Louis Columbus published an article in Forbes "10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018" where he highlights:
- Improving manufacturing yields, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
- Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today.
- Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC
- McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.
- Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well.
- Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%
- Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance.
- Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more.
- A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning.
- Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE).