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A recommendation engine for manufacturing operations leverages advanced data analytics, machine learning, and artificial intelligence to provide actionable insights and optimize various aspects of production.


The Recommendation Engine 

think.360 has the below functionalities built in to provide quick and relevant recommendations

Dynamic Production Scheduling

 is a method to continuously adjust and optimize production in response to real-time data, changes in demand, availability of resources, and unforeseen disruptions.

Zero Shot Demand Forecasting

is a machine learning scenario where an AI model predicts and categorizes demand for products or services without having seen any examples of those specific demand patterns beforehand.

Predictive Maintenance

Uses historical and real-time data to predict when equipment is likely to fail, allowing for proactive maintenance.

Maintenance Diagnosis using LLM

involves leveraging advanced language models to assist in diagnosing issues related to equipment, systems, or processes.

Ability to learn from operator feedback
Learns once and never forgets
Becomes more accurate over time
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