6 Jun
2018

10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018

Machine learning algorithms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations.

Artificial Intelligence
10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018
Artificial Intelligence
Artificial Intelligence
Food & Beverages Processing
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Consumer Product Goods
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Material Building & Construction
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Original Equipment Manufacturer (OEM)
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Pharmaceuticals & Supplements
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Packaging & Co-manufacturing
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Louis Columbus published an article in Forbes "10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018" where he highlights:

  1. Improving manufacturing yields, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
  2. Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today.
  3. 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
_Header_blogue_1920x800_Preventive_maintenance
  1. McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.
  2. Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well.
  3. Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%
  4. Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance.
  5. Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more.
  6. 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.
  7. Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE).

SOURCE: https://www.forbes.com/sites/louiscolumbus/2018/03/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2018/#1b56484523ac

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