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10 Benefits Of Machine Learning in Logistics Industry

by Aishwarya Gaikwad
10 Benefits Of Machine Learning in Logistics Industry

Artificial intelligence (AI) is taking part in the modern technological revolution of all industries, including logistics. Machine learning (ML) enables discovering patterns and mechanisms that are beneficial for optimizing supply chain processes and more efficient cost and time management. Some ways in which ML can positively influence logistics businesses are probably to be discovered with the future development of AI. However, we have prepared a list of 10 benefits of machine learning in the logistics industry, which can already be used in most companies.

Demand forecasting

AI algorithms make it possible to analyze real-time data and forecast demand for specific products based on it. Thanks to this application, suppliers can always be sure that they will not run out of the most popular products and demanded goods will always be ready to ship to the consumer in a short amount of time.

Supply planning

As we have already mentioned, AI analyzes data in real time. That is why companies can update their supply planning parameters according to the most recent data. It is a great way to reduce product wastes and optimize supply chain flow.

Product inspections

It may happen that products get damaged while moving around the warehouse or transported from one place to another. Faulty products can easily lead to a lack of satisfaction among customers. AI-powered computer vision technology enables companies to identify potential damages that a human eye could miss.

Predictive maintenance

Predictive maintenance refers to foreseeing potential machine and device failures by analyzing data collected by sensors located in these machines. Thanks to predictive maintenance, engineers are able to take action and fix minor failures before the device completely breaks down. This application significantly contributes to undisturbed product flow across your supply chain.

Smart warehouses

Machine learning turns out to be very helpful in programming autonomous machines and robots that can be used in modern smart warehouses. Such machines can replace people in physically demanding tasks like unpacking or placing products in proper places.

Travel costs optimization

ML enables real-time route optimization. Algorithms analyze road conditions and weather to recommend the best route to reduce travel time and costs.

Supply chains and IoT

Machine learning can help to understand the whole supply chain cycle properly. With the use of AI, logistic companies are able to track the exact location of transported goods. What is more, special sensors enable to monitor conditions in which parcel is being delivered (that’s the part done thanks to the Internet of Things). These parameters include, among others, temperature, humidity, and vibrations.

Delivery price prediction

It is self-explanatory that shipping costs may vary depending on the distance the goods need to travel. Some prices, such as fuel costs, change every day and are hard to predict. Machine learning algorithms combine historical data with current information in order to estimate travel costs. ML can be used to compare carrier offers and choose the most advantageous one if a company does not own any transporting vehicles.

Dynamic pricing

Dynamic pricing refers to real-time pricing. The price of a product responds to current changes in supply, demand, or market situation, which includes prices of subsidiary products or ones set by competition. There are various types of pricing software that analyze customer’s historical data to respond to any market fluctuations immediately. Using this solution will help any logistic company to propose competitive prices and be more likely chosen by customers over their competitors.

The reduction of no fault found (NFF) cases

It may happen that systems and human workers will not notice a reoccurring fault of the product during a production stage, and a damaged unit will be given to the customer. When an unsatisfied customer makes a complaint reporting damage, this kind of anomaly can be uploaded to the system so that ML algorithms are able to detect it in the event it occurs once again and prevent it.

Applications of AI in the logistic industry are truly versatile. We’ve listed the ones that are most common among supply chain professionals. ML is of great importance for this sector as it can be used during every stage of logistic processes. By introducing ML solutions to your firm, you will reduce costs, cut downtime needed to perform specific actions, and improve your company’s products’ quality and overall efficiency.

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