Since 2005, the Fleet Challenge team has completed dozens of fleet reviews (more than 150) for public and private sector fleets in all business categories from municipalities to over-the-road trucking.
Fleet Analytics Review (FAR) is a proprietary Fleet Challenge software tool that was developed by our team in 2016 for predictive analysis, data modeling, and completing fleet reviews and audits.
The Fleet Challenge Fleet Analytics Review™ (FAR) software tool uses historical operating data for go-forward predictive analysis and forecasting. FAR makes our fleet review processes thorough and efficient.
Baseline Analysis
The road ahead must begin by knowing the starting point. In fleet planning, this is the current-day baseline. The FAR process uses historical cost metrics and vehicle operating data (i.e., miles/kms-driven, engine hours, fuel usage, repair and maintenance costs, unit age, acquisition cost, cost of capital, downtime, residual value, etc.) to establish financial and service level baselines (i.e., utilization, availability/uptime).
FAR highlights the exception units – vehicles that are performing in a sub-standard way in terms of cost and performance; it identifies the reason(s) why thereby enabling management action(s).
The Fleet Management Balancing Act
Optimized fleet management is a complex juggling act. Capital investment, operating expenses, depreciation, preventive maintenance levels, fuel consumption, aging of the fleet, availability, utilization, emissions, and inflation are interconnected issues. Making a change to any one of these critical considerations impacts all of them.
For example, deferred capital spending will result in an aging fleet, in turn resulting in higher reactive repair rates, more downtime, higher fuel consumption, increased operating costs, and, ultimately, a larger overall fleet size to allow for more spare vehicles to compensate for the reduced reliability of primary vehicles. Counter to this, if vehicles are replaced too soon, value may be lost.
Fleet Challenge believes that the key to success is knowing the optimal economic lifecycle for each type of vehicle in a fleet. With that information, fleet managers can balance their go-forward capital spending to align with service level (uptime) and operating expenses (Opex), and other essential success measures.
Predictive Data-Modeling is A Key Step
It is said that “the past predicts the future” and based on this popular maxim we know that predictive data modeling is a critical step in fleet planning. In fleet management, costly errors from guesswork can multiply quickly. For example, if a fleet acquires a vehicle type that turns out to be a poor performer the impact can be negative and costly. But multiply that impact many times – such as when a fleet which has chosen to standardize on purchasing dozens, or even hundreds of these same poor-performing units, the cost increases exponentially — it can become insurmountable for some enterprises.
The same logic applies to management and operational practices – data modeling can show the way to success. Making a business process change – for example, outsourcing maintenance, or perhaps leasing versus owning vehicles – any significant change being considered should be researched fully beforehand. That’s why data modeling in FAR is a first step in making changes.
Go-Forward Action Planning
Once today’s baseline has been established through the fleet review process, and options for fleet improvements have been data-modeled and their cost-effectiveness evaluated in FAR, a go-forward fleet action plan is developed. For options that are found to deliver ROI, a business case based go-forward plan is developed for our client’s consideration.
FAR analysis is the first step in developing go-forward recommendations for our clients. It provides defensible, business case based strategies based on actual historical data.
Software tools, including FAR which we use in our consulting projects are incuded in the overall project price and we license the tools we use in perpetuity at no charge to our consulting clients for their own use post-project. Optionally, the software tools can be purchased for clients own use.