Business context We are a department of about 450 people. 70% of our work are projects that can be sub-divided into 12 project types with a standardized template. We execute about 1500 projects/year. Our performance drivers are productivity and planning adherence. Improvement potential Projects are supported by different teams that have specific skills. We need […]
We are a department of about 450 people. 70% of our work are projects that can be sub-divided into 12 project types with a standardized template. We execute about 1500 projects/year.
Our performance drivers are productivity and planning adherence.
Projects are supported by different teams that have specific skills. We need to capture actual execution start and finish dates and milestones across those teams and connect this data to get an end-to-end view. Quality of data is of utmost importance for the trust in the generated KPIs. Gathering performance data is intensive.
Additionally, we want to predict the performance based on demand planning and forecast. Our performance strongly depends on the demand volatility. When we see performance drops in the mid-term future (3-6 months), we want to take action.
How Binocs helped
We started with measuring the demand quality and more specifically the moment at which a project was confirmed. Project teams tended to delay the confirmation of the final delivery milestone with as a consequence that the upstream activities that had to be started in order to deliver at the milestone date were not confirmed. In “the new normal” no unconfirmed work was started.
In the next step we measured the number of times the delivery milestone target date was changed and the actual delivery milestone (aka slip chart).
Meanwhile, we started registering all actual start and finish dates of the project tasks. We used the Binocs concept of standard work, which gives us now the possibility to benchmark the performance. This is not so much from a productivity perspective. More important is making correct due date promises. By applying data science analytics on the lead times we were able to revert the demonstrated lead times to the standardized workmaster data. This resulted in a more realistic delivery plan and better promising to the customer.
Based on two years of data, we measured a 25% increase in teamwork throughput and a 30% increase in planning adherence. As we permanently measure and assess the demand quality and master data quality, the more than 400 users have built trust in the information and reports that Binocs provides.