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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nats Esquejo & Kevin Miller & Kevin Norwood & Ivan Oliveira & Rob Pratt & Ming Zhao, 2015. "Statistical and Optimization Techniques for Laundry Portfolio Optimization at Procter & Gamble," Interfaces, INFORMS, vol. 45(5), pages 444-461, October.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:5:p:444-461
    DOI: 10.1287/inte.2015.0802
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    References listed on IDEAS

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    1. Charles Audet & Jack Brimberg & Pierre Hansen & Sébastien Le Digabel & Nenad Mladenovi'{c}, 2004. "Pooling Problem: Alternate Formulations and Solution Methods," Management Science, INFORMS, vol. 50(6), pages 761-776, June.
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