Below are five examples of where AI is making waves in manufacturing and supply chain:

1) Machine learning algorithms and apps, running AI, can analyze large and diverse data sets faster and can improve demand forecasting. With the ability to process large amounts of data, and learn along the way, prediction becomes more accurate. In recent years, manufacturers have leaned towards AI to predict build-to-order processes, and make-to-stock production workflows more effective. Manufacturers are reducing supply chain latency for components and parts, used in their most heavily customized products, by using machine learning. CGN has implemented machine learning algorithms to analyze large amounts of data, find patterns and get clear insights. A great example of this implementation comes from an incomplete raw data-set, where the ask was to find any patterns in the data. CGN used data mining techniques, clustering and forecasting algorithms to predict the missing data; giving greater insights into the data.

2) Reducing freight cost, improving supplier delivery performance, and reducing supplier risk are a few benefits machine learning can provide in collaborative supply chain networks.  Companies look at supplier assessments, audits, and credit scores when deciding which supplier best suits their needs. With the help of AI, the data gathered can help supplier selection be more predictive and comprehensible. Components of a certain product can be easily tracked and traced that are inbound from suppliers.

3) Predictive analytics: The Internet of Things (IoT), with production machine sensors, has been gaining a lot of traction around the world. The data collected can help improve overall equipment efficiency (OEE), improve preventative maintenance that in turn can improve OEE and production quality.

1. Improve OEE: Most companies use AI to predict machine failures, but miss out on the true potential of AI to improve OEE. When working on change-over time reductions, single minute exchange of die (SMED) techniques measure time from the last part of the previous run to the first good part of the next run. However, there is still time wasted, during this procedure. AI can identify gaps, analyze the data from sensors, and help find patterns that lead to loss of time. Using time stamp data of each job can help in improving OEE and reduce downtime and changeover time.
2. Improving Preventative Maintenance Program: Sensor generated data can be analyzed using unsupervised learning algorithms to find deviations that can give an early warning of a component nearing failure.
3. Improving the Quality of a Product: When machines are trained (supervised machine learning) with a library of visual data and then combined with sensors to analyze a product, the results can help identify even the smallest of deviations.

4) Chatbots for Procurement: Conversational interfaces (Chatbots) can potentially be used to help businesses by reducing transaction cost and sales cycle time. Suppliers can speak to your bot and get information or plan orders. Additionally, bots can be used to send orders, do paperwork, deal with invoices and payments, without human intervention. Even though companies are slowly adopting chatbots, the technology has a few miles to go before becoming completely autonomous and being used in procurement.

5) Machine Learning for Warehouse Management, Logistics and Shipping:  Today AGVs are more autonomous, and with the integration of data from warehouse management software and control systems, AGV’s can do almost everything that a human can do manually.

AI is making its way into logistics control tower operations (LCTO) to provide new insights into how every aspect of SCM, collaboration, logistics and warehouse management can be improved.

At CGN Global, we strive to improve global supply chains for our customers. Using predictive analytics and machine learning, CGN can help businesses win in their supply chain efforts, making supplier relationship management, risk analysis, transportation management and demand forecasting much easier.