Generative AI in Manufacturing. How to do Predictive Maintenance

Manufacturing relies heavily on raw materials and labor, both of which are subject to heavy price fluctuations caused by economic headwinds. With the harsh economic climate, cost reduction has become the talking point of many boardroom meetings as companies struggle to stay afloat and maintain profit margins.

According to a study by Douglas Thomas of the National Institute of Standards and Technology (NIST), the US loses an average of $222 billion to inefficient maintenance practices [1]. Manufacturers can significantly cut costs (and losses) by embracing effective maintenance strategies, such as shifting from reactive to predictive maintenance with the help of generative AI.

Read on as we explore how manufacturers can use artificial intelligence for predictive maintenance and other uses to cut costs and boost productivity.

How to apply Generative AI in manufacturing

The manufacturing industry is about boosting productivity and improving efficiency while cutting operation costs. Generative AI in manufacturing is a game changer since it allows manufacturers to streamline workflows, reduce downtime, and improve output with maximum efficiency.

This revolutionary technology uses advanced algorithms and machine learning to continually self-improve and offer previously unattainable insights with conventional methods.

Here are some of the most popular use cases of AI in manufacturing:

• Optimizing Supply Chains

Disruptions in the supply chain are incredibly detrimental to manufacturers. Missed deadlines and logistical issues lead to dwindling profits and ruin their reputation. AI can help improve visibility over the entire supply chain, which enhances:

• Demand forecasting
• Inventory management
• Risk mitigation

When it comes to supply chains, manufacturers can also harness predictive analytics to optimize resource allocation and anticipate disruptions. That way, they can ensure a steady stream of their products to customers and maintain a solid reputation with their customers. Gen AI can act like a de facto logistics advisor, providing data-backed recommendations for the best travel routes and suppliers while optimizing production schedules and inventory levels.

• Automating product development

Product development is a crucial step in the manufacturing process, which typically involves several sub-processes, including:

• Market research
• Conceptual design
• Prototyping
• Testing
• Iterative refinement

These subprocesses are resource-intensive and time-consuming, prolonging a product’s time to hit the market. Gen AI can automate various aspects of product development, particularly design, testing, and refinement, to reduce the time a product takes to enter the market. Manufacturers who use artificial intelligence can, therefore, stay ahead of the game by being the first to launch their products.

• Document acquisition and generation

Technicians in manufacturing have to sift through hundreds of pages to find specific information concerning a piece of equipment or machinery. This leads to delays during equipment installation, repairs, and maintenance and prolonged downtimes. Downtimes that cost industrial manufacturers in the US up to$55 billion a year[[2].

Customer service reps face the same issue when giving product quotes or answering customer queries about specific products. They usually have to rummage through dozens of pages just to create a customer quote. While the search feature might be helpful, sometimes a search term appears thousands of times in a document.

With Artificial intelligence, technicians, customer service teams, and others can find the exact information they need with a simple prompt. Gen AI can also summarize the information or simplify it for better understanding. In doing so, AI reduces response times and minimizes operational delays, leading to significant cost savings and better customer satisfaction.

• Boosting innovation

Today’s competitive manufacturing sector leaves little room for stale and outdated designs or concepts. Only innovative companies can pierce through the competition by pushing the boundaries and venturing into new territory.

Still, innovation in manufacturing in the modern age can be complex because of rapidly shifting trends and strict regulatory requirements. Through gen AI, manufacturers can come up with plenty of innovative solutions on the fly. The technology can analyze tons of data in seconds to come up with viable and innovative product designs. Some of the data it analyzes include:

• Market trends
• Customer preferences
• Track records (historical performance)
• Prototyping feedback
• ROI metrics

AI uses this data to quickly identify trends and develop innovative designs and solutions. That way, manufacturers can respond to market demand and meet customer expectations, not to mention gain an edge over their competition.

• Automating customer service

After-sales service is crucial for success in manufacturing, but effective customer service is a steep climb for most manufacturers. To make it worse, the bar for proper customer service keeps rising. In fact, according to HubSpot research, 90% of customers expect instant replies[3] to their customer service queries. This is a difficult feat for manufacturers to achieve unless they use AI.

Manufacturers can use AI-powered chatbots and virtual assistants to communicate with customers in real time.

These tools can tap into data from the internet and company databases to provide correct responses to queries regarding:

• Product information
• Product Operation
• Product troubleshooting
• Returns
• Replacement parts
• Service scheduling

The information from these AI tools is not only helpful but also enriches the customer experience and significantly reduces the time to resolve customer complaints. Keep in mind that 89% of customers will likely make a repeat purchase [4] after a pleasant customer service experience. This means that manufacturers that adopt AI for customer service will likely experience improved profit margins.

