Digital Twins Support Supply Chain Optimization

Technology Category

Analytics & Modeling - Digital Twin / Simulation
Analytics & Modeling - Predictive Analytics

Applicable Industries

Chemicals

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance
Supply Chain Visibility

Services

Data Science Services

The Challenge

The chemicals industry is complex with a small number of raw materials often transformed into hundreds of thousands of final products. The industry employs expensive, heavy, and complex manufacturing assets that can cover the full spectrum of process operations: continuous, semi-continuous, or batch. Shutting down and then restarting the process is expensive; time consuming (think days, not hours); and has environmental, health, and safety implications. A hyper compressor’s job is to build up pressure needed in the conversion process. These compressors typically go down many times a year. Mitigating this problem could be worth millions of dollars to chemical companies.

About The Customer

The customer in this case study is the chemicals industry. This industry is characterized by the transformation of a small number of raw materials into hundreds of thousands of final products. The industry employs expensive, heavy, and complex manufacturing assets that can cover the full spectrum of process operations: continuous, semi-continuous, or batch. Shutting down and then restarting the process is expensive, time-consuming, and has environmental, health, and safety implications. A key piece of machinery in this industry is the hyper compressor, which is used to build up the pressure needed in the conversion process. These compressors typically go down many times a year, which can lead to increased manufacturing costs and service failures for end users.

The Solution

AspenTech offers a solution that includes optimized production scheduling based on an integrated digital twin maintenance model. The digital twin concept combines the ideas of modeling and the Internet of Things (IoT). Most often, the digital twin concept has been applied to assets. A piece of machinery generates data on vibration, temperature, pressure, and other things. That data is used to predict a machine’s failure and apply preventive maintenance to help avoid costly unplanned downtime. For supply chain planning, plant machinery is the key area for which an asset’s failure could lead to increased manufacturing costs and service failures for end users. The supply chain model does not look to predict asset failures, but could use inputs from the digital twin maintenance model to improve scheduling.

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.