By 2035, Artificial Intelligence (AI) has the power to increase productivity by 40 percent or more, according to Accenture. For manufacturing companies, integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. AI improves the manufacturer’s bottom line through intelligent automation, labor and capital augmentation, and innovation diffusion. For example, by analyzing incidents in real time, AI can provide early warning of potential problems and propose alternative solutions. These benefits mean that AI has the potential to boost profitability an average of 38 percent by 2035.
Why AI is the future for discrete manufacturers
AI helps discrete manufacturers unlock trapped value in their core businesses. Machine-based neural networks can understand a billion pieces of data in seconds, placing the perfect solution at a decision maker’s fingertips. Your data is constantly being updated, which means your machine learning models will be updated, too. Your company will always have access to the latest information, including breaking insights, which can be applied to rapidly changing business environments. Three important AI benefits are:
- Make decisions faster and with more confidence. How do you know what to fix first at your manufacturing plant? AI can automate and prioritize routine decision-making processes so your maintenance team can decide what to fix first with confidence.
- Access immediate, actionable insights from Big Data. One of the most exciting opportunities with AI is its ability to identify and understand patterns in Big Data that humans currently cannot. AI can predict future opportunities and recommend concrete actions your manufacturing company can take today to capitalize on these opportunities.
- Protect sensitive data. AI helps to eliminate human error, which improves output quality and strengthens cybersecurity. Strong cybersecurity is important for protecting sensitive, proprietary data in manufacturing and ensuring your competitive edge.
How Trenitalia uses Big Data and AI for predictive maintenance and productivity
The Italian train operator Trenitalia used AI and IoT to streamline maintenance and increase productivity. The Italian company has a 400 million euro operating income and transports 60 million passengers per year. Unnecessary downtime for repairs hurt productivity and wasted valuable resources on maintenance costs. The company wanted to perform all required interventions (and only those interventions that were necessary) at the exact right time, ensuring availability of the right resources for maximum uptime. The goal was simple: no unplanned downtime and higher asset utilization.
“Every year we spend €330 million on parts and on repairing parts which are subject to continual wear and tear,” says Trenitalia’s Chief Finance Officer, Enrico Grigliatti. “Having advance warning when each part of the machinery deteriorates means better management of inventory and ad hoc maintenance. All the more so given that today 60% of trains’ control costs is cyclical, consisting of planned maintenance, but the remaining 40% is corrective, consisting of unforeseeable faults that cause expenditures to go through the roof and infuriates passengers. Big Data allows us to determine how and when to take action.”
Trenitalia owns and operates a fleet of around 2,000 electro-trains, 2,000 locomotives and 30,000 coaches and wagons. The company equipped 9,000 trains of their trains, locomotive, coaches, and wagons with 6 million sensors that gather information on the train’s operating performance.
Traditional maintenance policies adopted by Railway operators can be significantly sub-optimized and create both unnecessary costs and lower asset utilization. AI is changing this. Highly granular telemetry data provides a complete picture of current and projected asset conditions. A “predictive” software brain then extrapolates and analyzes this data, predicting the perfect moment to perform maintenance. Dynamic maintenance plans reflect the specific status of each and every component of the train. This predictive maintenance approach helps Trenitalia achieve maximum productivity through maintenance efficiency.
Next steps: Using AI to boost your company’s productivity
AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. To capitalize on these benefits, manufacturing companies need a partner that can simplify and streamline the AI and IoT integration process.
Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Industrial Machinery and Components. Explore how to bring Industry 4.0 insights into your business today: Industry 4.0: What’s Next?