Improve production process continuity by forecasting employee absences

Background

Our client is a multinational company specializing in air conditioning and cooling systems with more than 100 production entities around the world. The company wants to set a fixed production output each day, which requires insight into employee absences on the production floor.

Challenge

To be able to predict absences, the company needed to combine internal data (like contract information, long-term illness, labor planning, availability of temp staff, etc.) with external data (like flu predictions, holidays, weather data, etc.). By leveraging many years of information on employee scheduling and availability, our client was looking for an advanced solution to forecast employee absences.

Approach

- Define and collect all relevant internal and external data. - Analyze historical data to define the real drivers of absences. - Build and test an advanced algorithm that forecasts absences. - Create a user-friendly interface allowing business users to access relevant information.

Key results

01
Feeding and testing of the predictive model to ensure a reliable outcome
02
A user-friendly tool that can be used without any IT knowledge
03
A clear view of the forecasted presence and absence of employees

Benefits

The solution significantly decreased costs by reducing production process downtimes, hence improving supply chain continuity. It offers a clear forecasted view on the available workforce for the upcoming days and weeks. This makes it possible to optimize the interim management workforce and guarantee that qualified staff is available at the right machine at the right time.

Technology partners

Python

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