Total Unduplicated Reach and Frequency Optimization at Procter & Gamble

References

• Balas E, Jeroslow R (1972) Canonical cuts to the unit hypercube. SIAM J. Appl. Math. 23(1):61–69.Google Scholar
• Camm JD (2014) ASP, the art and science of practice: A (very) short course in suboptimization. Interfaces 44(4):428–431.Link, Google Scholar
• Camm JD, Chorman TE, Dill FA, Evans JR, Sweeney DJ, Wegryn GW (1997) Blending OR/MS, judgement, and GIS: Restructuring P&G’s supply chain. Interfaces 27(1):128–142.Link, Google Scholar
• Church R, ReVelle C (1974) The maximal covering location problem. Papers Regional Sci. Assoc. 32(December):101–118.Google Scholar
• Downs BT, Camm JD (1996) An exact algorithm for the maximal covering problem. Naval Res. Logist. 43(3):435–461.Google Scholar
• Dwyer R, Evans J (1981) A branch and bound algorithm for the list selection problem in direct mail advertising. Management Sci. 27(6):658–667.Link, Google Scholar
• Ennis J, Fayle C, Ennis M (2012) eTurf: A competitive TURF algorithm for large datasets. Food Quality Preference 23(1):44–48.Google Scholar
• Farasyn I, Humair S, Kahn JI, Neale JJ, Rosen O, Ruark JD, Tarlton W, Van de Velde WM, Wegryn G, Willems SP (2011) Inventory optimization at Procter & Gamble: Achieving real benefits through user adoption of inventory tools. Interfaces 41(1):66–78.Link, Google Scholar
• Institute for Operations Research and the Management Sciences (2004) Procter & Gamble wins top decision-making award at operations research meeting. Accessed June 4, 2021, https://www.eurekalert.org/pub_releases/2004-10/ifor-pg102104.php.Google Scholar
• Kreiger A, Green P (2000) TURF revisited: Enhancements to total unduplicated reach and frequency analysis. Marketing Rev. 12(4):30–36.Google Scholar
• Louviere JJ (1991) Best-worst scaling: A model for the largest difference judgments. Working paper, University of Alberta, Edmonton, AB.Google Scholar
• Miaoulis G, Free V, Parsons H (1990) TURF: A new planning approach for product line extensions. Marketing Rev. 2(1):28–40.Google Scholar
• P&G (2020) P&G 2020 annual report. Accessed June 4, 2021, https://www.annualreports.com/HostedData/AnnualReports/PDF/NYSE_PG_2020.pdf.Google Scholar
• P&G (2021) Policies & practices. Accessed June 4, 2021, https://us.pg.com/policies-and-practices/purpose-values-and-principles/.Google Scholar
• Ritz W (2021) TURF: New methods for implementation. Accessed May 1, 2021, https://trcmarketresearch.com/whitepaper/turf-new-methods-for-implementation/.Google Scholar
• Sandholm T, Levine D, Concordia M, Martyn P, Hughes R, Jacobs J, Begg D (2006) Changing the game in strategic sourcing at Procter & Gamble: Expressive competition enabled by optimization. Interfaces 36(1):55–68.Link, Google Scholar
• Sawtooth Software (2021a) Exhaustive TURF limitation. Accessed May 1, 2021, https://sawtoothsoftware.com/help/maxdiff-analyzer/manual/index.html?exhaustive-turf-limitation.html.Google Scholar
• Sawtooth Software (2021b) Stepwise TURF algorithm. Accessed May 1, 2021, https://sawtoothsoftware.com/help/maxdiff-analyzer/manual/index.html?stepwise-turf-algorithm.html.Google Scholar
• Serra D (2013) Implementing TURF analysis through binary linear programming. Food Quality Preference 28(1):382–388.Google Scholar