April 14, 2022

AI in Logistics & Freight: Use Cases of Artificial Intelligence Scaling & Automating Processes

AI in Logistics & Freight: Use Cases of Artificial Intelligence Scaling & Automating Processes

While automation and artificial intelligence (AI) was already becoming prevalent in many areas for logistics companies, the past two years have spurred rapid change. After the pandemic disrupted the global supply chain and workforce, companies are in need of many intelligent tech solutions.

According to the 2022 MHI Annual Industry Report, 87% of logistics company leaders say the pandemic has altered the strategic importance of supply chain operations. Additionally, 78% note supply chain transformation has accelerated because of the pandemic.

Why Artificial Intelligence is a Good Fit for Logistics & Freight Processes

Supply chain disruptions have been rampant over the past two years. The changing landscape of logistics means many complex tasks are able to be automated, reducing the demand on employees. Automation and AI solutions can be used to support human workforces, speeding up their processes and improving productivity.

The 2022 MHI Annual Industry Report also found that 64% of logistics companies say they are increasing their investments in supply chain technology, and 40% are piloting new technologies.

Examples of Logistics & Freight Process AI Use Cases

Artificial intelligence in logistics has had a big impact on the industry over the past few years. The rapid changes in what is possible have allowed people to step back from certain jobs that don’t need human creativity, insight or discernment. Companies that chose early adoption of AI solutions saw logistics costs drop by 15%, inventory levels improve by 35% and service levels boost by 65%.

The clear progress of logistics processes when powered by AI solutions has led to even more IT vendors entering the scene. As the complexity of the supply chain has grown and demand has increased, automation and AI have become necessary to scale in a sustainable way.

Here are some of the top AI solutions and how they improve operations for logistics companies.

Demand Forecasting

AI is able to reduce error rates significantly when used to forecast with real-time data. It goes far beyond traditional forecasting models like AutoRegressive Integrated Moving Average, ARIMA or exponential smoothing methods.

Demand prediction allows manufacturers to optimize their manpower, machinery and available vehicles to plan for production. Holding costs can be lowered by determining the likely need of consumers. Plus, accurate demand forecasting helps manufacturers and retailers avoid frustrating stockouts that lower customer satisfaction.

Dynamic Supply Planning

Another key part of reducing stockouts is keeping necessary supplies and materials on hand. Most supply chains were shown to be fragile and easily disrupted during the fallout of the pandemic.

To help optimize supply chain flow and find the right partners, companies can use AI systems powered by real-time analytics. A dynamic supply planning process will reduce waste and the likelihood of supply chain disruption.

Warehouse Automation Bots

Robots can be used for picking, sorting, shelving, packing and transporting goods in a warehouse. Amazon may be the most well-known example of efficient AI in the warehouse. Since Amazon acquired Kiva Systems in 2012, they’ve increased their robotics to include 200,000 warehouse bots working the floors in 26 fulfillment centers.

Quality Control

Bots are able to detect damaged products and improve quality control for companies. With fewer defects, a company can reduce the number of expensive returns and frustrated customers. Alerting workers of problems on the line can also help achieve faster solutions, resulting in fewer damaged items.

Predictive Maintenance

Ideally, failures in machines would never occur. When failures happen, unplanned downtime causes interruptions to the supply chain and might mean more expensive repairs. Reducing failures and breakdowns can also help improve worker conditions.

Machines are now predicting potential failures to help catch problems before they cause unnecessary delays or expenses. Sensors can help catch possible failures as well as predictive analytics and timed reminders. When a possible problem is detected, maintenance can be scheduled for the least disruptive time.

Self-Driving Vehicles

One of the slower solutions to form has been autonomous vehicles. Companies like Tesla and Google are investing in machines that are self-driven with hopes to reduce carbon emissions and fuel use. Automated vehicles could also reduce strain on drivers and help boost road safety if implemented correctly.

However, most experts believe few delivery vehicles or freight trucks will be driverless any time soon. The concern for safer solutions is part of what makes full vehicle automation so slow to see implementation in real-world settings. This could offer a solution to help improve flow at delivery docks and ports to reduce the bottleneck holding up drivers during the loading and unloading process.

Delivery Drones

Not a far cry from the self-driving vehicle exists the self-piloted delivery drone. Smaller and more manageable than vehicles, drones have already taken up delivery tasks for small shipments.

Drones have proven especially valuable for delivering materials or products in areas where ground transportation is not safe, reliable or sustainable. For some products, like medications or food, short shelf lifespans mean immediate delivery is necessary.

Intelligent Pricing

Using real-time data, AI software can determine competitive pricing models for logistics costs or product pricing. For logistics companies like freight forwarders, AI technology can streamline procurement and increase the success rate of the pitch process. Not only can technology be used for dynamic pricing predictions, but it can also respond to inquiries for quotes automatically.

Freight Management

Optimizing the route and managing equipment availability are crucial processes that can be improved with the use of AI in transport logistics. Software can be used to find the most efficient routes and keep track of available trailers to manage upcoming loads.

Automated Data Entry

Most freight forwarders and logistics companies end up with a barrage of emails and paper invoices that need to be transferred to their existing systems and platforms. Without data entry, the information is scattered. AI has been able to speed up the process and drastically reduce manpower needed by automating this process.

Expedock is an example of AI-powered software that can take a variety of documents and quickly convert them to structured data while simultaneously auditing them against existing estimates. With the integration of Expedock, Rose Containerline was able to save over 80% of its operating expenses by optimizing day-to-day processes.

Customer Support

Chatbots and AI emails can provide automated responses to customer inquiries. By offering round-the-clock support through AI, companies can offer an instant connection with their audience. Leads and customers don’t have to wait for an answer to their questions if automated bots are on standby.

This technology can also help customer support teams by staying on top of important reminders and reducing the strain of redundant questions. Offering basic customer support technology to solve the most common issues and questions frees up customer support teams to handle more complex issues.

Business Process Automation

From scheduling and tracking to generating reports, AI can help with a number of back-office tasks. Robotic process automation (RPA) and process mining can be paired with AI to help identify areas where productivity could be improved with a better process.

Lead Scoring and Automated Marketing

Targeting the right audience means growing your customer base with qualified leads. Automation has become an expected part of modern marketing. Examples of automation in marketing include chatbots, lead scoring and automated emails. Marketing can be personalized to appeal to specific accounts based on their past behavior and engagement.

Today’s clients and customers want easily accessible information. Using automation and AI, marketers are able to stay on top of multiple channels and the increasing demand for content.

Benefits of Using AI in Logistics and Freight Processes to Scale and Automate

Companies using AI in supply chain processes can create sustainable growth and optimization when they implement the right modern solutions. Examples of the top benefits for logistics companies using AI solutions include:

  • Automated solutions to improve manpower resources
  • Faster processes and turnaround times for customers
  • Improved organization without increasing employee tasks
  • Lowered operating expenses and shipping costs
  • Strengthened relationships with vendors and customers
  • Clarity to help drive informed strategies and goals
  • Real-time insight to spark immediate improvements
  • Deeper connections with customers based on data analytics
  • Fewer accounting mistakes due to overcharges and unnecessary fees
  • Reduced waste and efficient allocation of resources
  • Improved supply chain with fewer holdups or shortages
  • Resiliency for supply chain disruptions and industry changes

There are many benefits to using AI for logistics and transportation. Finding the right technology for your company is a smart move if you want to stay competitive in this quickly changing business. Investing in AI could help your business scale and achieve long-term success.

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