Downtime is already a multi-billion-dollar problem for manufacturers. While downtime is undoubtedly the barrier to production efficiency and effectiveness, Robato Smart Monitoring Solution not only takes care of the machine downtime, but also helps manufacturers to improve the usefulness of the asset by monitoring asset KPIs. Nonetheless, to reduce machine downtime, manufacturers can count on the following three things.

1. First and foremost, know how you currently track your OEE and downtime

The manufacturers who are not tracking OEE (Overall Equipment Effectiveness) or downtime at all, chances are they are losing money every day. The concern here is not to gain insights into whether the manufacturing site has downtime occurring or not – every plant does – but to know “how much” and “how can” manufacturers decrease downtime and improve OEE.

The larger the plant, more essential tracking production efficiency and downtime becomes. The method of manual tracking is widely used, but it is rapidly shrinking with the adoption of predictive maintenance technology. It offers a simple way to monitor machine performance, thus fine-tuning OEE and minimizing downtime. The technique utilizes one or more sensors to stream machines’ real-time data into a computerized maintenance management system (CMMS) and predicts potential failures in real-time. Here are the 10 must-have sensors to track OEE and machine downtime. The most significant advantage of predictive maintenance is speed. It allows operators, maintenance technicians, and production supervisors to fix downtime issues as they happen, instead of weeks later. Accuracy is another benefit. The system is not only limited to informing plant managers about how much downtime occurs, but also track precisely when, where, and why a downtime happens. To troubleshoot downtime problems quickly and efficiently, this kind of speed and accuracy are crucial.

2. Understand which plant employees can have the remarkable impact on downtime and inspire them to do their jobs well

To understand the effect of small mistakes on big disasters – ask the scientists and engineers who worked on the Mars Climate Orbiter and lost a $327.6 MM spacecraft due to confusion in converting mathematical units of measurement.

In a scenario, when 70% of machine downtimes can be attributed to user error, understanding which plant employees are having the remarkable impact on downtime is one of the determining factors in running machinery smoothly. A proven operator will not only diagnose and fix the machines but can prevent future downtime events through maintenance schedules and accurate documentation. Now, what if manufacturers enable them to upskill and understand their role in boosting productivity and better service machines? They may even have suggestions on how to minimize downtime or improve production functionality. Henceforth, by inspiring those team members who are having the remarkable impact on downtime, manufacturers can expect fewer operator-instigated slowdowns.