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 proven roadmap towards a predictive, IIoT-centric ecosystem that is backed by two decades of expertise of working with leading OEMs since 1999.
STEP 1. CONNECTING MACHINES
The “Path to Predictive” begins with the installation of secure, stable and integrative hardware that connects with both modern and legacy machines.
STEP 2. COLLECTING MACHINE DATA
The second step involves gathering the data required to understand machine operations. An increasing number of IIoT standards, such as OPC-UA and PackML, simplify the process of gathering such machine data.
STEP 3. TRACK KEY PERFORMANCE INDICATORS
To simplify the implementation of a monitoring solution, a practical approach is to begin with a standard information collection model. This model entails tracking KPIs and relevant data, which are then analyzed to identify areas in need of improvement. More specifically, data from securely connected machines is analyzed in the cloud to generate performance metrics, delivered to personnel through web pages, dashboards, reports, and mobile apps. This performance information is also seamlessly integrated with computing systems, ERP, and business software using RESTful APIs for data exchange.
STEP 4. DISCOVER IMPROVEMENT OPPORTUNTIES
The fourth step of the process is to scale up operations in a sustainable manner.A linked, cloud-based data transfer system can bring in data from multiple machines across multiple plant sites. The top reasons for machine stoppage are easily revealed– independent of customer use, product, geography and many more factors. This ease allows users to pinpoint the most prevalent root causes of issues, optimizing their operations on a global scale.
STEP 5. DEVELOP PREDICTION MODELS
Building on the data collected in a fleet’s machines, artificial intelligence models can be utilized to predict the most common causes of unplanned downtime. These predictive models are programmed into cloud-based applications and can issue preventative maintenance alerts, notifying assigned individuals of any operational anomalies.
STEP 6. 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.