The Revolutionary Impact of AI in Manufacturing Today

#DrivingExpertLedTransformation

According to Marketsandmarkets.com, the artificial intelligence market is expected to read USD 16.7 billion by 2026. AI in manufacturing is rapidly advancing, mirroring the widespread adoption of this technology in industries like automotive and aerospace worldwide. KPMG survey on artificial intelligence adoption shows that industries including tech, financial service, retail sectors have a high adoption rate. Although the survey sheds light on the growing concern over job disruption and rapidity, another report from Grand View Research states that this upward trend in AI results from Artificial Neural Networks (ANN) replacing traditional machine learning systems to offer more accurate analysis and results.

AI in manufacturing

According to an Oxford Economics report, 34% of manufacturing leaders are investing in AI in manufacturing to solve critical workplace challenges that will result in long-term business transformation. The two driving factors for adopting AI are machinery inspection application and predictive maintenance for players in the manufacturing industry. As a machinery-dependent industry, these two offerings will hugely benefit the industry as there is a reduction in operations cost and machine downtime. With the adoption of AI in production, the manufacturing industry can transform its processes and supply chain, improve employee retention and efficiency, and drive meaningful engagement with customers.

Manufacturers must adopt a data-driven business model to remain competitive. As Industry 4.0 booms, AI has become an integral component of the shop floor. Businesses have understood the vast number of uses artificial intelligence has to offer. Over time, several of the capabilities have been incorporated. Some of them are
  • IoT devices, machine learning algorithms, sensors, MES data are used to prevent failures through recommended predictive maintenance schedule.
  • Machine learning and AI helps manufacturers define supply chain optimization and improve in-house inventory management.
  • Improve process parameters to maintain product quality with AI through historical data analysis.
  • Virtual Reality (VR) and Augmented Reality (AR) used for training can help employees learn by stimulating real-life incidents for better reactiveness.
  • AI offers predictive analytics that can be used to understand demand trends and modify processes accordingly.
  • Reduce operation costs by monitoring energy consumption.

AI in production powered by Microsoft Azure

Microsoft Azure Synapse Analytics has evolved from Azure SQL Data Warehouse. The solution can empower manufacturers to process Big Data in real-time and deliver actionable insights to create intelligent supply chains, improve product innovation, and enhance factory productivity. Combining enterprise data warehousing, data integration, and big data analytics into a single service, the solution caters to the needs of machine learning, data engineering, and BI without creating silos in processes and tools.

Service offerings of Microsoft Azure Synapse Analytics

  • A unified analytics platform that performs data integration, data warehousing, data exploration, Big Data analytics, and ML tasks.
  • Serverless and dedicated options support both data lake and data warehouse use cases.
  • Enterprise data warehousing.
  • Code-free hybrid data integration ingests data from more than 95 native connectors.
  • Deeply integrated Apache Spark and SQL engines enhance collaboration among data professionals.
  • Cloud-native HTAP for insights from real-time transactional data.
  • Data lake exploration brings together relational and non-relational data and query files.
  • Integrated AI and BI with deep integration of Azure Cogniti• Integrated AI and BI with deep integration of Azure Cognitive Services, Azure Machine Learning, and Power BI.
  • End-to-end management and monitoring.

Benefits of Microsoft Azure Synapse Analytics

  • Reduce unplanned downtime by applying advanced analytics and ML models with no data movement to optimize predictive maintenance.
  • Maximize overall equipment effectiveness (OEE) through powerful insights on Azure.
  • Connect data sources throughout the supply chain to enhance supply chain visibility and resilience.
  • Combine operational and analytic technologies to optimize the supply chain with real-time insights.

Conclusion

Korcomptenz can help businesses identify opportunities for implementing advanced analytics capabilities without disrupting existing processes.

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