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Manufacturing and Supply Chain

AI / ML Applications and Use Cases in 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 in the manufacturing sector, detailed below, all of which have been developed in close association with various clients, have relied heavily on using Advanced Anomaly detection, Gen AI and Transparent Machine learning capabilities of EazyML.


Production Line: 

RUL (Remaining Useful Life) prediction for industrial equipments: RUL employs sensor data, machine learning, and IoT technologies to estimate the remaining operational life of machinery and equipment.

Predictive Maintenance: Guided Machine Maintenance leverages IoT sensors, data analytics, and machine learning to predict when industrial machinery or equipment is likely to be serviced. By proactively identifying maintenance needs, organization can reduce downtime, lower maintenance costs, and improve overall production efficiency.

Anomaly Detection in the Production Line: Missed signals along the way, leading to major failure – can be avoided with staying on top of deviations from the norm – the anomalies – by proactively monitoring and detecting all the times. AI to identify and diagnose irregularities or faults in the production flow.

Product Quality Intelligence: AI for product quality intelligence to diagnose and recommend changes, based on the collection and analysis of multiple data points throughout the production process.


Supply Chain: 

Customer Churn Prediction and Demand Estimation: Identify when key customers have either slowed down in terms of orders or have completely stopped. The former behavior is known as “soft-churn” vs the latter is known as “hard-churn”. This is an ongoing pattern which is very difficult to track manually. This essentially is an automated churn-alert indicator system.

DIFOT Prediction: Delivery In Full On Time (DIFOT) prediction helps make necessary arrangements to make delivery or set customer delivery expectations accordingly. This helps in lesser customer complaints.

Capacity Planning: Planning for production capacity to meet the demand. The benefits of appying AI/ML to plan resources and schedule include consistent quality performance and produce on time delivery.

Dynamic Pricing: Price per unit increase/decrease is a factor for churing customers. ML Models can help resolve this by dynamic price creation for about to churn customers. Dynamic pricing hinges on seamless integration of AI algorithms that continuously decipher myriad real-time data points