Machine Learning in the Logistics Industry: 7 Use Cases Showing Benefits of Anomaly Detection

Logistics anomalies can create problems in the shipping process, contributing to issues that affect the business and customers. They put a kink in an otherwise good process to cause problems within the supply chain and affect business operations as a whole.

Technology can help. In particular, businesses can rely on machine learning in logistics as a solution to anomalies. What is anomaly detection, and how can machine learning be used in logistics to help with anomalies? This article covers the answers to these questions.

In addition, we present seven use cases of machine learning detecting anomalies in shipping activity or spend. These cases show how machine learning in the logistics industry facilitates finding these anomalies, allowing the company to take action and correct the mistake.

How Machine Learning in the Logistics Industry Provides Value

What is machine learning in logistics? An article by Addepto explains that it can carry out the tasks of analyzing data sets to improve operation methods. It facilitates this benefit in numerous ways, including by streamlining:

  • Spend optimization
  • Demand forecasting
  • Demand accuracy
  • Inventory optimization
  • Procurement responses

The Addepto article further shares ways companies can improve performance tasks with machine learning. It finds supply chain patterns that impact logistics operations the most. Through this data, businesses can see ways to perform better in all aspects of logistics, including warehouse management and analysis, demand prediction, supplier selection, route optimization, and supply chain planning.

Improving performance in these logistics processes means reduced theft, better supply management balanced with demand, reduced shipping costs, and more. These insights turn into efficient business processes, better profits and business growth.

10 Examples of How Machine Learning Provides Benefits in Logistics

  1. Reduction of costs
  2. Optimization of inventory control and management
  3. Management of cost load
  4. Management of unforeseen circumstances
  5. Data and analytics
  6. Recognition of visual patterns
  7. Optimization of routes
  8. Natural language process
  9. Technological innovation
  10. Anomaly detection

The Role of Anomaly Detection Empowered by Machine Learning

Let's take a closer look at the last one on the list of logistics benefits from machine learning: anomaly detection. This is a frequent purpose for relying on machine learning. This purpose enables companies to discover outliers to protect the company from threats like adversarial attacks, fraud, and network intrusions.

What Is Anomaly Detection?

As the term implies, anomaly detection is the finding of anomalies. What is an anomaly? In basic terms, it's something that deviates from the standard. For instance, you might notice a significant increase in activity that doesn't fit the usual pattern.

An anomaly like this can be cause for concern. It often indicates a problem, which could include an attack, fraud, an error, or something else that needs to be addressed. It's important to look for and catch these anomalies when they occur, which is where anomaly detection comes into play.

In logistics, anomaly detection catches exceptions and outliers that are impacting efficient warehouse management and shipping processes, keeping the supply chain running as smoothly as possible.

Machine learning provides a way to look through massive amounts of data and catch outliers faster and better than humans can do on their own. Plus, you gain the support of technology rather than putting staff hours toward this task.

Anomaly Detection in Freight & Shipping Data

With the support of artificial intelligence, machine learning can find anomalies in data related to freight and shipping. Once it is set up, machine learning takes care of this task independently without needing supervision. It can help when you know something is wrong but are struggling to find the source of the problem. It also seeks out anomalies on its own and can identify them before you realize there is a problem.

Machine learning in logistics can search through data in systems like TMS, OMS, and WMS. It seeks and finds data glitches, errors, and other problems that are not fitting standard patterns and interfere with proper business processes and customer satisfaction. Companies can use this information to identify and solve problems, thus streamlining logistics processes.

As supply chains become more complex and nuanced, it's crucial to have a powerful tool that uses true unsupervised machine learning to uncover instances of changes happening that will impact your transportation spend or your end customer experience.

Intelligent Audit's proprietary machine learning algorithms can do what no person or team of people could ever accomplish at a significant scale and speed. Backed by our team of experts who can explain the data, you pave the way for a more efficient, cost-effective supply chain.

