Customer description‍

A Dutch municipality with more than 150,000 inhabitants noticed structural peaks in civil affairs agreements, especially around vacations and elections. This led to long waiting times and ad-hoc solutions.

Challenge‍

Applications for passports, driver's licenses and removals came in waves, but schedules were made static based on averages. This resulted in queues, unnecessary overstaffing and inefficient staff deployment.

Solution‍

A solution was developed that uses a predictive AI model that predicts visitor flows based on historical requests, school holidays, events, seasonality, and demographics. Based on this, staff planning was dynamically organized.

Approach

1. Data analysis of historical request data
   Analysis of request volumes by day, week and month over the past years, linked to external factors such as vacations.
2. Developing a predictive model
   AI model trained on recognisable patterns in the demand for services, with automatic updates when circumstances change.
3. Connection with planning software
   Integration with the internal HR and roster system so that forecasts could directly affect staff deployment.
4. Training and simulation sessions
   The team was included in the use of the forecasts, with dashboards that provided insight into peak and trough times.

Results

• 20% less under or overstaffing
• Shorter waiting times for citizens
• Less stress and ad-hoc commitment for employees
• More control over staff deployment around known peaks (such as summer vacation)

Learnings‍

The use of AI brought peace and predictability to an environment that normally fluctuates strongly. By using data smartly, civil affairs were able to work more efficiently without sacrificing service quality.

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