A DSS to Optimize Facings on the Shelf of a Retail Store

This paper aims to maximize the profitability of a set of brand SKUs stocked at a particular retailer by optimizing the number of facings for each product. Based on the past data, a set of variables such as retail price of different SKUs, linear footage that a particular manufacturer manages from the retailer, number of facings for each SKU and volume of sales for each SKU are identified. We develop a set of models, which will measure the volumetric impact of these instances on the overall revenue dollars and volume of sales in particular. One of the major issues in this problem is the relationship between Volume of Sales Vi and the number of facings Xi. If this relationship were linear, this optimization problem could have been treated as a MIP and solved accordingly. We treat this non-linear program by an innovative approximation technique and solve the objective using a piecewise approximation function.