Optimize processes without risk & See the impact of changes before going live.
Minit Process Simulation & our machine learning models help you find out how changing the process variables changes the overall results.
Once the simulation confirms that the assumed change delivers the desired outcomes, you’ll know how to optimize the process.
No need to guess anymore, now you can:
- Test your process optimization hypothesis
- See impacts of overall process performance
- Understand change consequences beforehand
Optimizing a process is not an easy task. Too many variables, inputs, and steps involved affect each other. Uncertainty in changes can lead to an unintentional decrease in overall process performance.
Do you ask questions like:
- What would happen if I had a bigger team in the Accounting department?
- How much cost would I save with 3 RPA bots taking over the Invoice Retyping activity?
- Would my customer journey be faster if I rerouted the returning orders to pass our overloaded registry office?
Immediately see how changing the variables translates into the process performance, map, and statistics, and get the answers to your what-if questions.
Simply apply Minit Process Simulation on an existing process and custom-set variables affecting it, such as resources dedicated to a task, manhour rate, working hours and days, distribution of process starts, task duration, and others.
To make the simulations more accurate and precise, we have added the option to use machine learning models in each activity.
How Machine Learning (ML) Works in Simulations
Without ML in Simulations, each activity decides on random what the next path will be for any given case. This can cause irregularities in the newly generated log in many cases.
Imagine you have a process that consists of two variants. One is ABCE and the other is ACDE. So if a simulation is simulating such a process, variants such as ACE or ABCDE will occur, but this is not what the original process was supposed to look like.
So, if you include case attributes in simulation, you can teach the model that cases with certain values of case attributes are taking the first variant and cases with different case attributes are taking the other variant. This will result in precise simulations that will return generated logs matching reality.
Minit also allow using ML for choosing resources in every activity based on the original log and the duration of a case in any given activity.
Simulation is always bound to the currently open process view.
Simulation tiles allow you to view all simulations created in the process view and an overview of how they compare to the original process.
You also have the option to save the simulated process, together with its virtual logs for further before-after process comparison. This way you have a benchmark in which to reference real-life changes.
Moreover, you can see and edit the new process as a standardized BPMN diagram, adjust the process flow, and simulate the new flow afterwards.
Once the simulation confirms that the assumed changes deliver the desired results, you know what changes need to be applied and how to optimize the process. This allows you to make a risk-free decision, based on data, not assumptions.
Uncover hidden connections and causalities in process data & investigate where and why problems occur. More about AI-Powered Root Cause Analysis
We appreciate the speed and optimization of Minit’s algorithms, for analyzing of large data sets and clear visualization of the process made it easy for client to understand what the issues were.
David Slansky, Partner in Global Data & Analytics Team