Paccurate
Paccurate is the Packing Control System (PCS) for high-volume shippers. Powered by cost-aware cartonization, Paccurate helps brands, 3PLs, and distributors improve volume utilization, reduce shipping costs, and cut material waste. Our platform evaluates carrier rates, packaging constraints, and operational rules to determine the most cost-effective way to pack every order.
Shippers rely on Paccurate to run more efficient and consistent fulfillment operations, whether packing manually or through automated systems. Our goal is to ensure every order is perfectly packed.
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Epicor Connected Process Control
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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Model Predictive Control Toolbox
The Model Predictive Control Toolbox™ provides an extensive array of functions, an easy-to-use app, Simulink® blocks, and useful reference examples to streamline the development of model predictive control (MPC) systems. It effectively addresses linear problems by allowing the development of implicit, explicit, adaptive, and gain-scheduled MPC approaches. For more intricate nonlinear situations, users can implement both single-stage and multi-stage nonlinear MPC. Moreover, this toolbox comes equipped with deployable optimization solvers and allows for the incorporation of custom solvers as needed. Users can evaluate the performance of their controllers through closed-loop simulations within MATLAB® and Simulink environments. In the context of automated driving, the toolbox offers blocks and examples that comply with MISRA C® and ISO 26262 standards, which facilitates the rapid start of projects related to lane keeping assistance, path planning, path following, and adaptive cruise control. It enables the design of implicit, gain-scheduled, and adaptive MPC controllers that can solve quadratic programming (QP) problems while also facilitating the generation of explicit MPC controllers based on implicit designs. Furthermore, the toolbox accommodates discrete control set MPC for addressing mixed-integer QP challenges, thus expanding its versatility for various control systems. With its rich set of features, the toolbox guarantees that both beginners and seasoned professionals can successfully apply advanced control strategies in their projects. This versatility ensures that users across multiple domains can find relevant applications for their specific needs.
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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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