A retail store SKU promotions optimization model for category multi-period profit maximization

Consumer promotions are an important element of competitive dynamics in retail markets and make a significant difference in the retailer's profits. But no study has so far included all the elements that are required to meet retail business objectives. We extend the existing literatures by considering all the basic requirements for a promotional Decision Support System (DSS): reliance on operational (store-level) data only, the ability to predict sales as a function of prices and the inclusion of other promotional variables affecting the category. The new model delivers an optimizing promotional schedule at Stock-Keeping-Unit (SKU) level which maximizes multi-period category level profit under the constraints of business rules typically applied in practice. We first develop a high dimensional distributed lag demand model which integrates both cross-SKU competitive promotion information and cross-period promotional influences. We estimate the model by proposing a two stage sign constrained regularization approach to ensure realistic promotional parameters. Based on the demand model, we then build a nonlinear integer programming model to maximize the retailer's category profits over a planning horizon under constraints that model important business rules. The output of the model provides optimized prices, display and feature advertising planning together with sales and profit forecasts. Empirical tests over a number of stores and categories using supermarket data suggest that our model generates accurate sales forecasts and increases category profits by approximately 17% and that including cross-item and cross-period effects is also valuable

Improving promotional effectiveness through supplier-retailer collaboration

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.Includes bibliographical references (leaves 60-61).In the consumer products industry, retail chains and manufacturers run promotions to maintain consumer and brand loyalty. The two major issues in planning and executing promotions are to accurately forecast demand and to control Out-of-Stock at the shelf. This thesis addresses both these issues. At the strategic level, "Collaborative, Planning, Forecasting and Replenishment" is used to define a process for two companies to collaboratively plan and execute promotions. At an operational level, the single period multi-item newsboy concept with a budget constraint is used to define an optimization model that helps determine the right budget and order quantities for products under a promotion at a targeted service level to improve profit or sales. The concept of Supply Contracts is researched to identify some ways that can be used to optimize the whole supply chain rather than just the retailer's. The value of optimal collaboration was confirmed in the results shown by the model. When optimizing the entire chain, the maximize profit optimization model achieved combined profit improvements of 37% as compared to an actual promotion.(cont.) When only the retailer profit was maximized, the optimization model resulted in 5.9% profit improvements for the retailer and 0.3% profit improvements for the supplier as compared to an actual promotion. Finally, the revenue maximization model showed that after a certain point, increasing the budget did not result in increased service levels. This research can also be applied to new product launches, seasonality of products as well as daily replenishments.by Gautam Kapur and Bin Liu.M.Eng.in Logistic

Demand Forecast in Retail Assortment Optimization—Based on an Empirical Analysis of Beverage Sales

This paper focus on establishing the demand forecasting model to optimize product assortments from a set of SKUs in the same category. The aim of the model is to achieve revenue maximization. Based on the attribute level, the demand model considers the consumers’ preference and the possibility of substitution between different attributes. Then it divides the product’s specific attributes and multiplies these attributes effects. Furthermore, one beverage case was applied to the demand model to do empirical analysis. Top beverage categories were selected and e-commerce sales data were collected to represent the pre-sale of whole categories. Moreover, a store named S with some beverage SKUs is assumed and applied to the model, which predicted sales volume of each existing SKU and the total revenue

PROMOTION OPTIMIZATION IN COMPETITIVE ENVIRONMENTS BY CONSIDERING THE CANNIBALIZATION EFFECT

This study proposes a new model to optimize sales promotion in competitive markets and examines the impact of competition on sales promotion planning and business performance in retail chains. The model can be used to determine the best promotional discount for different products with a cannibalization effect when competitors are present in the retail market and offer the same products with different discounts. An integer nonlinear programming problem is proposed to model the above issue. To solve the model, it is reformulated as a mixed-integer linear programming problem. Consequently, a MIP solver can be used to solve the model in a reasonable CPU time. Several examples are solved and a sensitivity analysis of the model parameters is performed. The results of our numerical study show interesting findings that considering different competitors is very important in promotion planning and optimization. Failure to take them into account can lead to loss of profits

A model and case study for efficient shelf usage and assortment analysis

In the rapidly changing environment of Fast Moving Consumer Goods sector where new product launches are frequent, retail channels need to reallocate their shelf spaces intelligently while keeping up their total profit margins, and to simultaneously avoid product pollution. In this paper we propose an optimization model which yields the optimal product mix on the shelf in terms of profitability, and thus helps the retailers to use their shelves more effectively. The model is applied to the shampoo product class at two regional supermarket chains. The results reveal not only a computationally viable model, but also substantial potential increases in the profitability after the reorganization of the product list. © 2008 Springer Science+Business Media, LLC

Three Essays on Optimization and Decision-Making Solutions in Grocery Retail Operations

In this dissertation titled “Three Essays on Optimization and Decision-Making Solutions in Retail Operations,” we explore various techniques aimed at optimizing the operational efficiency in a grocery retail store. Specifically, the first essay examines a store manager’s decision of which stock-keeping units (SKUs) from a given category to assign to a promotional display space. We develop a decision support tool that consists of an estimation model and an optimization model. Using a grocery store sales transaction dataset, we introduce a methodology to measure the incremental lift in sales of placing a particular SKU on promotional display space. Our optimization model includes the incremental lifts (from the estimation method) combined with the estimated base-sales rates and profit margins of each SKU so that the profitmaximizing SKU can be chosen for a promotional display space for each week of the year. The second essay offers a novel methodological solution on the appropriate identification and analysis of submarkets in product categories. Our research contributes to the literature in the following ways. While a vast amount of literature in both marketing and operations management investigate retail decision tree structures, limited information exists on developing algorithms that allow to generate, analyze, and test data-driven decision trees. Understanding how decision trees may drive consumer preferences is critical to a retailer’s choice of product category assortment. We provide a methodology on empirically constructing and evaluating the best fitting decision tree structures using easily accessible and readily available scanner data. The third essay studies the mechanisms retailers can use to facilitate sales of reduced packaged products, which have a number of advantages that are attractive to retailers, manufacturers, and consumers. Large product packaging creates logistical and operational challenges for retailers who carry such products since these products require more space to be stored and displayed, and more manpower to handle it. In contrast, products in smaller packaging have fewer such problems, and, thus, positively contribute to the retailer’s operational efficiency. We discuss and empirically test two levers that retailers may utilize to influence the sales of reduced packaged products. Using sales data for liquid detergents, we show that retailers with market power are able to announce their preferences for reduced packaged detergents, which results in an industry-wide shift toward reduced packaged detergents. We also show that retailers, with varying degrees of market power, may select higher ratios of reduced packaged detergents and achieve convex levels of sales of reduced packaged detergents