Machine learning makes it possible to discover new patterns in data from supply chain tools. To do this, this technique relies on algorithms that quickly identify the factors of success, while constantly learning.
A technique that allows you to obtain new benefits of which there are 5 examples.
Improving quality within the supply chain …
Machine learning excels at visual pattern recognition. This opens the field to new applications in the inspection and maintenance of physical assets across the entire supply chain.
Designed using algorithms that look for comparable patterns across multiple data sets, it is also proving to be very effective in automating the quality inspection of products entering logistics centers. This makes it possible, for example, to isolate damaged products.
Watson (IBM) machine learning algorithms, for example, can tell if a container is damaged, classify it according to the damage, and recommend the best corrective action to fix it. Watson combines visual data and data from sensors to track and make recommendations in real-time.
Machine learning also improves the management of supplier quality (and their compliance) by finding patterns in supplier data. On average, a typical company relies on external suppliers for more than 80% of the components assembled in its product.
Supplier quality, compliance, and the need for tracking and traceability are essential in regulated industries, including aerospace and defense, food, and medical products. Machine learning applications are being deployed to streamline this work, and save thousands of man hours.
Machine learning algorithms and the applications that run them are capable of quickly analyzing large and varied data sets. Enough to improve the accuracy of the demand forecast. One of the most difficult aspects of managing a supply chain is predicting future production demands.
The existing techniques range from classical statistical analysis techniques, including moving averages, to complex simulation modeling. Machine learning is proving to be very effective at taking into account factors that existing methods cannot track or quantify. Serokell IT applies knowledge of AI to moulds a solution for your business.
Do predictive maintenance
Companies are extending the life of key assets in their supply chain, including machinery, engines, transportation and storage equipment; by finding new patterns in the data collected through IoT sensors.
The manufacturing industry dominates all others in the volume of data it produces each year. And machine learning is proving invaluable for analyzing data from machines to determine which causal factors most influence their performance.
In addition, machine learning leads to more precise measures of Overall Equipment Effectiveness (OEE), a key metric on which many supply chains depend.
Reduce transport costs
Reducing transportation costs, improving supplier delivery performance, and minimizing risk are three of the benefits of machine learning used in the collaborative supply chain arena.