Many companies set big and often unachievable goals from the very beginning of the implementation of process mining. It would seem that there is nothing wrong with ambitious plans, but nevertheless, disappointment may come by the end of the project. The tasks set did not correspond to the possibilities, causing a large amount of time and financial losses.
The described vector leads to lost profits and does not even give process analytics tools the opportunity to reach their design capacity.
Step-by-step development
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You shouldn’t start with all processes at once, you need to decide on a specific one, improve it in a short time, evaluate the results and only then scale it up. Such an approach allows you to create the necessary momentum and deduces the hypothesis that the sum of the momentums equals the benefit. The emotional side of the issue should not be underestimated: when people (from simple performers to top management) see the achieved results, their inspiration and confidence in the right decisions grows.
Step 1: choose a strategy
2 primary questions, the answers to which will greatly simplify the task – why and for what purpose is it necessary to implement process mining? Perhaps the business wants to improve operational efficiency. But what does this mean? What does a good process look like? What model should companies focus on?
An effective method is to decompose the bp. Example: within the framework of research and implementation, you can divide o2c into several subprocesses, focusing on improving individual areas, and then formulate hypotheses based on the information obtained.
Another example: there are regular failures in incident management, leading to the transition of small violations to large problems. The reasons may lie in the plane of poor-quality equipment, or be justified by the peculiarities of the region. Ultimately, at this step it is necessary to have both a high-level view and specific goals that will guide the team in the future.
Step 2: analysis of information systems
The next step starts with identifying the systems that need to be studied to confirm or refute the hypotheses. After that, the data needs to be extracted from the is for further use. At a minimum, the business needs 3 types of data: case id, steps, and timestamps (so there is no need to extract additional attributes at the initial stages).
Step 3: create event logs
With the results of the is research in hand, it is the use of letters in phone numbers dates back possible to create a basic data model in
3 steps:
Data purity check,
Creating a simple version of an event,
Checking the compliance of the version with the process under study.
It is important to note that at this stage it is necessary to verify the data, which guarantees the accuracy of subsequent conclusions and their initial coincidence with the real state of affairs.
Step 4: create a reference bp model
Here it is necessary to go beyond the initial understanding of bo directory the process and detail it in order to create the “perfect sample”.
It is worth noting that this step is optional – everything depends on the context and the hypotheses identified in the previous phases. But it is this stage that provides an understanding of how the process should work (the “To be” model), which can be compared with how it is carried out in reality.
Step 5: analysis
Using the information collected in the previous steps, you can begin to explore the process within the following criteria:
Frequency of execution,
Time,
The presence of “Bottlenecks”
Conformity with the ideal model.
A good tool allows you to visualize these factors, see exactly where problems are occurring, and present the data in a way that is easy for any stakeholder to understand.
Step 6: finding opportunities
Finally, it is necessary to take what has been learned in terms of process execution and translate it into real process improvement. This step goes back to the original question: what does a good bp look like? And what are the factors holding it back? The answers to these questions will help you decide what the next steps should be. Depending on the situation, this may mean automating certain parts of the process. Or adding people to eliminate a bottleneck.
Step 7: The described vector leads
Another common mistake is a pilot project built around solving the biggest problems. In this case, the principle “more does not mean better” works. Why? Cumbersome processes take time, and the key idea lies in the plane of rapid acquisition of benefits, so the best start is to start with small but significant things.
Having analyzed the two hypotheses, a logical question arises: how to achieve the results described above and increase the chances of effective project implementation? It’s simple: you need to segment the strategy into 6 main steps.
