Aerospace

QUAISR APPLICATION

Maximise fleet uptime via real-time maintenance scheduling

Link with ERP systems with heterogeneous data sources to collate spare-parts availability. Run custom scheduling logic for aircraft maintenance.

Challenge.: Optimal scheduling of aircraft maintenance can require custom algorithms and inputs from diverse data sources. Scheduling teams rely on manual processes, which can be time consuming and often rely on heuristics. A real-time understanding is only possible with data pipelines and automated update mechanisms in place.
Solution.: Use Quaisr to link diverse data sources – including ERP systems, spreadsheets, and legacy databases – and run custom logic to generate scheduling scenarios for aircraft maintenance.
Impact.: Maximise overall fleet uptime and reduce business interruption costs.

QUAISR APPLICATION

Correlate electricity consumption data with machining actions, and propose alternative pathways to reduce carbon footprint.

Challenge.: Legacy CNC machining equipment can be inefficient, increasing the OPEX and energy costs. As upfront equipment replacement isn’t often viable, due to large CAPEX costs, transition plans are required to meet ambitious energy reduction targets while minimising expenses.
Solution.: Use Quaisr to source real-time data feeds, including multi-axes milling head position and energy consumption data. Apply built-in statistical algorithms and machine learning, with expert inputs, to generate optimal machining pathways that reduce energy consumption.
Impact.: Reduce energy consumption of legacy machining operations, meeting carbon reduction targets without material upfront investment.

QUAISR APPLICATION

Exploit historical test data from legacy aircraft to identify low-cost materials that meet requirements.

Challenge.: Identifying, fabricating and testing high-performance UAV materials is challenging. Engineers must navigate large parameter spaces via physical experiments, which are often expensive and time consuming.
Solution.: Use Quaisr to screen materials with custom machine learning models trained on historical test data. Run promising candidates through physics based solvers, confirming technical performance prior to physical testing.
Impact.: Reduce physical testing and costs, while accelerating time to market.

Additional solutions

Chemicals: Reduce physical experimentation.
Supply chain carbon footprint.
Virtual laboratory.; Learn more
Consumer Goods: Reduce physical experimentation.
Increase product shelf life.
Reduce line breakdowns.; Learn more
Defence: Verification and validation.; Learn more
Energy: De-risk scenario planning.
Offshore wind optimisation.
Accelerate carbon reduction targets.; Learn more
Pharmaceuticals: Process selection.
Sustainability compliance.
Protocol design.; Learn more