As companies increase their reliance on AI for demand planning and procurement, the supply chain industry is expanding its exploration of its use in other key areas such as process standardisation and optimising last-mile delivery.
While COVID-19 sparked a rise in AI adoption in the supply chain, the evolution of generative AI - driven by the population of ChatGPT - has upended the beliefs of what is possible.
Understanding the application of generative AI in the supply chain
Based on data, generative AI is trained to create new content such as images, text, audio, or video. While this isn’t new technology, recent advancements made in the field have made it much simpler to use and realise value. As such organisations are working to understand the implications, business use cases, and ways to exploit the benefits.
“For those who diligently pursue innovation guided by strategy and an understanding of the limitations — not by an impulse to chase after the latest shiny object — generative AI can prove to be an agile co-advisor and multiplier in strengthening supply chains,” says EY.
Abilities of generative AI:
- Classifying and categorising information based on visual or textual data
- Quickly analysing and modifying strategies, plans and resource allocations based on real-time data
- Automatically generating content that enables faster response times
- Summarising large volumes of data and extracting key insights and trends
- Assisting the retrieval of relevant information quickly and providing instant responses by voice or text
Applications of generative AI in supply chains
Below are some of the ways that generative AI is currently being used in supply chains.
When it comes to supply chain planning, many organisations are using AI to analyse large historical data sets, market trends, and other variables to create real-time demand models. Taking demand forecasting a step further, generative AI can optimise inventory levels, production schedules, and distribution plans to be more efficient when meeting customer demands.
Other ways in which generative AI is being used in the planning phase include production planning, scheduling sequences and allocating resources to minimising bottlenecks, as well as risk management, scenario simulation, and mitigation strategies.
For those that operate in the sourcing function, leveraging natural language processing (NLP) can provide greater insights from supplier communications and data points for supplier management. It can also support, monitor, and analyse supplier interactions; identify potential issues; and improve supplier relationships.
Beyond supplier management, sourcing can also benefit from generative AI to support the selection process by analysing data and generating insights to provide recommendations or rankings for making informed decisions.
Contract analysis can also benefit by automating the key information from contracts and generating summaries or insights. As well as reviewing and comparing terms, identifying risks and ensuring compliance.
When it comes to the making of products, generative AI can rapidly produce and evaluate hundreds of alternative product designs based on predefined criteria to significantly speed up the innovation process. Learning from machine data on the factory floor, generative AI can create new predictive maintenance plans to correlate with when the equipment is likely to fail.
For those working in materials science and engineering, generative AI can help to discover new materials and optimise existing ones.
Finally, when it comes to the distribution of products, generative AI can help in a number of ways from global trade optimisation, to optimising logistics network designs, and last-mile dynamic route optimisation.
While generative AI is a useful and powerful tool, EY emphasises that it comes with limitations and is not a strategy. Businesses looking to adopt generative AI into their business should be guided by three key steps:
- Focus on domain-wide transformation
- Coordinate organisation collaboration
- Keep an open mind, and guard against risks
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When it comes to future applications of generative AI in supply chains, EY sees risk management as one of the most promising areas for the tools, particularly in preparing for risks that supply chain planners haven’t considered.
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