Accelerate product discovery based on modelling of components - potentially millions of input options can be modelled to ascertain the most promising.

Predict risk of failure for physical assets (e.g., military, infrastructure) and recommend proactive maintenance

Sensors can be expensive, hard to maintain or simply unavailable for what can be important data. Using data from other sources can enable the predictive modelling of other data sets. Note that this can create a series of new risks, especially if historic patterns break down or feedback effects occur.

Construction work is very reliant on the quality of both staff and site management, roles which may be harder to deliver in hard to access or inhospitable locations. Machine vision can be used to ensure that quality standards are being met and errors minimised and to ensure a rapid feedback loop to avoid potential cost (and engineering safety) issues.

Improve preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Predictive maintenance predicts when certain products or devices are in need of maintenance what sort of maintenance, the likely maintenance and replacement materials, and technician skill sets.

Maintenance, repair, and operations equipment inventory optimisation balances kit inventory with predicted maintenance needs in order to reduce inventory costs and minimise obsolete and excessive inventory.

Determine root causes for quality issues originating prior to the manufacturing process. This might include supply sources or logistic process issues. Close human analyst oversight recommended.

Identify design problems in pre-production to reduce ramp-up time to maximum output. Modelling potential scenarios will help establish capacity planning constraints,

Identify root causes for low product yield (for example input, tool or machine specific issues) in manufacturing. This analytical work will likely require working closely with human analysts.

Predict failure and recommend proactive maintenance for production equipment

Detect defects and quality issues during production using visual and other data. This process will potentially be impacted by unexpected issues - e.g. a change in the quality of the lighting on a production line.

Optimise complex manufacturing process in real time, for example to determine where to dedicate resources to reduce bottlenecks and cycle time. Resources might include automated factor input, machine re-alignment or signal for necessary human intervention.

Predict future demand trends and potential constraints in supply chain