Shelf Space Allocation Research Articles

Retailer shelf-space management with trade allowance: A Stackelberg game between retailer and manufacturers

Motivated by the significant influence of trade allowance on store operation and the efficiency of shelf-space allocation, this article develops a model for optimizing category-level shelf-space management. A category shelf-space allocation framework with trade allowance is presented and a multi-player Retailer Stackelberg game is introduced to model the interaction between retailer and manufacturers. With this framework, a retailer maximizes profit by taking the manufacturers' trade allowance response into account, which provides a realistic approach of simultaneously determining the shelf-space and trade promotion decisions under the consideration of interactions among products. A piecewise linearization method is employed to reformulate the Mixed Integer Nonlinear Programming (MINLP) problem into a linear MIP problem for optimal solutions. Numerical examples demonstrate significant advantage of the proposed framework and quantitative analysis leads to a variety of managerial insights.

The role of store brand positioning for appropriating supply chain profit under shelf space allocation

We consider a retailer’s decision of developing a store brand (SB) version of a national brand (NB) and the role that its positioning strategy plays in appropriating the supply chain profit. Since the business of the retailer can be regarded as selling to NB manufacturers the shelf space at its disposal, we formulate a game-theoretical model of a single-retailer, single-manufacturer supply chain, where the retailer can decide whether to launch its own SB product and sells scarce shelf-space to a competing NB in a consumer good category. As a result, the most likely equilibrium outcome is that the available selling amount of each brand is constrained by the shelf-space available for its products and both brands coexist in the category. In this paper, we conceptualize the SB positioning that involves both product quality and product features. Our analysis shows that when the NB cross-price effect is not too large, the retailer should position its SB’s quality closer to the NB, more emphasize its SB’s differences in features facing a weaker NB, and less emphasize its SB’s differences in features facing a stronger NB. Our results stress the importance of SB positioning under the shelf-space allocation, in order to maximize the retailer’s value appropriation across the supply chain.

Retailers and new product acceptance in India's base of pyramid (BoP) markets

PurposeRetailers' new product acceptance in base of pyramid (BoP) markets is crucial to marketers in this segment. This paper seeks to develop propositions for research on factors that affect retailers in new product introduction. The propositions also aim to make a distinction between urban and rural BoP markets.Design/methodology/approachThe paper provides a broad description of India's BoP market (one of the world's largest BoP markets) to better understand context. It uses literature from developed country context to BoP markets to arrive at research propositions for further research.FindingsThe key research propositions derived relate to exogenous and endogenous factors. Exogenous factors relate to store trading area, competitive environment, shopper characteristics and product diversity. The endogenous factors include store atmosphere, assortment and shelf space allocation, price and promotion. The differences across rural and urban BoP markets are highlighted for each proposition.Practical implicationsUnderstanding differences between rural and urban BoP retailers can help make crucial new product introduction decisions. Considering endogenous and exogenous factors that influence retailer acceptance decisions will make product introduction decisions successful.Originality/valueBoP literature has been replete with research on marketers and products but less on retailing. This paper addresses that gap. In addition, very few papers make the distinction between urban and rural BoP markets and mostly across countries but not within a country. This paper places the distinction within the country. Finally, explaining how various factors influencing retailing differ in urban and rural contexts and developing propositions is a major original contribution of this paper.

Game theory methodology for optimizing retailers’ pricing and shelf-space allocation decisions on competing substitutable products

This paper examines the influence of competition among supply chain partners on product demand. A power law demand function that depends on product pricing and shelf-space allocation (SSA) is used. The exponents in the power law are given by the elasticities of demand. In order to achieve the optimal pricing and SSA strategies in the presence of competition, game-theory-based methodologies—Cournot and Stackelberg games—are employed. For each type of game, a Nash equilibrium is achieved by optimizing the profit as a function of demand and price. A case study is presented to demonstrate the potential of this methodology. The results of this study indicate (1) how to achieve optimal pricing and SSA strategies, (2) how manufacturers can influence demand for a product, (3) that both prices and profits decrease using the Stackelberg game as compared with the Cournot game, and (4) that coordination beyond simple knowledge of price would be beneficial for improving overall profits.

