Maintenance of the production line is timely and costly. Knowing when to maintain an asset for peak performance is critical.

Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle.In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.

Plausible Analytics, a privacy-friendly alternative to Google Analytics, faced a significant challenge as it scaled its services. Since its launch in April 2019, the platform had grown to service over 5000 paying subscribers, tracking 28,000 different websites and more than 1 billion page views per month. However, the original architecture using Postgres to store analytics data was unable to handle the platform’s future growth. The loading speed of their dashboards was slow, taking up to 5 seconds, which was not conducive to a good user experience. The team realized that to continue their growth trajectory and maintain customer satisfaction, they needed a more efficient solution.

GEM Analytics specializes in helping corporations optimize operations efficiently. They provide a full stack business optimization platform to visualize insight from Big Data.GEM Analytics performs activity-based research on technology adoption, business optimization and solution ROI as well as work with solution providers, systems integrators, consulting firms and agencies to deliver thought leadership, market forensics, research and analytics to fulfill client potential.

Noodle.ai offers pioneering business solutions in Enterprise Artificial Intelligence, a unique collaboration among business executives, process experts, and Artificial Intelligence technologies (e.g., Machine Learning, predictive data analytics, data science). The company creates and implements these solutions to solve complex business challenges and drive dramatic improvements in customer, product, and enterprise operations.

Fluence Analytics is a manufacturer of industrial and laboratory systems that produce continuous Data Streams. These measurements, combined with powerful, proprietary analytical tools, enable realtime optimization of process control and faster R&D for polymer and biopharmaceutical manufacturers.

Immersive analytics builds upon the fields of data visualisation, visual analytics, Virtual Reality, computer graphics, and Human-Computer Interaction. Its goal is to remove barriers between people, their data, and the tools they use for analysis by presenting relevant data as needed in real time. Immersive analytics aims to support data understanding and decision making, both by people working individually and collaboratively. While this may be achieved through the use of immersive virtual environment technologies, multisensory presentation, data physicalisation, natural interfaces, or responsive analytics, the field of immersive analytics is not tied to the use of specific techniques.

Predictive quality analytics uses statistical algorithms and Machine Learnings to anticipate quality and safety risks before they occur, offering the opportunity to take timely and targeted countermeasures. As a first step, all available external and internal data sources are prioritized, consolidated, and correlated. Comprehensive data analyses are then performed and predictive models are developed in an iterative process, making use of a variety of evaluation techniques. Depending on the parameters which flow into the model, it is possible with these methods to forecast not only defects that appear shortly in the future but also ones which could lead to a warranty claim in the long term. The models are thus capable of integrating data sources, making efficient Data Mining possible and leading to user-friendly evaluations which in turn can be converted into easily readable reports for the end-user or managers at the reporting level. Wisely used, predictive quality analytics can lead to significant savings in warranty costs, improve customer satisfaction, and reduce scrap rates.