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Emerging trends in the interplay between analytics and operations in MSMEs

Author

Listed:
  • Mani Venkatesh

    (University of Manitoba)

  • Samuel Fosso Wamba

    (Toulouse Business School)

  • Angappa Gunasekaran

    (Penn State Harrisburg)

  • V. G. Venkatesh

    (EM Normandie Business School, METIS Lab)

Abstract

This editorial synthesizes the principal research trends and prospective directions emphasized in the special issue titled Emerging Trends in the Interplay between Analytics and Operations in MSMEs, published in Annals of Operations Research. The contributions underscore the transformative impact of analytics on Micro, Small, and Medium Enterprises (MSMEs), accentuating the integration of Industry 4.0 technologies, artificial intelligence (AI), machine learning, deep learning, blockchain, and big data analytics into operations management. Furthermore, the discussions illuminate emerging trends concerning the application of these technologies in MSMEs, presenting a future roadmap and directions regarding the interplay between analytics and operations. This also affirms a renewed focus on data analytics capabilities in enhancing operational efficiency, resilience, and sustainability within MSMEs. Prospective research directions encompass the development of transparent and responsible AI models, the addressing of implementation challenges related to business intelligence tools, and the fostering of dynamic capabilities to navigate the evolving digital landscape.

Suggested Citation

  • Mani Venkatesh & Samuel Fosso Wamba & Angappa Gunasekaran & V. G. Venkatesh, 2025. "Emerging trends in the interplay between analytics and operations in MSMEs," Annals of Operations Research, Springer, vol. 350(2), pages 355-364, July.
    • Handle: RePEc:spr:annopr:v:350:y:2025:i:2:d:10.1007_s10479-025-06704-7
    DOI: 10.1007/s10479-025-06704-7
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    References listed on IDEAS

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    3. Sabri Boubaker & Tu D. Q. Le & Thanh Ngo & Riadh Manita, 2025. "Predicting the performance of MSMEs: a hybrid DEA-machine learning approach," Annals of Operations Research, Springer, vol. 350(2), pages 555-577, July.
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