Modern manufacturing technology is underway to incorporate machine learning throughout the production process. Predictive algorithms are being used to devise machine maintenance adaptively rather than on a fixed schedule. Meanwhile, quality control is becoming more and more automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject defects. In this post we look at some recent advancements in industrial applications of machine learning.


These ten ways machine learning is revolutionizing manufacturing include the following:

1. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. These results to rise in production capacity up to 20% while reducing material consumption rates by 4%.

2. Machine learning has the potential to bring a completely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible. Thus providing more appropriate data so finance, operations, and supply chain teams can better manage factory and demand-side constraints.

3. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming prevalent, as revealed by announcements from Amazon, Google, and Microsoft. Thus these are the proven real use cases that machine learning improves deterrent maintenance with greater predictive accuracy.

4. Enabling condition monitoring processes that provide manufacturers with the scale to conduct Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%.

5. Machine learning is revolutionizing relationship intelligence. ML-powered tools are now helping scale the efforts of sales teams by gleaning useful patterns from data, finding successful courses of action, and taking care of the bulk of the work in addressing customer needs and grievances.

6. Manufacturers often are challenged with making product and service quality to the workflow level a key part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met.

7. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more sophisticated, customized products to use more of their production capacity, and machine learning help to maximize the best possible selection of machines, trained staffs, and suppliers.

8. The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioned to launch new businesses that provide a subscription rate for services and scale globally.

9. For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.

10. Machine learning is extending what enterprise-level price optimization apps provide today. One of the most important differences is going to be just how optimizing pricing along with proposed strategies to close deals accelerate sales cycles.


The Rise of the Machines

Machine learning has already shown its capacity to learn and analyze. Machine learning could lead to a whole new realm of manufacturing and material innovation that would be otherwise unimaginable. To usher in this machine-driven future, access to the latest advances in research, coupled with human nuance and understanding, will be critical to ensure the very best machine learning systems. Without this, we could be missing out on vital insights to revolutionize future manufacturing.