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ABSTRACT The Journal of Business Logistics has been the top location for publishing logistics and supply chain‐related technological research for over forty years. With digital transformation, reshoring of manufacturing, labor shortages, decreasing birth rates, and aging workforces, companies are increasingly adopting artificial intelligence‐supported robotics to increase the ability of supply chains to react quickly and effectively to changes in customer demand, market conditions, or disruptions. This paper analyzes the use of hardware robots across the logistics fulfillment process. The study addresses the evolution of robotic training from explicit programming to machine learning and continues with a detailed discussion of generative machine learning. We then provide an overview of key hardware robots driven by generative machine learning models that are used in the fulfillment process. The paper examines the challenges that robot adoption presents to organizations and concludes with explicit directions for further research using the Theory of Resource Orchestration.
Rainer et al. (2025) studied this question.