Manufacturing & Supply chain

Within manufacturing industries, a significantly large amount of data have been collected for many years by distributed control systems. Often, those large amounts of data have been mainly used for routinely technical checks within the process rather than detailed analyses connected to the quality outcome. By implementing smart algorithms which are continuously updated along the process, time- and cost waste caused by inconsistent quality can be reduced. 

AI can improve forecasting accuracy by 10 to 20 percent through analyzing underlying causal drivers of demand rather than prior outcomes. This translates into a potential 5 percent reduction in inventory costs and revenue increases of 2 to 3 percent.

By implementing smart algorithms, more constraints and factors can be taken into consideration when making scheduling decisions. Consequently, forecasts regarding availability of products in stock and delivery times to customers will be optimized. Furthermore, by implementing such artificial intelligent tools within the business, an automated and sustainable supply network can be achieved. 

Using data from connected, smart machines can help you minimize machine failure and down-time, thereby reducing cost of production, labor and equipment, while increasing safety and revenue.

In practice, large volumes of sensor data from machines and equipment (such as vibrations, temperature or time-in-use) are run through a machine learning model and connected with alarm systems that help you prevent failure.