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The 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.

This 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.

The 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.

This study suggests that managers should allocate shelf space for SKUs on the basis of not only case pack quantity but also consumer demand.

To empirically analyse the relationship between the shelf space assigned to brands and several factors related to store management.

The data come from a study of 40 product categories in a sample of superstores in Spain. The variables are: shelf space occupied by private labels, private labels market share, number of promotions, assortment (number of brands and number of varieties) and prices gap between private labels and national brands. A neural network analysis is then applied to the data. Methodologically, this method is shown to have better predictive power than a multiple regression.

There is a direct relationship between the space occupied by store brands and the market share, and an indirect relationship between the space and the price differential gap, the number of national brands and the range of choice.

The main limitation of this research is the time spent on collecting the data. Another limitation is that some variables have not been included in the study such as inventory. The conclusion can be used to account for behaviour of retailers in their outlets and could be used by manufacturers to determine which factors have an influence on the location of their brands on the shelves.

It has practical implications for retailers because their “over‐merchandizing” of own brands can damage the overall profitability of the category. On the other hand, national leading brands will have to invest more resources in advertising to sustain customer loyalty.

This article shows the impact that store brand shelf space has on different variables related to profitability, market share, and assortment. It has value for three agents: academic researchers, manufacturers and retailers.

Acknowledges that the effect of displayed inventory on retail sales is widely recognized in the logistics, marketing and operations management literature and has been empirically verified. However, neither the marketing literature (shelf‐space allocation models) nor the operations management literature (inventory control models) has appropriately modeled this effect. The displayed‐inventory news‐vendor problem is developed and analyzed, utilizing a simple model to illustrate the interdependencies between the inventory and space‐allocation decisions. The model is then extended to the multi‐item case, which can be incorporated as part of a comprehensive shelf‐management system.

The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.

The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.

The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.

The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.

The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.

Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.

Because 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.

The 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.

The 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.

The 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.

The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Shelf 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.

The paper develops three heuristics to solve a shelf space allocation problem. It compares three heuristics with existing heuristic using empirical study.

In 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.

The 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.

The proposed heuristics are applied to a case of existing retail store which gave more profit than the current allocation scheme.

The paper proposes new initial constructor and neighbourhood move strategy to develop efficient heuristic. Heuristics proposed in this paper are competitive with existing heuristics.

This paper seeks to analyze the shelf management actions undertaken by dealers between manufacturer and store brands, from the manufacturer perspective. Particularly, to know whether there is an agreement in manufacturers' perceptions on the merchandising of the different brands on the shelf or, on the contrary, whether different groups of manufacturers can be identified – in the latter case, to characterize these groups of manufacturers.

The data come from a survey aimed at the business units of mass commodity companies in Spain. The variables are: seven items on agreement degree regarding the shelf management carried out by distributors, manufacturer's descriptive variables and manufacturer's competitive strategy variables. The manufacturers' perceptions are analyzed by univariate and bivariate descriptive analysis techniques. Different groups of manufacturers in relation to their merchandising and shelf space perceptions are identified by multivariate techniques of cluster and hierarchical segmentation.

On average, manufacturers consider that retailers are favoring unequal competition terms between manufacturer and store brands through better merchandising management for their own brands. Nevertheless, different groups of manufacturers are identified according to their perceptions.

The potential contribution of this research lies in the identification and characterization of different groups of manufacturers regarding their opinions about shelf management actions undertaken by retailers. Moreover, the results evidence an increasing power of retailers and show manufacturers how they can respond.

The objective of this paper is to investigate the impact of products' characteristics on the performance of three distribution strategies: traditional warehousing (TW), cross-docking pick by line (XDPL) and cross-docking pick by store (XDPS).

Based on a case study of an FMCG “Fast Moving Consumer Goods” company and a major French retailer, we empirically analyse the impact of the products' characteristics on the performance of the three distribution strategies. We consider a three-echelon supply chain composed of one supplier DC, one retailer DC and multiple retailer stores. The inventory at each echelon is controlled according to an order-up-to (OUT) level policy. The demand is forecasted by means of a single exponential smoothing method. A sensitivity analysis is also conducted to analyse the impact of the supply chain parameters on the comparative performance of the strategies when the parameters' values deviate from the empirical base case.

The empirical investigation shows that the use of XDPL results leads to an increase in the supply chain total cost, whereas XDPS reduces the cost. Moreover, we show that for a service-level target, cross-docking strategies should be selected for products with low variability, high shelf space, low value and short lead-time. For an inventory reduction target, these strategies should be selected for products with high demand volume. We also propose a managerial framework for choosing the right strategy for each product.

This paper fills a gap in the literature by presenting empirical results based on a real business case of a multi-echelon supply chain. Both cost and service are used to evaluate the performance of the strategies.

Our work has the limitation to ignore the transportation cost implications when selecting the right distribution strategy. Hence, including such cost in the analysis would constitute an interesting extension of this work. Moreover, our empirical analysis represents a practical rich context that makes the scope for transferability of findings learned from this article substantial. However, for the generalisability of the findings, larger datasets in the retail supply chain would be interesting to consider

As other worldwide sourcing industries the retail sector is also prone to various forms of corruption. In particular large retail chains doing business in developing countries are often faced with corrupt bureaucracy and struggle with dubious administrative processes. On the other hand the purchasing divisions of large retailers decide upon million dollar deals with their suppliers which may tempt manufacturers to pay bribes for winning the deal. While such forms of corruption may be found also for other businesses there are other practices which may be recognized as corruption which are typical in the retail sector. One of the most controversial discussions concerns the practice of so-called slotting fees which are charged to manufacturers as a contribution to the handling costs of the retailer. Since such fees are negotiated in secrecy and not broken down by categories of expenditure they are often seen as a bribery-like payment demanded for getting contracts or staying in business. In the following chapter we will analyze these practices from an economic perspective. We will provide some empirical findings on how such payments are assessed in practice and conclude with some ethical considerations concerning the practice and the effects of slotting fees.

This study assumes image size and location, product substitution, and product supply to be factors influencing the purchase decision. Under such assumption, shelf-space allocation and inventory theories are integrated, and operating profit and cost of the online store under the supply policy that prevent stockouts are analyzed to develop an optimal model for inventory control and product image allocation. The paper aims to discuss these issues.

The developed model takes both order cost and holding cost into account when calculating the total cost. As the model presented is an integer non-linear programming problem, this study adopts genetic algorithm to solve the problem.

Numerical examples are provided in this study to demonstrate the applicability of the model and to illustrate the search for parameters that possess greater influence over the operating profit of the store.

This study provides a mixed integer non-linear programming model for the joint optimization of graphic design and inventory control problem. Online store owners may take the results of this study as a reference for decision-making purposes.