From the first assembly lines to the robotics revolution, the manufacturing industry continually strives to find new ways to boost productivity while lowering costs. Today, major trends are driving the need for further transformation, and generative AI is helping pave that path forward.
Factors like supply chain disruptions have wreaked havoc on bottom lines, with 45% of the average company’s yearly earnings expected to be lost over the next decade. Closer to home, companies are struggling to fill critical labor gaps, with over half (54%) of manufacturers facing worker shortages.
Challenges like these demand new solutions. And gen AI has the potential to deliver them. It can transform maintenance workflows and troubleshoot issues in real time. It can recommend ways to make production lines more efficient or less wasteful. It can even design new parts or products to take a manufacturing business to the next level.
By enhancing manufacturing processes, gen AI can reduce downtime, improve output, realize cost savings, and boost end-user satisfaction. No wonder 82% of organizations considering or currently using gen AI believe it will either significantly change or transform their industry (Google Cloud Gen AI Benchmarking Study, July 2023).
How to apply gen AI in manufacturing
With its unique ability to process and understand vast amounts of data, gen AI can be used across a wide array of applications — not just to improve productivity or efficiency. Here are five use cases that put gen AI to work in transforming the manufacturing industry.
1. Machine-generated events monitoring
Predictive maintenance is the best-practice strategy that identifies and rectifies possible equipment failures before they happen. According to Deloitte, it increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.
Gen AI can play a key role in transforming maintenance workflows and staying one step ahead with predictive maintenance. It helps manufacturers optimize operations by interpreting telemetry from equipment and machines to reduce unplanned downtime, gain operating efficiencies, and maximize utilization. If a problem is identified, gen AI can also recommend potential solutions and a service plan to help maintenance teams rectify the issue. Manufacturing engineers can interact with this technology using natural language and common inquiries, making it accessible to the current workforce and attractive to new employees.
Watch this video to see how gen AI helps a transport company fix a problem with a faulty locomotive.
2. Customer service automation
The bar for after-sales service in manufacturing is getting higher. According to Salesforce, 80% of business buyers expect companies to respond and interact with them in real time, and 82% say personalized care influences their loyalty.
To deliver on these expectations, manufacturers are increasingly turning to gen AI — which provides a helpful, value-added customer service experience that automates and accelerates time-to-resolution for common interactions like product troubleshooting, ordering replacement parts, scheduling service, product information, and product operation.
Watch this video to see how gen AI improves customer service for an automotive manufacturer, delivering real-time support to the vehicle owner who sees an unexpected warning light.
3. Document search and synthesis
In manufacturing, product and service manuals can be notoriously complex — making it hard for service technicians to find the key piece of information they need to fix a broken part. Ordering and quoting can be very complex, too, with sales teams often having to decipher a huge array of information before creating a customer quote.
Gen AI can quickly sift through generations of documents throughout the product lifecycle, extracting and summarizing the information needed by sales teams and technicians. For example, it can present servicing instructions in an easily digestible, step-by-step format so technicians can get straight to work. And it can synthesize purchase orders and quickly provide customers a quote, eliminating the need for sales teams to manually cross-reference emails with inventory availability.
4. Product/content catalog discovery
Using gen AI, manufacturers gain an efficient method to match requirements to the specifications of products they buy, and provide the same service to their customers.
Gen AI-enabled sales applications can provide sales recommendations based on historical sales data, in-stock data, master data, and more. The sales recommendations can be generated using special machine learning algorithms equipped with continuous or real-time feedback functions to optimize the suggested results. Results could be combined with more descriptive statistics on sales data joined with meta-information that is uploaded by sales agents, giving a clear visibility into the buying process.
5. Supply chain advisor
As noted above, supply chain disruptions are having a significant impact on manufacturers. As well as dealing with these long-term disruptions, manufacturers are increasingly tasked with more responsible, ethical, and sustainable sourcing of materials. To enable this, visibility across the supply chain is the top priority for supply chain executives.
Gen AI can act as a supply chain advisor, providing greater visibility across complex networks and delivering recommendations for best-suited suppliers based on relevant criteria — such as bill of materials specifications, raw material availability and delivery schedules, or sustainability metrics. Adept at extracting provisions using natural language processing from legal and contractual documents, it can deliver real-time insights into supply chain performance to help improve decision-making.
