RAVI DHARMALINGAM
Ravi develops cutting-edge solutions at Google as a Senior Application Engineer, leveraging his expertise in full-stack development and machine learning to build intelligent, agentic workflows on Google Cloud Platform. Ravi is pursuing a master's degree in computer science with a specialization in machine learning at Georgia Tech.
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Leveraging Generative AI for Efficient Product Briefing Document Generation in Supply Chain
Shaswat Kumar
Gary Borella
Youjin Zhu
Krish Mohan
International Journal of Management, IT & Engineering, 12 (2024), pp. 87-95
Preview abstract
Generative AI enables an efficient product briefing document generation approach in the supply chain based on its leveraging to streamline the information flow and decision making. Product briefing documents are typically produced using a highly labor intensive process of manual data extraction, synthesis, and formatting. Because generative AI can process large amounts of data and generate coherent and structured outputs, generative AI can substantially improve the efficiency of this process. Using machine learning models, AI can use its ability to automate the development of briefing documents that integrate key data points — product specifications, supplier information, market trends, and logistical issues. This also saves time, effort and keeps everything accurate and consistent throughout. The use of AI in supply chain helps businesses create real time updates which facilitate quicker response to market changes, inventory fluctuation and supplier dynamics. In addition, all possible stakeholders can utilize AI generated document with their custom derived according to their needs for better communication and alignment between departments. With an increasingly complex supply chain, the usage of Generative AI helps solving the problem of responsible high volume data, which in turn creates operational efficiency and strategic decisions. This represents a big step toward digital transformation in the field of supply chain management.
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