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Inventory Optimization at Procter & Gamble: Achieving Real Benefits Through User Adoption of Inventory Tools

Author

Listed:
  • Ingrid Farasyn

    (Procter & Gamble Services Company NV, 1853 Strombeek-Bever, Belgium)

  • Salal Humair

    (Harvard School of Public Health, Boston, Massachusetts 02115)

  • Joel I. Kahn

    (PS Analytics, The Procter & Gamble Company, Cincinnati, Ohio 45202)

  • John J. Neale

    (Boston University, Boston, Massachusetts 02215)

  • Oscar Rosen

    (The Procter & Gamble Company, Cincinnati, Ohio 45202)

  • John Ruark

    (Logility, Inc., Burlington, Massachusetts 01803)

  • William Tarlton

    (The Procter & Gamble Company, Hunt Valley, Maryland 21030)

  • Wim Van de Velde

    (Procter & Gamble Services Company NV, 1853 Strombeek-Bever, Belgium)

  • Glenn Wegryn

    (The Procter & Gamble Company, Cincinnati, Ohio 45202)

  • Sean P. Willems

    (Boston University, Boston, Massachusetts 02215)

Abstract

Over the past 10 years, Procter & Gamble has leveraged its cross-functional organizational structure with operations research to reduce its inventory investment. Savings were achieved in a two-step process. First, spreadsheet-based inventory models locally optimized each stage in the supply chain. Because these were the first inventory tools installed, they achieved significant savings and established P&G's scientific inventory practices. Second, P&G's more complex supply chains implemented multiechelon inventory optimization software to minimize inventory costs across the end-to-end supply chain. In 2009, a tightly coordinated planner-led effort, supported by these tools, drove $1.5 billion in cash savings. Although case studies show the mathematics employed, of equal importance is the presentation of the planning process that facilitates inventory management and the decision tree that matches a business to the optimal inventory tool depending on the requirements of the business. Today, more than 90 percent of P&G's business units (about $70 billion in revenues) use either single-stage (70 percent) or multiechelon (30 percent) inventory management tools. Plans are underway to increase the use of multiechelon tools to manage 65 percent of P&G's supply chains in the next three years.

Suggested Citation

  • Ingrid Farasyn & Salal Humair & Joel I. Kahn & John J. Neale & Oscar Rosen & John Ruark & William Tarlton & Wim Van de Velde & Glenn Wegryn & Sean P. Willems, 2011. "Inventory Optimization at Procter & Gamble: Achieving Real Benefits Through User Adoption of Inventory Tools," Interfaces, INFORMS, vol. 41(1), pages 66-78, February.
  • Handle: RePEc:inm:orinte:v:41:y:2011:i:1:p:66-78
    DOI: 10.1287/inte.1100.0546
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    References listed on IDEAS

    as
    1. John M. Bossert & Sean P. Willems, 2007. "A Periodic-Review Modeling Approach for Guaranteed Service Supply Chains," Interfaces, INFORMS, vol. 37(5), pages 420-436, October.
    2. Stephen C. Graves & Sean P. Willems, 2000. "Optimizing Strategic Safety Stock Placement in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 2(1), pages 68-83, June.
    3. Salal Humair & Sean P. Willems, 2011. "TECHNICAL NOTE---Optimizing Strategic Safety Stock Placement in General Acyclic Networks," Operations Research, INFORMS, vol. 59(3), pages 781-787, June.
    4. Ingrid Farasyn & Koray Perkoz & Wim Van de Velde, 2008. "Spreadsheet Models for Inventory Target Setting at Procter & Gamble," Interfaces, INFORMS, vol. 38(4), pages 241-250, August.
    5. John J. Neale & Sean P. Willems, 2009. "Managing Inventory in Supply Chains with Nonstationary Demand," Interfaces, INFORMS, vol. 39(5), pages 388-399, October.
    6. Salal Humair & Sean P. Willems, 2006. "Optimizing Strategic Safety Stock Placement in Supply Chains with Clusters of Commonality," Operations Research, INFORMS, vol. 54(4), pages 725-742, August.
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    Citations

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    Cited by:

    1. de Kok, Ton & Grob, Christopher & Laumanns, Marco & Minner, Stefan & Rambau, Jörg & Schade, Konrad, 2018. "A typology and literature review on stochastic multi-echelon inventory models," European Journal of Operational Research, Elsevier, vol. 269(3), pages 955-983.
    2. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    3. Salal Humair & John D. Ruark & Brian Tomlin & Sean P. Willems, 2013. "Incorporating Stochastic Lead Times Into the Guaranteed Service Model of Safety Stock Optimization," Interfaces, INFORMS, vol. 43(5), pages 421-434, October.
    4. Fernando Bernstein & Yang Li & Kevin Shang, 2016. "A Simple Heuristic for Joint Inventory and Pricing Models with Lead Time and Backorders," Management Science, INFORMS, vol. 62(8), pages 2358-2373, August.
    5. Gihan S. Edirisinghe & Thamer Almutairi, 2023. "Multi-Echelon Inventory Optimization for Practitioners: a Predictive Global Sensitivity Analysis Approach," SN Operations Research Forum, Springer, vol. 4(2), pages 1-20, June.
    6. Fichtinger, Johannes & Chan, Claire (Wan-Chuan) & Yates, Nicola, 2019. "A joint network design and multi-echelon inventory optimisation approach for supply chain segmentation," International Journal of Production Economics, Elsevier, vol. 209(C), pages 103-111.
    7. Steffen T. Klosterhalfen & Stefan Minner & Sean P. Willems, 2014. "Strategic Safety Stock Placement in Supply Networks with Static Dual Supply," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 204-219, May.
    8. Klosterhalfen, Steffen T. & Willems, Sean P. & Dittmar, Daniel, 2023. "Safety stock placement in supply chains with expediting," European Journal of Operational Research, Elsevier, vol. 307(2), pages 745-757.
    9. Grace Hua, N. & Willems, Sean P., 2016. "Analytical insights into two-stage serial line supply chain safety stock," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 107-112.
    10. Asmae El Mokrini & Tarik Aouam & Nadine Kafa, 2023. "A tailored aggregation strategy for inventory pooling in healthcare: evidence from an emerging market," Operations Management Research, Springer, vol. 16(1), pages 209-226, March.
    11. Stephen C. Graves & Tor Schoenmeyr, 2016. "Strategic Safety-Stock Placement in Supply Chains with Capacity Constraints," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 445-460, July.
    12. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    13. Eruguz, Ayse Sena & Sahin, Evren & Jemai, Zied & Dallery, Yves, 2016. "A comprehensive survey of guaranteed-service models for multi-echelon inventory optimization," International Journal of Production Economics, Elsevier, vol. 172(C), pages 110-125.
    14. Jeffrey D. Camm & Jeremy Christman & A. Narayanan, 2022. "Total Unduplicated Reach and Frequency Optimization at Procter & Gamble," Interfaces, INFORMS, vol. 52(2), pages 149-157, March.

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