Machine builder’s roadmap towards a predictive, IIoT-centric ecosystem

Predictive analytics is becoming an increasingly important facet of global business operations as it provides a crucial differentiator amidst stiff competition. The question is, what are the numerous, practical ways in which machine builders can implement this model? This whitepaper outlines ei3’s step-by-step guide towards a predictive, IIoT-centric ecosystem that is backed by two decades of expertise of working with future-forward clients since 1999.  


  • Connect Machines: The “Path to Predictive” begins with the installation of secure, stable and integrative hardware that connects to old and new machines. If you’re a machine supplier yourself, you can develop connect-ready machines that add value to your client’s factories. With remote access, service technicians can monitor and calibrate machines, reducing travel costs and creating instant ROI by reducing startup and warranty costs. 
  • Collect Machine Data: The second step is to accumulate the data needed to understand machine operations. By measuring the information being processed by the Programmable Logic Controller or the embedded computer in a machine, data can be collected online by cloud servers and stored in a database. This information, such as the set points, feedbacks, states, and faults of a machine, are the baseline information used by the analytic software improving machine efficiency.
  • Track Key Performance Indicators: For many, it seems daunting to implement a complex monitoring solution. In that case, starting with a standard information collection model is prudent. This model includes tracking KPIs and relevant data, which can then be analyzed to determine which areas need improvement and attention.
  • Discover Improvement Opportunities: The fourth step is to scale up in a sustainable manner. Finding root causes of unplanned downtime, machine failure and so on is simply the beginning. A linked, cloud-based data transfer system can allow users to optimize their operations/machines on a global scale. Tracking stop codes, displaying them clearly and acting upon them remotely has never been more straightforward! 10-15% reductions in downtime are a common result of this methodology. 
  • Develop Prediction Models: Building on the data collected in a fleet’s machines, analytic methods can be utilized to predict the most common causes of unplanned downtime and to send alerts to prevent them. Predictive models look for patterns in machine operational data, and effective models are a valuable resource for pursuing a competitive advantage.
  • Improve and Measure Progress: The final step is implementing repeatable, proven processes for predictive action. Prediction involves rigorous modeling in terms of what works and what doesn’t. It’s backed by actual, complex data so that your decisions are never in vain. Communicating these value propositions and backing them up with results is a surefire way to support your sales team even as they arm themselves with models that do everything: from replenishing consumables before time and examining operational conditions that lead to zero downtime. 

Download the Whitepaper