Getting people and goods from one point to another is fundamental in the world economy. Improving transportation has never been more important in today’s climate, and many are turning to predictive analysis. The desire to adopt this approach is sound in its possibilities, but it’s not an easy road. Multiple factors are making this an arduous task for organizations.

What’s Driving the Demand for Predictive Analysis in the Transportation Industry? 

One of the most significant drivers is the demand for e-commerce deliveries. For example, FedEx has handled more than 12 million packages a day in 2021, which represents a significant increase over 2020. The increase in online orders has also led to an increase in subsequent returns.

With demand high, there are other considerations, such as a lack of drivers and operators. Supply chain woes continue, and transportation is often the weak link, especially in last-mile deliveries. Customers have elevated their expectations about deliveries and aren’t as forgiving as they used to be.

Logistics companies are striving for digital transformation by using data to make better decisions. That data, however, comes from disparate systems, making its analysis difficult.

With all these competing and intersecting challenges, employing predictive analytics in a strategic, accessible way is promising.

Let’s look at specific ways predictive analytics can help the transportation industry adapt to new market pressures and digitization.

How Predictive Analysis Supports Transportation 

Here are 10 ways you can leverage predictive analysis in the industry. 

1. Adjust shipment patterns

When you have data on the entire shipment process, you can use that data to improve the process. Although there are many parts to a shipment’s journey, it’s no longer a mystery where the challenges are, whether they’re at the beginning or the end.

You can use this data to adjust your shipping patterns when you discover the culprits of delays.

2. Provide on-time deliveries through the most efficient routes possible

Efficient route planning could be vital in increasing your on-time deliveries. The best way to achieve this is to apply Internet of Things (IoT) sensors to vehicles and shipping containers. These sensors ingest a lot of data that can lead to optimized routes.

3. Reduce the risks of cargo inventory errors and miscalculations

Reliable predictive analytics reporting can prevent calculation errors regarding inventory. With this capability, once data indicates there are cargo errors, you have the information necessary to intervene immediately.

4. Predict disruptions before they happen

Foresight is something every business would like to have. Predictive analysis makes that possible. Many disruptions can impact your shipping operations. Some of those disruptions are more predictable than others. Using sensors and historical data, you may be able to foresee things that could jeopardize logistics, including traffic issues, supply chain problems, and fleet management issues.

5. Create visibility into seasonal buying patterns and forecasts

Certain times of the year will have higher levels of demand, such as the holiday season. Transportation companies are aware that this uptick will happen, but they may not have clarity on the details.

Visibility requires looking inside the data, and a 3D visualization is one of the best ways to do that. Companies can look at historical data and manipulate their models with different variables, ensuring they create data-driven forecasts to plan for the increasing demand.

6. Practice proactive fleet maintenance

Taking care of your fleet is essential to keeping things moving. When breakdowns occur, you lose valuable time. By collecting data from your fleet and analyzing it via sensors, you can move to a proactive approach to maintenance, which reduces unplanned downtime.

It can also reduce overall maintenance costs each year. Identifying potential issues before they arise is key to achieving this. 

7. Increase warehouse efficiencies

Being more efficient in the warehouse means you can be more efficient when you’re on the road, on the rail, in the air, or at sea. A well-run, organized, and data-driven warehouse uses IoT sensors to find inefficiencies.

8. Manage fuel efficiency

Those IoT sensors used for route efficiency and predictive maintenance can also help with fuel efficiency. First, maintained vehicles tend to get better gas mileage. Additionally, if routes have a better flow and idle time is decreased, they’ll use less fuel.

Fuel also fluctuates in pricing based on a variety of factors. Analyzing these factors could help you allocate fuel more efficiently.

9. Optimize and reduce costs

Whenever you use data to make better decisions on routes, maintenance, and forecasting, you’re going to reduce costs associated with every aspect of transportation.

Through a continued analysis, you’ll be able to identify cost drivers and possible actions you can take to reduce their impact.

10. Address last-mile delivery challenges

The last-mile delivery problem is growing, and it currently comprises about 53 percent of overall shipping costs. You can look forward by analyzing historical data around this part of the delivery process. You can find recurring patterns through data visualizations that help you predict what routes will be like in the future.

Using the data, you may be able to optimize everything from routing to timing based on traffic patterns, or where to plan your next warehouse to improve proximity.