Read more: Streamlining Manufacturing: 30% Reduction in Manual Work with AI [Case Study]

• Digital simulation

Manufacturers can utilize gen AI to create simulations of their real-world assets and processes. These simulations, or digital twins, are virtual replicas of actual physical systems that mimic their parts, processes, and interactions. Manufacturers can use simulations to experiment with new designs, test different scenarios, optimize operations, and identify bottlenecks and potential issues.

The best part about using simulations is that manufacturers don’t have to incur heavy costs with real-world trials. Furthermore, simulations eliminate the risk associated with experimental testing in physical environments. Manufacturing companies can literally explore different scenarios, make iterations, and solve issues without putting their assets and employees at risk.

• Improving decision making

One of the most popular use cases of gen AI in manufacturing is enhancing decision-making. Leaders in the manufacturing industry have to make pivotal decisions that could either propel the company forward or result in major setbacks. They could use AI to help them make more informed decisions for their company’s benefit.

Manufacturers can for instance use AI technology to analyze historical data, including customer feedback, market trends, and past inventory levels, and draw valuable insights. Gen AI can then provide recommendations for strategic planning, budgeting, marketing, and inventory management, among other processes.

Read more: Gen AI in Manufacturing – Use Cases

• Predictive maintenance

This maintenance technique focuses on predicting when failure will occur and taking adequate steps to prevent this failure. It’s a much better approach to maintenance than reactive maintenance, which addresses issues as they arise.

Complex AI algorithms can utilize data from different sources, including sensors and equipment parts, to identify anomalies. That way, manufacturers can schedule maintenance before the anomalies worsen, causing full-blown failure. By using AI for maintenance, manufacturers can reduce downtime and the costs associated with unplanned repairs or replacements.

How to do predictive maintenance using AI

As mentioned, AI is an incredibly useful tool for maintenance that manufacturers can use to optimize maintenance operations and save a bundle in maintenance and repair costs. Here’s a brief breakdown of the best use cases for AI in equipment and machine maintenance.

• Anomaly detection

Manufacturers can use artificial intelligence to analyze data from sensors and machine parts in real time. This data provides information on equipment performance, operational conditions, and wear and tear. The AI then uses complex algorithms to detect patterns that typically lead to abnormal functioning and equipment failure. Manufacturers can then address these issues before they get out of hand.

• Fault diagnosis

Manually diagnosing faults in large-scale factory machinery and equipment is difficult and time-consuming. Technicians typically spend hours sifting through data logs, inspecting various components, and troubleshooting potential issues. The result is protracted downtimes and increased labor costs. There’s also the possibility of delayed production schedules and missed deadlines.

Instead of manual inspections, technicians can use artificial intelligence to compare data from sensors generated in real time with past data indicative of anomalies and fault conditions. That way, technicians can swiftly pinpoint issues with machines and their components.

• Optimizing spare parts inventory

Manufacturers can optimize their spare parts inventory with AI, ensuring they always have spare parts when replacing worn-out or faulty parts. AI technology can analyze historical demand patterns and usage trends as well as the lifespans of different parts. That way, manufacturers can ensure they have the relevant spare parts in case of equipment or part failure. This provides operational continuity and reduces downtime.

Final thoughts on Generative AI in manufacturing

Artificial intelligence is transforming the manufacturing industry, ushering in a new age of unparalleled efficiency, innovation, and profitability. With AI, manufacturers can boost their maintenance exercise by identifying anomalies and faults early on and taking adequate steps to prevent the issues from escalating. The result is reduced downtime, longer equipment lifespans, and considerable savings in repairs and maintenance costs.

With multiple use cases, all indicators point to a future where AI will be crucial for success in production and manufacturing. And generative AI in manufacturing will play a big role here. Forward-thinking companies have already integrated AI into their maintenance operations and are reaping the benefits. Even small-scale manufacturers can incorporate AI into their operations, provided they use complementary implementation for a smooth transition.

References

[1] ncbi.nlm.nih.gov. Maintenance Costs and Advanced Maintenance Techniques in Manufacturing Machinery: Survey and Analysis. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890517/. Accessed on June 7, 2024
[2] Forbes.com, Unplanned Downtime Costs More Than You Think. URL:
https://www.forbes.com/sites/forbestechcouncil/2022/02/22/unplanned-downtime-costs-more-than-you-think/?sh=168e57a436f7. Accessed on June 7, 2024
[3] blog.hubspot.com, Live Chat Exposes a Fatal Flaw in Your Go-to-Market. URL: https://tiny.pl/d14zc. Accessed on June 7, 2024
[4] sfdcstatic.com, State of the Connected Customer. URL:
https://c1.sfdcstatic.com/content/dam/web/en_us/www/documents/research/salesforce-state-of-the-connected-customer-4th-ed.pdf. Accessed on June 7, 2024