5 Reasons Why Big Companies Are Opting for Machine Learning in 2018

Machine learning is increasingly making way in all kinds of industries. Manufacturers are more interested in finding new ways to grow, enlarge product quality, and at the same time make short production runs with customers. New business models often bring with them the paradox of new product lines that affect existing ERP, CRM, and PLM(Product lifecycle management) systems, as customer performance needs to be constantly improved. At present, new products are being increased in production and delivery windows are being adapted.

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Why Big Companies Are Opting for Machine Learning in 2018


Here are 5 ways people are adopting machine learning for their performance based manufacturing:


  1. Semiconductor Output Improvement

The improved semiconductor output power is up to 30%, which reduces the rejection rate and optimizes the manufacturing steps that can be achieved with ML. Reduce power reduction in semiconductor manufacturing by up to 30%, reduce scrap rates based on machine learning root cause analysis, and reduce test costs with ease AI optimization where machine learning will improve semiconductor manufacturing. McKinsey also found that predictive maintenance with artificial intelligence from industrial equipment would result in a 10% reduction in annual maintenance costs, a 20% reduction in downtime, and a 25% reduction in inspection costs.


Read also: TensorFlow: A Cutting-Edge Framework for Machine Learning


  1. Inventory tracking, Supply chain visibility, and Inventory optimization

Asset Management, Supply Chain Management, and Inventory Management are the key areas of artificial intelligence, machine learning, and the Internet of things in today’s manufacturing. The World Economic Forum (WEF) and the recent study by AT Kearney on the future of production conclude that manufacturers are exploring how to combine new technologies such as, AI and machine learning that improve the accuracy of inventory tracking, Supply chain visibility, and inventory optimization.

  1. Reduced Forecasting Error

McKinsey predicts that machine learning will reduce forecasting errors in the supply chain by 50 percent and reduce revenue losses by 65 percent through improved product availability. Supply chains are the soul of every manufacturing company. ML is expected to reduce transport and storage costs and supply chain management by 5 to 10% and 25 to 40%, respectively. Thanks to ML, it is possible to reduce inventory by 20 to 50%.


Read also: PyTorch Vs TensorFlow: Choosing a Deep Learning Framework


  1. Automating inventory

Automating inventory optimization through machine learning has improved service levels by 16 percent and increased inventory performance by 25 percent. AI-based algorithms and modeling and machine learning optimize the inventory scale across all distribution locations, taking into account external and independent variables that impact dthe emand and performance of the delivery to the customer.

  1. Reduced Testing and Calibration Time

One manufacturer reduced test and calibration time by 35% by accurately predicting the calibration and test results through ML. The aim of the project was to reduce the testing and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a set of machine learning models that would predict test results and learn over time. The next workflow in the process was able to isolate bottlenecks, accelerate test and calibration times.

Read More About Machine Learning Use Cases– IoT, Predictive Maintenance, Sentiment Analysis and Recommendation


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