COVID-19 forced many organizations to accelerate their digital transformation initiatives. But with global supply chain disruptions, record demand for freight delivery, and a desperate need to fill many essential positions amid a worker shortage, the logistics industry as a whole has fast-tracked digitization to keep up.

Much like the transportation industry, which was covered in “Using AI in the Transportation Industry,” the broader logistics industry is adopting artificial intelligence (AI) and machine learning technology to automate operations and improve analytics. 

Logistics manages the flow of information, goods, and services within a supply chain, including the storage, handling, inventory, packaging, and transportation and delivery of these goods.


Benefits of AI in Logistics

In today’s data-reliant business environments, analyzing logistics is a highly complex undertaking, and many organizations are finding that humans alone simply cannot scale to the extent needed to manage and analyze this volume of data quickly or efficiently.  

AI and machine learning tools use algorithms to automate the processing and analysis of structured and unstructured data collected from an array of sources, including sensors and manual input, to mimic and scale up decision-making and problem-solving.

AI supercharges traditional compute power and processing capabilities, which improves logistics in several keys ways:

Increase Safety

AI can improve safety both in the warehouse and on the streets. Automated processes and collaborative robots are being used to perform high-risk tasks and heavy lifting that are dangerous or impossible for humans. On the transportation side, sensors and route optimization analysis help drivers avoid high-congestion or crash-prone areas, thereby minimizing the likelihood of accidents or delays.

Improve Efficiency

Machine learning and automation are being used to increase process efficiency and decrease waste throughout the logistics industry. AI is used to predict maintenance before equipment or fleet vehicles break down, which reduces costly downtime. Machine learning technology is able to track and manage inventory and predict user demand to eliminate wasted product and reduce storage costs.

Minimize Impact of Staffing Shortages 

During the pandemic, online purchasing hit a historic high, which increased the demand for delivery drivers and warehouse workers—both jobs that are experiencing high turnover and low hiring rates. 

Increased adoption of AI-driven technology, such as drones and autonomous vehicles, is easing the strain somewhat and will have an even greater impact on logistics as autonomous technology reaches the mainstream.

Challenges to AI Adoption in Logistics

Although there are many benefits to adopting AI and machine learning for logistics, it is also important to consider the challenges. 

Data Management

AI adoption can be difficult for organizations that are working with siloed data. Without a centralized data management strategy, your AI solutions will be using poor-quality, incomplete, and often inaccurate data sets for analysis and insights.

Costs of Artificial Intelligence

There are costs associated with integrating AI with current systems. Depending on the number and type of legacy systems an organization is supporting, the cost of customization can be high. AI also increases some operational overhead costs, such as utility bills and the AI machine maintenance, including battery replacement, hardware upgrades, and software updates.


Job Loss

AI and automation are designed to perform tasks that humans aren’t well suited for, but in some cases, the technology will be used to replace jobs that are currently held by people. The threat of potential job loss may make it difficult to get employee buy-in for the initiative, which can slow adoption and ROI.


How the Logistics Industry Is Utilizing Artificial Intelligence

As enterprises grapple with high demand, low employment numbers, uncertain economics, and data overload, AI and machine learning solutions are helping companies navigate their logistics challenges. Here are four ways organizations are using AI in logistics to improve efficiency, reduce costs, and improve customer satisfaction:


Automated Warehousing

With fewer workers to fill warehouse positions, AI is becoming an essential tool for e-commerce and fulfillment companies. Warehouse automation tools move inventory in, out, and around warehouses and help ensure customer orders are filled accurately and quickly with minimal human intervention. 


Predictive Maintenance

Predictive maintenance uses machine learning tools to analyze sensor data and spot patterns in parts and equipment failures. This data shows which equipment is likely to fail and when, so maintenance technicians can replace the necessary parts before a breakdown occurs.

Supply Management

Supply chain disruptions are causing major upheaval for businesses in every industry. AI and machine learning can help mitigate some of the disruption by forecasting demand and automating supply tracking so the right inventory is on hand when it’s needed with fewer delays and little waste.

Freight Management

AI can help companies optimize freight delivery routes and yield large cost savings. By analyzing route lengths, traffic congestion, fuel usage, and other factors that can impact road travel, businesses can cut fuel costs, maximize load size, identify alternate routes, and increase shipping speeds.


