• Predictive maintenance: predict when and how a device will fail and what replacement and maintenance parts and service personnel skills will be required to preempt the failure.
  • Loss prevention: monitor device and network usage to flag unusual usage situations that may be indicators of revenue loss and theft.
  • Asset utilisation: monitor and predict asset utilisation under a number of different usage scenarios in order to improve asset, device, and node utilisation.
  • Inventory tracking: monitor inventory levels and inventory assets to minimise loss and waste and improve inventory utilisation.
  • Disaster planning and recovery: model different disaster scenarios and likely device and network usage requirements to proactively plan for disaster situations (e.g., storms, tsunamis, bush fires, …).
  • Downtime minimisation: leverage predictive maintenance and inventory tracking to identify high-probability downtime situations and ensure that the right maintenance and replacement parts are available, as well as the right skilled service personnel.
  • Energy usage optimisation: optimise energy usage given current and historical device performance, historical and predicted energy costs, and device performance requirements.
  • Device performance effectiveness: monitor and optimise individual device performance/throughput based upon historical performance, given certain workloads and environmental conditions and coupled with a detailed profile of the performance behaviors of that device or node.
  • Network performance management: monitor and manage/fine-tune the performance of a network of devices given current load, required performance requirements (service level agreements), and forecasted performance requirements.
  • Capacity utilisation: reallocate device resources and jobs to optimise network and device performance given the history of device interactions and current and forecasted performance requirements.
  • Capacity planning: predictive and prescriptive analytics that model product and device usage and in real time, make ressource allocations and automate the provisioning of new capabilities (turning on and off capacity as dictated by the predictive models) in order to ensure the required capacity at the optimal price.
  • Demand forecasting: leverage device behavioral models, actual usage patterns and trends, and external factors (weather, traffic, events) to forecast longer-term network configurations and product and network build out.
  • Pricing optimisation: understand device usage patterns, coupled with demand forecasting, to optimise device and network pricing — lowering pricing when demand is low and increasing pricing when demand is higher; almost like surge pricing, but hopefully without the same customer satisfaction issues.

Thinxtra is bringing to Australia & New Zealand a full eco-system of IoT solutions & suppliers as well as leading IoT platforms to develop the right applications to enable the non-connected to connect, to increase productivity, accelerate decision making, improve quality of service or simply solve problems in an economic & connected manner.

Contact us to discuss your specific requirements.