AI driven Snotra
Manufacturing Execution System

What can you takeaway as a user using AI driven Snotra MES?

MES Modeling (site, area ,operator, product, equipment, data, process planning)

Flexible site modeling, equipment modeling, product modeling, process modeling, data modeling support for manufacturing managment supports product procss planning and customised process flow control rules for each customer.
 

Work order and progress management, BOM, material, lot, scribe, carrier, batch operation

It is possible to control the production of various material (product, durable, consumable) units, so it supports actual production volume receipt and payment and quality barges based on interrelationships.

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WIP (Work In Process) Tracking and Equipment, Recipe Operation.

It is possible to track movement from process input to sites, areas, and equipments. Basically, tracking of unit process units is supported by various operations (start/issue, trackin/out/ rework/hold/store/ship, etc. 50 functions). Especially, unattended automation is possible through operation RPA for collaboration between equipmments and related systems.

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Data Collection & Manufacturing Intelligence for AI

It is possible to collect trace data of less than 0.1 seconds per lot or scribe in units of equipment, process modules, and recipes, which enables process engineering. In Industry 4.0, even high-precision data or non-parametric data collection of equipment sensors is essential, but the legacy Industry 3.0 CIM purpose M#S cannot process it. Snotra MES with artificial intelligence capabilities enables Industry 4.0 implementation by processing even these data frames.