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.

Machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to imitate the way that humans learn. The “learning” aspect of machine learning refers to the system’s ability to improve its accuracy and knowledge set over time based on experience.

Machine learning has a lot of practical applications, but one of the most powerful ways this technology is being used today is in big data analytics.

Applying machine learning algorithms to generating, aggregating, and analyzing large and complex datasets provides several key benefits, including:

  • Improving process efficiency
  • Reducing need for human intervention
  • Scaling for big data applications
  • Performance monitoring for sensors in remote, hazardous, or unmanned locations


How Machine Learning Is Currently Being Used in the Energy Sector

Data analytics in general, and machine learning specifically, is proving to be a valuable asset in the growth and management of the energy sector. 

With a widespread shortage of skilled labor, increased connectivity and reliance on smart technology, and a push for more sustainable and cost-effective energy sources, machine learning will play an integral role in shaping the future of the energy industry.

Here are seven ways the energy sector is harnessing the power of machine learning as part of a data analytics strategy to drive performance improvements and increase ROI:

Predictive Maintenance

Machine learning makes predictive maintenance possible by analyzing historical data and real-time data across multiple sources to predict which systems and parts are likely to fail and when. Creating AI-driven predictive models to monitor the condition of equipment allows maintenance teams to proactively schedule repairs and replacement of vulnerable components and systems.  

By addressing potential problems before they occur, predictive maintenance helps reduce the number of failures and breakdowns, which increases system availability, cost savings, and customer satisfaction. 


Grid Management

One of the big challenges of managing power grids is that power generation and power demand need to be equivalent at all times. Otherwise, utilities risk blackouts from insufficient energy on one end and wasted capacity on the other. Machine learning can help maintain balance and increase resilience, especially for renewable energy grids. 

For example, machine learning algorithms can identify changes in usage patterns, which allows utilities to quickly redirect stored energy to areas where it is needed most while decreasing load in regions with lower demand. 


Demand and Load Forecasting

Machine learning algorithms make it possible to analyze a variety of influencing factors from disparate sources, such as historical demand, temperature, time, wind speed, weather patterns, and day of the week. 

With the ability to compare analytics from many sources and weather models, utilities can make more accurate predictions about future load and demand requirements, which reduces the amount of capacity they have to hold in reserve “just in case.”


Reduced Energy Consumption

Smart metering, a technology that uses machine learning to track energy usage patterns over time, is an effective way to reduce the amount of wasted energy and save money. Machine learning algorithms can analyze data down to the device level and identify which business systems, appliances, and even recurring activities consume the most energy so steps can be taken to improve efficiency and reduce waste.

The Future of Machine Learning in the Energy Sector

The energy sector is at a crossroads, with reliance on fossil fuels slowly giving way to increased usage of renewable energy sources. As this transition plays out, artificial intelligence and machine learning technology will play an integral role in several key use cases.


Reliable Renewable Energy Forecasts

Machine learning algorithms can accurately predict the amount of electricity a wind turbine or other renewable energy source can generate during a given time period. This knowledge makes it possible for utilities to forecast supply versus demand with a high level of confidence.


Drone/Image-Based Damage Detection

Drones are revolutionizing how utilities monitor and detect damage to transmission and distribution infrastructure in remote or dangerous regions. However, the increased volume of images generated by drone-based detection makes non-AI-augmented review cost- and resource-prohibitive.

Machine Learning Is Powering Growth and Sustainability in the Energy Sector

The energy sector is in transition, and machine learning is helping organizations streamline the process. Smart technology, including sensors and IoT devices, are ushering in the big data era, and generating plants, utilities, and renewable energy providers are harnessing the power of AI to make that data usable.

The fourth industrial revolution (Industry 4.0) is well underway, as traditional manufacturing and industrial practices are increasingly augmented with “smart” technology to automate processes, monitor performance, and diagnose issues.

