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The state of artificial intelligence: Procurement versus sales and marketing

In: Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31

Author

Listed:
  • Spreitzenbarth, Jan
  • Stuckenschmidt, Heiner
  • Bode, Christoph

Abstract

Purpose: Sales and procurement are the main boundary spanning functions of an organization - each with a specific focus and partly different views and objectives. They are often considered as two sides of a coin that struggle with one another for relative competitive advantage. The digitalization of procurement functions and the introduction of enterprise resource systems have led to a seeming data abundance. However, the results especially in the area of artificial intelligence are not yet satisfactory in practical application. In addition, few academic works are steered towards procurement. In fact, some expect that procurement is less likely to benefit from the application of methods of artificial intelligence (AI) emphasizing the potential benefits in functions such as finance, production, marketing and sales. Why is that? What can we do about it? Methodology: Explanatory study considering the needed decisions and available data of procurement in contrast to sales and marketing. The manuscript is structured in three sections based upon the "Memorandum of Design-Orientated Business Informatics" with analysis, draft, evaluation and diffusion (Österle et al., 2010). Findings: There is a need for research on the purchasing-marketing interface, not just for AI but also for AI applications and analytics in general. Procurement scholars and managers must speed up data and analytical development, especially since our negotiation partners are benefiting from the rapid development of AI technology. Five propositions have been derived from a master thesis analyzing the needed decision and available data as well as during discussions at the 2021 European Research Seminar to facilitate practical application for management and direct further research. These are more perceived value, more data, better technological solutions, more skills and training, and different role in the value chain. Originality: This is an original work of in-progress research.

Suggested Citation

  • 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.
  • Handle: RePEc:zbw:hiclch:249617
    DOI: 10.15480/882.3990
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    References listed on IDEAS

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    1. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    2. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    3. Alexandra Brintrup & Johnson Pak & David Ratiney & Tim Pearce & Pascal Wichmann & Philip Woodall & Duncan McFarlane, 2020. "Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3330-3341, June.
    4. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    5. 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.
    6. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    7. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    8. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    9. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    10. Nam, Dalwoo & Lee, Junyeong & Lee, Heeseok, 2019. "Business analytics use in CRM: A nomological net from IT competence to CRM performance," International Journal of Information Management, Elsevier, vol. 45(C), pages 233-245.
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    Artificial Intelligence; Blockchain;

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