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

Nats Esquejo (), Kevin Miller (), Kevin Norwood (), Ivan Oliveira (), Rob Pratt () and Ming Zhao ()
Additional contact information
Nats Esquejo: Procter & Gamble, Newcastle-Upon-Tyne NE27 0QW, United Kingdom
Kevin Miller: Procter & Gamble, Cincinnati, Ohio 45202
Kevin Norwood: Procter & Gamble, Cincinnati, Ohio 45202
Ivan Oliveira: SAS, Cary, North Carolina 27513
Rob Pratt: SAS, Cary, North Carolina 27513
Ming Zhao: Department of Decision and Information Sciences, Bauer College of Business, University of Houston, Houston, Texas 77204

Interfaces, 2015, vol. 45, issue 5, 444-461

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.

Keywords: pooling; blending; optimization; response surface; design of experiments (search for similar items in EconPapers)
Date: 2015
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