Challenges

According the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. Traditional preventive maintenance processes require machines to be repaired at intervals based on time or usage. These methods, however, still result in significant instances of equipment failure resulting in idle workers, increased scrap rates, lost revenues and angry customers. In addition, preventive maintenance may replace parts that still have significant working life, which can be a waste of time and money.

Opportunity

AI based predictive maintenance uses a variety of data from IoT sensors imbedded in equipment, data from manufacturing operations, environmental data, and more to determine which components should be replaced before they break down. AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. McKinsey and Company found that AI based predictive maintenance typically generates a 10% reduction in annual maintenance costs, up to a 25% downtime reduction and a 25% reduction in inspections costs.