Let's go through 7 use cases Intelligent Audit has already uncovered in anomaly detection powered by machine learning.

7 Use Cases of Anomaly Detection

It's one thing to read about a concept and another to see how it works in action. The following use cases help to show how machine learning in the logistics industry works in real life to detect anomalies. Through these cases, you'll better understand the types of anomalies this technology can find in logistics.

1. Excess Shipping Fees

  • A shipping company was excited to launch an e-commerce initiative expected to generate significant revenue.
  • The launch did not go as expected but instead led to a cost increase of over 250 percent on some new parcel shipper accounts.
  • The company did not know the source of the problem. Searching KPIs did not answer their questions and did not have the budget for this unexpected cost increase.
  • The machine learning program did not know about the e-commerce launch yet still found the anomaly: large package surcharges and extra handling fees on shipments that exceeded certain dimensions.
  • The company responded to this information by changing its shipping settings for large packages and giving customers new options for delivery.
  • Finding this anomaly allowed the company to stop the excess charges and improve its margins.

2. Wrong Shipping Carrier

  • A global manufacturer had established and contracted carriers for its routing guide.
  • The company found that the cost per shipment had gone up 50 times for an infrequently used lane for one day.
  • The machine learning system found the anomaly, a new carrier with 50 times higher rates.
  • With this information, the logistics and procurement teams could prevent any further shipments by this carrier in this lane, saving on any more exorbitant costs from using the wrong carrier.

3. Human Error

  • An American company brought in new team members, who were European, at a small distribution center.
  • Due to cultural differences, the team used commas instead of periods, so they typed 5,00 lbs. instead of 5.00 lbs., turning the shipment weight to 500 instead of 5 lbs.
  • To account for the perceived extra weight, the shipment went through the wrong carrier and transportation mode, costing the company thousands of dollars.
  • Machine learning discovered the problem and alerted the customer in real-time.
  • This anomaly detection encouraged the customer to create a new system that would now flag future shipments over 100 lbs.

4. Unexpected Charges

  • A procurement team compared shipment prices, asking each carrier for an all-in cost. It chose a less expensive carrier when making an apples-to-apples comparison.
  • Machine learning detected the anomaly of a spike in beyond charges from this lane and this carrier, which were unexpected.
  • The information showed the team that the carrier had been disingenuous in providing an all-in cost for the RFP and instead had a clause for separate beyond charges.
  • The team responded by switching to a new carrier with transparent pricing.

5. Detention Fees

  • A company has a negotiated contract that includes detention charges.
  • Machine learning found an anomaly of a fee increase.
  • Account management used machine learning information to identify the ports with specific problems and develop solutions.
  • The company used the knowledge of the increasing fees to update budget projections and otherwise make adjustments.

6. A New Fee Not in the Carrier Contract

  • Machine learning identified a new pattern of fees on its own.
  • The customer realized they were being charged a new accessorial fee that they had no warning of, which significantly increased their shipment costs.
  • Our account managers and the customer created an action plan to address the problem and manage future costs.

7. A New Accessorial Fee

  • A banking customer consistently used same-day shipping.
  • Machine learning found an anomalous charge and identified that the fee was connected to distance to destination calculations on packages. It was an accessorial fee for excess mileage.
  • This data showed that the fee was an error. It could be compared to historical data to show the bank had not been charged this fee on the same type of package with the same carrier previously.
  • The carrier agreed this was an error and credited the bank the cost of the erroneous fees.

Machine Learning Keeps You Ready for the Unexpected

Anomalies in storage and shipping can interfere with the supply chain, costs, or customer experience. Machine learning in the logistics industry provides a way to detect these anomalies better than employees can do on their own. The list of seven use cases demonstrates how effective machine learning can identify various anomalies so companies can understand and address them. Machine learning helps to quickly prevent or address mistakes to keep logistics operating as smoothly as possible. Contact us today to learn more about how Intelligent Audit's proprietary machine learning and anomaly detection can provide you with peace of mind.  

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