Stellar is an industrial design/build firm focused primarily on the food and beverage market. They were seeking an industrial IoT platform that could help the company reduce total cost of ownership for its industrial refrigeration customers.
In an industry where assets can remain in the field for decades, change takes time and determination. Leaders at Stellar realized that embracing new technology was key to delivering greater value to its customers. The company created an innovation division to explore alternative solutions. Their first goal was to develop a crisp definition of customer challenges. These included:
Stellar recognized that providing insight into the true costs of building ownership would be a powerful change agent. “And to do that, we recognized we would need to collect significantly more data,” explains Luke Facemyer, Divisional Vice President. “We wanted to expand our service business and help customers move to a more proactive maintenance approach.” The first solutions targeted for the company’s digital transformation journey were:
To support its digital transformation initiatives, Stellar sought an IoT solution that could be quickly implemented with minimal business impact. Other selection criteria included a superior application design user interface (UI) and user experience (UX), personalized audit navigation, on demand training modules, and drag-and-drop connectivity with ERP and other external software.
"As the construction and industrial refrigeration industries continue to evolve, it is imperative to have the right technology partner in place to ensure our systems are meeting customer demands. With increasing regulatory requirements and an aging workforce that holds untapped knowledge of system operation, now more than ever, embracing technology such as cloud, IoT, and AR is vital."
First out of the gate was the Digital Process Safety Management (PSM) solution. It took less than three months to develop the replacement PSM solution, which is now being deployed across the Stellar customer base. Early feedback has been overwhelmingly positive. The new solution enables customers to more easily manage their compliance documentation and enables greater confidence in the event of an audit. With convenient cloud deployment, the solution can also be easily upgraded as regulations evolve.
The first MAP (Maintenance Advisor and Predictor) pilot was introduced shortly thereafter. The new system brings visibility, efficiency, and cost savings to customers through a system health dashboard. At the heart of the MAP solution is an analytics engine, powered by ThingWorx Analytics, built from large data-sets specific to each customer installation.
Traditional Maintenance Issue
|Equipment failure occurs, refrigeration and production stops, storage may spoil||Analytics engine detects anomalies and diagnoses future issues before they occur|
Upon equipment failure, production must accommodate maintenance
A. Emergency parts must be expedited and costly machine repairs performed
B. Equipment repairs may be delayed due to long-lead-time parts
Issue detection provides time to resolve before a future issue occurs
Diagnosis provides information on root cause of future issue before it arises
|Small unseen issues cause larger costly issues||Engine detects anomalies and diagnoses issue before it occurs|
|Maintenance or technician overtime is required||Issue prediction from anomalies is provided before issue occurs
|Parts are preventatively replaced well before the end of their useful life||Issue detection and diagnosis allows parts to be replaced only when they are at the end of their useful life|
|Energy-use impacts of system changes hidden and hard to quantify||The system dashboard provides real-time system conditions and historical data|
|Staff uses majority of time collecting and reporting system information||Data is automatically collected and logged|
|Staff troubleshooting experience and knowledge requires substantial training to pass on or it is lost||Issue diagnosis is performed by the Analytics Engine|
- Courtesy of Stellar
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