Nanda Balasubramanian
Nanda is a seasoned Business Systems Architect and Delivery Leader with over 22 years of experience driving digital transformations for Organizations. He specializes in architecting and delivering innovative solutions in the domains of Master Data Management (MDM), Product Lifecycle Management (PLM), and Supply Chain Management (SCM). Nandakumar is passionate about leveraging the power of GenAI to enhance PLM processes, optimize product development, and navigate the complexities of modern manufacturing. He is a strategic thinker and a results-oriented leader with a proven track record of success in delivering impactful business solutions.
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The accelerating pace of innovation is
fundamentally reshaping product development,
creating a complex environment that demands rapid
decision-making and efficient information
management. To remain competitive, organizations
must integrate Generative AI (GenAI) tools into
their Product Lifecycle Management (PLM)
processes. This integration is crucial because
traditional PLM systems, often built on decades-old
architectures, struggle to manage modern product
complexity, vast data volumes, and interconnected
supply chains.1 Limitations such as data silos,
inflexible change management, and inadequate
collaboration capabilities hinder the agility required
today.3 GenAI offers transformative potential by
automating complex tasks, enhancing data analysis,
and facilitating more dynamic design and
collaboration within the PLM ecosystem.5 This
integration is not merely an upgrade but an
essential evolution to overcome the inherent
architectural and process constraints of legacy
systems, which impede the speed and data fluidity
necessary in the current market.
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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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