AI for business, machine learning, and data science are quickly becoming part of our daily lives. While the world of science fiction has created such amazing ideas of what AI can do, the reality is often much more practical. Some of these technologies are so seamlessly integrated that we aren’t even aware of the work they are doing. One of the fields that AI/machine learning and data science are quietly transforming is the logistics business.

The most significant potential of AI for business lies in the supply chain, where data science applications are already dramatically increasing efficiencies. AI has the potential to improve productivity, reduce costs, and improve overall profitability across every phase of the supply chain.

Here are some practical applications of AI for business, machine learning, and data science that are already revolutionizing the logistics business.

1. Supply Chain Planning (SCP)

Even the most comprehensive Supply Chain Planning (SCP) is limited in its ability to forecast every possible scenario. Machine learning can analyze more data than ever before. The analysis of massive data sets through intelligent algorithms is continually monitoring inventory, supply, and demand. This type of AI is also capable of continual machine learning, meaning it can learn from its mistakes and further refine predictions as more data becomes available.

2. Improving Shipping Efficiency

Data science has excellent potential for increasing efficiency in shipping. The latest AI for business tools combine the power of GPS, the Internet of Things (IoT) and machine learning to create the most optimal shipping routes. From overall destination planning to turn-by-turn directions, companies can save time, reduce accidents, and offer more accurate delivery estimates.

3. Robotics

Robotics has long been part of logistics but faced the limitation of only being able to perform entirely predictable, repetitive tasks. Now, robotics, paired with data science and AI for business, allows intelligent robots to complete tasks that once required human monitoring and interaction. Paired with machine learning algorithms, robotics are now capable of autonomous decision making. This means that machines can now bring increased efficiency and reduced errors into tasks like identifying, picking, sorting, and counting.

4. Extending Asset Lifespans

Supply chains are comprised of hundreds of different essential assets that work together to keep the chain moving. AI for business is taking advantage of the latest developments in data science to extend the life of assets and ensure that potential downtime is minimized. With the advance of IoT sensors, machines can compile data from supply chain assets and analyze that information to understand the factors that impact the performance of machinery. Machine learning can improve maintenance schedules, predict failures, and recommend machinery replacement in time to avoid supply chain issues.

These are just a few applications of AI/machine learning and data science in the logistics business. The potential to increase efficiencies is limitless. As companies continue to collect more data, data scientists will develop more applications to analyze the information to positively impact supply chain efficiencies.