Régaz Optimizes its Gas Network Inspection Routes with DCbrain
Régaz uses DCbrain to reduce distance travelled thanks to dynamic planning of its inspection routes.
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The gas network operator Régaz-Bordeaux inspects a pipe network covering more than 3500 km and 46 towns in Gironde, France. Before DCbrain, the group was having difficulties optimizing the planning of its routes and taking into account the network’s various requirements.
In order to meet the legal inspection requirement and with a view to reducing the environmental footprint of its operations, Régaz needed to model its gas network in greater detail.
The planning of inspection routes at Régaz was static and manual, involving the use of paper cards. Furthermore, there were significant requirements to be integrated: more than 3500 km of networks with inspection points on both sides of the road, which are sometimes difficult to access or have no GPS references. This organizational complexity meant that routes could not be dynamically planned.
Thanks to DCbrain we have gained in productivity, reduced the number of kilometers traveled and the carbon impact of our rounds while meeting our regulatory obligations to control our pipeline network.
Geraldo Alves
Director Regaz Bordeaux
Context
Why ?
Régaz wants to reduce the amount of time it spends modelling its gas network and to be able to integrate requirements. The group ultimately wants to reduce the distance travelled and, therefore, its CO2 emissions.
Who For ?
The solution is used by the network’s inspection department (aroute 10 users), with access for the department manager and the deputy CEO
Before DCbrain
The planning of gas network inspection routes was burdensome, static and manual, involving the use of paper cards.
With DCbrain
- Simple integration of pipe location and footprint data via automatic file imports
- Automatic suggestion of inspection routes
- Visualization of routes in progress and to be scheduled for managers and users
- Setting up of tracking indicators
- Choice of the point of departure of routes
- Ability to forbid the algorithm from using specific sections of the road network in routes
Data Used
- Past route plan
- Network topology
- Job-related rules: speed, U-turns, side of inspections (on the left or right-hand side of the road), etc.
Projected Gains
- Less distance covered
- Reduced CO2 emissions
- Less time spent planning routes
- Increased productivity by removing tasks with low added value
- Automation of data and continuous updates
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