How Generative AI Is Shaping the Manufacturing Supply Chains of the Future

Summary

The last twelve months have shone a spotlight on how fragile global supply chains can be. From geopolitical conflicts to component shortages, manufacturers have faced numerous complexities that have disrupted production and underscored a need for more resilient systems. Supply chain disruptions cost organizations an estimated average of $184 million per year, highlighting the urgent need for effective management strategies. Firms that fail to manage supply chains effectively are missing out on revenue through poor prediction of upcoming demand or overproduction of low-demand products.

To address these issues, 93% of supply chain executives plan to improve their supply chain’s resilience, and 90% of them plan to increase their use of digital technology to do so.  Many are already using analytics and AI to improve the performance of their supply chain and manufacturing operations. This technology provides manufacturers with more information to help improve the resiliency, security, and efficiency of their supply chains.

Recent breakthroughs in Generative AI have opened another door for revolutionary change. While traditional AI primarily focuses on making predictions and optimizing processes based on patterns in historical data, GenAI goes a step further. The technology can also create new data and insights by analyzing vast amounts of structured and unstructured data that leads to creative and innovative solutions. This allows manufacturers to democratize data and analytics access within their organizations, making it easier to identify patterns and make informed decisions.  Here are three key areas where GenAI can enhance the manufacturing process:
 

Giving manufacturers a crystal ball

Almost every industry is trying to predict future demand and vulnerabilities of the supply chain. 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.
GenAI simplifies this process by integrating structured and unstructured data in real time from multiple sources. Managers can analyze the data to predict flash points and divert resources, getting a much more holistic picture of their operations and potential demand. For example, GenAI can predict demand fluctuations by looking not only at customer orders and transactional data but also macro-variables like social media trends, economic indicators, and even weather patterns. This gives manufacturers a glimpse into what may drive consumers in the coming months.

Likewise, GenAI can perform complex reasoning tasks by linking multiple pieces of information together, known as Chain-of-Thought (CoT) activities. This helps solve intricate problems and make well-informed decisions by analyzing past supplier performance metrics, contract documents, financial statements and other unstructured content to create dynamic and up-to-date supplier risk profiles. In turn, manufacturers can make more informed decisions about who they work with to take advantage of these moments in time quicker than competitors.
 

Boosting supply chain resiliency

Any disruption in a supply chain results in a cost for a manufacturer. For example, if a supplier goes bust, or experiences a cyberattack, manufacturers need to work out who else can supply that material, who is best placed to offer the replacement, and when it can arrive. During this decision-making process, the factory risks product shortages and reduced production, leading to significant revenue loss. For instance, in early 2022, car production in the UK dropped as firms struggled with part shortages, leading to almost 100,000 fewer cars being built in the first three months of 2022 compared to the previous year.

GenAI can optimize this process, enabling manufacturers to recover quicker from unplanned disruptions with minimal impact on their operations. By analyzing structured and unstructured data from distributers, suppliers, and the factory, GenAI improves overall visibility of the supply chain. This helps manufacturers identify bottlenecks quicker and easier, avoiding potential disruption before they happen.

With its ability to hold intelligent conversations with human-like understanding, GenAI can provide ample support in customer service, maintenance, or even operational decision-making, which allows manufacturers to quickly address concerns and streamline communications across the supply chain.

For example, GenAI can generate a variety of scenarios and responses to potential disruptions. It can rapidly model questions such as “Which facilities are at risk of running out of stock?” or “What is the impact of moving inventory from plant A to plant B?” This provides recommendations without the need to manually navigate 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 create more efficiencies, leading to less time spent on repairing broken machinery, streamlined use of resources, and greater productivity for manufacturers. For example, US Steel is using Google Cloud’s generative AI to lower downtime and speed up repairs. Achieving this requires IoT devices and data to be combined with GenAI. However, once achieved, it allows manufacturers to identify how machines are working, and when maintenance might need to be carried out.

GenAI can assist maintenance teams by generating new content with little or no guidance. The technology can comb 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.

Getting started with Gen AI solutions requires a deep understanding of the technology’s capabilities. As such, manufacturers looking to integrate AI into their operations must work with trusted providers who can bring the skills and expertise. Additionally, preparing a roadmap for adopting AI solutions that include all stakeholders is crucial for generating impactful results. Manufacturers that build GenAI foundations now will benefit from quicker, more informed decisions and a more resilient and productive business overall.