If you're a small to medium business, you probably don't have the spare resources to look inside your processes. Sometimes the final outputs can be acceptable but, like a swan gliding along on the surface of a lake, there's a lot of action below the waterline, some of which is unnecessary and causing waste. This tip might help you have x-ray vision at minimum cost.
Dashboards are usually employed to display a range of parameters across a business in order to see a high level view of day to day performance. However, they can also be used to create a simple form of process mining by displaying what is really happening in various stages of an end to end process.
Doing this, rather than just looking at final outputs, can highlight when a particular part of the process may be going out of statistical control and possibly allow a correction to be made before the deviation reaches the final output. Even if you are unable to make a real-time correction, you'll see that there was a problem and exactly where to make further analysis in order to avoid a repetition.
The technique involves the collection of data, either from systems or by sampling, and some statistical analysis in the form of control charts. The latter are essential to prevent overcompensation or tinkering, which can make matters worse. They use upper and lower control limits (shown in the example below as dotted red lines) to evaluate statistical stability against a number of rules. Specification limits can also be added, where available. Data can then be added over time to keep a rolling view of process performance.
The general method would be to:
Select an end to end process.
Map it out at a high level.
Select a key end to end metric, for example the process time.
Collect input/output data for that metric, at each step of the process.
Set up a dashboard of control charts, one chart for each process step.
Observe the charts over time, looking for instability at each step of the process.
Provide corrective action at the points of instability, but take care not to 'tinker'.
A simple example is shown below, where there was a problem in step 2 which was noticed by the operator in step 3 and mitigated before it was passed on to step 4 and the final customer. For example, it might be an indication of an exception not being effectively managed or a supply chain delay. Someone just looking at the output of step 4 would not have seen any instability and so would not have been aware that there was a problem with the end to end process. But having a view at every step will immediately indicate where the problem first occurred.
You'll need some Statistical Process Control (SPC) software but there's no need to get a high end stats package. There are some very good applications that work within MS Excel. They'll do the job, be easier to use and won't cost a fortune.
The example shown was created using QI Macros, which I’ve found to be very user friendly, but there are a number of similar applications on the market. This type of add-on will normally guide you through the use of control charts and indicate when special causes are present, so you don't have to be an expert to use them effectively.
Discovering what is going on 'under the waterline' with your processes can lead to a more efficient operation and less waste, so it's well worth the trouble to have a look.