AI-Based Yield Optimization for Global Chemical Company
An international chemical company wanted to identify use cases for AI/ML models across different portions of their operations. One proof of value was developed to stabilize and optimize yield in a core chemical reactor system.
- Disconnect between operational team & HQ: Initial push to roll-out AI/ML didn't lead to successful use cases with operational impact.
- Potential of AI/ML to create impact: Senior Leadership critical about actual operational impact and benefits of applying AI/ML. :
- Expertise and capabilities to deliver fast results: Designing and developing AI models quickly to show value.
We identified pain points across operations, reliability, and maintenance, and detailed them into AI/ML use cases together with data scientists and domain experts.
Services:
- Fast-track innovation with Design Sprints: Our project leveraged design sprints with local teams across operations, reliability, and maintenance to identify pain points and translate them into AI/ML use cases.
- Industrial AI/ML: Our digital experts across Siemens Advanta Consulting, Digital Industries, and Technology helped the client to develop an AI model to optimize yield.
Together with our Digital Industries and Technology colleagues, we enabled our client to identify relevant use cases in their operations and directly develop one key use case to show operational impact as proof of value.
90+ use cases for AI/ML identified across three main sites.
15 use cases short-listed and detailed out.
$6+ million in annual savings achieved through yield optimization use case.
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