Consumer Goods

QUAISR APPLICATION

Reduce physical experimentation

Apply simulation & AI based modelling workflows for characterising experimental outcomes.

Challenge.
Current design cycles are extended and expensive due to the large numbers of physical experiments that need to be undertaken.
Solution.
Use Quaisr to screen and select which physical experiments to conduct.
Impact.
Automate processes to save worker hours and consumables costs, while accelerating time to market.

QUAISR APPLICATION

Increase product shelf life

Democratise complex modelling tools with intuitive applications – empowering non-specialists to explore self-service "what-if" scenarios.

Challenge.
Accurate shelf-life predictions require coordinating data, simulation and machine-learning models from multiple teams. Experimentalists and domain experts are often locked out of the underlying tooling due to high complexity and steep learning curves.
Solution.
Use Quaisr to connect and share modelling workflows from multiple teams, creating end-to-end predictions of product shelf life.
Impact.
Empower faster decision-making and time to market by democratising data-driven applications among your R&D and experimental teams, backed by state-of-the-art simulation and data-science tools. Generate actionable insights and improve team productivity, without new investments in training.

QUAISR APPLICATION

Reduce breakdowns with plant monitoring and predictive maintenance

Monitor process parameters for change point detection using hybrid simulation and machine learning models with real-time streaming.

Challenge.
Most processing operations rely on manual operator heuristics, failing to leverage process models and data for maintenance planning, scheduling and real-time feedback.
Solution.
Use Quaisr to integrate state-of-the-art machine-learning models with process simulations and live streaming data. Detect anomalies and deviations from normal process operating conditions, preventing unplanned disruptions.
Impact.
Increase asset life, process reliability and production throughput, while reducing costs.
20% - 40%
Reduction in physical experimentation
30x - 50x
Experimentalists empowered
10% - 15%
OPEX reduction