Seeing is Believing
In some cases, disparate data points alone are not enough to compel a manufacturer to take action, especially when operators can't physically see the problem the data has identified. Rather, it's combining data in a way that defines root causes that offers compelling enough evidence to drive actionable improvements.
When a performance management software system at a large beverage manufacturer pointed to a large amount of downtime in a tunnel heater machine, operators simply didn't believe the data because they weren't seeing the machine stop. Investigation into the process revealed the machine was stopping several times per second because the machine's heaters were undersized; an issue identified by monitoring the temperature inside the heaters. The heaters were shutting due to overheating, stopping and then starting up again after cooling down -- all within a split-second.
At other times, it may be easy for operators to identify that action is needed, but it's unclear where the priorities should be. This was the case for a large petroleum and petrochemical manufacturer in the Northeast. One of the producer's winding machines was extremely complex, operating in dozens of different states of running versus not running. When the machine would go down, operators had no way of identifying which state of not running the machine was in.
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| OEE Water Fall -- shows the cumulative effect that Availability, Quality, Performance have on OEE / TEEP. |
The standard procedure during downtime was to start at the beginning of the line and check for potential problems until the problem part was identified. After installing a performance management software system, the manufacturer was able to log a specific event for each state, allowing operators to pinpoint the specific cause of downtime. Instead of floundering in the dark trying to identify a root cause, operators could go right to the area of the machine causing the issue, saving large amounts of time (and cost) in the process.
In both cases, a single OEE point was not enough to spark change on the plant floor. Instead, leveraging additional objective, fact-based data points from the control system and using them to identify root causes and specific improvements that would reduce costs were eye opening.
