Fleet software specialist Bynx has released its latest software version, further supporting fleets with vehicle management and also helping with the transition to electric vehicles.

Gary Jefferies, sales and marketing director at Bynx

The new Bynx 12.13 release includes important technology stack changes and an update to the Oracle 19c database, enhancing data optimisation and diagnostics to help users achieve better operational, analytical and workload performance.

It also brings bespoke support for fleets introducing electric vehicles or managing existing EVs. This includes increased manufacturer model data and reporting capability, which can be used to provide insights to help manage the fleet mix optimally and in line with both business and environmental goals.

Gary Jefferies, sales and marketing director at Bynx, said: “Introducing electric vehicles into the fleet mix is a step into the unknown for fleets, especially in terms of budgeting for total cost of ownership (TCO). In Bynx 12.13, we’ve added more EV manufacturer and market data and made it easier for fleets to supplement this with their own findings in order to paint the complete picture. This can then be turned into meaningful insight that will aid decision-making about the future fleet mix and the utilisation of EVs within it.”

The latest release also helps commercial fleets through enhanced maintenance management, providing increased functionality, more streamlined job processing and control of authorisation processes. Supporting commercial fleets with vehicles using ancillary products fitted from different suppliers, the new maintenance functionality enables users to create service patterns against vehicles, ancillaries and at a model type level.

Fleets can also now select applicable service patterns and build these specifically into their full service operating lease quotations, and can combine service patterns into a single view.

And Bynx 12.13 also includes improved reporting capability, in particular when it comes to data centred around residual value optimisation.