Factory equipment management uses IoT-enabled sensors to monitor and track the performance and location of manufacturing equipment to optimize processes, reduce theft and misuse, and prevent unscheduled downtime.
Unexpected downtime in manufacturing can be costly – resulting in the loss of not only product, but wages, utilities, and then the cost of emergency maintenance to downed equipment. The average outage costs companies $17,000. And although equipment failure is a major contributor to unexpected downtime, a 2017 survey found that 70 percent of companies lacked a complete awareness of when the equipment was due for maintenance or upgrade.
Factory equipment management solutions allow for a more complete understanding of manufacturing equipment’s performance and utilization, ensuring not only that the equipment is operating properly, but that its being used efficiently by staff on-site. By constantly monitoring equipment, systems can alert companies when equipment is due for maintenance or is under-performing and enables them to schedule maintenance before the need is urgent and prevent an outage.
Using RFID or Bluetooth low-power equipment management solutions can track the performance and location of manufacturing equipment. Retrofit kits and sensors allow companies to track performance without replacing legacy equipment.
Increase production yield while reducing scrap, rework, and recalls with industrial artificial intelligence (AI) and predictive quality analytics
Monitor equipment status and performance, manage user and asset groups, build tailored insights and dashboards, and customize alerts and notifications.
Track machine performance and anticipate failures before they occur to help improve productivity and reduce wasted time and costs.
Remote monitoring enables users to track trends in machine performance over time to improve the efficiency of their operations long term.
Falkonry Clue is a plug-and-play solution for predictive production operations that identifies and addresses operational inefficiencies from operational data.