Transformative Power of Generative AI in Supply Chain: Examining different Drivers and Barriers
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Abstract
As the capabilities of generative artificial intelligence increases, so does the need forintegration into various industries. The purpose of this study was to explore the drivers,barriers, and future direction of integration of generative artificial intelligence in a supply chainindustry.
The existing research on generative Artificial Intelligence in the supply chain is verylimited and therefore there are many gaps in existing research. From the drivers that pushintegration to the barriers that serve as a hindrance, there is little available data. We also wantedto understand how this field will develop going forward and the research on this is also limitedso there was a need for exploratory research to help close these gaps. To help with this, valancetheory and dual factor theory was invoked.
The method used in the study was media discourse analysis, by collecting secondarydata theories were made from existing data, while data could come from a large number ofdiffering opinions, expertise and representatives. The data was found online and consisted ofvideos, articles, and blog posts. By utilizing Gioia’s method, the data collected was coded andused to answer our research questions.
The findings of this study include the drivers, barriers, and future development of theintegration of generative artificial intelligence technology into the supply chain, the findingsreaffirming the drivers, and barriers found in existing research while offering new insights intothe future development as well as drivers and barriers absent from current research.The study offers both theoretical and practical implications where the research could beused to help close existing gaps, using media discourse analysis, a method that as far as we cantell has never been used in a supply chain and generative AI context. Further it can be used asguides for organizations, regulators, and industry professionals in the supply chain industry