Optimise resource allocation to support branch and ATM network. Factors that may be open to alternative deployment and focus include staffing, cash delivery trucks, opening hours or mobile assets

Reduce unplanned downtime to identify, monitor and pre-emptively predict the failure of the drivers of unplanned network downtime. Model complex networks and analyse historic data to understand probable causes of major problems and interruptions.

Examine network traffic to triage network traffic bottlenecks and provide real-time incentives and/or intervention to reduce or re-route traffic during overload situations. Load balancing identifies and rebalances network traffic based on current and forecasted traffic needs and current network capacity.

Discover anomalies across fleet of vehicle sensor data to identify potential failure risks. This may enable companies to pre-empt expensive and embarassing recalls, often driven by negative PR.

Predict failure and recommend proactive maintenance for farming and production equipment saving costs and reducing downtime.

Network performance optimisation predicts and optimises field network performance across multiple usage scenarios (network traffic, weather, seasonality, holidays, special events) - potentially in real-time.

Forecasting network demand (average demand, surge demand, minimal viable demand) based on predicted network usage behaviours, patterns, trends and likely upcoming events (e.g. cold weather). Helping determine future capacity needs (e.g. retail locations, new plant, new networks) ensures better planning outcomes including potential (what if) working situations.

Network layout optimisation optimises network layout in order to minimise traffic bottlenecks and deliver higher volume network bandwidth and throughput.

Optimise field force labour allocation - engineers and support staff. This is especially important at moments of network crisis (e.g. in the event of natural disaster) - although this may also be when humans are most likely to override any algorithmic decisions.

Use technologies, such as machine vision, to better detect quality control issues during key processes - vegetable sortign for quality for example. This will potentially help generate a better understanding of which internal processes, workflows and factor contribute most and least to meeting quality objectives.

Use satellite image feeds to assess likely commodity prices - this could include likely crop yields for agricultural commodities or shipment levels for mining products.

Forecast asset pricing based on market patterns. This can be for investment banking trading and back office teams.