
AnalyticsCreator helps Microsoft data teams turn governed design into deployable data solutions without introducing a proprietary runtime layer.
Teams use AnalyticsCreator to define warehouse structures, transformation logic, historisation rules, relationships and dependencies in a central model. From that model, the application can generate native implementation assets for technologies such as SQL Server, SSIS, Azure Data Factory, Microsoft Fabric and Power BI.
The approach is designed for organisations that want to standardise how data warehouses and data products are engineered while keeping full control of the resulting code and project artefacts. Generated outputs can be integrated into existing Git, Azure DevOps and CI/CD workflows for versioning, review and controlled deployment across environments.
AnalyticsCreator supports dimensional, 3NF and hybrid modelling as well as common engineering patterns including delta loading, Slowly Changing Dimensions, snapshots and historisation. Documentation, lineage and dependency information are maintained alongside the project design, making it easier to assess the impact of proposed changes and keep implementation aligned with the underlying model.
The AnalyticsCreator Governed Control Model provides the foundation for this process by keeping business meaning, technical structures and implementation logic connected. Design Intelligence builds on that context by making governed project metadata, lineage, dependencies and design rules available to authorised AI tools and agents.
Typical use cases include modernising SQL Server and SSIS estates, building Microsoft Fabric solutions, standardising Power BI delivery and creating repeatable data warehouse and data product engineering processes.
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Epicor Connected Process Control offers an intuitive software solution designed to create and manage digital work instructions while maintaining strict process control, effectively minimizing the chances of errors in operations. By integrating IoT devices, it captures comprehensive time studies and detailed process data, including images, at the task level, providing unprecedented real-time visibility and quality oversight. The eFlex system is versatile enough to accommodate countless product variations and thousands of components, catering to both component-based and model-based manufacturers alike. Furthermore, work instructions seamlessly connect to the Bill of Materials, guaranteeing that products are assembled correctly every time, even when modifications occur during production. This advanced system intelligently adapts to variations in models and components, ensuring that only the relevant work instructions for the current build at the station are presented, enhancing efficiency and accuracy throughout the manufacturing process. In this way, Epicor empowers manufacturers to maintain high standards of quality control while adapting to the dynamic nature of production demands.
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Cybernetica CENIT
Cybernetica is dedicated to delivering Nonlinear Model Predictive Control (NMPC) by leveraging mechanistic models. Our cutting-edge software, Cybernetica CENIT, boasts a flexible architecture designed to tackle a wide array of industrial challenges while producing optimal control strategies. This encompasses sophisticated multivariable optimal control, predictive control techniques, and smart feed-forward methods, all while adeptly managing various constraints. Additionally, our adaptive control features utilize state and parameter estimation, allowing for the integration of feedback derived from indirect measurements through the process model. Employing nonlinear models facilitates effective performance across broad operational ranges, significantly improving the management of complex nonlinear processes. Consequently, this approach reduces the dependence on step-response experiments and enhances the precision of state and parameter estimations. Moreover, we provide tailored control solutions for both batch and semi-batch operations, efficiently overseeing nonlinear processes that endure varying conditions. Our technology also guarantees optimal transitions in product grades during continuous operations, ensures the safe management of exothermic reactions, and controls unmeasured variables such as conversion rates and product quality. Ultimately, these advancements lead to decreased energy consumption and a minimized carbon footprint, while simultaneously boosting overall process efficiency. In conclusion, Cybernetica is fully committed to pioneering industrial control solutions that not only enhance performance but also promote sustainability in various sectors. Our relentless pursuit of innovation positions us as leaders in the field, enabling us to adapt to the evolving needs of our clients.
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COLUMBO
A universal multivariable optimizer, designed for closed-loop systems, aims to improve the performance and quality of Model Predictive Control (MPC) systems. This optimizer harnesses data from Excel files derived from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson, facilitating the development and fine-tuning of precise models for various multivariable-controller variable (MV-CV) pairs. This cutting-edge optimization solution does away with the need for step tests that are usually required by Aspen Tech and Honeywell, functioning entirely in the time domain to maintain user-friendliness, compactness, and efficiency. As Model Predictive Controls (MPC) often involve numerous dynamic models—sometimes tens or even hundreds—there is a significant risk of utilizing incorrect models. Inaccurate dynamic models in MPCs can introduce bias, which appears as model prediction errors, leading to inconsistencies between expected signals and actual sensor measurements. COLUMBO emerges as a robust tool to bolster the precision of Model Predictive Control (MPC) models, effectively leveraging either open-loop or fully closed-loop data to guarantee peak performance. By tackling the risks associated with errors in dynamic models, COLUMBO not only enhances the reliability of the control system but also contributes to a more efficient operational framework. Ultimately, its implementation is expected to yield substantial advancements in control system effectiveness across various applications.
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