Case Studies
Digital Twins Support Supply Chain Optimization
Overview
Analytics & Modeling - Digital Twin / Simulation Analytics & Modeling - Predictive Analytics | |
Chemicals | |
Maintenance | |
Predictive Maintenance Supply Chain Visibility | |
Data Science Services | |
Operational Impact
The AspenTech offering is the first that ARC has been briefed on that includes optimized production scheduling based on an integrated digital twin maintenance model. | |
In many industries, this solution would be overkill, but not in the chemicals industry. Chemicals firms stand to gain a great deal through the ability to predict failures in hyper compressors used in LDPE production. | |
Other asset-intensive industries like power, metals & mining, and transportation could also potentially obtain significant value from optimizing maintenance across the supply chain. | |
Quantitative Benefit
Aspen Mtell can provide more than 25 days of advance warning of a central valve failure. | |
This can allow scheduling of less-expensive maintenance downtime rather than reacting to unplanned downtime. | |