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Verizon Uses Advanced Analytics to Rationalize Its Tail Spend Suppliers

Author

Listed:
  • Hossein Abdollahnejadbarough

    (Verizon, Basking Ridge, New Jersey 07920)

  • Kalyan S Mupparaju

    (Verizon, Basking Ridge, New Jersey 07920)

  • Sagar Shah

    (Verizon, Basking Ridge, New Jersey 07920)

  • Colin P. Golding

    (Verizon, Basking Ridge, New Jersey 07920)

  • Abelardo C. Leites

    (Verizon, Basking Ridge, New Jersey 07920)

  • Timothy D. Popp

    (Verizon, Basking Ridge, New Jersey 07920)

  • Eric Shroyer

    (Verizon, Basking Ridge, New Jersey 07920)

  • Yanai S. Golany

    (Verizon, Basking Ridge, New Jersey 07920)

  • Anne G. Robinson

    (Verizon, Basking Ridge, New Jersey 07920)

  • Vedat Akgun

    (Verizon, Basking Ridge, New Jersey 07920)

Abstract

The Verizon Global Supply Chain organization currently manages thousands of active supplier contracts. These contracts account for several billion dollars of annualized Verizon spend. Managing thousands of suppliers, controlling spend, and achieving the best price per unit (PPU) through negotiations are costly and labor-intensive tasks handled by Verizon strategic sourcing teams. Verizon engages thousands of suppliers for many reasons—best price, diversity, short-term requirements, and so forth. Whereas managing a few larger spend suppliers can be done manually by dedicated sourcing managers, managing thousands of smaller suppliers at the tail spend is challenging, can often introduce risk, and can be expensive. At Verizon, a unique blend of descriptive, predictive, and prescriptive analytics, as well as Verizon-specific sourcing acumen was leveraged to tackle this problem and rationalize Verizon’s tail spend suppliers. Through the creative application of operations research, machine learning, text mining, natural language processing, and artificial intelligence, Verizon reduced spend by millions of dollars and acquired the lowest PPU for the sourced products and services. Other benefits Verizon realized were centralized and transparent contract and supplier relationship management, overhead cost reduction, decreased contract execution lead time, and service quality improvement for Verizon’s strategic sourcing teams.

Suggested Citation

  • Hossein Abdollahnejadbarough & Kalyan S Mupparaju & Sagar Shah & Colin P. Golding & Abelardo C. Leites & Timothy D. Popp & Eric Shroyer & Yanai S. Golany & Anne G. Robinson & Vedat Akgun, 2020. "Verizon Uses Advanced Analytics to Rationalize Its Tail Spend Suppliers," Interfaces, INFORMS, vol. 50(3), pages 197-211, May.
  • Handle: RePEc:inm:orinte:v:50:y:2020:i:3:p:197-211
    DOI: 10.1287/inte.2020.1038
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    References listed on IDEAS

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    Cited by:

    1. William A. Muir & Daniel Reich, 2021. "Using Machine Learning to Improve Public Reporting on U.S. Government Contracts," Interfaces, INFORMS, vol. 51(6), pages 463-479, November.
    2. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Aditya Kamat & Saket Shanker & Akhilesh Barve & Kamalakanta Muduli & Sachin Kumar Mangla & Sunil Luthra, 2022. "Uncovering interrelationships between barriers to unmanned aerial vehicles in humanitarian logistics," Operations Management Research, Springer, vol. 15(3), pages 1134-1160, December.
    4. Yi Wang & Yafei Yang & Zhaoxiang Qin & Yefei Yang & Jun Li, 2023. "A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

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