Challenges and Opportunities of Artificial Intelligence on Supply Chain Management: A Case of Delta Beverages, Zimbabwe.

This study examines the challenges and opportunities presented by Artificial Intelligence (AI) in the supply chain management (SCM) of Delta Beverages. The objectives were to identify critical factors influencing AI adoption, assess its impact on financial performance, and propose policy reforms to support effective integration. The study is guided by two theories namely the Resource based View and Technology-Organisation-Environment. A mixed-methods approach, grounded in both positivist and interpretivist research philosophies, was employed. Data were collected from 80 respondents through structured questionnaires with a five-point Likert scale, 20 semi-structured interviews, and direct observations. Findings revealed that AI significantly enhances demand forecasting, inventory management, predictive maintenance and real-time logistics decision-making, thereby improving operational efficiency and reducing costs. However, challenges persist, including limited supply chain visibility due to data quality issues, a shortage of skilled personnel, integration difficulties, employee resistance to change, high initial investment costs and uncertainty regarding return on investment (ROI). The research has led to the conclusion that despite AI having the potential to revolutionize Delta Beverages’ supply chain, its implementation will be successful only if skill gap issues, data governance challenges and costs are taken care of. Some of the most effective recommendations that can be made in this regard would be implementing effective AI training programmes within the organization, developing better data management processes, utilizing incentives provided by governments and industries for seamless integration and implementing effective change management approaches, among others.

Artificial Intelligence, Supply Chain Management, Challenges, Opportunities

1. Abioye, A., Adeleke, A., and Mojisola, O. (2021). Application of AI and ML in Demand Forecasting in Southern Africa. Journal of African Supply Chain Management, 9(2), pp.45-67. [Google Scholar] [Crossref]

2. Alsheibani, S., Cheung, Y., and Messom, C. (2019). Factors inhibiting the adoption of artificial intelligence at organisational level: A preliminary investigation. *Proceedings of the 25th Americas Conference on Information Systems*, 1–10. [Google Scholar] [Crossref]

3. Antonopoulos, I., et al. (2020). Artificial intelligence and approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899. [Google Scholar] [Crossref]

4. Awa, H. O., Ojiabo, O. U., and Emecheta, B. C. (2020). Integrating TAM, TPB and TOE frameworks and expanding technology acceptance model for mobile commerce adoption. *Journal of Business Research*, *116*, 567–578. https://doi.org/10.1016/j.jbusres.2020.04.029 [Google Scholar] [Crossref]

5. Barney, J. (1991). Firm resources and sustained competitive advantage. *Journal of Management*, *17*(1), 99–120. https://doi.org/10.1177/014920639101700108 [Google Scholar] [Crossref]

6. Baryannis, G., Validi, S., Dani, S., and Antoniou, G. (2023). Artificial intelligence in supply chain management: Theoretical frameworks and future directions. Journal of Business Research, 154, 113345. [Google Scholar] [Crossref]

7. Brynjolfsson, E., and McAfee, A. (2021). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton and Company. [Google Scholar] [Crossref]

8. Brynjolfsson, E., McElheran, K., and Nanda, R. (2021). AI adoption: A firm-level perspective. Harvard Business Review, 99(4), 22–25. [Google Scholar] [Crossref]

9. Canhoto, A. I., and Clear, F. (2020). Artificial intelligence and as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193. [Google Scholar] [Crossref]

10. Chironga, M., De Grandis, H., Mylenko, N., and Zouaoui, Y. (2020). How artificial intelligence can improve demand forecasting in South Africa. McKinsey and Company. [Google Scholar] [Crossref]

11. Choi, T. M., Kumar, S., Yue, X., and Chan, H. L. (2022). Disruptive technologies and supply chain management: A review and research agenda. *International Journal of Production Research*, *60*(4), 1387–1412. https://doi.org/10.1080/00207543.2021.1971778 [Google Scholar] [Crossref]

12. Choi, T. M., Wallace, S. W., and Wang, Y. (2022). Big data analytics in operations management: Recent developments and future directions. International Journal of Production Economics, 238, 108151. [Google Scholar] [Crossref]

13. Davenport, T. H., and Ronanki, R. (2020). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. [Google Scholar] [Crossref]

