Limited Shelf Space Research Articles

De vuelta a lo básico: la simplicidad del empaque y la percepción prémium

In a limited shelf space and with a wide product offering, the visual noise of packaging can be overwhelming. This study proposes that packaging with a simple design positively impacts the consumer: firstly, it is perceived as premium packaging, and secondly, it increases purchase intention. The methodology uses an intra-subject experimental design. Participants were randomly exposed to two product images, in this case, liquid milk: one with a simple packaging design and the other with a complex design. Next, they completed a survey where they indicated their perception of the product as premium and their purchase intention. The results support full mediation: compared to complex packaging, simple packaging increased the perception that the product is premium, which in turn increases purchase intention

Omnichannel product selection and shelf space planning optimization

As the retail industry is evolving toward an omnichannel paradigm, we study a product selection and shelf space planning problem faced by omnichannel retailers. We aim to maximize aggregated profits across channels by determining the optimal product offerings for the offline and online channels while optimizing the allocation of limited shelf space to these products in brick-and-mortar stores. Furthermore, the showroom effect of physical stores on online sales is also taken into account. We propose a corresponding model that jointly optimizes these decisions. To solve the nonlinear nonconvex model of practical scale, we first reformulate it into a mixed integer quadratic programming by exploring the structural properties. Next, we use the reformulation linearization technique to further improve the computational speed. Our numerical studies validate the efficiency of the proposed solution approach. In addition, we derive some managerial insights, including that retailers should adopt joint optimization and consider how the involved effects and parameters may influence their profits and decisions.

Decision support for managing assortments, shelf space, and replenishment in retail

Efficiently managing retail space is critical as the increase in product variety is in conflict with limited shelf space and instore replenishment constraints. This paper develops a general framework for retail space management and presents a decision support model with the related problems within the framework of optimizing assortment, shelf space assignment and replenishment. An integrative approach to these planning problems becomes particularly relevant for fast-moving consumer goods and groceries, where stores are regularly replenished from distribution centers. The planning problem at hand is a multi-product shelf space allocation problem where demand is a composite function of the shelf space allocated and assortment-related demand substitution, and actual replenishment practices from retail are incorporated. The model developed extends existing models of shelf space management by jointly considering space-elastic demand and assortment-based substitution and integrating restocking constraints. For the latter, we consider real-world replenishment processes of retailers that distinguish between period-based and ad-hoc replenishment from the backroom. We develop three solution approaches that are based on efficient pre-processing and a nonlinear binary integer programming formulation of the problem. The computation tests based on retail data show the efficiency of the solution approaches in terms of computation time and solution quality. We reveal the improvement in profit levels that can be achieved from integrating assortments, shelf space planning and replenishment where challenges arise in obtaining feasible solutions with limited shelf space and replenishment constraints. We also use sensitivity analyses to demonstrate the high impact of replenishment constraints on profits and solution structures.

Increasing product variety: Conflicts in shelf space allocation

The increasing product variety trend prevalent in most industries today does not stop at retailers. Unfortunately, this trend is a mounting task due to limited shelf space. Based on a survey of 376 people, this research provides an insightful picture of the connection between product variety and impulse buying decisions. One of the study's main findings is that product variety offered by retailers is one of the main in-store factors driving impulse purchases. Since the survey results show the importance of product variety for retailers, the paper also presents some concepts as to how retailers can deal with the numerous product varieties under the condition of limited shelf spaces, e.g., by allowing manufacturers to compete for shelf space.

Variety and Inventory Trade-off in Retailing: An Empirical Study

We investigate the trade-off between variety and inventory in retailing using a proprietary data set from a bookstore chain. In general, more variety is expected to generate higher traffic to store and higher conversion for incoming customers. Another argument is that too much variety reduces sales due to the confusion it creates in the customers’ minds (choice paradox). Higher variety also implies lower inventory per product due to limited shelf space in the stores. Hence, a critical question for a retailer is to determine where their stores are currently positioned on the variety-sales curve, and whether or not they should increase/decrease variety by extending/cutting the long tail of slow-moving products. We consider number of subcategories, number of products in a subcategory, average inventory per product, and inventory dispersion as measures of, respectively, macro variety, micro variety, inventory depth, and inventory policy effectiveness. We exploit the differences between stores and variations over time to investigate the net impact of variety and inventory on sales of total category and of top-selling products. We control for store traffic and seasonality, and introduce instrumental variables to control for endogeneity of variety and inventory. We find that both micro and macro variety increase total category sales. Further, variety increases sales of the top-selling products as well—a somewhat surprising positive effect of presenting the long-tail of products to customers. We also demonstrate how a retailer can significantly increase its revenue by optimally swapping shelf spaces across product categories based on the estimated category-specific variety effects.

