Digital Twins Support Supply Chain Optimization

技术

分析与建模 - 数字孪生/模拟
分析与建模 - 预测分析

适用行业

化学品

适用功能

维护

用例

预测性维护
供应链可见性(SCV)

服务

数据科学服务

挑战

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.

关于客户

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.

解决方案

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.

运营影响

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.

数量效益

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.