The refrigerated supply chain (cold chain) is a $250B industry globally. Trucks pick up fresh produce. Sea vessels transport food across the ocean. Neighborhood grocery store refrigerators maintain ideal climate-controlled conditions until you make the purchase. It’s all about keeping things fresh. Because of that, unplanned downtime is costly in repairs, associated produce loss, and disruptions to the supply chain.
Panasonic is a world-leading maker of cold-chain equipment. Among its customers is a supermarket multinational, which uses Panasonic refrigeration systems. Under service contracts, these systems are monitored in real-time. Yet unplanned equipment downtime still leads to hundreds of millions of losses annually. Preventive maintenance—replacing parts based on regular wear-and-tear schedules—has helped reduce downtime, but is still far from perfect.
Panasonic and the industry’s holy grail is predictive maintenance: being able to predict—weeks ahead—the
probabilities of failure of various systems and their individual components, so that highly targeted truck rolls can be dispatched and the exact components can be maintained or replaced just ahead of likely failure. Less than that and you get catastrophic downtime. More than that and you waste on unnecessary maintenance expenses.
The main challenge for all machine-learning-based predictive maintenance solutions to date is that there are insufficient labeled failure data, for a variety of reasons. Either data has not been collected, or there is data but actual failure events are insufficient for ML training, and equipment models are always changing under different operating conditions, etc.
In other words, Panasonic and other companies have tried, but have been unsuccessful at building and deploying real predictive maintenance solutions, i.e., predictive maintenance with actual Fault Prediction.