AI / ML Applications and Use Cases in Manufacturing and Supply Chain
Manufacturing and Supply Chain
Gestalt brings pre-built AI analytics enabled solutions and has invested (& perfected) building a methodology which helps our clients institutionalize AI/ML solutions within their organization. We bring to every project well defined frameworks, templates and best practices which accelerate the delivery and adoption of AI enabled solutions.
Key solutions detailed below, which have been developed in close association with various clients, have relied heavily on using advanced Anomaly detection and Transparent Machine learning capabilities of EazyML.
The traditional demand forecasting models – ARIMA and Prophet – don’t consider the input parameters, rather just trend the forecast. EazyML’s LSTM factors in various parameters that influence the sales, couple it with its proprietary lag determination, to forecast demand accurately. Best of all, EazyML’s Transparency – Explainable AI, in particular – makes the model transparent, vitally important for trusted collaboration between the human expert and the intelligent machine.
Predict Shipment Delivery:
Based on EazyML insight from both internal and external data, derive a set of algorithms to predict the shipment delays in real time. Insights enable you to take remedial action about improving the on time delivery or their prediction for the delivery date, for its customers in multiple geographies. Traditional analytics can only provide insights along formulated rules & that have proved woefully inadequate & often late, after the fact. Not to mention, these have often got plagued with analyst’s bias.
Quality Assurance of the Product (lets work with images)
Derive insight from a large portfolio of imaging data and quality ratings and thus assist with Quality Assurance of the end product. EazyML’s patent pending confidence score can enable Quality Assurance teams to determine which images of product should be used for assessing product quality and which should not, so as to not be misled.