Competition, demand and unprecedented global events continue to shift the economic landscape. This means fleet leaders must run smarter, more efficient operations just to stay afloat. With each passing year, we’re seeing a shift from manual, more traditional fleet management practices to digital, automated and even predictive processes.
Some fleets still rely on manual, pen-and-paper tracking, while others may be more tech-savvy with a couple of digital processes. Needless to say, there’s a wide variety of approaches when it comes to fleet management, but some are certainly more effective than others.
An increase in digital solutions can help tackle the top fleet challenges, including hiring and retaining drivers, more accurate ETAs, reducing vehicle downtime and improving driver performance. Manual processes simply can’t achieve these improvements. Naturally, there are various levels in a fleet’s digital transformation and we categorize them into three different phases: static, dynamic and predictive.
Static is the first phase. Static processes are the easiest to implement. They include manual procedures such as calling a driver and asking for their ETA or managing schedules via pen and paper. Not surprisingly, processes that fall within the static phase offer the lowest value to an organization as they can often be inaccurate along with a greater chance of human error.
A static ETA would be a route based on distance and the estimated speed. However, unexpected road conditions and incidents make static ETAs unpredictable. Traffic, port congestion, vehicle profile, vehicle reliability and other important factors are not considered with a static ETA. Instead, drivers are left to overcome these obstacles themselves. A static ETA requires near-perfect conditions for it to be accurate and is often inaccurate by the time the driver leaves with their load.
Second, is the dynamic phase. In this stage, visibility, dynamic ETAs and automated workflows are introduced. There is more room for optimization as processes can leverage real-time data, such as traffic or weather data, to create more accurate ETAs and automate customer alerts. Dynamic process can also manage and analyze dwell times, create efficient tour plans, improve driver-facing applications – which can have a big impact on driver safety – and take analytics to the next level.
A dynamic ETA would be comprised of accurate estimates obtained through route matching and actual traffic. A dynamic ETA would adjust in real-time and opens the door for a fleet operator to automate customer alerts about the changing ETA. Leveraging real-time location intelligence at this stage also allows for post-trip performance analysis and data to support driver coaching.
Building a dynamic process requires third party data for weather, traffic, truck-specific routing, road regulations and other critical pieces of information. While this makes the initial build more complex, it results in greater efficiency and value to the organization.
The final phase is predictive. Predictive logistics utilizes data analytics to create and execute prescriptive strategies during unforeseen challenges. By identifying possible disruptions and leveraging historical and real-time data, fleet and supply chain leaders can predict, with a reasonable degree of certainty, possible outcomes and levels of impact. Understanding the range of possible outcomes helps evaluate capacity, price, prioritization, and timing in an efficient and cost-effective way.
Location technology is required in each phase, but it is most apparent in the predictive phase because it is a unifying layer of data. It enables companies to create a digital network where functional silos are replaced by end-to end visibility, collaboration, responsiveness and risk mitigation, agility, and optimization. The processes in this phase rely on high-quality data for training, testing and maintenance, implementation can be complex, but the resulting value is high.
Automated workflow management and decision-making replace time-consuming, manual tasks. Automated processes enable or improve load and capacity optimization, multi-route optimization, multimodal predictive ETAs, risk analytics, carbon footprint reporting and more.
When it comes to predictive ETAs, the following variables are all taken into consideration: historical traffic patterns, driving behavior, seasonality, derived features, routing history, brake data and KPIs which can all be adapted for customer requirements and measured directly. And keeping customers informed is made simple with automated ETA updates. This all results in a top-tier experience for customers that would be nearly impossible to replicate any other way.
With location-powered predictive processes, fleet leaders can streamline their operations and optimize middle- and last-mile journeys with advanced routing based on truck attributes and predictive traffic rather than best-guess estimations. State-of-the-art tour planning algorithms can account for real-time conditions, time windows and job constraints, which can reduce human error and make dispatchers more effective.
Whether your business processes are static or dynamic, location technology can help transform them. See how logistics companies are creating more resilient, efficient and intelligent operations at here.com/logistics.
