How is Machine Learning Reshaping Manufacturing?

Share

Machine Learning – Revolutionizing Manufacturing

We are rapidly moving in the Fourth Industrial Revolution (Industry 4.0) where technologies and trends are transforming the way we live and work. It is the amalgamation of physical and advanced digital technologies such as Machine Learning, Artificial Intelligence, and Internet-of-things (IoT). These trends are not only changing the way we deal with information and computers but also, revolutionizing the manufacturing sector.

Increasing demand for customized products at affordable rates has been the primary driving force towards the need for AI and ML in the manufacturing process. According to a report by McKinsey, it is predicted that Machine Learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.

The following disruptive technologies are bringing change in the manufacturing sector:

1. Predictive Maintenance for Reducing O&M Costs

According to PwC, manufacturer’s adoption of Machine Learning and analytics to improve predictive maintenance is predicted to increase by 38% in the next five years. Companies around the world are trying to reduce equipment downtime using various AI technologies. With the help of cognitive AI, smart sensors, and an interconnected network of machines, the manufacturers can monitor the devices on the floor. In return, the constant monitoring will allow floor managers to generate predictive analytics which will help in preventing unexpected equipment failures. The underlying fact remains that maintenance should be performed at a scheduled point in time when the activity is most cost-effective and before the equipment loses performance within a threshold.

Digital Twin technology was restricted to high-end applications such as running spacecraft simulations at NASA for many years. However, with the rapid increase in adoption of AI and other technologies, this technology can be used in analyzing the data which is collected from a complex array of sensors. Also, it can track anomalies and diagnose failure situations. Gartner predicts that by 2021 almost half of the large industrial giants will use digital twins which will result in gaining a 10% improvement in the effectiveness of those organizations.

2. Condition Monitoring and Quality Control for Improved Supply Chain

Use of Artificial Intelligence and Machine Learning will improve all areas of supply chain including transportation, logistics, production, packaging and customer service. For example, Watson Supply Chain System of IBM uses cognitive AI technology to monitor the processes. It collects and analyzes the data from different sources thereby, helping in adapting to changing market situations easily. Rather than relying on manual quality checks, AI can help in identifying the defects with efficiency and accuracy based on historical data of defects and errors.

3. Process and Inventory Optimization

When a process is to be optimized, it is important to maximize one or more of the process specifications while keeping all other constraints same as before. By using Machine Learning algorithms, it is possible to optimize the best possible set of machines for a production and provide real-time insights for machine-level loads and production schedule performance. In real-time, it helps in analyzing how each machine’s load level impacts the overall production schedule performance which leads to making better decisions thereby, managing each production run. Using AI and ML algorithms, automating inventory optimization has improved service levels by 16% while simultaneously increasing inventory turns by 25%.

4. Demand Forecasting

It is possible to integrate AI and Machine Learning algorithms with procurement, strategic sourcing and cost management areas. This will improve the accuracy of demand forecasting thereby, reducing energy costs. Yield rates can be improved by combining Machine Learning and Overall Equipment Effectiveness (OEE) since manufacturing companies use OEE to define production effectiveness. OEE combines availability, performance, and quality to drive the factors which impact manufacturing processes and performance. If this data is integrated in Machine Learning models, it will be easy to learn quickly about the impact and take necessary actions.

Ushering in the Modern Manufacturing Industry

AI and machine learning are the foundation stones of Industry 4.0. The deployment of cutting-edge technologies like AI, Machine Learning, IoT will cause massive disruption in the manufacturing sector and is already helping in automating the core manufacturing processes. 

By,

Priya Pariyani
Consultant – Pre-Sales, RapidValue

Please Share Your Thoughts & Comments Below.

How can we help you?