IIC Journal of Innovation 6th Edition - Page 11

How Democratized Artificial Intelligence Can Move Manufacturing to a New Evolution Pace Then you will be able to adjust your production planning and slow down or accelerate for the next production period based on your targets of overall efficiency, costs, quality and demand satisfaction indicators. In manufacturing some tools are using, for instance, simple linear regression 13 and ARIMA 14 . identify abnormal behaviors and patterns in a set of data qualified as normal data thru mostly two approaches, supervised and 15 unsupervised models . The purpose of these approaches is to detect a repeatable and non-repeatable, unexpected, undesirable behavior hidden in an expected, normal behavior. This AI domain is now also included in the Digital twins 16 approach for Assets and Process. 2. Machine learning (ML) is the second AI area also embedded in manufacturing business applications (predictive maintenance, production monitoring, human resources, finance, supply chain, marketing and sales). This technology is using a large set of historical data to Let’s go back to our predictive maintenance case; you already know and set up your monitoring thresholds for a Figure 2: Example of now + 10 minutes data prediction approach 13 https://en.wikipedia.org/wiki/Linear_regression 14 https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average 15 https://blog.clearbrain.com/posts/the-two-types-of-machine-learning 16 Digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of Things device operates and lives throughout its life cycle. http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf; http://research3.fit.edu/camid/documents/doc_mgr/1221/Origin%20and%20Types%20of%20the%20Digital%20Twin.pdf - 10 - November 2017