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Product Information on Eyelit Technologies Advanced Planning and Scheduling
What is Eyelit Technologies Advanced Planning and Scheduling?
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Overall experience with Eyelit Technologies Advanced Planning and Scheduling
“Implementation and product is working great!”
About Company
Company Description
Eyelit Technologies is a provider of integrated software solutions that optimize factory and multi-factory productivity across industries such as semiconductor, automotive, medical device, electronics, and aerospace & defense. Its AI-powered suite of planning, scheduling, and execution solutions enables businesses to improve production processes, enhance asset utilization, and streamline scheduling. Eyelit Technologies empowers organizations to drive profitable growth, reduce costs, improve delivery performance, and gain greater visibility, improved decision support, and decision execution. Expanding on this foundation, Eyelit’s purpose-built, industry-specific solutions extend the capabilities of existing platforms to optimize decision-making across supply chain, planning, and execution. These tailored solutions improve outcomes by addressing critical elements such as orders, quality, assets, materials, labor, and suppliers.
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Eyelit Technologies Advanced Planning and Scheduling Reviews and Ratings
- Associate1B-10B USDManufacturingReview Source
Implementation and product is working great!
The implementation process and overall use of the product has been fantastic, however from the MLP side we have noticed room for improvement for the error handling on the planner. I understand that due to the way the algorithm works it is difficult to pinpoint and say exactly where the issue is, but something that could tell us what the Order is that is having issues with a job time window instead of the "Job ID" would provide a start as we can reference which constraints would be used by that order. Additionally on the ramping constraints we are unable to establish which constraint was causing the infeasible error directly and have to go through a long process of running the planner multiple times with different combinations of constraints until we are able to get it to run successfully and identify the constraint with the issue.
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