Adding predictive capabilities to Fleet Management Software to increase competitive advantage

Over the past decade, CloudMade has worked with some of the largest automotive OEMs in the world. During this time, the team has gained valuable knowledge, skills, insights and software-IP. CloudMade’s Fleet Predictions as a Service was created as a result of a pressing need amongst Fleet Management Software (FMS) providers, who were struggling to manage the increasing amount of data being collected and the lack of structure to gain intelligent insights from
it.

FMS-providers were also receiving increased requests from fleet owners to provide additional capabilities, such as predictive forecasting, improved fleet analysis and real-time predictions to support fleet operations, reduce costs,
and achieve goals regarding CO2 emissions reduction.

As a result, CloudMade’s Fleet Predictions as a Service offering was created to provide customizable technology that met the needs of the FMS-providers and could be integrated into their existing software, all while increasing the
value for the end-customer – in other words, a low-hanging source of competitive advantage, upselling, and growth.

What is Fleet Predictions as a Service?

CloudMade’s Fleet Predictions as a Service is a unique offering which provides access to our patented machine learning platform through an API, a mobile app or through a dashboard. Our platform offers customers a wide range of different
inference engines and predictive models. These in turn enables valuable use cases and data-driven insights about the fleet operations.

Fleet optimization

Fleet management and optimization refers to managing the vehicles that an organization needs to run smooth operations, whilst considering financial impact, customer satisfaction, regulations, climate impact, and working conditions
for employees.

From a commercial perspective, we consider fleet optimization to be a ‘no brainer’. By using data from vehicles, telematics, or driver mobile phones, CloudMade’s models uses predictive capabilities to coach the driver, affecting the
choice of the driver for specific vehicles or routing. This will reduce the fuel consumption and need for maintenance, which leads to lower costs and CO2 emissions.

Also, if a fleet manager understands which vehicles will likely require maintenance soon, this will reduce the risk of a breakdown of the vehicle. Achieving proactive maintenance is necessary to maximize uptime of the vehicles and
reduce the total cost of ownership, so additing this capability to your software will become invaluable to your customers.

Forecasting and analysis

Forecasting and analysis can help fleet managers to acknowledge the future demand of the operations and will lead to a better understanding of what vehicles are required in the short and long-term future.

A fleet manager needs to consider what cost, fuel consumption, and CO2 emissions can be expected and base future strategies around that. Machine Learning can be used here to analyze a large amount of data and draw better conclusions
and predictive forecasting.

This forecasting is essential in the current climate. The lead time for ordering new vehicles is now 8-12 months due to the ongoing semiconductor shortage, which is showing no signs of improvement in the near future. Considering these
lead times, fleet managers simply must have access to data and insights that enable forecasting and predictions around demand. If your FMS does not provide this capability, you risk losing customers to FMS-providers who are responding
to these urgent needs.

Fleet electrification

The number of fleet owners interested in electrifying their fleet is on the rise in 2022 – and so is their demand for insights from you as their FMS-provider on how to go about this efficiently and cost-effectively. In this instance,
CloudMade’s solution combines telematics data with asset and infrastructure data to help organizations make informed decisions regarding the electrification of their fleet. These decisions will be based around aspects including:

• Residual value and CO2-emissions;
• Maintenance organization of existing vehicles; and
• Making sure that the routes support electric vehicle charging.

Optimizing fleet operations requires fleet managers to carefully consider which vehicles are most beneficial to convert to electric while ensuring that the new electric vehicles are supported by relevant maintenance and charging opportunities.

Not using the data effectively when electrifying fleets can result in:

• The risk of electrifying the “wrong” vehicles which lead to increased cost and CO2-emissions;
• Not having the right composition of vehicles to support the operations; and
• Not having fleet management optimized for electric vehicles (considering charging strategies and routing etc).

Optimising job allocation, scheduling, and routing manually or by a rule-based system is also difficult. This task is optimally handled by AI (ML-models). By efficiently allocating jobs, the energy costs are lower as well as down-time.
This would therefore reduce the costs for the fleet manager.

Your competitive advantage

Incorporating predictive capabilities to Fleet Management Software will add value to the end-user (the fleet managers), meaning that you can charge for the added capabilities, use cases and features.

This also means that you can achieve increased growth and upsell on existing customers. Ultimately, the result is simultaneously increasing competitive advantage and securing a position as a premium player with a high-tech FMS offering.