How to Increase Profitability in Manufacturing using Machine Learning

How to Increase Profitability in Manufacturing using Machine Learning | SPONSORED

The Internet of Things (IoT) is creating a new dynamic in the industrial space, specifically manufacturing. Large manufacturing companies are moving to leverage IoT technologies and predictive analytics as a part of increasing profitability and staying competitive.

In the U.S. alone, existing capital stock worth over $6.8 trillion dollars is being fitted with sensors to drive key insights and create connected environments. This next wave of industrial evolution, coined as Industry 4.0, will generate $14.2 trillion of global output by 2030, according to the World Economic Forum.

Predictive analytics using automation and machine learning technology is key in monetizing these connected plant environments. Predictive analytics of the past are becoming incredibly intelligent, giving manufacturers data-driven insights that significantly contribute to overall business profitability; insights that:

• Reduce maintenance costs
• Optimize production lines
• Increase the remaining useful life of equipment

The challenge with connected industrial environments is that they are highly dynamic, have multiple sources of data generation and are incredibly data intensive. Thus, new feats in efficiency will be driven by a combination of deep knowledge and speed. More about Connected Manufacturing: What it is and Why it is Important

Automation is key for operationalizing predictive insights, but automation alone cannot achieve such depths of knowledge discovery required for such massive volumes of data being generated within connected plant environments. Machine learning is the critical foundational element in which automated analytics must be built upon in order to uncover efficiencies hidden deep within complex industrial data.

Download “Increasing Profitability in Manufacturing” white paper sponsored by Canvass to find out more about:

• The Connected Industrial Environment
• The Need for Automated Analytics within Plants
• How to Enable Faster and More Meaningful Insights with Machine Learning
• Machine Learning’s Role in Automation
• Three Key Attributes of Machine Learning
• Predictive Insights with Canvass
• Canvass’ Unique Capabilities using Machine Learning