Generative AI Brings Real-Time Supply Chains Closer to Reality


Embedding Generative AI into supply chains isn’t just a technology project, it also requires a profound shift in the way organizations think about creating value and getting work done.

Generative AI is impacting, or promises to impact, many sectors, and supply chain networks are ripe for transformation. Generative AI is poised to significantly boost real-time interactions and information across supply chains, from planning to sourcing, product manufacturing, and fulfillment.

The impact on productivity across all these processes is notable. More than four in ten companies, 43%, of all working hours across end-to-end supply chain activities could be impacted by generative AI, a new study out of Accenture calculates. In addition, 29% of working hours across supply chains could be automated by generative AI, while 14% of working hours across supply chains could be significantly augmented by generative AI.

This rising technology has potential for the entire supply chain, from design and planning, to sourcing and manufacturing to fulfilment and service, the report’s authors, Maria Rey-Marston and Jaime Lagunas, both with Accenture, state.

In total, 58% of the 122 supply chain processes analyzed in the study are ripe for reimagination through generative AI. The technology can serve as the “missing link that helps bridge the gap from the linear supply chains of the past to the truly interconnected, intelligent supply chain networks of the future.”

Performance gains can be seen across everything from sourcing and planning, through manufacturing and fulfilment, to after-sales and service,” Rey-Marston and Jaime Lagunas state. The capabilities generative AI bring to the supply chain table include the following:

  • Providing contextual understanding: “Enhances forecasting and decision-making with contextualized insights from larger volumes of previously inaccessible unstructured data.”
  • Enabling conversational capabilities: “Streamlines access to insights and creates new automation opportunities through user-friendly interactions with AI agents in everyday language.”
  • Designing and engineering: “Generative AI can streamline design processes, using historical and external data sources to rapidly produce new designs to specification, reducing time and repetitive effort. Examples include generating new sustainable packaging designs.”
  • Planning: “Through simple-to-use interfaces, employees can query for recommendations in everyday language and receive explanations they can easily understand and action. At the same time, generative AI can be used to bring a broader set of unstructured data sources (such as market reports, news results and social media) into forecasting calculations. It can instantly summarizing meeting action points, comparing plans with actual outcomes, building dashboards of key metrics, even generating draft plans themselves.”
  • Sourcing: Generative AI could provide business users an assistant buyer. “When they needed to buy something, the assistant could guide them to the right buying channel, support any call off or spot buy, and, if needed, connect with a professional buyer to handle the purchase.” In addition, the Accenture team notes, “while teams often spend significant time on strategy alignment, sourcing, and data reconciliation, generative AI presents an opportunity to streamline operations, bridge information gaps, and improve access to a broader array of data sources, enabling faster insights and simplified processes.”
  • Making: “If companies can bring their IT data together with their operating and engineering data, generative AI will help them achieve a consistent level of quality and operational excellence in their manufacturing operations, particularly in areas such as asset maintenance and empowering the workforce with actionable predictive insights. It can also offer new insights into product design and quality control.”
  • Quality assurance: “AI will also be increasingly applied to the wealth of insights in operational digital twins, expediting diagnoses and root cause analyses. And the combination of classical and generative AI offers the promise of significantly streamlining access to predictive maintenance insights, real-time data analysis and failure diagnostics by making the information more consumable through easy-to-use Q&A interfaces.”
  • Fulfilling: “That includes enhancing hyper-personalized customer experiences and extracting new revenue opportunities from insights based on large volumes of omnichannel data. Fulfillment operators can also use generative AI to suggest ways to optimize transportation management and improve forecasting by considering a broader range of factors from unstructured information (such as weather forecasts and competitor activity.”
  • Managing regulatory forms: A generative AI-powered import/export document generator “could transform shipping and export processes. Generative AI can be applied to a comprehensive collection of multi-modal unstructured information, including historical internal records and governmental regulations, across various formats, including PDFs and tablets. Shipping and export documents can then be automatically populated for human experts to review and verify, reducing opportunities for error while saving time and manual effort.”

A final word on the promise of Generative AI

Embedding AI into supply chains isn’t just a technology project, it also “requires a profound shift in the way an organization thinks about creating value and getting work done,” the Accenture co-author’s suggest. “It means approaching generative AI not merely as the latest in a long line of software implementations, but rather as an enterprise transformation, with a clear focus on end-to-end business capabilities and implications for areas like data, people, ways of working, processes and responsible adoption.”

In the process, “AI-powered reinvention helps bridge the gap from the linear supply chains of the past to the truly interconnected, intelligent supply chain networks of the future.”


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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