By 2035, Artificial Intelligence (AI) has the capability to build productivity by 40 percent and the sky is the limit from there, as indicated by Accenture. For manufacturing industries, coordinating AI into information and communications systems will convey noteworthy cost, time and procedure rapidly. Man-made artificial intelligence improves manufacturer’s bottom line through intelligent automation, labor and capital augmentation, and innovation diffusion. These advantages imply that AI can possibly increase profitability an average of 38 percent by 2035.
As expanding evidence shows the advantages of smart systems, more boardroom decision-makers are gaining a better understanding of what AI can really offer. EY research clarifies “organizations enabling AI at the enterprise level are increasing operational efficiency, making faster, more informed decisions and innovating new products and services.”
The first businesses that use AI systems across the board can achieve competitive advantage, reduce operating costs and eliminate head counts.Because of the significant operational changes, the introduction of these technologies will likely trigger a problem with unions and job security.
Why Trenitalia allows predictive maintenance and performance using Big Data and AI
To streamline maintenance and boost productivity, Italian train operator Trenitalia used AI and IoT. The Italian company has an operating profit of EUR 400 million and holds 60 million passengers annually. Unnecessary repair downtime affects productivity as well as the cost of maintenance wasted valuable resources. The business wanted to carry out all necessary interventions (and only those necessary interventions) at the right time, ensuring that the best tools are available for maximum uptime. The goal was simple: no unplanned downtime and better use of resources.
“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 network of nearly 2,000 electric trains, 2,000 locomotives, and 30,000 coaches and buses. The company fitted 9,000 trains of their trains, locomotive, coaches, and wagons with 6 million sensors that collect information about train’s operating performance.
Traditional maintenance practices implemented by railway operators can be considerably under-optimized, generating even additional costs and lower usage of properties. This is being improved by AI. Highly granular information on telemetry provides a complete picture of current and predicted conditions of assets. This data is then extrapolated and interpreted by a “predictive” brain software predicting the ideal time to perform maintenance. Dynamic maintenance plans reflect each and every train component’s specific status. This predictive maintenance approach, through maintenance efficiency, helps Trenitalia gain maximum productivity.
Why AI impact productivity of discrete manufacturers
AI allows discrete producers in their core businesses to unlock trapped interest. Machine-based neural networks can recognize in seconds a billion pieces of data, bringing the right solution at the fingertips of a decision-maker. Your data are continually updated, which means your machine learning models will be updated, too. Your company always has access to the latest data, including ideas that can be applied to quickly change work environments. Four important AI benefits are:
Make decisions more easily and with greater confidence. How do you know what to repair at your production facility first? Routine decision-making processes can be streamlined and prioritized by AI so that the maintenance team can determine what to repair first with confidence.
Use Big Data’s instant, actionable insights. One of AI’s most exciting opportunities is its ability to identify and interpret trends in Big Data that humans are actually unable to grasp. AI will predict future opportunities and suggest concrete actions to capitalize on these opportunities that your manufacturing company should take today.
Protect data that is sensitive. AI helps reduce human error, improving performance efficiency and enhancing cybersecurity. To protect sensitive, confidential data in manufacturing and to make sure your competitive edge, strong cybersecurity is important.
By intelligent automation and diffusion of innovation, AI can reverse the cycle of low profitability.
