At Enablon, innovation 4.0 is in our DNA. Recognized as one of the most progressive companies in our field, we push the boundaries of process and product development. To advance our work, the Enablon Innovation Labs team collaborates with internal and external stakeholders to develop ideas for innovative, game-changing technologies.
We encourage our customers to test our products and co-innovate with us. To ensure our innovations meet real needs, we engage customers early in the product development process. Our product managers elicit customer feedback via regular calls, site visits, workshops and user groups. In addition, we hold our annual SPF conference, where customers can take part in our Customer Innovation Challenge, a mini-hackathon partnered with the Enablon product team, to help prototype innovative ideas that solve big industry problems. We also welcome submissions from clients for new projects designed to solve specific problems. These measures ensure we incorporate the customer voice in all our product innovations.
Through Innovation Labs, we have created a partnership program to help us build proof-of-concept schemes and pilots for technological innovations. Specifically, we work with industry leading technology partners and suppliers, such as TapRoot, OSIsoft, and more. Working together, we bring our expertise to bear on key innovation focus areas, as defined below. Learn more about our technology partners here.
Across Enablon, we have access to many brilliant minds and bright ideas. To help drive innovation, we hold internal events & hackathons that bring people together to discuss our new product roadmap, sharing product news and information on a regular basis. The Enablon Spark Week, held every year, is a major internal event bringing technology and people in the same place for a full week dedicated to innovation with our Product and Engineering teams. It hosts a one-day hackathon to foster collaboration and innovative ideas on how to improve our customer-focused solutions.
We’ve been able to pull in different areas: inspections, environmental permitting, incident analysis. We’re seeing results of having all that data in a single solution – being able to do analysis and trending to identify correlations that we may not have previously realized. With Enablon, we’re capturing a wealth of data that we’re able to use to make better business decisions with.
Superimposing a computer-generated object onto a visual perspective. Use case: Displaying checklists and interactive guides for technicians performing inspections.
VR creates a full three-dimensional simulation of an environment with which workers can interact. Use case: creating industry-specific VR scenarios to help train workers without putting them in danger.
The interconnection of computing devices and sensors, enabling them to send and receive data. Use case: employing wearables and RTLS beacons to help companies determine if workers are wearing proper PPE gear in designated areas.
Remote-controlled pilotless aircraft or aerial vehicles equipped with cameras and sensors. Use case: capturing video footage to enable remote safety inspections, saving time and enhancing safety.
Training a computer to learn and understand the users’ intents to automate and/or assist the user with tasks at hand. Also, exploiting machine learning and statistical methods to surface insights from data.
Technology used by workers, embedded in or worn outside a PPE; outfitted with biometrics and other sensors to monitor movements and help predict and prevent injuries.
Real-time location detection of an object or person. Use case: actively monitoring safety issues and alerting employees accordingly; determining if employees are in risk areas, such as hazardous interactions or choke points.
With geofencing, workers are notified whether a specific type of personal protective equipment (PPE) is required in an electronically defined area, or that they are entering a restricted hazardous area.
Training a computer to understand the context and extract information from image and videos feeds. Use case: detecting workers not wearing mandatory PPEs from surveillance cameras and the automated generation of at-risk observations.
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