Home Bots & Business Lessons learned using process mining – part 3

Lessons learned using process mining – part 3

by Guest

This is the third – and last – article on the lessons learnt during process mining implementation. You can read Part 1 here and Part 2 here.

Correct data is important to build trust

Data quality is often regarded by companies as a roadblock to start implementing process mining. Companies face issues with, for instance, incorrect or insufficient logging of data or imprecise timestamps, and also sometimes struggled with incorrect translation of business logic to the process mining tool. The business logic was not always implemented the first time right, for example which users should be considered automated or what is the definition of a delivery being late.

Incorrect logic leads to false analysis and conclusions. A good practice that we identified during the project is to check the data for errors using manual inspection. Always use experts from the business process domain for a sanity check of the data and the analysis conclusions. After development is finished, we perform validation step. Once it is confirmed that the developed functionality works as specified, it is released to the users.

We position process mining as the tool that allows to analyze processes based on facts instead of subjective opinions. If you skip the data quality checks and present conclusions based on data that turns out to be wrong, you will lose the trust of the business forever.

Spot where process mining can support existing initiatives 

Process mining discloses process inefficiencies and bottlenecks. However, if the problems are obvious and significant, process experts and continuous improvement specialists likely have already identified them and improvement projects have already been started to address them. It is important to have a broad view on the running improvement initiatives within the company and stay closely connected to multiple stakeholders to spot overlapping activities.

Take into account that project pace and stakeholders’ involvement are much higher when a burning platform and potential “big bang” does already exist and efforts already have been done. In this situation the one should identify how process mining can help to accelerate the improvement process; in particular to support the analysis phase, complementary to or an extension of existing analysis methods, and monitoring the effect of improvement actions.

Strong change management is essential 

Process mining can be considered as a transformational project, and as in any transformational activities change management aspect is crucial.

The process mining improvement projects result in changes to the way people operate their work. As such, the improvement actions might affect people on personal level and create a shift in the kinds of jobs that people do. Make sure you have a change manager particularly skilled at managing communications, leading organizational change and addressing human dynamics.

The company culture is a very important aspect as well as starting point. If already a strong shared continuous improvement culture exists in a global company, various people from different backgrounds and cultures already have adapted to this.

It is crucial to recognize universal approaches to change might not serve their purpose in every setting and should be tailored based on a particular situation you target.

Use these practical lessons and take advantage of organizational best practices to transition from your current state to the next level and avoid potential mistakes during implementation of process mining within your company.

Ekaterina Sabelnikova is Innovation Consultant at Philips Engineering Solutions

Misschien vind je deze berichten ook interessant