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Five use cases for manufacturers to get started with generative AI

October 9, 2023
Charlie Sheridan

Technical Director, Industry Solutions, Manufacturing

Matthias Breunig

Director, Global Automotive Solutions

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.


Ready to get started?

Leading manufacturers are hitting the ground running with gen AI.

Global airline supplier, GA Telesis, has integrated Google Cloud’s gen AI technology to revolutionize sales processes. CEO Abdol Moabery said, “In aerospace, GA Telesis will deploy Google Cloud’s generative AI technology to revolutionize the sales and service processes for the parts the company supplies to major global passenger and cargo carriers.”

US Steel is building applications using Google Cloud’s generative artificial intelligence technology to drive efficiencies and improve employee experiences in the largest iron ore mine in North America.

“We’ve meaningfully accelerated digitization at U. S. Steel through our work with Google Cloud. Faster repair times, less down time, and more satisfying work for our techs are only some of the many benefits we expect with generative AI,” said David Burritt, president and CEO of U. S. Steel. “I’m thrilled that the U. S. Steel team is a manufacturing leader in this work.”

GE Appliances helps consumers create personalized recipes from the food in their kitchen with gen AI to enhance and personalize consumer experiences. GE Appliances’ SmartHQ consumer app will use Google Cloud’s gen AI platform, Vertex AI, to offer users the ability to generate custom recipes based on the food in their kitchen with its new feature called Flavorly™ AI. SmartHQ Assistant, a conversational AI interface, will also use Google Cloud’s gen AI to answer questions about the use and care of connected appliances in the home.

You can realize the transformative benefits of gen AI, too. Download our latest eBook, The executive’s guide to gen AI, for more details on jumpstarting your journey.

About the Google Cloud Generative AI Benchmarking Study

The Google Cloud Customer Intelligence team conducted the Google Cloud Generative AI Benchmarking Study in mid-2023. Participants included IT decision makers, business decision makers, and CXOs from 1,000+ employee organizations considering or using AI. Participants did not know Google was the research sponsor and the identity of participants was not revealed to Google.

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