The Future of Logistics Is AI-Driven

The logistics industry is changing at lightning speed, driven by supply chain issues, high demand for shipped goods, and insufficient staff to keep it all moving. To overcome these challenges, many organizations are adopting artificial intelligence tools to streamline operational processes and improve analytics that drive decision-making.

Supply chain disruption has impacted business operations in every industry over the past two years. Organizations almost across the board are navigating inventory shortages, scarcity of raw materials, and lack of essential components.

The transportation industry has landed in the spotlight during this crisis. Inadequate staffing, high fuel prices, and blocked access to ports and offloading sites are increasingly affecting business continuity and inventory management for organizations around the globe.

Although there is little transportation sector leaders can do to mitigate raw materials and component shortages, many are looking to technology—such as artificial intelligence and machine learning—to overcome some of the transportation-specific supply chain problems.


What Is Artificial Intelligence?

Once the purview of science fiction plot lines, artificial intelligence (AI) has reached almost mainstream status in business and industry. At a high level, AI is technology that uses algorithms and data to mimic the decision-making and problem-solving capabilities of the human brain, only faster, more efficiently, and at a larger scale.

AI and machine learning systems can process and analyze much larger and more complex data sets than humans can, which makes this technology an essential analytics tool in today’s sensor-driven, data-hungry transportation industry.


The Benefits of Adopting AI in Transportation

In our challenging business environment, saving money and increasing efficiency are two of the top priorities for most organizations. For companies that rely on transportation and logistics to keep their businesses running, implementing AI can help them achieve these goals and more:


Cost Savings

AI helps businesses reduce their transportation costs by identifying best practices for optimizing travel routes, reducing labor costs, and maximizing fuel efficiency.



AI analytics increases transportation efficiency by improving capacity planning, predicting maintenance that will decrease downtime, and maximizing loads to minimize wasted fuel and labor.

Customer Satisfaction

AI technology can create a better user experience by reducing customer wait times, improving transportation reliability, and providing dependable delivery estimates.


AI improves transportation safety using technology such as traffic pattern analysis to prevent accidents, and to power autonomous and semi-autonomous vehicles that mitigate crashes due to operator error.


Top Use Cases for AI in the Transportation Industry

Self-driving, or autonomous, vehicles get the most press when discussing the uses for AI in transportation. However, as illustrated previously, AI has many practical uses within the industry.

Traffic Management

AI can help cities improve traffic management by automatically adjusting resources to match current demands and conditions. Using data collected from sensors, cameras, and other connected devices, AI systems can analyze the data, predict traffic patterns, and redirect drivers and emergency responders to faster or less congested routes.

Predictive Maintenance

Predictive maintenance provides both cost savings and efficiency improvements for fleet managers. AI systems collect data from vehicle sensors and analyze the data to identify which parts are likely to fail, causing a vehicle to be out of service. Tracking this data over time allows maintenance teams to proactively make repairs to avoid costly downtime.

Route Optimization

Using AI algorithms to optimize travel routes enables a business to identify the most efficient routes for its vehicles with regard to fuel usage, driver time, and speed of delivery or passenger arrival. By adjusting routes to changing conditions, businesses are able to decrease fuel costs, maximize driver time, and improve customer satisfaction.

Demand Forecasting

Using AI for demand forecasting allows transportation providers to better match resources to demand. Collecting and analyzing passenger or shipping data enables the right number of vehicles to be dispatched, thus minimizing wasted space on trucks or low rider counts on public transport.


The Business Case for Adopting AI in Transportation

As with any new technology adoption, implementing AI in the transportation industry isn’t always smooth sailing. IT teams may find it difficult to get leadership buy-in for technology that is less established and that lacks clarity around regulatory and compliance requirements. Without sign off from higher ups in the company, obtaining a budget for AI initiatives may be difficult, if not impossible.

Even with challenges such as these, the investment is worth it. 

In 2018, a survey by McKinsey found that AI early adopters in the transportation and logistics sector with a proactive strategy for AI were seeing profit margins greater than 5 percent. The study also found that those respondents in the same industries who had not adopted AI were in the red.

Three years and one global pandemic later, digital transformation initiatives have accelerated by leaps and bounds, and AI is one of the reasons the transportation industry is succeeding in digitizing both operations and analytics. Artificial intelligence has exponentially increased the amount of transportation data that IT teams can extract actionable information from. This allows companies to drive cost savings and efficiencies, as well as improve safety and customer satisfaction. 

Learn more about the role of AI in revolutionizing the transportation industry.