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We have witnessed unparalleled progress in artificial intelligence (AI) and machine learning (ML) applications in the last two decades. The AI technologies have accelerated advancements in robotics and automation, which have significant implications on almost every aspect of businesses, and especially supply chain operations. Supply chains have widely adopted smart technologies that enable real-time automated data collection, analysis, and prediction. In this study, we review recent applications of AI in operations management (OM) and supply chain management (SCM). Specifically, we consider the innovations in healthcare, manufacturing, and retail operations, since collectively, these three areas represent a majority of the AI innovations in business as well as growing problem areas. We discuss primary challenges and opportunities for utilizing AI in those industries. We also discuss trending research topics with significant value potential in these areas.

This study provides a systematic review of 76 relevant wine business studies published in the last 30 years. Our meta-analysis investigates six commonly used variables to explain wine innovation: absorptive capacity, technology adoption, sustainable practices, export orientation, firm size, and firm age. We also investigate the association between innovation and financial performance, using the reported effect sizes in the literature. Our meta-analysis reveals that absorptive capacity, technology adoption, sustainable practices, export orientation, and firm size positively correlate with innovation efforts, and innovation is positively associated with financial performance. However, we find no correlation between firm age and innovation. In addition to the meta-analysis, we apply basic text analytics and narrative review methodologies to identify a taxonomy of wine industry innovation according to four types of innovation. Based on our systematic literature review results, we make a series of managerial and policy recommendations for wine firms. Finally, we identify gaps in the literature and suggest future research directions.

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