4.5 out of 5 stars
(108 Ratings)
AlisQI is a modular, cloud-based Quality Management platform for process and batch manufacturers who want to reduce firefighting, improve predictability, and stay compliant by default.
Unlike traditional EQMS platforms that were built around documents and later adapted for analytics, AlisQI was designed from the start as a data-first system. Quality, lab, and production data are structured and connected in one operational backbone.
That foundation now enables practical AI capabilities inside daily workflows. Manufacturers can automatically extract data from diverse supplier COAs without predefined templates, generate structured digital forms from existing files or plain language, query their QMS conversationally, and detect recurring incident patterns across sites.
Core modules include Document Control, Training, Deviations, CAPA, Audits, Risk Management, Supplier Quality, SPC, and EHS, supported by targeted out-of-the-box Solvers that address specific operational problems.
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4.9 out of 5 stars
(4 Ratings)
Epicor Connected Process Control (CPC) is a flexible no-code/low-code MES and manufacturing operations platform that helps manufacturers improve quality, consistency, and visibility across production and assembly operations. Create digital work instructions with multimedia content, guide operators through critical tasks, connect equipment and devices across the shop floor, and capture production and quality data without extensive programming.
CPC provides product traceability with a complete historical record of each product's build and inspection history. Real-time insights into quality, performance, defects, rework, and production trends help teams identify issues faster, reduce waste, and support continuous improvement.
Manufacturers use CPC to standardize production and assembly processes, improve operator consistency, and support assembly verification, error proofing, quality inspections, and production monitoring across the shop floor.
Whether deployed in a single work cell, across multiple production lines, or enterprise-wide, CPC scales to support manufacturers of all sizes. Available on-premises or in the cloud, it adapts to your operational requirements and manufacturing processes.
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MPCPy
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.
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