
Epicor Connected Process Control (CPC) is a flexible no-code/low-code MES and manufacturing operations platform that helps manufacturers standardize production and assembly processes, improve quality, and increase visibility across the shop floor.
Using digital work instructions with multimedia content, manufacturers can guide operators through critical tasks, enforce process control, and improve consistency across products, workstations, and production lines.
CPC connects equipment and devices to collect production and quality data in real time, helping manufacturers identify issues faster, reduce waste, and support continuous improvement.
CPC provides product traceability with a complete historical record of each product's build and inspection history. Manufacturers use CPC to support assembly verification, error proofing, quality inspections, operator guidance, and other connected manufacturing initiatives.
For manufacturers with complex product variations, CPC can dynamically present the correct work instructions based on the product or configuration being built, helping ensure products are assembled accurately and consistently.
Available on-premises or in the cloud, CPC scales from a single work cell to enterprise-wide deployments.
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The automatic process initiates as soon as you either transfer or incorporate a domain into your account. You have access to well-established and reliable libraries that facilitate your work. This setup minimizes the chances of your application experiencing downtime due to DDoS assaults. You can enhance the redundancy of your zones by allowing them to be mirrored to alternative DNS providers. Furthermore, you can redirect any emails from your domain directly to your current inbox. There are no restrictions on the number of records you can maintain within your zones. Each domain transfer comes with an additional one-year extension to your registration. To register, transfer, or renew domain names, a DNSimple subscription is necessary. Please note that the fees associated with domain registration, transfer, and renewal are separate from your subscription costs. This comprehensive approach ensures that your domain management is both efficient and effective.
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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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COLUMBO
A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.
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