Abstract
Backrooms are an important echelon of the retail supply chain. However, research focus has been mostly targeted to optimise both distribution centres and stores' sales area. In this paper, we propose two mathematical programming formulations to solve the grocery backroom sizing problem. This problem consists of determining the dimension of each storage department in the backroom area to optimise its overall efficiency. The first formulation is a bottom-up approach that aims to reduce the backroom life-cycle costs by determining the optimum floor space and storage height for each department. The second is a top-down approach based on Data Envelopment Analysis (DEA), which determines the efficient level of storage floor space for each backroom department, based on a comparison with the benchmarks observed among existing stores. Each approach has distinct characteristics that turn the models suitable for different retail contexts. We also describe the application of the proposed approaches to a case study of a European retailer. The application of this methodology in the design process demonstrated substantial potential for space savings (6% for the bottom-up model and 16% for the top-down model). This space reduction should either allow higher revenues in the sales area and/or lower backroom-related costs.

Abstract
Purpose - The design of retail backroom storage areas has great impact on in-store operations, customer service level and on store life-cycle costs. Moreover, backroom storage in modern retail grocery stores is critical to several functions, such as acting as a buffer against strong demand lifts yielded by an ever-increasing promotional activity, stocking seasonal peak demand and accommodating e-commerce activities. The purpose of this paper is to propose a framework to design retail backroom storage area. Furthermore, the authors aim to draw attention to the lack of literature on this topic, while clarifying the relationship between this promising research stream and the considerable body of research regarding the design and operations of conventional warehouses, as well as retail in-store operations. Design/methodology/approach - The key literature on backrooms, grocery retail, in-store operations, warehouse design and operations was reviewed. This allowed an understanding of the gap in the literature regarding the design of backrooms. Moreover, a case study methodological approach was conducted in a Portuguese retailer to extend the literature review. Findings - Despite having functions similar to conventional warehouses, backroom storage facilities have particularities that deserve a distinct analysis. Thus, the authors stress these differences and demonstrate how they influence the development of a novel backroom design framework. Originality/value - This paper fills a gap by proposing a framework to design backroom areas. Furthermore, this research may help practitioners to better design backroom areas, since this process currently lacks a formal and standardized procedure.

Abstract
The design of retail backroom storage has a great impact on in-store operations, customer service levels and store life-cycle costs. Moreover, backroom storage in modern retail stores is crucial to several functions, such as acting as a buffer against strong demand lifts yielded by an increasing promotional activity, seasonal peak demand and e-commerce activities. Despite having similar functions to a distribution center, backroom storage facilities have particularities that deserve a distinct analysis. In this paper we aim to draw attention to the lack of research about this topic.

Abstract
In this paper, the main complexities related to the modeling of production planning problems of food products are addressed. We start with a deterministic base model and build a road-map on how to incorporate key features of food production planning. The different "ingredients" are organized around the model components to be extended: constraints, objective functions and parameters. We cover issues such as expiry dates, customers' behavior, discarding costs, value of freshness and age-dependent demand. To understand the impact of these "ingredients", we solve an illustrative example with each corresponding model and analyze the changes on the solution structure of the production plan. The differences across the solutions show the importance of choosing a model suitable to the particular business setting, in order to accommodate the multiple challenges present in these industries. Moreover, acknowledging the perishable nature of the products and evaluating the amount and quality of information at hands may be crucial in lowering overall costs and achieving higher service levels. Afterwards, the deterministic base model is extended to deal with an uncertain demand parameter and risk management issues are discussed using a similar illustrative example. Results indicate the increased importance of risk-management in the production planning of perishable food goods.