Check out how important machine learning in logistics can be!
By: Joseph Hendrix
Tags: MLB, Logistics, Machine Learning, AI, Logistics Industry

I’ve read on several platforms about artificial intelligence’s potential value in logistics — it’s a massive opportunity that every company will need to embrace to stay competitive. Using AI in operations is not as simple as flipping a switch. A single solution does not exist for every logistical challenge faced by companies using artificial intelligence. Addition of technology to different parts of the supply chain is most likely to be the most popular adoption model. Machine learning as a method of using data to improve different aspects of a business is already being used by many companies.

A major challenge of the logistics industry has always been to filter insights from enormous data sets. While data scientists will always be useful (arguably more so now than ever before) for interpreting data, machine learning programs can handle a lot of the heavy lifting. The applications of machine learning in logistics are vast. As a predictive method, it finds patterns in large data sets and can detect anomalous behavior. Due to the COVID-19 pandemic, machine learning can assist companies to adjust their operations in a proactive manner to deal with public health concerns and fluctuations in demand.

The infrastructure to build a more predictive logistics environment was in place even before the pandemic. Data can be collected and analyzed in real-time with the help of the Internet of Things (IoT). The immediate feedback from this technology allows logistics professionals to make real-time adjustments to the supply chain, which is imperative in an era of such volatility. The IoT sensors can be viewed as the infrastructure for machine learning efforts as they provide the latest and most accurate data for machine learning.

Furthermore, companies can use this data to gain more oversight and control over the entire supply chain. It can be easy for information about security breaches or fraud to be overlooked when certain parts of an operation lack visibility. Anomalies can easily be detected, and verification processes can be improved to safeguard critical infrastructures. In the case of problems that require immediate attention, machine learning can provide the critical information needed to respond.

As well as providing insights into the strategic layer of operations, machine learning can provide insights into the practical side of a business. Various types of assets such as equipment, vehicles, and machinery can be evaluated based on their usage patterns. As a result of these insights, companies are able to maximize lifespan and gain more accurate estimates of overall equipment effectiveness (OEE), a valuable metric in an industry with many moving pieces. Monitoring assets using machine learning has shown to reduce maintenance costs by up to 10% and increase productivity by up to 20% for businesses that have implemented it.

Significant portions of logistics companies are already using machine learning, which is likely to increase as time goes on. By reducing equipment costs, taking into account unusual activity, and improving visibility, these tools have proven effective. It is important for logistics companies to develop an adoption strategy that integrates this technology into their existing strategic framework. For a detailed and a little more technical article, you may check out machine learning in logistics article here.