Bouygues DTP Mining: modernizing its maintenance processes

Bouygues DTP Mining is a subsidiary of the Bouygues Travaux Publics group specializing in mining, from drilling to transportation.

Challenges

Bouygues DTP Mining wanted to modernize its maintenance processes for mining vehicles operating in Africa. The group was facing several challenges:

Increasing Data Volume:
Challenges and Management Imperatives

The company was operating more and more connected mining vehicles. This was generating a growing volume of data from multiple streams (OEM data, laboratory oil analysis reports) that needed to be processed.

Predictive Maintenance:
Meeting Growing Customer Expectations

Bouygues DTP Mining’s customers wanted increasingly detailed information on the maintenance status of their equipment and assurance of a limited number of breakdowns that would slow down production.

Processus Artisanaux:
Moderniser la Collecte et l'Analyse de Données

The collection of maintenance data was manual, very often from emails. Their transformation and analysis were then tedious, with some flows requiring resource-intensive calculations and others relying on unsuitable tools (Excel macro).

Solutions

Bouygues DTP Mining has implemented the DataOps Platform to design more efficient maintenance data analysis processes.

Efficient collaboration:
Putting Saagie at the Heart of Data Analysis

Only 10% of the group’s data is currently used on Saagie, with a desire to position Saagie as the solution for standardizing all of Bouygues DTP’s data integration and analysis.

Data efficiency:
Speed of Action and Use Case Construction

Strong collaboration between the group’s business teams and Saagie’s Professional Services, enabling their use cases to go into production in just a few weeks.

Operational Revolution:
A New Chapter for Bouygues DTP Mining

Productivity gains in man-days through the automation of many data collection and analysis operations and increased team autonomy on data subjects through customized training.

You want to go into production and launch your data projects?