A recent study predicted that the implementation of Machine Learning (ML) to enable predictive maintenance is anticipated to increase among producers by 38 percent because of its capability to advance the profit margin by removing unscheduled work stoppages.
FREMONT, CA: The chief growth strategy for manufacturing businesses today is enhancing shop floor potency by investing in Machine Learning (ML) platforms that deliver the acumens required to improve product quality and production yields. ML transforms an industrial operation into a network of systems that can get products to market quicker at a low cost so the business can remain competitive and keep its clients happy.
Industrial transformations attributed to ML involve:
The prospect of predicting disruptions to a production line in advance can be priceless to manufacturers. It facilitates the manager to schedule downtime at the most beneficial time and eliminate unscheduled downtime. Impromptu downtime hits the profit margin hard and can result in the loss of the customer base. Furthermore, it also disrupts the supply chain, causing the transportation of excess stock.
The need to bring in the additional workforce can cost a lot of money as well. A recent study predicted that the implementation of Machine Learning (ML) to enable predictive maintenance is anticipated to increase among producers by 38 percent because of its capability to advance the profit margin by removing unscheduled work stoppages.
IT/OT Convergence and Network Security
The expansion of ML will also drive many business model alterations in a manufacturer’s usual operating procedures. The approach is particularly true in the organizational framework of an enterprise. Furthermore, the computer network, which is the revered ground of the Information Technology (IT) department, ought to be co-located with the operational sensors on production machinery. The data, therefore, can be collected and sent to the information warehouse as training data for ML purposes.
There needs to be an ease of collaboration and cooperation between the internal groups. After all, the technicians and floor operators will be considerably impacted if the network is not dependable or gets hacked, which can bring production to a stop. The Operational Technology (OT) devices and sensors will be affected as much as the computers and IT network.
Digital Twin Development
The ultimate objective of AL and ML is to facilitate the development of a digital twin of the production floor. The formation of a digital twin needs to take place under a model-based system engineering process using ML algorithms and knowledge gained as a base. The digital twin can serve as a platform for running what-if scenarios to learn what one does not know today. It can also be employed as a model for designing higher reliability parts and regulating the communications between production-line machines to advance performance.