14. Duan, Y., Edwards, J. S., and Dwivedi, Y. K. (2021). Artificial intelligence for decision making in the era of Big Data. International Journal of Information Management, 48, 63–71. [Google Scholar] [Crossref]

15. Dubey, R., Gunasekaran, A., and Childe, S. J. (2019). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. *Management Decision*, *57*(8), 2092–2112. https://doi.org/10.1108/MD-01-2018-0039 [Google Scholar] [Crossref]

16. Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., and Giannakis, M. (2021). Big data analytics and artificial intelligence pathway to operational performance. International Journal of Production Economics, 226, 107599. [Google Scholar] [Crossref]

17. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., and Roubaud, D. (2021). Upstream supply chain visibility and complexity effect on the focal company’s sustainable performance. Annals of Operations Research, 290(1), 343–367. [Google Scholar] [Crossref]

18. Dubois, E. (2020). Artificial intelligence in supply chain management: A systematic literature review. Journal of Cleaner Production, 276, 123215. [Google Scholar] [Crossref]

19. Dwivedi, Y. K., Hughes, L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J., and Upadhyay, N. (2021). Impact of COVID-19 pandemic on information management research and practice. International Journal of Information Management, 55, 102211. [Google Scholar] [Crossref]

20. Feizabadi, J. (2020). demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 1–23. [Google Scholar] [Crossref]

21. Gupta, M., and George, J. F. (2020). Toward the development of a big data analytics capability. *Information & Management*, *57*(1), Article 103169. https://doi.org/10.1016/j.im.2019.103169 [Google Scholar] [Crossref]

22. Haider, N., Baig, M. Z., and Imran, M. (2020). Artificial intelligence and in 5G network security. arXiv preprint arXiv:2007.04490. [Google Scholar] [Crossref]

23. Ivanov, D., and Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience. International Journal of Integrated Supply Management, 13(1), 90–102. [Google Scholar] [Crossref]

24. Ivanov, D., and Dolgui, A. (2020). Viability of intertwined supply networks. International Journal of Production Research, 58(10), 2904–2915. [Google Scholar] [Crossref]

25. Ivanov, D., and Jackson, I. (2023). A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting. Transportation Research Part E, 180, 103360. [Google Scholar] [Crossref]

26. Ivanov, D., Blackhurst, J., and Das, A. (2021). Supply chain resilience and its interplay with digital technologies. International Journal of Physical Distribution and Logistics Management, 51(2), 157–176. [Google Scholar] [Crossref]

27. Kietzmann, J., and Pitt, L. (2020). Artificial intelligence and: What managers need to know. Business Horizons, 63(2), 131–133. [Google Scholar] [Crossref]

28. Kolinski, A., Sliwczynski, B., and Walczak, M. (2021). Artificial intelligence in supply chain management: Challenges and opportunities. Logistics, 5(2), 27. [Google Scholar] [Crossref]

29. LeCun, Y., Bengio, Y., and Hinton, G. (2021). Deep learning. *Nature*, *521*(7553), 436–444. https://doi.org/10.1038/nature14539 [Google Scholar] [Crossref]

30. LeCun, Y., Bengio, Y., and Hinton, G. (2021). Deep learning. Nature, 521(7553), 436–444. [Google Scholar] [Crossref]

31. Marcus, G., Davis, E., and Gureckis, T. (2020). The atoms of neural computation. Science, 346(6209), 551–552. [Google Scholar] [Crossref]

32. Marcus, G., Davis, E., and Gureckis, T. (2020). The next decade in AI: Four steps towards robust artificial intelligence. *arXiv preprint arXiv:2002.06177*. [Google Scholar] [Crossref]

33. Mikalef, P., Pappas, I. O., and Krogstie, J. (2020). Big data analytics capabilities: A systematic literature review and research agenda. *Information Systems and e-Business Management*, *18*(1), 1–38. https://doi.org/10.1007/s10257-019-00435-6 [Google Scholar] [Crossref]

34. Mwamburi, J., and Njenga, K. (2020). Barriers to artificial intelligence adoption in developing countries: A case of Kenyan manufacturing firms. *African Journal of Science, Technology, Innovation and Development*, *12*(5), 601–612. https://doi.org/10.1080/20421338.2020.1727135 [Google Scholar] [Crossref]

35. Mwangi, J., and Kamau, M. (2021). Enhancing demand forecasting in Kenyan retail using AI-based models. African Journal of Business Management, 15(3), 45–54. [Google Scholar] [Crossref]

