Shop floor data management systems will track employees’ time as they switch between tasks and the process time for machines. However, this automated data collection does not eliminate the need for traditional time studies. Here are a few reasons why time studies should at a minimum supplement automated data collection of business processes.
Dealing with Questionable Data Quality
Shop floor data management systems may collect data, but the data it collects may not be accurate. For example, when the data entry demands get in the way of doing the work, someone may not log in and log the work done until it is done. Then you get a sixty second cycle time for the five minute repair. Accidental errors like forgetting to log out and getting an extra hour of cycle time over lunch will skew the results, too. This is aside from the risk that people will maximize the time they’re logged in to give themselves more grace when the task is challenging or increase their billable time.
The automated data collection can be skewed in other ways. What happens when a driver turns off GPS so they aren’t tracked when going to lunch at a seedy place or when the GPS is interfered with via a spoofer? The AI analyzing the data may not be able to record it. That’s aside from software glitches and database issues themselves.
Collecting Data on Everything Else in Your Process
While software may log the cycle time for the machine, it may not track the full set-up and tear-down time. It probably won’t capture the preparations workers needed like moving raw materials into place or manual checks. Time studies will. It won’t count the couple of minutes someone else invests when they’re mentoring a new hire or helping troubleshoot a problem. An accurate time study will capture related tasks that may not be tracked by automated systems, such as when an IT technician is researching an error message but isn’t on the phone with the customer.
Time studies will capture the cycle time and process steps that software probably will not such as the steps customers go through before they interface with your business processes. And the tracking of this data may not be accurately captured in the logs, though it is essential to providing good customer support. Alternatively, the tool may not fully account for two people’s time during a service hand-off, causing you to underestimate the effort involved.
The Hidden Issues Data Collection Can’t Capture
What gets measured gets managed. The problem with this is that data collection and managing to the metrics can alter behavior. One of the most glaring examples is Amazon’s tracking of employees and docking them for going to the bathroom, leading to bottles of urine being found on the shelves by other employees. Customers struggling to retain their place in line while taking turns going to the restroom won’t necessarily affect wait time metrics, though this is clearly going to impact customer satisfaction.
If you only manage based on the amazing metrics, you’ll lose sight of the humans working within the system and the problems they face. This could range from shortcuts that create safety hazards to routines that are impossible to maintain over the long term. Falsely optimistic metrics may undermine process improvement efforts, because things seem better than they really are.