The Future of Fleet Optimization

fleet optimization
7 min read

While innovations in data and technology solutions have the potential to completely transform fleet management operations, a whopping 85% of shippers and consignees believe their industry is still lagging behind others when it comes to implementing these new technologies. Meanwhile, the logistics companies who have been early to embrace tools like artificial intelligence (AI), predictive analytics, and Internet of Things (IoT) devices are realizing high ROI and improved efficiency for their businesses. Now, digital enterprise tools are becoming central to the monitoring, analyzing, and execution of sustainable fleet optimization.

As devices become smaller and digital tools become more affordable and capable, fleet logistics companies of all sizes will be able to more easily leverage them to optimize their fleet and workforce with real-time visibility and automation. The ROI from integrating these solutions speaks for itself: As an example, operators at the Port of Rotterdam now use real-time intelligence to generate an estimated $80,000 in savings every time they dock.

These powerful technologies include telematics and IoT, AI-powered analytics, and workforce management applications all of which will be key tools needed to work together to shape the future of fleet optimization. Let’s take a look at three of these technology classifications and how they can advance the current state of fleet management.

Connected Technologies: Telematics, IoT, and Vehicle Tracking

Fleet Optimization
Picture Credit: TBOS

Real-time data on equipment performance, location, and maintenance is becoming an increasingly common part of industry-wide operations. In fact, McKinsey has found that IoT and other smart sensor applications, such as telematics and vehicle tracking, are some of the most widely developed among emerging logistics technologies today. The potential global economic value of these IoT technologies is estimated at $1.9 trillion, according to a report by Cisco and DHL. 

IoT and vehicle tracking systems are enabled by telematics, which provide fleet operators with remote monitoring capabilities. Deloitte reports that 40% to 45% of U.S. fleet vehicles are currently equipped with a telematics device, updating operators with real-time visibility into asset location, status, and activities. Telematics-enabled service offerings are already driving fleet efficiencies, where 56% of operators using telematics cite improved productivity and 53% cite reduced fuel consumption as real benefits.

Real-time vehicle tracking has also evolved beyond GPS, helping operators route and schedule fleets dynamically, and even incorporating live weather and traffic data for accurate predictions. With both personalized vehicle monitoring (e.g., theft protection) and improved fleet operation features (e.g., environmental metrics), vehicle tracking has become a means for optimization within each broader fleet management network. Using this method, fleet operators can use the data to increase fuel efficiency, implement preventative maintenance, and create more proactive operations.

Now, IoT—which encompasses telematics devices and connectivity, as well as sophisticated analytics—is also having a broad impact on fleet optimization, allowing for real-time routing, dynamic vehicle scheduling, and the optimization of both resource and workforce management. IoT systems can provide operators with detailed analysis, reporting on individual vehicle fuel efficiency or failure rates, and even receive as detailed insights as which truck drivers are most likely to “brake hard” or receive a speeding violation.

One IoT system, Geotab, provides statistics on how replacing a group of specific trucks can increase a fleet’s overall fuel efficiency. The system also uses sensor data to determine if and when vehicle electric systems will fail, which is the cause of 60% of larger fleets’ maintenance issues. By using this data to do more accurate preventive maintenance, fleet companies have the potential to see incredible cost-savings.

As these solutions become implemented on a mass scale over time, telematics, IoT, and vehicle tracking will all contribute to environmental initiatives. According to Forbes, tracking solutions will allow fleet operators to “literally take control over idling, speeding and other inefficient driving behaviors that waste fuel and produce excessive CO2 emissions that are released into the atmosphere.”

Artificial Intelligence: Analytics, Forecasting, and Predictive Optimization

Picture Credit: Transmetrics.

In 2019, McKinsey estimated that the transportation and warehousing industry has the third-highest automation potential of any sector. After all, AI is foundational to broader supply chain and operations automation, with a wide variety of applications in areas like tendering, last-mile delivery, and predictive optimization through augmented intelligence, among others. McKinsey predicts that AI will create an entirely new “logistics paradigm” by 2030 as it continues to outperform humans at repetitive but mission-critical tasks.

