IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v50y2020i3p197-211.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2020.1038
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.2020.1038?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Talluri, Srinivas & Narasimhan, Ram, 2003. "Vendor evaluation with performance variability: A max-min approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 543-552, May.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    4. Talluri, Srinivas & Narasimhan, Ram, 2004. "A methodology for strategic sourcing," European Journal of Operational Research, Elsevier, vol. 154(1), pages 236-250, April.
    5. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    6. Kleinsorge, Ilene K. & Schary, Philip B. & Tanner, Ray D., 1992. "Data Envelopment Analysis for monitoring customer-supplier relationships," Journal of Accounting and Public Policy, Elsevier, vol. 11(4), pages 357-372.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Visani, Franco & Boccali, Filippo, 2020. "Purchasing price assessment of leverage items: A Data Envelopment Analysis approach," International Journal of Production Economics, Elsevier, vol. 223(C).
    2. Silvia Saravia-Matus & T. S. Amjath-Babu & Sreejith Aravindakshan & Stefan Sieber & Jimmy A. Saravia & Sergio Gomez y Paloma, 2021. "Can Enhancing Efficiency Promote the Economic Viability of Smallholder Farmers? A Case of Sierra Leone," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    3. Yash Daultani & Ashish Dwivedi & Saurabh Pratap, 2021. "Benchmarking higher education institutes using data envelopment analysis: capturing perceptions of prospective engineering students," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 773-789, December.
    4. Ying Li & Yung-Ho Chiu & Tai-Yu Lin & Tzu-Han Chang, 2020. "Pre-Evaluating the Technical Efficiency Gains from Potential Mergers and Acquisitions in the IC Design Industry," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 525-559, April.
    5. Amar Oukil & Slim Zekri, 2021. "Investigating farming efficiency through a two stage analytical approach: Application to the agricultural sector in Northern Oman," Papers 2104.10943, arXiv.org.
    6. Olawale Ogunrinde & Ekundayo Shittu, 2023. "Benchmarking performance of photovoltaic power plants in multiple periods," Environment Systems and Decisions, Springer, vol. 43(3), pages 489-503, September.
    7. Park, Jaehun & Lee, Dongha & Zhu, Joe, 2014. "An integrated approach for ship block manufacturing process performance evaluation: Case from a Korean shipbuilding company," International Journal of Production Economics, Elsevier, vol. 156(C), pages 214-222.
    8. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2020. "An Efficiency Perspective on Carbon Emissions and Financial Performance," Ecological Economics, Elsevier, vol. 175(C).
    9. Toloo, Mehdi & Hančlová, Jana, 2020. "Multi-valued measures in DEA in the presence of undesirable outputs," Omega, Elsevier, vol. 94(C).
    10. Babak Daneshvar Rouyendegh & Asil Oztekin & Joseph Ekong & Ali Dag, 2019. "Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach," Annals of Operations Research, Springer, vol. 278(1), pages 361-378, July.
    11. Andreas Eder & Bernhard Mahlberg, 2018. "Size, Subsidies and Technical Efficiency in Renewable Energy Production: The Case of Austrian Biogas Plants," The Energy Journal, , vol. 39(1), pages 185-210, January.
    12. Maha Kalai, 2019. "Nonparametric Measures of Capacity Utilization of the Tunisian Manufacturing Industry: Short- and Long-Run Dual Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 318-334, March.
    13. Cook, Wade D. & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2017. "Within-group common benchmarking using DEA," European Journal of Operational Research, Elsevier, vol. 256(3), pages 901-910.
    14. Wei, Guiwu & Chen, Jian & Wang, Jiamin, 2014. "Stochastic efficiency analysis with a reliability consideration," Omega, Elsevier, vol. 48(C), pages 1-9.
    15. Shuangjie Li & Hongyu Diao & Liming Wang & Chunqi Li, 2021. "Energy Efficiency Measurement: A VO TFEE Approach and Its Application," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    16. Kai Li & Zhili Ma & Guozhou Zhang, 2019. "Evaluation of the Supply-Side Efficiency of China’s Real Estate Market: A Data Envelopment Analysis," Sustainability, MDPI, vol. 11(1), pages 1-18, January.
    17. Olawale Odewole, Philip & Oyesola Salawu, Rafiu, 2021. "Assessment Of The Efficiency Of Selected Public Sector Entities In Nigeria," Ilorin Journal of Economic Policy, Department of Economics, University of Ilorin, vol. 8(1), pages 18-30, June.
    18. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    19. Dean Uèkar & Danijel Petroviæ, 2021. "Efficiency of banks in Croatia," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 39(2), pages 349-379.
    20. Chowdhury, Hedayet & Zelenyuk, Valentin, 2016. "Performance of hospital services in Ontario: DEA with truncated regression approach," Omega, Elsevier, vol. 63(C), pages 111-122.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:50:y:2020:i:3:p:197-211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.