THE PATH
TO PREDICTIVE

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. 

Benefit: Reduced travel time and costs by 80% for machine builders; Improved production efficiency, reduced downtime, and increased cost savings for machine owners.

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.

Benefit: Measuring key machine data points to analyze processes and their implications. Additionally, data stored in the cloud can be utilized for compliance reporting, offering traceability and secure off-site backups.

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.

Benefit: Effective tracking of KPI, reported to improve plant floor OEE by 5% per year for machine owners. Machine builders can generate additional after-sales revenue by offeringSaaS subscriptions.

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.

Benefit: 10-15% decrease in downtime and enhanced machine designs across global operations for machine owners.

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.

Benefit: Machine owners predict downtime causes with preventative maintenance alerts; Machine builders gain a competitive edge and improve equipment design.

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. 

Benefit: Increase in aftermarket sales and ongoing predictive model refinement for improved production efficiency for machine owners .

Download the Whitepaper