A Heuristic Approach to Shelf Space Allocation Decision Support Including Facings, Capping, and Nesting

Shelf space on which products are exhibited is a scarce resource in the retail environment. Retailers regularly make decisions related to allocating products to their outlets’ limited shelf space. The aim of the paper was to develop a practical shelf space allocation model offering the possibility of horizontal and vertical product grouping, representing an item (product) with facings, capping, and nesting, with the objective of maximizing the retailer’s profit. Because real category-management problems address a lot of retailer’s rules, we expanded the basic shelf space allocation model, using shelf constraints, product constraints, multi-shelves constraints, and category constraints. To solve the problem, we proposed two adjustable methods that allowed us to achieve good results within a short time interval. The validity of algorithms was estimated, using the CPLEX solver and illustrated with example problems. Experiments were performed on data generated on the basis of real retail values. To estimate the performance of the proposed approach, 45 cases were tested. Among them, the proposed approach found solutions in 34 cases, while CPLEX found solutions only in 23 cases. The profit ratio of the proposed approach is, on average, 94.57%, with minimal and maximal values of 86.80% and 99.84%, accordingly.

Faculty Perceptions of Online Law Journals

Online-only law journals (OLJs) are sometimes referred to as “companions” or “supplements.” They are law journals that are only distributed in a digital medium. The Yale Law Journal Pocket Part became the first OLJ in 2005. In the brief existence of OLJs, they have experienced rapid growth in both prestige and prevalence. There were already four OLJs in the top 100 of the Washington & Lee law journal rankings by 2015. In 2020, there are now seven OLJs in the top 100. Using the impact factor measure, there are ten OLJs in the top 100. The Forum—Harvard’s OLJ—is ranked ahead of the flagship print journals North Carolina Law Review and Connecticut Law Review. The performance of OLJs in rankings is even more impressive when considering how a key measure used in the rankings—impact factor—disadvantages OLJs because they publish shorter articles. OLJs generally have less stringent footnote requirements and a faster turnaround time from acceptance to publishing. OLJs publish a greater variety of articles than their print counterparts. Book reviews, short essays, responses, current event musings, and even poetry and satirical pieces are published in OLJs. Some scholars applaud this flexibility, while others criticize it as unpredictable. There is great variance in OLJ article lengths, but they are generally shorter than flagship print journal articles. Some OLJs have an explicitly stated maximum of only 2,500 words including footnotes. Others allow submissions of over 15,000 words. Demand for printing and purchasing physical law journal editions has been steadily decreasing due to cost, limited library shelf space, and increased popularity of digital legal research. Physical distribution of the Harvard Law Review has decreased almost 90%. Even libraries at elite law schools, such as Columbia, are canceling subscriptions to hundreds of print law journals. Some commentators argue persuasively that certain flagship journals should move exclusively online. The budget cuts many colleges face from coronavirus will likely accelerate this trend of transitioning toward OLJs.This article first examines current conceptions for weighting OLJs compared to print law journals, criticism of OLJs, and praise for OLJs. It then reports the findings of a survey of business law faculty to measure perceptions of OLJs. Finally, faculty demographic factors are also analyzed to determine what role they may play in forming these perceptions.

Demand, challenges, and marketing strategies in the retail promotion of local brand milk

AbstractThis article addresses the demand and challenges for local brand milk and focuses on the marketing strategies for retail promotion. While federal and state government make efforts to promote local food purchases and consumption at the farm level, local foods face significant distribution challenges in grocers, especially in larger retail stores. Limited shelf space and high pricing by retailers impedes consumers’ purchases and results in weak competitiveness for local brands. Therefore, it is essential for local producers and “buy local” programs to understand what types of promotion and marketing strategies might be more effective in stimulating demand in retail outlets. Using 2010‐2011 Nielsen Retail Scanner data in the Northeast region, we estimate a random‐coefficient discrete choice model of demand to determine the effects of nutritional characteristics, price, packaging, and distribution strategies on the consumers’ choice to purchase locally branded milk. Results show that local brand milk faces significant demand challenges in addition to high prices and limited outlets in competing with lower‐priced products. Marketing strategies, such as price cut, one‐gallon package offering, and expanded presence in retail stores, can significantly stimulate the demand for locally branded milk, with one‐gallon package offering being the most effective strategy.

Data mining-based algorithm for assortment planning

With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem.

