Statistical and Optimization Techniques for Laundry Portfolio Optimization at Procter & Gamble

Abstract

The Procter & Gamble (P&G) fabric-care business is a multibillion dollar organization that oversees a global portfolio of products, including household brands such as Tide, Dash, and Gain. Production is impacted by a steady stream of reformulation modifications, imposed by new-product innovation and constantly changing material supply conditions. In this paper, we describe the creation and application of a novel analytical framework that has helped P&G determine the ingredient levels and product and process architectures that enable the company to create some of the world’s best laundry products. Modeling cleaning performance and other key properties such as density required P&G to develop innovative quantitative techniques based on visual statistical tools. It used advanced mathematical programming methods to address challenges that the manufacturing process imposed, product performance requirements, and physical constraints, which collectively result in a hard mixed-integer nonlinear (nonconvex) optimization problem. We describe how P&G applied our framework in its North American market to identify a strategy that improves the performance of its laundry products, provides targeted consumer benefits, and enables cost savings in the order of millions of dollars.