How manufacturers can use gen AI to rethink the supply chain

The last 12 months have spotlighted how fragile global supply chains can be. Geopolitical conflicts, shortages in key components, boats stuck in canals and-recently-the collapse of bridges over major waterways have all had an effect on manufacturers. Even without these high-profile events, supply chain disruptions cost organizations an estimated average of $184 million per year.

When manufacturers fail to manage supply chains effectively, they are more likely to poorly predict upcoming demand or overproduce low-demand products, both of which can have a devasting impact on revenues. For this reason, many are already using analytics and artificial intelligence (AI) to improve the resiliency, security and efficiency of their supply chains and manufacturing operations.

Recent breakthroughs in generative AI have opened another door for revolutionary change. Gen AI allows far more people in an organization to take advantage of the power of data and analytics delivered by traditional enterprise AI applications.

To reap the benefits of generative AI, however, manufacturers need to understand where it can have the most impact in the supply chain, and how they can take advantage of it.

Gen AI: a supply chain crystal ball

Almost every industry is trying to predict the next big moment. Manufacturers that get it right can significantly boost their revenue, which can then be put back into improving the company.

However, getting it wrong can result in piles of excess stock or running out of supply at a crucial time, leading to huge losses. While many manufacturers already use AI to analyze past data to predict and resolve these flash points, generative AI is set to make the process even simpler. Because generative AI can integrate structured and unstructured data in real time, from multiple sources, it can provide managers with a much more holistic picture of their operations and potential demand. These insights can then be used to plan more effectively for these moments.

For example, generative AI can derive demand fluctuations looking not only at customer orders and transactional data, but also at macro-variables like social media trends, economic indicators and even weather patterns. With these insights, manufacturers can get a glimpse into what may drive consumers in a few months' time.

Likewise, generative AI can swiftly analyze past supplier performance metrics, contract documents, financial statements and other unstructured content to create dynamic and up-to-date supplier risk profiles. Manufacturers can then make more informed decisions about who they work with to take advantage of these moments in time more quickly than competitors.

Boosting supply chain resiliency

Any supply chain disruption results in a cost for manufacturers. For example, if a key supplier goes out of business or experiences a catastrophic event like a cyberattack, the manufacturer needs to identify alternative suppliers, determine which one is best placed to offer the replacement and find out when the materials can be delivered. While this decision process is happening, the factory runs the risk of parts shortages and reduced production, resulting in lost revenue.

For instance, in early 2022, car production in the UK dropped as firms struggled with parts shortages, leading to almost 100,000 fewer cars being built in the first three months of 2022 compared with the previous year.

AI-powered analytics can optimize this decision process, meaning manufacturers can recover more quickly from unplanned disruptions with minimal impact to their operations. Gen AI can improve overall visibility of the supply chain by analyzing structured and unstructured data from distributers, suppliers and the factory. Therefore, manufacturers can identify bottlenecks quicker and easier, avoiding potential disruption before it happens.

For example, generative AI can enable companies to generate a variety of scenarios and responses to potential disruptions. Questions such as, "which facilities are at risk of running out of stock? What is the impact of moving inventory from plant A to plant B?" can be rapidly modeled to provide recommendations without having to manually navigate through multiple applications. By increasing understanding of the supply chain, and the wider ecosystem, manufacturers can create a more resilient supply chain.

Creating a smarter factory

Smarter factories mean less time spent on repairing broken machinery, more efficient use of resources and greater productivity for manufacturers. For example, US Steel is using Google Cloud's generative AI to reduce downtime and speed up repairs. Achieving this requires IoT devices and data to be combined with generative AI. Once achieved, it allows manufacturers to identify how machines are working, and when maintenance might need to be carried out.

Generative AI can assist maintenance teams by combing through technical machine manuals, service history and maintenance logs to provide immediate support on equipment failure, without having to switch between systems. This reduces downtime and creates a more sustainable, efficient and profitable factory that can react more easily to any changes in the supply chain.

Putting generative AI to work in the supply chain

Getting started with generative AI requires a deep understanding of the technology's capabilities. As such, manufacturers looking to integrate AI into their operations need to work with trusted providers who can bring the skills and expertise manufacturers don't have access to.

Likewise, it is important to prepare an AI adoption roadmap that includes all the stakeholders in the business to create a system that can generate truly impactful results. Manufacturers that start now to build the foundations for generative AI will benefit from faster, more informed decisions, and a more resilient and productive business as a whole.

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