A leading global maker of medical devices had an ERP in place but did not have a formal plant planning and scheduling system to optimize detailed production workorders across a key plant. The planners relied on spreadsheets in a disjointed way to try to plan through and across plants. Without an intelligent planning and scheduling …
A leading global maker of medical devices had an ERP in place but did not have a formal plant planning and scheduling system to optimize detailed production workorders across a key plant. The planners relied on spreadsheets in a disjointed way to try to plan through and across plants. Without an intelligent planning and scheduling …
Agent EyeQ detects, diagnoses, predicts, and acts, powering autonomous and semi-autonomous manufacturing operations Holmdel, NJ — August 10, 2026 —Eyelit Technologies (Eyelit), a leader in optimized planning, scheduling, and execution systems for manufacturers, today announces Agent EyeQ, debuting in its 10.1 release. Agent EyeQ exposes more than 1,700 executable operations as tools across Eyelit’s manufacturing operations management (MOM) portfolio, spanning SIOP, APS, MES, QMS, SPC, Predictive Analytics …
Ask any production planner how often their schedule survives a full week untouched, and you’ll get a knowing laugh. In discrete and process manufacturing, replanning isn’t the exception. It’s the rhythm of the job. The question worth asking isn’t how do we stop replanning. It’s why does it keep happening, and is our planning stack actually built to handle it? Where APS Fits, and Why It’s Not ERP …
One of the most common pitfalls in manufacturing AI adoption is treating AI as a single, uniform technology. Organizations invest in a platform or a model, apply it broadly, and then are surprised when results are inconsistent, strong in some areas, underwhelming in others. The underlying reason is usually a mismatch between the type of …
Key advancements include Agentic AI, prescriptive inference, transparent forecasting, and end-user configurability Holmdel, NJ — July 2026 — Eyelit Technologies (Eyelit), a leader in optimized planning, scheduling, and execution systems for manufacturers, today announced the release of Eyelit Version 10.1 which highlights Agentic AI, Demand Planning and advancements across the fully integrated manufacturing operations management (MOM) applications suite. Built for discrete manufacturers …
Version 10.1 is the most significant release in Eyelit’s history bringing agentic AI, a reimagined demand planning platform, deeper execution intelligence, and hundreds of enhancements across MES, APS, and supply chain planning. Here is what’s new. Demand Planning Version 10.1 is a ground-up rearchitecting of demand planning for the AI era. The capabilities below represent …
In manufacturing, execution rarely aligns perfectly with the original plan. A machine goes into an unplanned maintenance state. A supplier delivers late. A quality hold pulls a batch off the line. An urgent customer order changes the demand picture mid-shift. Within hours of a production day starting, the schedule that looked optimal at the beginning …
There is a growing assumption in software that AI-assisted development is rapidly closing the gap between general-purpose tools and specialized applications. If a capable model can generate code quickly and iterate on it in real time, the argument goes, purpose-built software becomes less necessary. Domain expertise gets absorbed into the model, and the barriers to …
As manufacturers evaluate AI strategies, one of the most important architectural decisions is where AI inference should run and what type of model is best suited for shop floor operations. For many complex manufacturing environments, Small Language Models (SLMs) deployed at the edge offer meaningful advantages over large language models running in the cloud. Understanding those advantages …
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