Analyzing Big Data for Continuous Improvement

Digital technologies are changing the way companies operate and innovate. Now, with advances in artificial intelligence (AI) and machine learning, Internet of things is moving to a new level of possibilities. In the past, predicting what will happen and prescribing what actions to take usually required human interventions. But with AI and machine learning, systems can learn from data with no human interaction. That means the big data you collect from your operations and business systems can be analyzed in real time to make complex, evidence-based decisions. As the amount of your data grows, the “smarter” the analysis and the related machine decisions become.

Manufacturer can reap tremendous advantages when they use AI to predict maintenance and quality issues. For example, data from an asset’s sensors can be collected and compared using analytics and algorithms to predict when a part will fail or how much time is left in an asset’s useful life. Armed with these insights, the system uses AI to recommend repair or replacement before there is a breakdown.

Some of the areas where AI can help improve industrial operations are:

Monitoring

Sensors and applications on individual components generate huge amounts of data about a production asset’s basic health, utilization, availability and key performance indicators (KPIs). With AI and machine learning, this data helps you gain a deeper understanding of your production environments, identify issues and plan for improvements.

Maintenance

The right maintenance at the right time reduces unplanned downtime and extends an asset’s life. Using AI, machine learning, real-time data and predictive algorithms, production anomalies can be automatically identified along with maintenance recommendations.

Quality

Product defects can be costly, especially if they result in recalls. Analytics and AI enable the rapid identification of deviations by comparing live data to standard deviation models, aggregating data for analysis and modeling in a single data lake, and pinpointing the root cause of production problems.

Productivity

AI can be used to identify the root cause of bottlenecks in production processes, predict their direct impacts, help maximize workforce productivity and increase throughput yield of the products being produced.

Planning and Scheduling

Mitigating supply chain risk—including planning for, ordering, and managing raw materials and parts—is essential to maintain high productivity. With AI, you can use dynamic scheduling to proactively optimize production schedules and promptly implement countermeasures when process delays occur.

Energy and Consumables

Renewable energy growth is causing grid volatility and financial challenges. AI can help maximize profits and boost efficiencies from energy generation and trading to distribution and consumption.

Safety and  Compliance

By collecting data from machine sensors and videos, and applying analytics and AI, organizations gain insights that increase the safety and comfort of operators, and ensure environmental and regulatory compliance.

Engineering and Design

AI and machine learning deliver insights relating to maintenance, quality, productivity, energy and safety. These insights can help manufacturers design newer versions of assets with newer sensors that deliver the intelligence required for continuous improvement.

While Internet of things technologies connect everything and create huge pools of data around assets, AI and machine learning transform the data into insights that enable efficient automation of production processes. Please comment/share your thoughts on how you are applying AI in your context!

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