In one use case, Caterpillar became the first company to establish a connection between its optimal machine-learning system for cleaning fleet ship hulls and fleet performance improvements. According to a Harvard Business School report, this increase in maintenance improved overall fleet performance, leading to $400,000 in yearly cost savings per ship.

AI tools are driven by the availability of data provided by IoT and other sensor technologies, where predictive analytics has become among the most visibly successful applications to date. This supply chain data can cover anything from sales data to weather patterns, allowing logistics professionals to draw insights that will enhance future performance and profits.

AI predictive optimization tools for cargo transport, like that of Transmetrics, helps logistics planners improve fleet utilization and empty repositioning, yielding millions in potential savings for clients. This is achieved through a “human-in-the-loop AI” approach,  wherein decision-making is enhanced for high-skills logistics planners by combining human intelligence with processes automated by AI. It works by offering planners recommendations on actions to take, and then captures the final decisions and uses that input to further train the AI for future choices.

For example, our complete AI-driven data model for NileDutch helped the company build a global plan for fleet container reduction, empty repositioning, storage, repair, and maintenance for the whole network at the lowest possible cost. Now, the company’s logistics team can dynamically optimize empty container plans 10 to 12 weeks ahead of time. The team has established “one version of truth” with relevant logistics and financial data displayed in a single, powerful interface.

Read NileDutch Case Study…

Tools for Workforce Management Optimization in Fleet Operations

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Logistics planners across Europe, Asia, and the Americas are currently facing a global shortage of truck drivers, with a 50,000-person shortage in the U.S. alone reported in 2018. And in both 2018 and 2019, “Workforce” was among the 27% of chief supply chain officers’ (CSCOs’) top-three strategic business priorities—a dramatic increase from 2017.

Fortunately, data and technology solutions can help optimize workforce management in addition to fleet equipment utilization, dispatching, and maintenance. This is especially critical because workforces are becoming increasingly mobile in both fleet operations and fleet management. 

Individual tools allow fleet employees to track personal mileage, provide real-time reporting, and ensure compliance during individual interactions and transactions. These data inputs feed into predictive tools to help organizations optimize driver assignments, reduce drive turnover, and even analyze employee satisfaction.

For example, Navisphere is a TMS system with a virtual network that connects drivers and carriers to optimize truck posting and load management, including driver assignment and billing. The platform is broken down into two sides: a carrier version and a driver version. Drivers can use it to easily scan and upload documents, get load updates, and find the information they need to make deliveries. Meanwhile, carriers use it to gain deeper insights into actions like load searching and truck posting with more management tools and analytics reports. Used together, freight companies can create a new level of transparency in their supply chain.

As workforce management systems become more capable, standards and performance tend to improve. For example, mainstream auto insurance companies like Progressive and GMAC allow companies to adopt tailored “pay-how-you-drive” (PAYD) insurance packages based on the personalized movement profiles of their transporters. Fleet operators may find additional predictive functions for workforce management solutions, helping them to reduce turnover, improve driver assignments, and analyze employee sentiment data to benefit morale.

Build a Foundation for Your Future Fleet

The future of the fleet management industry ultimately lies in its adoption of IT, cloud, and data-driven solutions. According to Deloitte, fleet managers need to focus on digitization, process improvement, and operational efficiency. With these underlying technologies in place, fleet operators, CSCOs, and other fleet logistics leaders will embrace more practical, sector-specific technologies in areas like automated diagnostics, last-mile delivery, and augmented intelligence.

Supply chain leaders must begin by improving the reliability and scalability of existing, desirable processes, optimize data quality and analytics, and build internal competencies for the disruptive technologies that will streamline fleet processes. As their competencies mature, these leaders can foster a culture of innovation that will add lasting value as business models, technologies, and customer expectations evolve, paving the way for the future of fleet optimization.

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