When a manufacturing plant comes to a halt due to an unplanned maintenance issue, the cost of the downtime can quickly grow into the millions. While many tools attempt to predict the next maintenance issue using historical data, they often don’t provide insight on how to then handle it.
DecisionIQ and its Genesis IoT platform goes one step further.
“We improve plant availability and enhance equipment reliability by optimizing maintenance operations and logistics,” says CTO Nagi Gebraeel. “There’s a huge cost component associated with unplanned downtime since it impacts production. In the case of an energy plant, it will impact power generation.”
In 2014, Gebraeel and Andrew Lewis set out to commercialize Gebraeel’s IoT-focused industrial engineering research at Georgia Tech. The team joined the Flashpoint@GT startup program in 2015 to identify their market and true customer, and found an opportunity in the energy and manufacturing market.
“We’re able to combine advanced analytics with industrial engineering to solve our key value proposition,” CEO Lewis tells Hypepotamus. “What we learned is that predicting a failure is not a solution. It’s telling the operator what to do about the failure.”
The cloud platform monitors the manufacturing plant data in real-time, with a continuous improvement cycle and AI-based decision guidance. DecisionIQ tells the plant owner how to fix the problem, the best timing for the maintenance, and the best way to do the repair.
Once the Genesis platform identifies a potential maintenance problem, it narrows down the possible issues to the top two for the operator to get started on repairs.
To find the best time to replace parts, Genesis predicts the lifetime of all machinery components. This way, operators can proactively replace parts that may be partially degraded and could cause downtime at a later date.
“We not only integrate the sensor data, but also the reliability information and the economics that are driving the whole operation. For example, how much will it cost if I continue operating and deliver the orders by this time, versus the cost of risking failure?” says Gebraeel.
“You’re basically having a cost trade-off between the cost of unexpected failure and the cost of premature replacement.”
The SaaS startup has run successful pilots with Coca-Cola and Southern Company and currently has a multi-year deployment with a Fortune 100 company. They service the food, oil/gas, chemical, and energy industries.
DecisionIQ has thus far grown organically while remaining bootstrapped.
“Using the resources within Georgia Tech like ATDC and Flashpoint have given us a clear path to commercialization. We keep tight reins on our operation and have been able to self-fund to this point,” says Lewis.