Services for Ai Modelling and Process mapping analysis

 Modeling a process is complex because it requires extensive knowledge of the process at hand.

(Long discussions with the workers and the management are needed)

It is time-consuming and often prone to subjectivity.

 

This approach begins with process discovery

Process discovery is related to management trends such :

1. Business Process Reengineering (BPR);

2. Business Intelligence (Bi);

3. Business Process Analysis (BPA);

4. Continuous Process Improvement (CPi);

5. Knowledge Management (KM);

 

Process discovery can be used to (re) design processes.

The goal is to understand what is going on in reality.

Our experts provide methods that allow us to understand business processes by applying machine learning techniques.

• What is the empirical data within your business?

• Are you aware of your assets?

• How to leverage those assets for the greater good of your organization?

 

Deeper knowledge processes are 2-sided :

• Provide scientific knowledge, explain, predict, understand, and control phenomena;

• Enable practical applications to have a solid base to develop systems that efficiently support and control business processes;

 

Your discovery meeting kickoff with those discovery questions, and then we dig deeper :

• What data representations can be useful for modeling business processes?

• How can machine learning techniques be used for the clustering of process-related measures?

• Knowing that relevant clusters can be developed, how can they be used to make predictions?

• What kind of processes can be discovered from past process executions?

• Is it possible to extract process models from data?

 

Understand your data type, your asset can increase business valuation.

Aggregated data results from some transformations of raw data.

Benefit : Transform the original representation into a new and more compact representation to facilitate characteristics segmentation.

Aggregated data are the variables that result from operationalizing and simplifying the use case of process complexity.

Logistic homogeneous clusters :

Developing homogeneous clusters for a given process is relevant concerning the induction of predictive models for concrete directives for building data flow integration between management systems.

Sequence data describe the sequence of activities over time in a process execution.

• Process log

• Execution of the process steps

The goal is to derive a model explaining the events recorded. Machine learning techniques are instrumental when discovering a process model from noisy sequence data. Such a model can be further analyzed and eventually improved.