The Power of AI and Automation for Predictive Service in Manufacturing

The Power of AI and Automation for Predictive Service in Manufacturing

The future of manufacturing service starts by shifting from reactive to proactive strategies.

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As manufacturers continue to face supply challenges, labor shortages, and tight revenue margins, they can no longer afford to react to problems after they arise.

Innovations in artificial intelligence (AI)have the potential to transform many areas for manufacturers, including the shift from reactive service to proactive service. By managing repetitive and time-consuming tasks, AI can free up human workers to concentrate on more strategic and creative activities. It can also help manufacturers preempt and address potential issues before they happen, which reduces costs and improves efficiency.

AI requires manufacturers to have a ‌comprehensive data strategy in place. With a solid data foundation, manufacturers can use the power of embedded and generative AI to automate complex tasks, improve processes, and foster innovation.

In this blog, we'll take a closer look at the potential of AI and automation for manufacturing service. We'll explore the main value drivers of these technologies in this sector and the impacts it can have on your manufacturing service organization.

What you'll learn:

What are the key value drivers for AI and automation for manufacturing?
The transition to proactive manufacturing service with AI and automation
Use cases for proactive and predictive service in manufacturing
How AI and automation can affect manufacturing service
Conclusion

What are the key value drivers for AI and automation for manufacturing?

The integration of AI and automation for manufacturing presents several value drivers that can affect operations.

From a productivity perspective, AI can automate repetitive and complex tasks, which can enhance overall productivity. This optimizes resource allocation and fosters a culture of innovation and continuous improvement.

AI also facilitates making information accessible to everyone throughout the value chain. By removing data silos and providing real-time insights, AI equips decision makers with the intelligence to make well-informed decisions. This extends to Internet of Things (IoT) data, which AI can deliver in easy-to-understand formats, like automated workflows where insights on needed service can trigger action.

Cost reduction is one of the most compelling value drivers of AI and automation for manufacturing. AI algorithms can analyze large, diverse datasets to reveal inefficiencies in asset management and optimize processes like manual equipment inventories. Being able to forecast maintenance needs and improve resource allocation reduces product outages and leads to major cost savings.

Here's how this can be applied to manufacturing service.

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The transition to proactive manufacturing service with AI and automation

With AI and automation, manufacturers can shift from reactive to proactive strategies.

Reactive asset maintenance lends itself to unplanned downtime. In situations where unexpected service issues arise, production halts, revenue is lost, costs increase, and customers are left dissatisfied.

Manufacturing outages on average last four hours and cost $2 million. They can also leave customers frustrated by delays and quality issues, leading them to switch to competitors. 82% of companies have experienced at least one unplanned incident in the past three years.

Several factors contribute to this downtime, including lack of real-time visibility into operations, reliance on manual and paper-based data collection, or manufacturers having numerous information silos where key data gets stranded.

Now let's see how using AI and automation for manufacturing service help make the shift to a proactive and predictive approach.

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Use cases for proactive and predictive service in manufacturing

AI can help you monitor and analyze asset performance, allowing you to detect health issues and failure patterns. These insights can also help monitor if replacement parts or maintenance are needed before failures.

For example, let's say the motor in a commercial building's elevator is running at a high temperature. In the event that this issue isn't quickly addressed, there's a risk of motor failure, which could inconvenience‌ businesses within the building. However, if the overheating motor could be identified and fixed before it becomes an issue, such proactive service and maintenance can limit disruptions and increase customer satisfaction.

Being proactive by using AI extends to tracking assets, products, and warranties. These insights can help you upsell service plans, enhance warranty protection, or issue proactive inspections, which increase speed to market and reduce opportunity costs.

Once you stream asset data, you can better forecast maintenance needs and optimize resource allocation during non-peak times to reduce labor costs, truck rolls, and customer churn.

Fixing problems before they become an issue leads to a better customer experience. Customers will love it and you'll see a boost in customer satisfaction, your Net Promoter Score (NPS), and customer lifetime value.

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How AI and automation can affect manufacturing service

There are many areas you may consider implementing AI, so let's take a look at how it can impact manufacturing service from a crawl, walk, run approach.

To get started, identify the areas where out-of-the-box AI solutions can make an immediate impact. Asset health summarization, for example, provides a consolidated view of equipment health, enabling proactive maintenance. Similarly, AI can recommend the next-best maintenance action based on real-time data, minimizing downtime and maximizing asset uptime.

As you grow more comfortable, you can move on to predictive asset analytics, which uses both historical and current data to anticipate potential issues and optimize maintenance timing. This represents the shift from reactive to predictive maintenance that can lead to significant cost savings and improved operational efficiency.

To build on this, you can begin to implement solutions that use your data. One way to do this is by creating a central knowledge base where you can capture and share best practices, lessons learned, and insights across your manufacturing organization. You can also use warranty intelligence to identify the root causes of failures and improve the quality of your products.

Ultimately, AI can help transform manufacturing service from being reactive to proactive to predictive to prescriptive. For example, you can use AI and automation, coupled with asset performance data and asset service history, for service asset prediction. These insights help show the likelihood of a particular part failure based on asset health score, age, planned and unplanned maintenance events, warranty claims, and more. It can provide service agents with recommendations to enhance customer support during the upcoming potential maintenance event and suggest parts to swap out before a critical issue occurs.

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Conclusion

While AI has been used in the manufacturing industry for years, the new possibilities that embedded AI, generative AI, and automation will bring to manufacturing service are intriguing.

AI equips manufacturers with the ability to analyze data in real time, leading to more proactive decision-making and quicker problem resolution. If you're manually managing inventories on paper or spreadsheets, AI and automation for manufacturing can help with that. If you have limited insight into the health of your assets and don't know when your next failure might occur, AI can help with that, too. It can also help if you struggle to meet customer expectations because you can't anticipate their demands and needs. And so much more.

Taking a proactive approach to how you monitor assets has the potential to have an impact on your bottom line and on your customer service levels. By tracking an asset's lifecycle with insights into issues before they lead to failure, you can reduce the costly probability of unplanned downtime and keep your customers happy.

Learn how proactive and predictive strategies can take your manufacturing service operation from a cost center to a profit center in The Manufacturer's Guide: Transform the Service Experience.

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Nanda Chitrala is part of the Manufacturing and Automotive Industry Solutions and Product Strategy team. He is responsible for driving industry solutions and product go-to-market strategy for Manufacturing Cloud and Automotive Cloud across the Americas, Japan, and APAC regions. In this role, he... Read More serves as a customer industry advisor and helps influence product roadmaps based on customer gaps and inputs. Previously he served as Customer Digital Transformation Officer (CDXO) for the Americas region, supporting enterprise manufacturers like Caterpillar, Cummins, Honeywell, Trane, Dover, Cargill, and Daimler Trucks, among others.