Economic order quantity model for a product with expiration date, limited shelf space and freshness dependent selling price under the effect of trade credit financing

In practise, supplier offers the retailer a credit/delay period for settling the account and no interest is charged on the outstanding account if the account is settled by the end of the delay period. With the consideration of trade credit, we develop an economic order quantity model for a product with expiration date and freshness dependent selling price. Objectives of our model are twofold. The initial one is the consideration of the circumstance that the demand of the product is dependent upon the freshness condition as well as selling price of the product and the last one is relaxation of inventory level at the end of cycle. The solution procedure of proposed optimization model is illustrated analytically and numerically by a couple of examples. Concavity of the average profit function is shown by plotting graphs. To study the effect of fluctuating the value of all parameters in the proposed maximization model a sensitivity analysis is carried out.

Retail Shelf Space Planning – Differences, Problems and Opportunities of Applied Optimization Models

Shelf space optimization models receive high attention among researchers for years. The allocation of different products with a certain number of facings (a facing corresponds to one visible unit of the product on the shelf) to a limited shelf space is a catchy example of applying operations research methods for a practical use case. Some approaches are more of a generic kind in order to create a basic understanding of the possibilities of decision support models while others aim to investigate specific impacts of different factors (e.g., cross-space elasticity, cost functions). Further a set of approaches provide applicable decision support tools which can be used to determine (almost) final planograms for retail stores. Beside a lot of improvements provided by latest research retailers are subjected to a continuous development. The requirements for shelf space planning in practice are increasing with new technologies (e.g., planning software, data availability) and impacts from different research fields (e.g., marketing, food chemistry, logistics). When shelf space models are created these specifications should be considered closely in order to provide beneficial solutions. Indeed the intention of researchers and retailers doing shelf space planning might differ in many cases. During a collaborative research project with one of Europe's biggest food retailer over three years a lot of experience could be achieved and intensive comparisons between scientific work and practical requirements could be fulfilled. Based on the experiences made during our collaboration where several optimization models were developed and whose results were actually applied to stores, in this paper the key findings from the related processes shall be shared. The goal is to provide insights from practice and point to unnoticed problems in order to let both groups, retailers and researches, gain valuable information and simultaneously improve their work. Finally some rather unattended, but crucial, aspects of shelf space allocation are mentioned suggesting further areas of research.

Single-period assortment and stock-level decisions for dual sales channels with capacity limits and uncertain demand

This paper addresses a dual channel, clicks-and-mortar retailer's problem of determining which among a set of products with seasonal demand will occupy limited retail shelf space, which products will be offered via an online channel, and which items will be available through both channels. Using a consumer choice model in which the set of products offered influences each product's demand in each channel, we consider stocking and price decisions under uncertain demand in a single-period setting with a constraint on the probability of stocking out. The resulting model is a large-scale, chance-constrained, two-stage stochastic programme. We propose a sample average approximation (SAA) method that permits quickly arriving at near-optimal solutions for this complex problem class. We also exercise the proposed model to gain insights on the problem's key tradeoffs and properties of optimal solutions.

The shelf space and pricing strategies for a retailer-dominated supply chain with consignment based revenue sharing contracts

Consider a two-echelon supply chain consisting of two manufacturers and a dominant retailer, such as big supermarkets like Walmart. Under a consignment contract with revenue sharing, the two manufacturers sell through the retailer two substitutable products whose demands are dependent on their shelf space and sales prices. The two manufacturers may compete horizontally for shelf space and pricing by three scenarios: Nash game, Stackelberg game, and collusion, and play vertically the retailer-Stackelberg game with the retailer. For each of these horizontal scenarios, we present all participators’ equilibrium strategies and their corresponding profits, based on which the impacts of manufacturers’ cost difference and moving sequence are investigated. Additionally, we discuss whether a horizontal collusion among manufacturers occurs when they choose their scenarios and whether centralization is always beneficial for the entire chain under the considered consignment contract. The study reveals the following results: (i) When the manufacturers compete horizontally, the high-cost manufacturer always sets a high-price and less shelf space strategy, while the low-cost manufacturer always adopts a low-price and more shelf space strategy, which is not affected by their moving sequence. If they collude horizontally, it is just reverse. (ii) When the two manufacturers compete horizontally, all participators’ equilibrium strategies and their corresponding profits are significantly influenced by manufacturers’ moving sequence. (iii) A horizontal collusion between the manufacturers can occur only when their cost difference is relatively small; this finding supplements existing literature. (iv) When the cost difference between manufacturers is relatively big, then centralization may be detrimental to the entire chain, which can explain why several supply chains adopt vertical competition strategies in practice. In addition, we find that these results still hold for the limited shelf space scenario and shelf-space limitation enhances the horizontal and vertical competition intensity by increasing shelf space fee.

