Understanding future peak times at Civil Affairs counters

Municipality of Groningen

This algorithm, the forecasting model, provides insight into future peak moments at the municipality's Citizen Affairs desks. This allows additional staff to be hired in time for expected peak moments. This model also supports financial planning.

Last change on 27th of June 2024, at 13:10 (CET) | Publication Standard 1.0

• Publication category: Other algorithms
• Impact assessment: Field not filled in.
• Status: In use

General information

Theme

• Organisation and business operations
• Public finance

Begin date

2023-09

Contact information

[e-mail]

Link to source registration

https://data.groningen.nl/dataset/algoritmeregister-gemeente-groningen

Responsible use

Goal and impact

The purpose of the algorithm is to allow Civil Affairs staff to be better scheduled. Because passports have been valid for 10 years instead of five years since 2014, we expect a peak, and thus greater crowding, at the front desk in 2024. This algorithm can better predict when to hire additional staff for expected peak times, taking into account the necessary training period. Due to the special nature of the documents, not just anyone may do this work, so it is important to take this training period into account. This model also supports financial planning. For residents, this means that the Municipality of Groningen can respond better to expected crowds and there are fewer long waiting times at the Burgerzaken public counters.

Considerations

An advantage of the algorithm is that it allows for better management. Temporary staff can be hired at peak times while allowing sufficient time for training. This means the resident can be helped faster and better. In addition, a better departmental budget can be drawn up.

The downside is that it remains a forecast based on past trends. It does not take into account external events such as corona or legislative changes.

Human intervention

The algorithm is used to make a prediction. The prediction is used by the department to adjust policies accordingly. This is well monitored. In doing so, the algorithm does not make a decision on its own. It serves as an aid to the policy.

Risk management

The biggest risk is that the forecasts are not correct and that too many or too few staff are hired and the departmental budget is incorrect. Therefore, it is important that the data is correct. The forecasts have been extensively tested and checked. Furthermore, the dashboard always shows the monthly forecast and the actual realisation. There is constant insight into how reliable the forecast was over the past year.

Elaboration on impact assessments

There is currently no impact test done on this process. Data is extracted from the municipal basic register, but it only relates to the validity of documents.

Operations

Data

From 1995, records have been kept of the products issued by the civil affairs department as to how many were spent each month each year.

Data is retrieved on online and POS payments. Furthermore, the appointment system for the counters is accessed and the (numbers) of registrations of travel documents and deeds. From the RDW come data on digitally requested driving licences. Finally, data on the registration of non-residents (RNI) come from the National Service for Identity Data.

Technical design

The model focuses on the processes and products at Civil Affairs that proportionally involve the most work or revenue. It forecasts the expected issue of travel documents and driving licences, the number of removals and migrations, and marriages and registered partnerships. Combined with the average time taken by the processes, an expected capacity utilisation is made. For the selected products, a forecast is made for 24 months ahead. Annual forecasts are used as a starting point. Based on past monthly figures, a profile is drawn up, which determines for each month in the year what the share for that month is on the annual number.

For travel documents (passports and ID cards), the forecast is based on the end date of the travel documents. For driving licences, the forecast is based on an extrapolation from history; a fit function is used to capture the pattern from the past. The extrapolation takes into account damping over time and population growth. Relocation and migration is based on the population forecast for the municipality of Groningen. The number of expected marriages and partnerships is forecast based on past years. This trend is continued.

The profiles are used in combination with the annual number to produce a forecast at the monthly level. The forecast is also broken down for travel documents and driving licences by medium (internet or counter) and for migration also by specification (inside or outside the EU). By determining fractions for month, medium and specification, a forecast at this level of detail is made from the annual forecast.

External provider

This model was developed by the municipality of Groningen itself.