In today’s reality, data has become one of the key factors in driving business decisions. The emergence of big data has left organizations with more data than before and harnessing the value of this data is becoming ever more challenging. Analyzing such huge volumes of data and presenting this data to stakeholders in a meaningful and in a visually aesthetic manner is of paramount importance. It is no different in the field of telematics and fleet management. Fleet administrators are constantly faced with the challenge of measuring various key performance indicators (KPIs) and ensuring their fleet is utilized efficiently. In addition, fleet managers also need to ensure the drivers adhere to company policies regarding speeding, idling and use of company vehicles while not on duty.
To an extent, information collected from GPS modems helps the fleet administrator to monitor these KPI activities but it is challenging to determine if efficiency is truly being achieved. The true potential of this data is ignored as there is just too much to data being consumed, which results in many fleet managers being reactionary in the way fleet assets are managed. True optimization requires an analytics engine. Predictive Analytics is a branch of data mining that deals with extracting of information from data collected in the field and using this data to predict future possibilities, trends and behavior patterns. Predictive analytics has an important role in helping fleet managers mitigate risk, resulting in an exceptionally efficient and safe fleet. Telematics data on its own is relatively beneficial but, it isn’t until you begin modeling and cubing the data, the true value is realized.
Predictive analytics can be a powerful decision-making tool within various parts of fleet and asset operations. It’s used in many major industries such as retail, finance and insurance to forecast what is most likely going to happen in the future — it’s a popular risk assessment tool. To illustrate the potential uses in the world of fleet and asset management, safety and safety-related decisions provide a great example of how predictive analytics can impact fleet operations.
How predictive analytics empowers a fleet manager to make proactive decisions
Having access to information such as predicting driver behavior and the likelihood of a driver getting involved in an accident empowers fleet managers to operate a safe fleet by managing risk and implementing additional training if warranted. With the nature of fleet business often revolving around its employees being on the road, fleets are significantly impacted by an increase in fatalities and collisions occurring on Alberta roads.
Many company drivers average 24,000 kilometers per year, with more fleets experiencing an increase in preventable accidents. The primary cause of this uptick in preventable fleet accidents points to driver distraction that now contributes to the 25-30% of all fleet-related accidents. While driving records, accident reports and traffic violations provide information to identify high-risk drivers, it does not provide real-time information on drivers who operate under the radar but possess potential risk every time they drive a company vehicle.
Building predictive models utilizing telematics data can help determine future driver behaviors with a higher degree of certainty, which can in turn be utilized by the fleet administrators to develop customized driver safety programs to address risky driver behaviors. These models with a high probability can detect when a driver is likely to be involved in an accident and also the associated costs of an accident.
Predictive analytics also aids fleet managers better plan vehicle maintenance thereby increasing the efficiency of corporate assets and also mitigating costs associated with unplanned vehicle breakdowns (unscheduled repairs) and loss of warranty. Trending vehicle maintenance data and building predictive models using this data can help predict with a high degree of certainty when a component might fail and the costs associated with an unexpected breakdown as a result of a failed component. This valuable information helps maintain an efficient and well-maintained fleet. The data collected also helps fleet managers in their vehicle procurement strategies thereby driving fleet operational efficiencies (life cycle cost analysis).
Early adopters of optimization tools are greatly benefitted by predictive analytics because of the huge volumes of data accrued. However, the benefits and huge cost savings associated with predictive analytics propel more and more fleets to see the value in it. Considered the most revolutionary technological step of the “Big Data” era, predictive analytics has quickly become one of the most advanced forms of customized risk management.