Marketing Manufacturer and Retailer House Brands: A Study of Shelf Space in Two Product Categories

This article investigates shelf space allocation in the marketing of manufacturer brands (national brands) and retailer house brands (store brands) in the consumer packaged goods sector in a highly concentrated grocery retail environment. Of special interest is the degree to which the market mechanism is still at play in this kind of environment where retail chains wield considerable power over manufacturers supplying the respective grocery retail categories. The study focuses on two consumer packaged goods categories, and data collection is conducted via a combination of in-store category observation and in-depth interviews. The results of in-store category observation data indicate that the shelf space occupied by retailer house brands is largely proportional to the market share of the retailer house brands, which tends to negate the possibility of abuse of power within the product categories studied. Analysis of research interview data unveiled a number of themes that seem to collectively indicate that, both the manufacturer brand and the retailer house brand will continue to have a home in the respective categories, and performance on the part of manufacturer brands is expected for them to be able to justify occupation of valuable shelf space ahead of other competing manufacturer brands in the tightly contested shelf arena. The study points to the importance of participation of both manufacturer and retailer house brands if optimum category performance is to be achieved.

An integrated retail space allocation and lot sizing models under vendor managed inventory and consignment stock arrangements

In this paper, we develop integrated retail shelf space allocation and inventory models for a single item with a stock dependent demand. The integrated models are developed for a supply chain operating under vendor-managed inventory (VMI) and consignment stock (CS) agreement. More precisely, the supplier is responsible for initiating orders on behalf of the retailer and decides about the size of each order, the quantity to be displayed on the shelves, and the reorder point. In addition, the supplier owns the stock at the retailer’s premises until it is sold. We develop mathematical models to assess the benefits accrued by both parties as a result of the adoption of VMI–CS partnership. Results from the numerical experimental study show that such partnership is more attractive to all supply chain members when the retailer provides a flexible display capacity. Moreover, the supplier can use his/her selling price and the maximum allocated shelf space as negotiation means to benefit from the partnership.

A new model and a hyper-heuristic approach for two-dimensional shelf space allocation

In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function. We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithm with a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improved when compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.

An approach to products placement in supermarkets using PrefixSpan algorithm

With a great variation of products and user buying behaviors, shelf on which products are being displayed is one of the most important resources in retail environment. Retailers can not only increase their profit but, also decrease cost by proper management of shelf space allocation and products display. To solve this problem, we propose an approach to mine user buying patterns using PrefixSpan algorithm and place the products on shelves based on the order of mined purchasing patterns. The proposed approach is able to mine the patterns in two stages of process. In the first stage, the sequences of product categories are mined to place the product categories on the shelves based on the sequence order of mined patterns. Subsequently, in the second stage, the patterns (products) are mined for each category and then, rearrange the products within the category by incorporating the profit measure on the mined patterns. The experimentation is carried out on the synthetic datasets and the evaluation with two datasets showed that the proposed approach is good for product placement in supermarkets.

A piecewise linearization framework for retail shelf space management models

Managing shelf space is critical for retailers to attract customers and optimize profits. This article develops a shelf-space allocation optimization model that explicitly incorporates essential in-store costs and considers space- and cross-elasticities. A piecewise linearization technique is used to approximate the complicated nonlinear space-allocation model. The approximation reformulates the non-convex optimization problem into a linear mixed integer programming (MIP) problem. The MIP solution not only generates near-optimal solutions for large scale optimization problems, but also provides an error bound to evaluate the solution quality. Consequently, the proposed approach can solve single category-shelf space management problems with as many products as are typically encountered in practice and with more complicated cost and profit structures than currently possible by existing methods. Numerical experiments show the competitive accuracy of the proposed method compared with the mixed integer nonlinear programming shelf-space model. Several extensions of the main model are discussed to illustrate the flexibility of the proposed methodology.

A hierarchical decomposition approach to retail shelf space management and assortment decisions

Shelf management is a crucial task in retailing. Because of the large number of products found in most retail stores (sometimes more than 60 000), current shelf space management models can only solve sub-problems of the overall store optimization problem, since the size of the complete optimization problem would be prohibitively large. Consequently, an optimal allocation of store shelf space to products has not yet been achieved. We show that a hierarchical decomposition technique, consisting of two interwoven models, is suitable to overcome this limitation and, thus, is capable of finding accurate solutions to very large and complex shelf space management problems. We further conclude that other important variables (such as product-price) can be included into the methodology and their optimal values can be determined using the same solution technique. Our methodology is illustrated on a real-life application where we predict a 22.33% increase in store profits if our model's solution is implemented.

