Manufacturers face challenges such as defective products, on-time delivery slippage, excessive scrap and rework, unacceptable equipment downtime, and supply chain disruption. As the number of assets and production facilities increase, it becomes more difficult to diagnose and react to these challenges in a timely and scalable manner. Such difficulties can have cascading effects on production schedules and translate to higher production costs, lost profits, missed order fulfilment and reputational damage. Information silos and manual data collection add complexity to making nearreal-time operational decisions.

 

Consider a manufacturing enterprise that needs to operate at maximum production capacity to meet aggressive quarterly targets. It is imperative that its machines operate reliably and that parts and materials are available at every production step, to ensure that products being made meet quality standards. Lumada Manufacturing Insights from Hitachi leverages AI and machine learning (ML) techniques to provide answers to these challenges, by connecting data from man, machines, method and materials for situational awareness.

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Machine Productivity Optimization

Granular visualization of overall equipment effectiveness (OEE) in near real time allows production managers to quickly identify machinerelated issues and take corrective action. Track asset availability, performance and quality for various pieces of equipment, regardless of type, vendor, model or year.

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Maximize First-Pass Yield

Lumada Manufacturing Insights integrates with existing quality systems to track and alert on degrading quality trends, identify dominant quality issues via pareto analysis, identify root causes based on operational parameters or raw material batches, and forecast impending quality issues.

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• Enable real-time situational awareness of shop-floor operations and equipment.

• Maximize revenue by enhanced line productivity and throughput, as well as reduced cycle-time bottlenecks.

• Optimize quality and yield via historical trending, pareto analysis and forecasting.

• Proactively react to changing conditions on the shop floor, before issues occur or spread

• Gain visibility on equipment effectiveness. Draw insights from predictive and prescriptive analytics

• Significant reduction in operating costs

• AI enabled advance analytics

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