How many plant operators know when their equipment is due for maintenance, upgrade or replacement? And how much confidence do they place in whatever system tells them this? How about when assets are due to be replaced?
A survey by ServiceMax, a leader in field service technology, found that more than 70 percent of surveyed manufacturers don’t know when their equipment is due for maintenance, upgrade or replacement, and 74 percent don’t know when assets need to be replaced. The survey also found that unplanned downtime affected 82 percent of businesses over the past three years, with an average cost of $260,000 an hour. It’s reported that 42 percent of this downtime was caused by equipment failure at a cost of $50B annually (source: IndustryWeek).
Zero unplanned downtime is now a top priority for most manufacturers. One way to make maintenance smarter is through artificial intelligence (AI). By analyzing data collected from different sources, AI can deliver actionable insights on the status of a machine component or the machine itself. AI also has an unmatched ability to process large volumes of data, recognize patterns, make predictions, and give practical advice on actions to take.
According to digital transformation consultant Capgemini, machinery maintenance and quality are the leading AI transformation projects in manufacturing operations today. McKinsey says predictive maintenance can reduce machine downtime by up to 50% and increase machine life by up to 40%. Results like these are why global research and analyst firm, ABI Research, expects AI-enabled industrial devices to have an installed base of 9.8 million over the next two years.
