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In the new age of constant change (climate change, coronavirus, political turmoil, among other events), your company needs new tools. Companies that produce physical goods are especially exposed to all the risks and require the tools that bring them actionable data, at the right time, to the right people. Are you able to run simulations on how different events will impact your business in real-time?
No matter if you are starting to build your digitization roadmap, or have invested millions of dollars and years building data lakes and applying available algorithms, Logyc's solutions can complement your efforts and accelerate impact on your operational impact and your ROI.
Enhance the product formulation experience with data and simulation capabilities like never before.
Discover the difference between what product should cost vs. how much it actually costs in terms of capital, time, and even CO2 emissions.
Run simulation on how external and internal events will affect your production. From raw materials extraction to manufacturing to last-mile delivery.
Improve your business operations by being able to exchange data with other departments, vendors, and partners in real-time.
Augment your existing data with the knowledge to create a continues learning loop; while bringing actionable insights to the right people at the right time.
Logyc's Just-In-Time Data Engine is designed to bring real-time operational intelligence and discover signals hidden in the noise of Big Data.
Operational data is personalized to individuals depending on how it affects them, eliminating the extra noise and improving individual's productivity and accuracy.
The data is either captured in real-time or continuously updated to provide the most accurate information at the moment it is required; while historical data created is used for pattern discovery and recommendations.
Consolidating all the data into data lakes is extremely time-consuming and expensive. Logyc consolidates data, as needed, based on the variables that affect the outcome while autonomously discovering gaps in knowledge — significantly reducing the configuration and algorithm training costs.
By eliminating noise, the data is consolidated in half the time. By combining our system with customers' existing user interfaces, user adoption is twice as fast. Reducing the learning cycle by enhancing missing data with the collective knowledge
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