Machine 4.0: Making your Factory, Production and Maintenance Data Work

To leverage the potential of Big Data the manufacturing firms should intelligently integrate and connect their data sources on a unified platform and use machine learning to extract insights, analyze them, and derive results.

By Sundeep Sanghavi, Co-Founder and CEO, DataRPM Sponsored Post.

Data driven organizations are three times more likely to benefit from informed decision making, says a PwC Global Data & Analytics survey. So what do CIOs and COOs of manufacturing organizations with data driven initiatives require to thrive?

Well, they firstly need to intelligently integrate, connect and leverage their data sources on a unified platform and secondly use machine learning based technology initiatives to extract insights, analyze them and derive results.

Why machine learning?  

The potential of machine learning is huge considering the need to decipher the vast volume big data, since it is based on algorithms which learn from data and can crack the most complex data structures. With 50 billion smart connected devices in the next 5 years, the manufacturing world needs the weapon of machine learning to accurately collect, analyze and share data.

Here are four ways discrete manufacturing organizations armed with machine learning can achieve astounding results with their data stacks:

  1. Making data analysis intelligent - By applying sensor technology and intelligent data analysis, plant operators can continuously capture status data from machine components. This can then be combined with information from third-party systems such as ERP/CRM to analyse results and predict failures. The result? Speeding up maintenance processes and slashing down production outages
  2. Stepping up Overall Equipment Effectiveness (OEE) - By integrating sensor data with various operating parameters collected in short intervals over a period of time, manufacturers can leverage machine learning to schedule and plan maintenance processes while ensuring minimal loss of production. The outcome can be an impressive increase in OEE from 65% to 85%.
  3.  Amplifying quality on the factory floor- By applying machine learning algorithms smartly to existing data streams, manufacturers can transform product quality to ascertain which specific internal processes and factors directly impact the quality of output. Manufacturers also achieve the superior intelligence of predicting how their product quality can match Six Sigma standards on the factory floor
  4. Harnessing an ocean of relevant data- One of the biggest challenges manufacturing organizations face is disintegrated cross functional teams with limited access to unified IT systems. By using machine learning with accurate insights into these disparate teams, this pool of data is automatically made more relevant to create common workflows and value driven operational outcomes, to take uni-directional data driven decisions across the organization

Getting started

Every manufacturer can easily leverage machine learning to gather valuable insights for accurate decision making.

Machine learning algorithms can create absolute value from complex nodes of Big Data to keep manufacturing processes running seamlessly while bringing greater predictive accuracy. Many algorithms being iterative in nature can learn continuously and iterate in milliseconds, creating optimized outcomes in minutes versus months.

While data itself can mean a treasure cove for an organization, imagine creating value from that data to convert into actionable results. If a typical Fortune 1000 company can derive more than $65 million additional net income with just 10% increase in data accessibility, just imagine what possibilities exist for you and me.

It’s time to convert these endless possibilities into reality by taking advantage of automated approaches with machine learning now!