Inventory and credit decisions for deteriorating items with displayed stock dependent demand in two-echelon supply chain using Stackelberg and Nash equilibrium solution

With the prevalence of network technologies, the world is shrinking and it is observed that the decision policies of the players of the supply chain go hand in hand. Thus the optimal replenishment decisions of retailers cannot be taken in isolation and need to be integrated with that of the supplier. This research work establishes a supplier-retailer supply chain in which demand for the products is displayed stock dependent. Nowadays, trade credit is also seen as prime source of short term financing, thus the retailer takes the benefit of permissible delay in payments from the supplier. The objective of the proposed model is to obtain the optimal decisions of the supply chain under three different policies- centralized, Supplier-led Stackelberg policy and Nash equilibrium solution. In this study, the influence of trade credit offered by the supplier, replenishment decisions and integration among the players of supply chain through different centralized and decentralized policies is analyzed for deteriorating items where retailer faces displayed stock dependent demand with two storage facilities. The model is best suitable for the emerging retail markets or supermarkets with limited shelf space displaying consumable items such as grocery, consumer goods, etc. The results have been validated with the help of a numerical example. Sensitivity analysis has also been performed to study the effect of various parameters on the optimal solution.

Optimal pricing and lot-sizing policy for supply chain system with deteriorating items under limited storage capacity

This research work considers a two-echelon supply chain model for deteriorating items in which the retailer's warehouse capacity of display area is limited. Therefore, the retailer stores remaining units in the back room which has unlimited capacity. The demand rate is assumed to be dependent on the retailer's selling price and displayed stock level. The proposed approach explicitly models the interdependence among price of a product, demand for the product and the integration among the retailer and the supplier under four different policies: non-integrated, integrated, supplier-led Stackelberg policy and retailer-led Stackelberg policy. Furthermore, this paper also establishes several theoretical results to show that the solution provides the maximum total relevant profit. By using differential calculus method, it is proved that the optimal solution not only exists but also is unique. In this study, the impact of price, inventory level and the integration among retailer and the supplier is explicitly analyzed in the emerging retail scenario with limited shelf space.

A decision support system for retail assortment planning

PurposeBecause increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues.Design/methodology/approachThe findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice.FindingsThe author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure.Practical implicationsThe author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems.Originality/valueThe planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Managing limited retail space for basic products: Space sharing vs. space dedication

In this paper, we study the problem of managing limited retail shelf or storage space for basic products by considering two inventory management strategies: space dedication and space sharing. When space is dedicated to each product, there is more flexibility in planning as different products can be replenished independently. In contrast, when space is shared across different products, there is potential for saving space; however, replenishment has to be coordinated across products and this leads to additional costs due to the lack of flexibility in replenishing each product individually. We model this problem as a non-linear mixed integer program and develop an effective heuristic and an upper bound for each strategy. We introduce three different but consistent criteria to compare each strategy. Through an extensive computational study, we identify the most relevant factors that impact the relative benefit of space sharing over space dedication. In addition, we show that space sharing with an optimal replenishment scheduling program can on average reduce space consumption by 31%.

An integrated assortment and shelf-space optimization model with demand substitution and space-elasticity effects

Retailers must define their assortments and assign shelf space to the items included in these assortments. These two planning problems are mutually dependent if space is scarce. We formulate a model that maximizes a retailer’s profit by selecting the optimal assortment and assigning limited shelf space to items. This model is the first decision model to integrate assortment and shelf-space planning by considering stochastic and space-elastic demand, out-of-assortment and out-of-stock substitution effects. To solve the model, we develop a specialized heuristic that efficiently yields near-optimal results, even for large-scale problems. We show that our approach outperforms alternative approaches, e.g. a sequential planning approach that first picks assortments and then assigns shelf space by up to 18%, and a greedy algorithm by up to 16% in terms of profit.We test our model on two real data sets for perishable and non-perishable items and show how it can support retailers in increasing their profits by up to 25%. We then use the model to generalize these results and find that space elasticity and substitution effects have a significant impact on profits, assortment size as well as facing decisions, and that both effects reinforce each other. Using our model, we finally derive rules-of-thumb for planners in practice.

Effect of replenishment and backroom on retail shelf-space planning

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. Finally, we use the model to show the impact of backroom space availability and 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.

Inventory allocation models for a two-stage, two-product, capacitated supplier and retailer problem with random demand

The objective of this research is to develop an optimal inventory allocation methodology for a supply chain consisting of a capacitated retailer with limited shelf space, and two unreliable capacitated suppliers in an uncertain environment. We develop conceptual and analytical models that provide allocation preferences between shelf-space and warehouse in both deterministic and stochastic demand cases, and develop managerial insights based on them. For each case, we provide both a closed-form solution and a heuristic method, and illustrate the bounds on the optimal solution. Further, we show that the cost function is L⋄-convex in some cases. Finally, we prove that the expected profit decreases as the variance of demand increases.