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Intelligent purchasing: How artificial intelligence can redefine the purchasing function

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  • Allal-Chérif, Oihab
  • Simón-Moya, Virginia
  • Ballester, Antonio Carlos Cuenca

Abstract

Artificial intelligence (AI) can affect all of a company’s functions, not least the purchasing department. In addition to automating and optimizing existing processes, AI opens up new opportunities for purchasers to undertake new, strategic, collaborative, enduring missions. AI enables complex, strategic decision-making in an unpredictable, hostile environment. This article analyzes to what extent AI can improve the performance of the purchasing department. First, a review is undertaken of how AI is used in purchasing. Thereafter, the research follows an exploratory, inductive, and qualitative approach based on a multiple case study of the following technologies: (1) the Synertrade automated international purchasing system; (2) the Silex matching system; (3) SAP Ariba decision support; (4) Jaggaer supplier relations management; and (5) the Ideapoke collaborative ideation and innovative project management platform. The present study’s contributions lie in its redefinition of the purchasing function, of the purchaser’s role, of supplier relationship management policy, and of interdepartmental collaboration, involving, for example, Marketing and R&D.

Suggested Citation

  • Allal-Chérif, Oihab & Simón-Moya, Virginia & Ballester, Antonio Carlos Cuenca, 2021. "Intelligent purchasing: How artificial intelligence can redefine the purchasing function," Journal of Business Research, Elsevier, vol. 124(C), pages 69-76.
  • Handle: RePEc:eee:jbrese:v:124:y:2021:i:c:p:69-76
    DOI: 10.1016/j.jbusres.2020.11.050
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    1. Werner Liebregts & Pourya Darnihamedani & Eric Postma & Martin Atzmueller, 2020. "The promise of social signal processing for research on decision-making in entrepreneurial contexts," Small Business Economics, Springer, vol. 55(3), pages 589-605, October.
    2. Bleda, Mercedes & Chicot, Julien, 2020. "The role of public procurement in the formation of markets for innovation," Journal of Business Research, Elsevier, vol. 107(C), pages 186-196.
    3. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    4. Aragon-Mendoza, Juan & del Val, Manuela Pardo & Roig-Dobón, Salvador, 2016. "The influence of institutions development in venture creation decision: A cognitive view," Journal of Business Research, Elsevier, vol. 69(11), pages 4941-4946.
    5. Reis, Carolina & Ruivo, Pedro & Oliveira, Tiago & Faroleiro, Paulo, 2020. "Assessing the drivers of machine learning business value," Journal of Business Research, Elsevier, vol. 117(C), pages 232-243.
    6. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    7. Chung, Minjee & Ko, Eunju & Joung, Heerim & Kim, Sang Jin, 2020. "Chatbot e-service and customer satisfaction regarding luxury brands," Journal of Business Research, Elsevier, vol. 117(C), pages 587-595.
    8. Borges, Mauro & Hoppen, Norberto & Luce, Fernando Bins, 2009. "Information technology impact on market orientation in e-business," Journal of Business Research, Elsevier, vol. 62(9), pages 883-890, September.
    9. Nitesh Asthana & Manish Gupta, 2015. "Supplier selection using artificial neural network and genetic algorithm," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 11(4), pages 457-472.
    10. Legenvre, Hervé & Gualandris, Jury, 2018. "Innovation sourcing excellence: Three purchasing capabilities for success," Business Horizons, Elsevier, vol. 61(1), pages 95-106.
    11. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    12. Werner Liebregts & Pourya Darnihamedani & Eric Postma & Martin Atzmueller, 0. "The promise of social signal processing for research on decision-making in entrepreneurial contexts," Small Business Economics, Springer, vol. 0, pages 1-17.
    13. Allal-Chérif, Oihab & Makhlouf, Mohamed, 2016. "Using serious games to manage knowledge: The SECI model perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1539-1543.
    14. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Sylvie Crouzet, 2022. "Train your buyers in the hidden cost method! For a purchasing cost that incorporates evaluation of the impact of purchasing-related dysfunctions [Formez vos acheteurs à la méthode des coûts-perform," Post-Print hal-04223281, HAL.
    2. Spreitzenbarth, Jan & Stuckenschmidt, Heiner & Bode, Christoph, 2021. "The state of artificial intelligence: Procurement versus sales and marketing," 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 223-243, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Allal-Chérif, Oihab & Guijarro-Garcia, María & Ulrich, Klaus, 2022. "Fostering sustainable growth in aeronautics: Open social innovation, multifunctional team management, and collaborative governance," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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