal disturbance (Buttle, 1984). Moreover, a pallet shelf is a very convenient solution as they are reusable and can be moved to other locations for better accessibility in the store (Hübner and Schaal, 2017).…”

Application of the analytic hierarchy process in the selection of storage rack systems for e-commerce clothing industry

“…Also studies on "Warehouse Operator Selection" (Korpela, Lehmusvaara, & Nisonen, 2007), "Equipment Selection" (Dağdeviren, 2008) and "Packaging Selection" (Tümenbatur, 2016) are found. When the studies on "Storage Rack Systems" are investigated, most of the studies found are related to inventory optimization as listed in Table 1 like "Order Picking" (Hwang & Cho, 2006), (Tunç, Kutlu, Zincidi, & Atmaca, 2008), (Ofluoğlu & Baki, 2016) and "Shelf-Space Allocation" (Özcan, 2010), (Geismar, Dawande, Murthi, & Sriskandarajah, 2015), (Tsaia & Huang, 2015), (Hübner & Schaal, 2017) . Matson & White (1981) focused on optimization procedures for the design and selection of storage system alternatives, including block stacking, single-deep and double-deep pallet rack, and deep lane storage.…”