What manufacturing data analytics trends should you know?

1 Edge computing

Edge computing is the practice of processing data near the source, rather than sending it to a central server or cloud. This can reduce latency, bandwidth, and security risks, as well as enable real-time analysis and decision making. Edge computing is especially useful for manufacturing data analytics, as it can handle large volumes of data from sensors, machines, and devices, and provide faster feedback and control. Edge computing can also support predictive maintenance, quality control, and process optimization.

2 Artificial intelligence

Artificial intelligence (AI) is the ability of machines or software to perform tasks that normally require human intelligence, such as learning, reasoning, and problem solving. AI can enhance manufacturing data analytics by providing advanced capabilities, such as natural language processing, computer vision, and machine learning. AI can help manufacturers extract insights from unstructured data, such as text, images, and videos, and automate complex or repetitive tasks, such as defect detection, anomaly identification, and demand forecasting. AI can also generate recommendations, predictions, and simulations based on data analysis.

3 Digital twins

Digital twins are virtual replicas of physical assets, systems, or processes that can be used to monitor, simulate, and optimize their performance. Digital twins can integrate data from multiple sources, such as sensors, models, and historical records, and create a dynamic and holistic view of the reality. Digital twins can enable manufacturing data analytics by providing a platform for testing, experimenting, and improving scenarios, such as product design, production planning, and quality assurance. Digital twins can also facilitate collaboration and communication among different stakeholders, such as engineers, operators, and customers.

4 Data governance

Data governance is the set of policies, standards, and practices that ensure the quality, security, and usability of data. Data governance is essential for manufacturing data analytics, as it can help manufacturers manage the increasing volume, variety, and velocity of data, and comply with the regulatory and ethical requirements. Data governance can also enable data sharing, integration, and interoperability across different platforms, systems, and departments. Data governance can also support data literacy, culture, and strategy within the organization.

5 Data visualization

Data visualization is the process of presenting data in graphical or interactive forms that can make it easier to understand, explore, and communicate. Data visualization can enhance manufacturing data analytics by providing tools and techniques that can help manufacturers discover patterns, trends, and outliers in their data, and convey their findings and insights to others. Data visualization can also enable data storytelling, which is the art of using data to create engaging and persuasive narratives that can inform, educate, or influence the audience.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?