Business executives are responsible for the bottom line. Investing in innovation, such as Big Data, Data Science, and the Industrial IoT, is often motivated by fear of being left behind the industry. However, there is also a need to show return on the investment. Mitek Analytics helps with delivering effective operational applications of the IIoT analytics that leverage the investments into the IIoT data platform and Data Science capability to provide value added results.

Problems Addressed

We offer Analytics Engines that are in the center of value-added Industrial IoT analytics applications. Our solutions have been proven to improve the efficiency of the IIoT assets and processes in several industries. Our Analytics Engines ingest the data, automatically process it, and output actionable results in concise form. The engine can be demonstrated as stand-alone software in a proof-of-concept project. It can be then integrated with existing data management platforms, data science knowledge, and BI visualizations. Our Analytics Engines can be customized to best suite customer needs. With heavy-lifting data-driven modeling done by Predictive Analytics, our Prescriptive Analytics can be customized for a variety of business cases including but not limited to

  • Condition Based Maintenance (CBM) to reduce service cost
  • Asset health monitoring to reduce downtime and improve customer loyalty
  • Optimization of energy and fuel efficiency by identifying and eliminating bad actors
  • Reliability analytics, identifying supplier or operational issues
  • Analysis and optimization of sustainment and the logistics chain
  • Forecasting and predictive trending for a variety of applications
  • Risk estimation and management for a variety of applications
  • Stochastic optimization of electricity trading
  • Tracking product usage, e.g., aircraft structure usage analytics

Examples

In one example is our Analytics Engine was deployed for processing Industrial IoT data collected by airlines. Flight data are collected for a hundred of channels every second of every flight for each aircraft. Analyzing cross-fleet variation and trends for fuel performance of jet engines fleet allows identifying bad actors sooner. Targeted maintenance actions then lead to 2% fuel cost savings for the airlines. For the industries that are currently less tightly managed, the savings in similar scenario might be much bigger.

Another example is the Analytics Engine for cost-efficient demand-supply balancing in the power grid. Building data-driven joint stochastic forecasting models for power demand and spot electricity prices allows optimizing day-ahead electricity procurement to achieve savings of over 2% of the revenue.

Deployment Process

Mitek follows best practices deployment processes to deliver analytics engines that reliably and automatically perform in mission-critical applications. Our open architecture analytics can fully integrate with the in-house Data Science knowledge and operational engineering knowledge of your assets. Using Mitek’s software enables customer oversight of asset performance and health independently of OEMs. Our software can integrate into most existing business processes with little to no process redesign.

The typical Return on Investment (ROI) period when deploying Mitek’s software is 6 months or less. Furthermore, there are no licensing fees for the proprietary analytics software until the business case and ROI are established. Mitek tries to make sure that the upside is very real and the risk is minimal.