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Manufacturing

Deliver Agile and Flexible Manufacturing with Intelligent Machine Learning and Automation.

The combination of AI and sensors in sensor technology can further optimize manufacturing processes and develop individual production strategies. Applications of emerging tech clearly give manufacturers the critical ability to make data-driven decisions and optimize their operations in real-time.

AI and Manufacturing

Process Optimization

  • Discover production line discrepancies
  • Resource allocation optimization
  • Optimal decisions on manufacturing like optimal equipment use
  • Refine and execute plans based on real-time data

Performance Improvement

  • Health status assessment
  • Predict and avert breakdowns
  • Target appropriate maintenance activity
  • Improve product quality by giving input to operators and managers

AI in Supply Chain Management

  • Balance the inventories
  • Predict customer demand and supply variability
  • Predict the potential supply chain disruption and find the reason for such issues
  • Predict the production requirements and plan accordingly

Fact: A study shows 67% of manufacturing executives practice predictive analytics in their companies

Use cases

Predictive Maintenance

Manufacturers are able to assess the condition of machinery in real-time through AI. They draw on this information to develop maintenance plans that maximize equipment uptime and lifespan. Moreover, feedback concerning machine performance is given by organizations based on insights gained from AI. With such knowledge, operators will know how they can be more efficient while cutting down on downtime.

Supply Chain Optimization

Review of historical data; including supplier performance, transportation time, inventory levels etc., manufacturers can identify high-risk areas within their artificial supply chains. This allows them to use applications based on AI for forecasting possible disruptions so that they may switch suppliers if necessary, re-balance inventory positions or even optimize transport routes.

Quality Control

Integration of AI and Machine Learning into manufacturing processes can help catch defects early. This improves product quality and reduces waste as compared to human inspectors who may not notice any difference when analyzing sensor data with respect to cameras in which an artificial intelligence system could spot differences.

Case Study

Midalloy, a specialty welding consumables manufacturer and distributor of complex high-integrity components had product development challenges due to it's reliance on their only Senior Technical Expert. This created delays in response time and loss in productivity. When the expert was set to retire, the company needed a way to optimize the operation efficiently. Midalloy leveraged the skills of Techjays along with using an AI based utility model to remove / reduce the dependency on experts for queries. This helped Midalloy achieve a hassle free transition while maintaining services at high efficiency levels.

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