It is a fleet manager’s nightmare to maintain manual records on each and every vehicle or piece of equipment within the fleet that provides intricate details of the fleet operating metrics. This process may, to an extent, be feasible for a very small fleet of vehicles/ assets. However, as the number of vehicles increases, this method of maintaining vehicle logs becomes logistically impractical. Adopting a data-driven approach to fleet management provides a fleet manager with the insight and foresight to manage the fleet. Listed below are some of the challenges faced by a fleet manager.
Service companies typically need a lot of machinery to get the job done. Unfortunately, many contracting companies lack the ability and visibility to manage fleet utilization metrics. They don’t know whether their equipment is out in the field (and if so where), in transit, or undergoing maintenance. This inability to quickly check the status of every asset in the fleet leads many companies to buy or rent more assets than they actually need. Not having visibility into the status and whereabouts of your organization’s assets can be extremely costly. Without a fleet performance analytics tool, it’s difficult to tell whether equipment is on a job site, in transit, or just sitting idle in a yard somewhere. The result is that service companies believe assets in their fleet are all charged out, so they make the mistake of buying or renting equipment to fulfill demand, only to learn after the fact that some of their assets were sitting idle and waiting to be sent off to the next job. This is what asset under-utilization is all about, and it can be deadly for service companies because there are huge cost implications associated with it. It erodes margins, and it often creates a stressful environment to operate in. This is where an analytics solution can deliver efficiency!
Fuel costs due to extensive idling is one of the major expenses a fleet manager must deal with. During the cold winter months, it is understandable a vehicle has to idle to warm up the engine and the cabin which is beneficial for both the driver and the vehicle. However, extensive idling can lead to excessive fuel consumption and higher operational costs of a vehicle. Basic telematics data provides information as to how many hours a vehicle idled in a day. This data can only lead to a reactionary approach by a fleet manager. Analyzing multiple sources such as the location where excessive idling occurred, time of the day, and weather & road conditions on a particular day, historical driver behavior, etc. can provide invaluable insights to a fleet manager in making scheduling and dispatching decisions. An analytics engine can assist in correlating infractions to direct cost impact allowing you to trend this data, which then align to organizations auto-idling policies.
Leased vehicles can prove to be cost effective to a fleet manager only when the vehicle is operated with terms and conditions of the leasing company. It’s important for a fleet manager to maintain visibility on the mileage incurred on a leased vehicle in order to avoid any penalties arising from excessive mileage. Predictive analytics can provide a fleet manager with information as to the number of kilometers a vehicle is expected to run on a particular route based on historical data and help the fleet manager make tactical decisions as to the route to operate leased vehicles on. Alternatively, if a vehicle is forecast to run lower kilometers than what is actually outlined in the lease agreement, the fleet manager can increase the trips for this vehicle or negotiate the terms at the end of the lease with the leasing company.
Over the years, Analytics has proved to be a valuable tool for fleet managers. Data and numbers alone do not provide a story to drive cost efficiencies. It is only when a fleet manager interprets this data in a meaningful manner that the true value of data analysis becomes evident.