The sensors within these connected systems generate vast amounts of data across a countless number of variables. With the right analysis tools, this data can be used to drive efficiencies, reduce downtime, minimize unscheduled maintenance, and save the business time and money.


Industry 4.0 Data Analytics Challenges

The process of integrating high-tech monitoring and analytics into these complex industrial and manufacturing environments is not without its challenges. 

Although some of the data analytics obstacles found in Industry 4.0 are a natural part of integrating new technology into established systems and processes, others are more fundamental in nature.

Siloed Operational Knowledge

In a manufacturing setting, knowledge is often siloed by department, with little visibility between groups. When there is a performance problem, equipment operators, manufacturers, field technicians, and maintenance workers all may have a piece of the information needed to solve the problem. But with no way to explore the relationships between the pieces, getting to the root cause of an issue is a slow and inefficient process.

Difficulty of Extracting Meaningful Insight from Large Amounts of Data

The data gathered in industrial and manufacturing settings comes from many different sources, which makes it extremely difficult to find connections between how data derived from one component impacts the data from a seemingly unrelated operation. To further exacerbate the problem, data may also be structured or unstructured, making identifying interrelationships even more complicated.

Skilled Worker Shortage

The lack of skilled technology and data science workers isn’t an Industry 4.0-specific challenge, but with more types of enterprises now relying on technology and data to run their businesses than ever before, the issue is reaching critical mass. In fact, a recent Gartner survey found that IT executives rate lack of technology talent as the most significant adoption barrier to 64 percent of emerging technologies. That’s a huge increase from the 4 percent reported in 2020. 


How AI and Multi-Dimensional Data Visualization Help Solve Industry 4.0 Data Analytics Challenges

In today’s data-fueled industrial and manufacturing environments, connecting the dots between disparate data sources and using that information to drive action is critical. As Industry 4.0 continues to mature, data analytics must evolve to ensure enterprises realize the full potential of their embedded technology investments.

Flat, two-dimensional data representations can’t provide the level of insight needed to close information gaps and drive decision making. Artificial intelligence and multi-dimensional visualization tools are essential elements in this new industrial revolution. Together, these technologies provide deep visibility into interrelationships that isn’t possible with traditional analytics and visualizations.

Multi-Dimensional Visualization

Multi-dimensional visualizations enable analysts and stakeholders to find the “whys” behind the data. Knowing why equipment is behaving a certain way or why a sensor alert is frequently triggered helps teams maximize system performance, find the root cause of chronic issues, and take a proactive approach to preventing system failures and downtime.

In manufacturing and industrial services, data-driven decision-making is the most impactful; however, many mission-critical decisions are made by non-technical stakeholders. Data visualization takes complex, multi-sourced information and translates it into a visual model that is easily understood by data scientists and the C-suite alike. 

For example, applying AI routines in conjunction with data visualizations enables analysts to identify which variables in a dataset have the greatest impact on the target variable. Using this information, the data analytics application can recommend the most appropriate visualization to interpret and explain why the findings are significant and provide easy-to-understand insight or validation around an issue.

Predictive Analytics

One of the most valuable outcomes of Industry 4.0 is the ability to predict and proactively address system and component failures that lead to lost productivity and revenue.

Predictive modeling leverages machine learning and AI-driven pattern recognition processes to quickly identify anomalies and trends within the data. 

For example, predictive models can take both structured and unstructured data from disparate sources and determine which parts or systems are most likely to fail within a given period of time. This information is then integrated into maintenance workflows and systems, which ensures the at-risk components are inspected and replaced before a failure causes any downtime.


What’s Next for Industry 4.0 Data Analytics?

Looking ahead to the widespread rollout of technologies like 5G and the growing adoption of IoT in manufacturing and other industrial applications, it’s impossible to predict where Industry 4.0 will end and the next technology era will begin. However, we can be sure that data will play an even larger role in business and industry in both the short and long term. 

As the volume of data continues to grow, analytics capabilities will have to keep pace to ensure the data can be used to provide value and insights to drive decision-making and process and performance improvements.