Shelf Space Management When Demand Depends on the Inventory Level

Two factors that their influence on the demand has been investigated in many papers are (i) the shelf space allocated to a product and to its complement or supplement products and (ii) the instantaneous inventory level seen by customers. Here we analyze the joint shelf space allocation and inventory decisions for multiple items with demand that depends on both factors. The traditional approach to solve inventory models with a state‐dependent demand rate uses a time domain approach. However, this approach often does not lead to closed‐form expressions for the profit rate with both dependencies. We analyze the problem in the inventory domain via level crossing theory. This approach leads to closed‐form expressions for a large set of demand rate functions exhibiting both dependencies. These closed‐form expressions substantially simplify the search for optimal solutions; thus we use them to solve the joint inventory control and shelf space allocation problem. We consider examples with two products to investigate the significance of capturing both demand dependencies. We show that in some settings it is important to capture both dependencies. We consider two heuristics, each one of them ignores one of the two dependencies. Using these heuristics it seems that ignoring the dependency on the shelf space might be less harmful than ignoring the dependency on the inventory level, which, based on computational results, can lead to profit losses of more than 6%. We demonstrate that retailers should use their operational control, e.g., reorder point, to promote higher demand products.

Channel Coordination in Green Supply Chain Management: The Case of Package Size and Shelf-Space Allocation

Environmental consciousness has become increasingly important in present times, as evident by the examples from both everyday life and business practices. In the manufacturing sector, the efforts to reduce the impact of environmentally harmful activities have collectively been labeled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, relatively few studies have addressed the issue of coordinating the green supply chain. We address the issue of vertical coordination in a green supply chain between the players at the upstream (say, a manufacturer) and downstream (say, a retailer) levels of the chain. In this paper, we focus on an interesting problem faced by manufacturer-retailer pairs (e.g., P&G, and Walmart) in the consumer products supply chains. The manufacturer has to decide on the wholesale price and the package size of its product. The manufacturer is labeled as a green manufacturer, since reducing the pack size of its product would help him save on environmentally costly transportation cost. Reduction in pack size helps the manufacturer also save on product packaging cost. Moreover, the reduced package size also results in cost savings for the retailer in terms of handling and storage.The retailer decides on the retail price and the shelf space to be allocated to the 'green' manufacturer. The increase in allocation of shelf space has a demand expansion effect on the retail demand for the green manufacturer’s products. However, the decrease in pack size has a demand reduction effect on its demand, because larger pack sizes attract more consumer attention, and therefore, act as an advertising vehicle. Further, the retailer has limited shelf-space and has to balance the space allocation for the green manufacturer against the potential profitability of allocating that space to “other” manufacturers.In view of the above elements of complexity, it is interesting to examine the optimal prices charged, optimal reduction in size and optimal shelf-space allocation in this problem. We examine the problem for the cases of both integrated and decentralized channels. Our results show that the total profit is greater, pack sizes are smaller, and shelf-space allocation is greater in the integrated channel as compared to the decentralized channel. Retail prices can be greater or smaller in the decentralized channel as compared to the integrated channel, depending on certain values of the problem parameters. These results show that clearly there is scope for channel coordination in this problem setting for better profitability and environmental benefits. We propose a two-part tariff scheme which effectively coordinates the green supply chain in this problem setting.

Consumer‐driven retail operations

PurposeThe purpose of this paper is to investigate the direct and interaction effects of shelf space, case pack quantity, and consumer demand on shelf stockouts, i.e. stockouts at the shelf level when inventory is available in the backroom of a retail store.Design/methodology/approachThis study uses discrete‐event simulation based on data collected from the ready‐to‐eat breakfast cereal category with multiple stockkeeping units (SKUs) to model a retail supply chain consisting of a supplier, a retailer, and consumers.FindingsThe results indicate that shelf space and case pack quantity have direct effects on shelf stockouts. Furthermore, evidence is found for interactions among shelf space, case pack quantity and consumer demand. Though many retailers adopt simple heuristics for shelf space allocation, such as a multiple of case pack quantity, this study suggests that such heuristics tend to over‐ or underestimate shelf space requirements when consumer demand is ignored.Originality/valueThis study suggests that managers should allocate shelf space for SKUs on the basis of not only case pack quantity but also consumer demand.