36. Mwangi, J., and Ochieng, J. (2021). Cloud computing adoption in Kenyan supply chains: A TOE perspective. *Journal of African Business*, *22*(3), 345–363. https://doi.org/10.1080/15228916.2020.18268 [Google Scholar] [Crossref]

37. Panetta, K. (2021). Gartner Top Strategic Technology Trends for 2021. Gartner Research. [Google Scholar] [Crossref]

38. Roden, S., Nucciarelli, A., Li, F., and Graham, G. (2020). Big data and artificial intelligence in operations management. International Journal of Production Research, 58(3), 887–900. [Google Scholar] [Crossref]

39. Roden, S., Nucciarelli, A., Li, F., and Graham, G. (2020). Big data and the transformation of operations models: A framework and a new research agenda. *Production Planning & Control*, *31*(11–12), 929–944. https://doi.org/10.1080/09537287.2019.1695914 [Google Scholar] [Crossref]

40. Russell, S., Norvig, P., and Davis, E. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. [Google Scholar] [Crossref]

41. Saunders, M., Lewis, P., and Thornhill, A. (2019). Research Methods for Business Students (8th ed.). Pearson. [Google Scholar] [Crossref]

42. Shrivastav, S. (2022). Data quality challenges in AI adoption for supply chain management. *International Journal of Logistics Management*, *33*(2), 456–478. https://doi.org/10.1108/IJLM-03-2021-0156 [Google Scholar] [Crossref]

43. Singh, A., and Challa, S. (2016). techniques for pattern recognition and predictive analytics. Pattern Recognition and Artificial Intelligence, 29(2), 123–135. [Google Scholar] [Crossref]

44. Singh, A., and Challa, S. (2016). techniques for pattern recognition and predictive analytics. Pattern Recognition and Artificial Intelligence, 29(2), 123–135. [Google Scholar] [Crossref]

45. Soleimani, H. (2021). Applications of artificial intelligence in supply chain management. Supply Chain Forum: An International Journal, 22(3), 185–200. [Google Scholar] [Crossref]

46. Soleimani, S. (2021). Artificial intelligence in supply chain management: A systematic literature review. *Journal of Business Logistics*, *42*(4), 432–458. https://doi.org/10.1111/jbl.12272 [Google Scholar] [Crossref]

47. Stefanovic, D., and Stefanovic, M. (2021). Artificial intelligence-powered supply chains: A systematic literature review. Logistics, 5(3), 45. [Google Scholar] [Crossref]

48. Stefanovic, N., and Stefanovic, D. (2021). Supply chain management and artificial intelligence: A review and research agenda. *Supply Chain Management: An International Journal*, *26*(6), 701–720. https://doi.org/10.1108/SCM-11-2020-0575 [Google Scholar] [Crossref]

49. Tesfaye, A., Tadesse, M., and Zegeye, T. (2022). Application of artificial intelligence in optimising agricultural supply chains in Ethiopia. Journal of Agricultural Informatics, 13(2), 12–23. [Google Scholar] [Crossref]

50. Toorajipour, R., et al. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. [Google Scholar] [Crossref]

51. Tornatzky, L. G., and Fleischer, M. (1990). *The processes of technological innovation*. Lexington Books. [Google Scholar] [Crossref]

52. Validi, S., Baryannis, G., and Dani, S. (2020). Towards an artificial intelligence-based decision-making approach for supply chain risk management. Computers and Industrial Engineering, 139, 106003. [Google Scholar] [Crossref]

53. Wamba, S. F., Gunasekaran, A., and Akter, S. (2020). Big data analytics and firm performance: Effects of dynamic capabilities. *Journal of Business Research*, *112*, 356–365. https://doi.org/10.1016/j.jbusres.2019.11.081 [Google Scholar] [Crossref]

54. Wamba, S. F., Queiroz, M. M., and Trinchera, L. (2021). Industry experiences of artificial intelligence (AI): Benefits and challenges in operations and supply chain management. Production Planning and Control, 32(12), 975–990. [Google Scholar] [Crossref]

55. Zijm, H., and Klumpp, M. (2020). Logistics and supply chain innovation: Bridging the gap between theory and practice. Springer Series in Supply Chain Management. [Google Scholar] [Crossref]

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