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Problem Statement: Auto-organize a large, unorganized collection of documents by discovering hidden ‘themes’ present in each document using NLP and Deep Learning.
Client: Global leading FMCG player
Industry Segment: Manufacturing, Food Processing
Requirement: Conduct a thorough analysis of a specific manufacturing line, and draft a detailed solution architecture to Increase the product yield and improve the margins.
Outcome: Established deep understanding of complex interaction between various manufacturing processes. Defined an architecture for reducing waste by 5% leveraging existing sensors, additional sensors and closed loop real-time data-led controls.
Tech Stack Used: IoT sensors, Non-linear Multivariate Optimization, Dynamic Programming, Sensor Pattern Recognition, Time Series Forecasting, Statistical Inference.
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