Optimal Shelf Space Allocation Using System Dynamics Approach: A Case Study in Shoe Retailing Industry

Shelf space is a limited resource in a retailing store and multiple brands compete to get the maximum of this resource for their exposure to customer. However, a retailer's objective usually is to allocate space to each brand in such a way that yields maximum cumulative profit from the shelf space available. A retailer would make space allocation decisions based on various attributes possessed by the brands to be displayed. Such attributes may include: relative demand, price, profit margin, price elasticity, demand-space elasticity, cross-space elasticity, weather and temporal effects on demand of brands, demographic characteristics of visiting customers, and substitution relationships of a brand with competing brands to name a few. Hence an analysis of these variables together to find out an optimal brand space allocation decision that maximizes total profit of the shelf-space is a fairly complex and dynamic problem. This paper explores the use of system dynamics approach for studying shelf space allocation decisions in the context of shoe retailing stores. Based on data which is part collected, part hypothetical (but with reasonable assumptions) a system dynamics model is developed for finding a solution of the aforementioned problem. The objective of the model is to determine optimal shelf space allocation that maximizes net profit of the store at given set of retailing store's constraints. Further implications of the model results are discussed and future research directions are proposed.

A DYNAMIC PROGRAMMING HEURISTIC FOR RETAIL SHELF SPACE ALLOCATION PROBLEM

Shelf space allocation to products greatly impacts the profitability in a retail store. In this paper, we consider a retail shelf-space allocation problem where retailer wishes to allocate the available spaces of different shelves to a large number of products considering direct space elasticity in the product's demand. There is a great interest to develop efficient heuristics due to NP-hard nature of this problem. We propose a dynamic programming heuristic (DPH) to obtain near optimal solution in a reasonable time to solve this problem. The empirical results found that DPH obtained near optimal solutions for randomly generated instances of problems with size (products, shelves) varying from (100, 30) to (200, 50) within a few seconds of CPU time. The performance of DPH is benchmarked against an existing local search heuristic (LSH). It was found that DPH takes substantially less CPU time and attains a solution close to that obtained by LSH. Thus, DPH has great potential to solve the problem of realistic size within reasonable time. The proposed DPH is also applied to a case of an existing retail store.

Impact of Revenue Sharing Contracts on Retailers’ Shelf Space Allocation

The retailer’s shelf-space-allocation decision affects not only the retailer’s profitability but also the supplier’s. This paper studies channel coordination through revenue sharing (RS) contracts between a common supplier and two competing retailers in a two-echelon supply chain. The supplier, acting as a leader, grants the franchise to two retailers facing shelf-space-dependent demand. The supplier influences retailers’ decisions by charging portions of the revenue in exchange for the reduction in wholesale price. To maximize their profitability, the two competing retailers determine the order quantities under the given contract terms. We then investigate how the partners make decisions under the RS contracts and provide the condition under which both retailers stay in competition. Lastly we explore when the supply chain could reach the efficiency as in a centralized one.

Heuristics for retail shelf space allocation problem with linear profit function

PurposeShelf space is often retailer's critical resource. Growing number of products has posed a challenge to the retailers for efficient allocation of available shelf space to them. The paper aims to consider a retail shelf space allocation problem with linear profit function and aims to develop efficient heuristics to solve this problem.Design/methodology/approachThe paper develops three heuristics to solve a shelf space allocation problem. It compares three heuristics with existing heuristic using empirical study.FindingsIn an empirical study of 320 randomly generated instances of problems with size (products, shelves) varying from (25, 5) to (200, 50), it was found that all three new heuristics are competitive with existing heuristic. The best amongst three heuristics found solution with average objective value of 99.59 percent of upper bound in a reasonable central processing unit time.Research limitations/implicationsThe linearity assumption of the profit function is based on earlier findings that marginal returns to space first increase and then decrease in an S‐shaped curve. Hence, linearity assumption for profit function is justified by the fact that retails would want to operate on linear (or approximately linear) and more strongly increasing part of the curve.Practical implicationsThe proposed heuristics are applied to a case of existing retail store which gave more profit than the current allocation scheme.Originality/valueThe paper proposes new initial constructor and neighbourhood move strategy to develop efficient heuristic. Heuristics proposed in this paper are competitive with existing heuristics.

