Machine learning is a subset of artificial intelligence that allows an algorithm, software or a system to learn and adjust without being specifically programmed to do so.  ML typically uses data or observations to train a computer model wherein different patterns in the data (combined with actual and predicted outcomes) are analyzed and used to improve how the technology functions.

Machine Learning

Machine Learning (ML) models, based on algorithms, are great at analyzing trends, spotting anomalies, and deriving predictive insights within massive data sets. These powerful functionalities make it an ideal solution to address some of the main challenges of the supply chain industry.

5 Top Benefits Of Machine Learning In Logistics

1. Accurate Demand Forecasting

Anticipatory Logistics is no longer a figment of the imagination. Artificial Intelligence in logistics have the ability to evaluate thousands of disparate data sets and then recommend actions or even be programmed to act on the findings. From optimizing carrier selection, fixing on pricing and improving routing, Machine Learning can do it all. For example, traditional models looked only at intrinsic data but machine learning in logistics industry can dig much deeper than traditional correlations. It includes dynamic variables like weather, GPS systems, social media feeds as well as daily lane patterns. These algorithms can self-evolve over time and can keep finding more patterns and insights to remove inefficiencies.

Demand forecasting is an essential prerequisite for profits since cash can be tied up in stocks. The less time that inventory sits in a warehouse the less you spend. Knowing what the end customer can want at any given time or the ability to forecast multiple scenarios can improve supply chain agility. DHL has a predictive analytic model that uses over 58 variables. This is used to help freight forwarders know a week in advance whether average freight times can rise or fall and create contingency plans. By knowing what the trends in the market are, they can quickly move vehicles to areas with more demand and save operational costs.

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2. Cutting down on fuel costs

Logistics is a business where cutting down on a mile per vehicle each day can see savings of millions a year. Companies like UPS have actively used technology to drive their global logistics networks. One of these technologies is ORION (On-Road Integrated Optimization and Navigation). All UPS vehicles have systems and sensors that continually capture data. This feeds algorithms that in turn plan and optimize routes taken by UPS drivers. The millions of miles cut through optimized delivery routes is why ORION has become the standard to emulate.

Surprisingly, the most important insight provided by ORION was that the shortest routes are not necessarily the best. This supports the fact that AI can solve problems we didn’t even know existed. Turning left in countries with right-hand traffic and vice versa raises the ante on accidents as the driver is going against on-coming traffic. Waiting to turn also burns fuel needlessly. With this simple change, UPS burns 10 million gallons less fuel while delivering 350,000 more packages annually.

3. Predicting Price of a Load

There are over 500,00 trucking companies in the United States alone. Shipping a truckload from Chicago to Los Angeles will not cost the same as shipping from Los Angeles to Chicago. Prices change from season to season and from day to day. Price predictions are therefor the biggest challenge. Human experts are usually responsible for fixing prices based on their deep domain expertise. Yet this takes time and can only be learned by experience.

Machine Learning in Logistics are now removing the guesswork. They evaluate historical freight data along with concurrent data such as traffic and weather conditions to fix a fair price. Freight brokers can also use predictive models to run carrier analytics to find which carrier has moved what kind of product at what price. Choosing a carrier can thus become easier by matching freight to route and price.

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4. Robotics in Ware House Management

AI and Machine Learning for logistics bring up the world of robotics. Warehouse robots are no longer futuristic technology, there are already being used to track, locate and move inventory within a physical space. Robots have deep learning built into them, they have been trained using ML data capture  including computer vision to make autonomous decisions that cut down on time.

Tractica Research predicts that by 2022 most major players would have adopted warehousing and logistics robots. Sales are expected to reach a record of 30.8 billion dollars. British online grocer, Orcado has built a fully automated warehouse that uses space intelligently. The robotic machinery sorts and stores products with rarely ordered items in the bottom tiers. This ensures minimal time is required to sort orders and can clear 65,000 orders in just a week. Such flexible, scalable, robotic solutions will soon be a standard infrastructure, necessary to keep up with modern needs.

5. Autonomous Vehicles

Self-driving cars or autonomous cars from Tesla to Google to Uber are foreseeing the future for logistic carriers. Existing laws prevent drivers from driving for more than 11 hours without an 8-hour break. Autonomous vehicles will increase time on the road, increasing delivery volumes while cutting costs by 25%.

While driverless trucks might still not yet be here, machine learning is already setting the way to that goal. Automated systems like lane-assist, highway autopilot and assisted braking features are making long-haul driving easier. These driving systems are also using ML based data capture to provide  information for multiple trucks to drive in formations that cut down on fuel usage. Completely controlled through computer-driven communications, it reduces fuel by 4.5% for the lead truck and up to 10 % for trucks following.