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How generative AI is used in supply chains

Photographic portrait of Sumit Dutta
Sumit Dutta

Principal, Supply Chain & Operations, Ernst & Young LLP

3 minute read 01 Feb 2024

Implications of AI and GenAI for the future of supply chains

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Generative AI is gaining traction in supply chains. However, overcoming execution hurdles will take talent, data and systems integration.

Three questions to ask:

  • What are the key application areas of GenAI in supply chain management?
  • What are the primary challenges supply chain leaders face in adopting GenAI and how can they overcome them?
  • What are the hallmarks of a successful GenAI program?

Artificial Intelligence (AI) has come a long way over the past two decades, with transformative implications for businesses across the world. AI evolution has moved from machine learning to more elaborate forms such as deep learning, natural language processing and now, generative AI (GenAI). The latter is a subset that can produce content such as texts, images or codes, akin to its human counterparts.

Particularly in industries such as manufacturing, GenAI in supply chain management has provided cost advantages and valuable contributions to revenue growth.

The recent EY CEO Outlook Pulse Survey 2023 on AI points to a trend where businesses are eagerly investing in AI-driven products and services. In planning, AI’s role was evidently seen across supply chain control, digital twins for virtual modeling and streamlining of product development. In sourcing, it facilitates contract management, procurement 4.0 through real-time inventory analysis and risk assessments of vendors.

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In manufacturing, firms use AI in predictive scheduling, warehouse automation and collaborative task management. Lastly, in delivery, AI optimizes conversational customer service, manpower planning and dynamic pricing.

 

The progression of AI to GenAI has opened new avenues. GenAI adopts a form of advanced artificial neural networks that find patterns in the data without needing labels or human supervision, making them more adaptable to varying use-cases.

 

GenAI in supply chain management is particularly useful for aiding in areas such as demand forecasting, prototyping, error detection, production planning and last-mile delivery optimization. Major retail chains and health care industries are already piloting the use of GenAI for tasks such as summarizing and analyzing customer feedback and generating novel small-molecule entities for drug discovery.

 

While promising, adopting GenAI has its own roadblocks in terms of data security, talent procurement and incompatibility with legacy systems, to name a few. Added to this is the cost and complexity of achieving regulatory compliance. However, by employing a systematic strategy of preparation, prioritization, contemplative presentation, and thorough evaluation for timely corrective measures, businesses can steer through these hindrances effectively and leverage GenAI to its full potential.

 

GenAI in supply chain can offer a unique combination of efficiency, accuracy and better decision making. Interested readers can download (pdf) a new white paper to learn about the five implementation steps for GenAI in supply chains. When implemented following a structured and systematic approach, it may revolutionize supply chains and offer significant financial and other benefits. As GenAI continues to evolve, it’s certain to reshape our understanding of business processes and the future of work.

Special thanks to Sudhanshu Wasan, Karan Chowdhary and Runjhun Anurag from EY Global Services India LLP for contributing to this article.

Summary

Generative AI in supply chain management holds a promising future, offering diverse applications such as demand forecasting, procurement, manufacturing and logistics. However, there are myriad hurdles to implementation, such as data gathering to train models, talent gaps, integration challenges with legacy systems, evolving regulatory dynamics and costs of high compute environments. A successful strategy involves utilizing a structured framework for preparing, prioritizing, refining and presenting a formal plan for the transition to generative AI in your supply chain.

About this article

Photographic portrait of Sumit Dutta
Sumit Dutta
Principal, Supply Chain & Operations, Ernst & Young LLP
Supply chain aficionado. A true global citizen and explorer with experience working in over 25 countries. Father and personal tutor/homework helper to two daughters.

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