Docmosis is a self-hosted or SaaS template-based document generation solution. Integrate with custom-built software applications or popular third-party apps using the API.
Create templates using MS Word or LibreOffice. Add plain-text placeholders to control: the insertion of text/images/tables; conditionally add/remove any content; perform calculations; loop over repeating data; format data/numbers and much more.
Integrate with: Custom software built using Java, C#, Python, PHP, Ruby and more via a REST API; Low-code and no-code platforms like Appian, Bubble, Mendix, Outsystems; Third-party form builders or apps that can perform a webhook such as FormAssembly or Salesforce.
Used by customers in Finance, Health, Legal, Education, Government, HR, Insurance, Logistics, and Manufacturing to generate customized letters invoices, proposals, contracts, statements, reports and more.
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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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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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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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