Intelligent Process Control – Use Cases
Business leaders like CEO, COO are demanding their operations to produce consistent quality in the most optimal way at the highest level of automation possible and a complete overview on the critical steps in core processes that enables to take instant decisions which reflect business needs and can be automated along the development of Digital Twin concepts.
Process Control use cases leverage real-time OT and IT data to monitor an industrial process, predict quality of the process output and automatically take corrected actions, optimize energy and raw material usage, facilitate remote operations and, eventually, enable autonomous execution of the production process with no human supervision.
Benefits generated are lower operational costs and risks, reliable quality, reduced waste, increased overall yield
Production Operations and R&D
Production Operations and R&D
Long time to market for new vaccines and high cost of production due to batch waste (1 out 5 batches is discarded).
Atos solution provided real-time prediction of critical quality attributes, prescription of corrective actions if the process is going off-spec and automatic execution of corrective actions.
- Faster product development and time to market: reducing physical test cycles by 35%
- Robust quality, avoidance of deviations and improved yield: reducing production cost by 20%
Complex product quality: multiple quality parameters which need counter-acting actions to fix. Unplanned downtime due to production disturbance, typically clogging up the production machine or pipeline.
Atos solution provided real-time prediction of quality parameters such as Z-Strength and Bending force (7 parameters in total) supported by monitoring and alert system indicating the probability of going above & below specification boundaries.
- Extra time for preventive action to avoid production loss or quality impact
- Reducing the cost associated with overdosing chemicals
- Reducing wastage of paper rolls due to insufficient quality of paper
- Immediate information on paper quality
- Reducing the risk of web-breaks that require clean-up of the equipment and cause downtime
- Improving production efficiency and product quality
Wastewater treatment is an expensive and complex process, requiring high capital investments. Regulatory pressure, heavy penalties in case of water quality issues. Necessity to improve sustainability of the operations, reduce environmental impact.
Atos solution provided big data platform to acquire and integrate the client’s plant data as well as external data sources: weather, population, and consumption dynamics. Machine Learning models trained centrally in the cloud and deployed locally on Codex Smart Edge.
- 20% reduction in operational costs
- 5% reduction in asset investments
- Reduced environmental footprint due to lower energy consumption and chemical use
- Robust water quality, avoiding penalties
- Better prevision and control of energy consumption, reducing penalties from the energy providers
- Early detection of foam creation, reducing risk and potential costs
Atos engagement roadmap to realizing Digital Twin
A business driven ideation workshop to identify most promising use cases
Atos’ robust consulting approach (Digital Twin 4M-6C Methodology) is based addressing business challenges. The “4M” design thinking approach helps in identifying relevant digital threads which influences the targeted business KPI. Most promising use case is fast tracked into a Proof of Value (PoV) after a quick Digital Twin assessment using the “6C” maturity pyramid.
Digital Twin 4M-6C methodology is:
4M – What to capture (Digital Twin model fidelity)
6C – How to realize (Digital Twin functionality)