Epicor Connected Process Control
Epicor Connected Process Control provides a simple-to-use software solution that allows you to configure digital work instructions and enforce process control. It also ensures that operations are error-proof. Connect IoT devices to collect 100% time studies and process data, images and images at the task level. Real-time visibility and quality control on a new level! eFlex can handle any number of product variations or thousands of parts, whether you are a component-based or model-based manufacturer. Work instructions can be linked to Bill of Materials, ensuring that products are built correctly every time, even if changes are made during the process. Work instructions that are part a system that is advanced will automatically react to model and component variations and only display the right work instructions for what's currently being built at station.
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Predict360
Predict360, by 360factors, is a risk and compliance management and intelligence platform that automates workflows and enhances reporting for banks, credit unions, financial services organizations, and insurance companies.
The SaaS platform integrates regulations and obligations, compliance management, risks, controls, KRIs, audits and assessments, policies and procedures, and training in a single cloud-based SaaS platform and delivers robust analytics and insights that empower customers to predict risks and streamline compliance.
Happy with your current GRC but lacking a true analytics and BI tool for intuitive executive and Board reports? Ask about Lumify360 from 360factors - a predictive analytics platform that can work alongside any GRC. Keep your process management workflows intact while providing stakeholders with the timely reports and dashboards they need.
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INCA MPC
Advanced Process Control (APC) provides a highly efficient solution for enhancing your plant's performance without requiring any hardware modifications. By implementing an APC application, you can stabilize operations while simultaneously optimizing production or energy usage, leading to a deeper insight into your production processes. This term encompasses a wide array of methods and technologies that complement fundamental process control systems, which are primarily constructed using PID controllers. Some examples of APC technologies include LQR, LQC, H_infinity, neural networks, fuzzy logic, and Model-Based Predictive Control (MPC). An APC application continually optimizes plant operations every minute, round-the-clock, seven days a week, ensuring consistent efficiency. Among these technologies, MPC stands out as the most widely adopted within the industry, as it utilizes a process model to forecast the plant's behavior for the near future, typically ranging from a few minutes to several hours ahead, thus providing a strategic advantage in operational planning. Through the continual refinement of processes, APC not only improves efficiency but also contributes to long-term sustainability goals.
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Model Predictive Control Toolbox
The Model Predictive Control Toolbox™ offers a comprehensive suite of functions, an intuitive app, Simulink® blocks, and practical reference examples to facilitate the development of model predictive control (MPC) systems. It caters to linear challenges by enabling the creation of implicit, explicit, adaptive, and gain-scheduled MPC strategies. For more complex nonlinear scenarios, users can execute both single-stage and multi-stage nonlinear MPC. Additionally, this toolbox includes deployable optimization solvers and permits the integration of custom solvers. Users can assess the effectiveness of their controllers through closed-loop simulations in MATLAB® and Simulink environments. For applications in automated driving, the toolbox also features MISRA C®- and ISO 26262-compliant blocks and examples, allowing for a swift initiation of projects related to lane keep assist, path planning, path following, and adaptive cruise control. You have the capability to design implicit, gain-scheduled, and adaptive MPC controllers that tackle quadratic programming (QP) problems, and you can generate an explicit MPC controller derived from an implicit design. Furthermore, the toolbox supports discrete control set MPC for handling mixed-integer QP challenges, thus broadening its applicability in diverse control systems. With these extensive features, the toolbox ensures that both novice and experienced users can effectively implement advanced control strategies.
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