Clockwork has deep roots in data science. Thirty years ago, Clockwork Solutions was formed by a group of leading scientists, at Los Alamos National Lab, using simulation in nuclear physics applications. They pioneered ideas to increase production, lower costs and reduce the risk of failure of capital intensive, infrastructure related assets. Their targets were machines with significant on-going costs whose failure would have a catastrophic impact on customer service and profits. These early data scientists identified how the simplicity and accuracy of the modeling and simulation methods created during the Manhattan Project could also deliver powerful insight to improving asset performance and reducing costs.
From their initial work, Clockwork’s founders developed powerful predictive modeling and simulation technology that dramatically increased availability while significantly reducing costs. This technology continues to evolve to produce accurate predictive modeling of capital-intensive assets–aircraft and vehicle fleets, drill ships, power plants, generators, and other high-value equipment–across their life cycles. Clockwork’s predictive simulation platform has evolved over decades. Recently Clockwork launched Studio—an extensible platform that encapsulates analytic algorithms, data management processes, analysis, and visualization.
These innovative technology platforms coupled with our thirty years of predictive modeling expertise are the foundation of Clockwork’s leadership in applying data science to predictive analysis. Our deep domain expertise in customer industries is exercised together with our refined practice of simulation modeling, applied mathematics, statistics, data analysis, ML, and computer science.
Rapidly expanding quantities of available enterprise asset data feeds our predictive simulation models. Using predictive simulation platforms, Clockwork generates unique volumes of output from simulated future operations to derive predictive and prescriptive results and improve asset lifecycle management. We uncover unforeseen business challenges and identify root causes. The result is optimized performance with controlled cost and risk.
Clockwork applies the latest, evolving advances in Design of Experiments (DOE) to map the many possible future outcomes, across various response metrics, driven by input factors with inherent uncertainty such as component reliability, maintenance task times, operational tempo, and supply chain performance. From volumes of high-resolution simulation output, quantitative relationships between input factors and resulting outcomes are developed to identify the best choices for the long-term management and sustainment of capital-intensive assets.
Clockwork’s predictive models include multi-indentured representations of each serialized platform with serialized components and part-tracking along with a holistic representation of the global supply chain, multi-echelon maintenance, complex operations to capture the effects and relationships between multiple simultaneous physical phenomena.
Clockwork transforms evolutionary IIoT trends into clear views of the future for customers. These unique insights translate to vastly reduced costs, and improved performance across enterprise assets. Clockwork helps customers harness the power of the unprecedented, thriving advancements in APM—we are doing this today. We enable our customers to leverage our next-generation predictive analysis through services and software that realize unparalleled return on investment.
Clockwork has also stepped up to take on the most challenging data sets—those with large gaps and many errors. Over thirty years, we have developed conditioning techniques to overcome these data limitations. Clockwork applies the most advanced simulation experiment design methods to overcome lack of data and poor data quality. This data management expertise is captured using our Studio platform.
Clockwork operates over the full spectrum of data quality. We overcome data gaps in legacy and new asset fleets, and we also manage established, high-velocity, high variability, and high-volume data to deliver a holistic view of future fleet management that traditional predictive analytics overlook.