The biggest obstacles when searching for the perfect business process and how to overcome them
Process mining has great potential as it makes transparent what is actually happening in a company. There is a lot to be considered in order to derive efficient steps for improvement from IT-aided stock-taking. In this article, you will learn more about what software-based process optimization is all about.
Companies use process mining to analyze the log files of the company applications involved in a business process. The resulting process model does not only serve documentation purposes. A professional analysis of the actual processes also reveals potential for improvement. The following 6 tips will help you avoid common mistakes:
- Make data available and comparable
- Provide for a continuous digital process
- Ensure the quality of the analyses
- Involve employees and the works council
- Avoid perfectionism
- Rely on continuity
Make data available and comparable
The first thing you have to do when starting to work with process mining is to ensure that log files are always accessible and created flawlessly. Furthermore, all required fields must contain data. And what's more: time in minutes and hours, temperatures in Celsius and Fahrenheit, ordering processes in Euro, US and Australian dollars complicate direct processing and make it prone to errors. An analysis usually requires the data to be harmonized first.
Provide for a continuous digital process
Process mining detects possible weaknesses and bottlenecks. For this purpose, you need consistent data. This means that it is crucial that the processes to be analyzed do not involve any manual steps. If your employees move a work in process from one workstation to the next, this movement cannot simply be analyzed by software. Furthermore, you should approach your processes as holistically as possible. Accelerating one process might lead to a backlog or even overload elsewhere, which might eventually even slow down the overall process. In intralogistics, this might occur with the acceleration of a production step or the digitalization of the mailroom, for example, if specialist departments still have to release accounts payable manually
Ensure the quality of the analyses
If you accidentally only analyze a part of the data, you run the risk of drawing wrong conclusions. For instance, if you only analyze the e-commerce ordering processes of younger users born in and after 1990, the result might be quite different from the ordering processes of users older than 65. Therefore, the choice and volume of data to be analyzed is crucial to making the right decision
Involve employees and the works council
The process mining results can also reveal the performance of individual employees. This might not be the goal, though. To avoid this, you can pseudonymize data, for example. A meaningful analysis requires a broader perspective anyway, since performance metrics do not tell us anything about the actual performance. There might be various reasons why employee A takes longer than employee B to complete a task. For instance, they might often give others a hand. The key question of process mining is hence not "Who makes mistakes?" but "Why does somebody make mistakes?". You will get the best answers from your employees themselves. Therefore, consulting with the employees and your works council is the fastest way to finding constructive solutions.
You should also take into account that a process will never be perfect. A healthy portion of pragmatism already meets the main requirements of seamless processes. If you try to map the smallest exceptions, you will only waste money and time without gaining any real added value. A good eye is also useful for selecting the KPIs. In general, a handful of measurands is enough to track your efficiency.
Rely on continuity
Corporate environments and customer requirements are more dynamic than ever. They require a lot of flexibility, and so do the processes. It is worthwhile to check and readjust processes at regular intervals. One-time process mining is therefore only the first step on your path to increased efficiency in the long run.
As opposed to the usual modeling of steps, process mining helps you detect hidden and implicit process knowledge and make it tangible. This way, you can not only check, valuate, and improve existing and familiar processes. If applied properly, you can also detect unknown processes. This is a major step toward increasing your efficiency in the long run.