Multiple Regression Analysis Lowers Fleet Costs
Do you have a fleet management system? Does this calculate how much fuel is being consumed by each vehicle and by whom? This is very important information and very often nothing, or not enough, is being done with your information.
With the constant fluctuation in fuel prices, it can be difficult for transport companies to budget their fuel costs as accurately as possible. Many factors contribute to the total fuel cost.
Multiple Regression Analytics lower fleet fuel
Lowers Fleet Costs
Meniscus has used these factors to model the cost of fuel for transport companies. Through Multiple Regression Analysis, an 11% reduction in total fuel costs could be achieved, by reducing the number of idling hours of drivers and their harsh driving scores by 20%.
Multiple Regression Analysis allows a correlation between a dependent variable and many independent variables. The analysis provides the coefficients for each variable giving an equation in the format of y = β0+ β1X1+ β2X2 +…+βnXn. From the established regression equation, it is then possible to use the independent variables to estimate the dependent variable, in this case, fuel cost. Multiple Regression Analysis also allows us to determine the contribution each individual independent variable makes to the dependent variable value. We are in the early stages of analysing transport data and believe it is a sector that would see real benefit from our MCE (Meniscus Calculation Engine).
We are not saying you shouldn’t use the system you have. All we are saying is that maybe we can do more with data and help you become even more cost efficient. In other words, get more from your system and by analysing the data produced to reduce costs, giving you more time to focus on other pertinent areas of your business.
Watch this space for new amazing on developments.