Case Study 3-10810
IET analyzed downtime in order to predict required manning for a future production line.
This powertrain machining and assembly facility produces four speed and six speed transmissions for vehicles in production throughout North America.
The client’s production planning team wanted to accurately predict the required manpower for an upcoming machining line. The team wanted to calculate a baseline for future manpower using the performance of the current system. In order to do this, the production team needed to understand what level of operator interaction is required for repairs and how much idle time is represented for each downtime occurrence, but their downtime tracking software wasn’t able to answer these specific questions.
IET was brought in to analyze downtime so that the production planning team could move forward in their design.
IET put together a team of engineers to study each of the current automated machining cells. The engineers tracked performance statistics such as number of downtime occurrences including the mean time between failures, the reason for each occurrence, the duration of each occurrence, the fix required to bring the machine back up, machine idle time and repair time (mean time to repair). IET correlated all of this data in a user friendly matrix that was easily understood by the client’s production planning team.
This information was invaluable to the client and the production team. Not only was the production planning team able to move forward with their design, but they were able to identify all of the delays in the current downtime reaction plan. It allowed the client to adjust their preventative maintenance schedule to be more effective and redesign the reaction plan to minimize idle downtime. It allowed the team to make capital investment for improvements because they were able to confidently predict the return on the investment using projected uptime improvement. By calculating and analyzing the mean time between failures and the mean time to repair the failures, the team was able to ensure the appropriate level of accumulation was on the machining line to ensure optimum productivity.
By measuring actual performance on the production floor, the client was able to make statistically guided decisions for future planning and current performance improvement which realized bottom line results in a short period of time.