Competing for Shelf Space

In recent years, the competition for shelf space has intensified, as more products now compete for a retail space that has remained roughly constant. In this paper, we analyze the dynamics of this competition in a multi‐supplier retail point. Assuming that sales are shelf space dependent, we consider a retailer that optimizes its shelf space allocation among different products based on their sales level and profit margins. In this context, product manufacturers set their wholesale prices so as to obtain larger shelf space allocations but at the same time keep margins as high as possible. We analyze the equilibrium situation in the supply chain, and find that generally the retailer's and the suppliers' incentives are misaligned, resulting in suboptimal retail prices and shelf space allocations. We however find that the inefficiencies induced by suboptimal shelf space allocation decisions are small relative to those induced by suboptimal pricing decisions.

Channel Coordination in Green Supply Chain Management: The Case of Package Size and Shelf-Space Allocation

Environmental consciousness has become increasingly important in present times, as evident by the examples from both everyday life and business practices. In the manufacturing sector, the efforts to reduce the impact of environmentally harmful activities have collectively been labeled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, relatively few studies have addressed the issue of coordinating the green supply chain. We address the issue of vertical coordination in a green supply chain between the players at the upstream (say, a manufacturer) and downstream (say, a retailer) levels of the chain. In this paper, we focus on an interesting problem faced by manufacturer-retailer pairs (e.g., P&G, and Walmart) in the consumer products supply chains. The manufacturer has to decide on the wholesale price and the package size of its product. The manufacturer is labeled as a green manufacturer, since reducing the pack size of its product would help him save on environmentally costly transportation cost. Reduction in pack size helps the manufacturer also save on product packaging cost. Moreover, the reduced package size also results in cost savings for the retailer in terms of handling and storage. The retailer decides on the retail price and the shelf space to be allocated to the “green” manufacturer. The increase in allocation of shelf space has a demand expansion effect on the retail demand for the green manufacturer’s products. However, the decrease in pack size has a demand reduction effect on its demand, because larger pack sizes attract more consumer attention, and therefore, act as an advertising vehicle. Further, the retailer has limited shelf-space and has to balance the space allocation for the green manufacturer against the potential profitability of allocating that space to “other” manufacturers. In view of the above elements of complexity, it is interesting to examine the optimal prices charged, optimal reduction in size and optimal shelf-space allocation in this problem. We examine the problem for the cases of both integrated and decentralized channels. Our results show that the total profit is greater, pack sizes are smaller, and shelf-space allocation is greater in the integrated channel as compared to the decentralized channel. Retail prices can be greater or smaller in the decentralized channel as compared to the integrated channel, depending on certain values of the problem parameters. These results show that clearly there is scope for channel coordination in this problem setting for better profitability and environmental benefits. We propose a two-part tariff scheme which effectively coordinates the green supply chain in this problem setting.

Joint Optimization of Product Price, Display Orientation and Shelf-Space Allocation in Retail Category Management

We develop a model that jointly optimizes a retailer's decisions for product prices, display facing areas, display orientations and shelf-space locations in a product category. Unlike the existing shelf-space allocation models that typically consider only the width of display shelves, our model considers both the width and height of each shelf, allowing products to be stacked. Furthermore, as demand is influenced by each product's two-dimensional facing area, we consider multiple product orientations that capture three-dimensional product packaging characteristics. That enables our model to not only treat shelf locations as decision variables, but also retailers’ stacking patterns in terms of product display areas and multiple display orientations. Further, unlike the existing studies which consider a retailer's shelf-space allocation decisions independent of its product pricing decisions, our model allows joint decisions on both and captures cross-product interactions in demand through prices. We show how a branch-and-bound based MINLP algorithm can be used to implement our optimization model in a fast and practical way.