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Companies know they need to integrate artificial intelligence into the manufacturing process, but the real challenge continues to be achieving it at scale. Scaling artificial-intelligence implementations beyond the proof-of-concept (PoC) level remains one of the biggest hurdles.
When the Capgemini Research Institute talked with manufacturers for the report Scaling AI in manufacturing operations: A practitioners’ perspective, the greatest amount of activity was in the maintenance (32%) and quality (26%) functions. The three most-implemented AI uses cases in operations were: intelligent maintenance, product quality inspection, and advanced simulation or digital twins.
It is not surprising that predictive maintenance is the number-one priority. In most cases, manufacturers can access significant amounts of historical and real-time data from machines to make reliable use cases, but it takes a change in mindset because many collected this data but did not look at it until it was too late.
The use of IoT sensors means manufacturers can access real-time data now. That opens up exciting possibilities. It means manufacturing execution can be made more efficient with fewer defects.
We worked with one automotive company on predictive models to identify robot failures. This proactive approach to maintenance improved quality and delivered real return-on-investment value.
Product quality is another area where manufacturers can improve efficiencies with AI. From stringent regulatory requirements to product specifications, non-compliance can lead to significant issues, from dissatisfied customers to fines and class-action lawsuits.
Detecting defects before they become a major issue is significant, and manufacturers should look beyond their own production lines when they compile data to find imperfections. For example, they should leverage the test data provided by suppliers. Manufacturers typically work with components from multiple sources, so assembling all the data from suppliers and the shop floor is critical for tracing back defects.
The challenge, however, is that suppliers keep data in different formats, so data engineering is required to combine it in a usable format. This is better than trying to make sense of a jumbled spreadsheet, and the ability to predict problems is worth the effort.
For example, one large manufacturer of computer equipment was able to predict a failure related to a heating issue before the product left the plant. It found that a cable connector was unable to withstand the required temperature, and that insight saved millions of dollars in warranty and recall costs. This type of root-cause analysis is vital.
Activating data in manufacturing is key to drive efficiencies and become a truly smart factory. To be successful, companies need to pick AI applications that can process real-time data from the shop floor and integrate with existing legacy IT systems. It will mean investing in data engineering, AI systems, and talent. But with the right governance and use cases, companies will find projects they can scale across multiple sites and factories.
Prasad Shyam is a Vice President, Insights & Data Global Practice at Capgemini. To learn more about how using data and analytics can improve business performance, contact him at firstname.lastname@example.org.
Vice President – Insights & Data Global Practice, CapgeminiIt is crucial to collaborate closely with your clients and truly understand their business challenges if you are to develop solutions to overcome those hurdles.I am a Vice President within Capgemini’s Insights & Data practice. In this role, I connect with the business and technology leaders of Fortune 500 organizations to understand their business challenges and partner with them to develop solutions they need to succeed. I am focused on the manufacturing, automotive, pharmaceuticals, oil and gas, and energy and utilities sectors.My business experience includes developing and delivering analytics and data-management service offerings for multiple industries. I also have extensive experience delivering program management, architecture, design-to-deliver, business intelligence, analytics, and performance-management solutions.I have managed I&D projects in India, overseeing more than 9,000 analytics professionals over three years. I have also led smart-insights solutions and platform development, smart-asset-management projects, and a smart-procurement-management implementation.Recent work includesProviding thought leadership solutions for vital business problems to understand customer pain points, working closely with technology partners to develop positive outcomesDriving analytics and data-management offerings across verticals with an extensive understanding of critical business drivers in global markets and industries.Within every team structure I am a part of, I promote a collaborative and constructive approach. This is crucial to ensure team members work effectively in a matrix organization across a global enterprise.And another thing…Before joining Capgemini, I worked as a VP and Global Head for Business Intelligence and Analytics at IGATE. I was also a General Manager and Business Head for Analytics and Information Management at a renowned tech company.I have been with Capgemini for over four years. I am currently based in Chicago, Illinois.
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