There are various techniques that can be used to drive process improvement and solve problems in your organizationâ€™s workflow. Some are more complex than others, and some require a specific set of circumstances for their use to really make sense. In any case, itâ€™s important for you as a leader to recognize the kinds of situations that call for the use of specific problem solving techniques, and of course, know what techniques are available at your disposal in the first place.
Youâ€™ll find that itâ€™s actually not that hard to ensure that every change in your organizationâ€™s workflow drives things to an improvement, but you will need to carefully measure and evaluate everything that youâ€™re doing. To this end, there are several popular techniques that you may find useful.
PDSA (Plan-Do-Study-Act) is a popular and relatively simple method that can lead to great results with relatively little effort. On the other hand, DMAIC (Define, Measure, Analyze, Improve, Control) can provide you with a slightly more complex set of tools for addressing each situation, but it can be a bit more versatile in the way itâ€™s used.
PDSA Works Well for Iterative Processes
If you have a process in your organization thatâ€™s constantly going through improvements in an iterative manner, then youâ€™ll probably want to look in the direction of PDSA. Itâ€™s designed to work in a feedback loop, constantly using the results of the last iteration as input for the next one, and it can work especially well when you are planning to run some process that promotes change for a very long period of time.
Itâ€™s critical that there is some sort of feedback loop present in order for PDSA to work correctly though, otherwise you will simply be starting over from scratch with each iteration of the cycle, and in that case you might as well not even use it.
DMAIC is Great for Data-Driven Improvement
On the other hand, if you have a certain process that constantly works based on external data, you might find DMAIC to be the perfect fit for your needs. The way DMAIC works makes it perfect for organizations that gather a lot of data about their operations, especially when there are adequate systems in place for sorting that data and filtering through it.
DMAIC can also be a bit more complex though, so make sure that youâ€™re prepared to take on that extra workload if youâ€™re planning to use it. In most cases, a little preparation can go a long way towards ensuring that your application of DMAIC is successful and doesnâ€™t take an excessive amount of effort, and once youâ€™ve gotten used to the way it works, youâ€™ll find it especially easy to drop DMAIC into a new situation and work with it.
There are some intricate details to observe when using DMAIC though, and youâ€™ll want to ensure that you always have enough data to work with. This is similar to PDSA, which relies on a good set of output data from the previous iteration of the cycle, but in this case, youâ€™ll need more information from your external sources.
Sometimes DMAIC can even promote improvement in your companyâ€™s data collection practices, as itâ€™s not rare to realize that they are inadequate for the needs of the current project.
DMAIC and PDSA may seem very different at first, but they have their similarities, and itâ€™s important to understand when a situation calls for either of the two. With time, you should get a pretty good sense for that, and youâ€™ll be able to make decisions that make sense in the long run.