Industrial Analytics: The Rosetta Stone of Automation?
The manufacturing landscape is undergoing a seismic shift. The rise of Industry 4.0, characterized by increasing automation, interconnectivity, and data-driven decision-making, is forcing manufacturers to adapt and embrace new technologies. At the forefront of this transformation lies a powerful tool: industrial data analytics.
By effectively utilizing the vast amount of information generated by automated manufacturing processes, companies are unlocking a treasure trove of insights that are revolutionizing how they operate. This data-driven evolution is propelling manufacturers towards a future of unparalleled efficiency, innovation, adaptability, and competitiveness.
Understanding the Role of Data Analytics in Industry 4.0
“Industry 4.0” is engineering jargon for the 4th industrial revolution, which is all about digitalization and AI. Enter the other current industry buzz word: digital transformation, which is all about mechanizing as many aspects of a business’ operations as possible. What do these have to do with industrial data analytics? Almost everything.
Industrial analytics refers to the process of gathering, organizing, culling, integrating, and translating raw data into understandable information that is able to be acted upon. Industry 4.0 and digital transformation would not be possible without it.
Consider an automated facility: every sensor, alarm, piece of equipment is constantly generating data. For that to be worth anything to the person standing at the control system panel, that ocean of stats and measures needs to make sense. That requires an integrated system that allows constant, clear, complex communication between devices across the network, and a central HMI that is able to access, affect and generate that information into a context the operator can utilize.
You need industrial analytics to assess, plan and design an automation system, and an automation system also requires analytics to perform all its functions correctly. You can’t have some without the others; Industry 4.0, digital transformation and industrial analytics are inextricably intertwined.
How Industrial Analytics Has Changed the Automation Ecosystem
Generally, there are four types of data analysis used in industrial automation solutions:
- Descriptive Analytics: This is the what-happened stage. It uses historical data to understand past performance, identify trends and get a general sense of how things have been operating.
- Diagnostic Analytics: This is the why-it-happened stage. Diagnostic analytics examines historical data to pinpoint root causes of issues or anomalies. It helps identify problems and malfunctions within the automated system.
- Predictive Analytics: This is the what-could-happen stage. It uses historical data and statistical models to forecast future events like equipment failure, production bottlenecks, or potential obsoletion.
- Prescriptive Analytics: This is the what-to-do stage. By analyzing all the previous data, prescriptive analytics recommends specific actions to optimize operations, prevent problems and/or improve outcomes. It suggests the best course of action based on the insights from all the other stages.
These data analytics happen in a continuous cycle where descriptive analytics sets the foundation, diagnostic analytics refines the understanding, predictive analytics forecasts future scenarios, and prescriptive analytics suggests actions to optimize those scenarios.
This is a distinct development from Industry 3.0, which was the first emergence of computers, robotics and automation. Data analytics were rudimentary and limited, which led to systems that were opaque, mysterious and problematic. What good is an automated process that generates raw data if, when something goes wrong, there’s no salient way to access a coherent report that tells you why?
Data analytics can be thought of as something of a Rosetta Stone of automation. Where we once had infinite spools of raw data we could barely piece together, leaving much of it unintelligible mess, we now have the technology to translate that data into a usable format, allowing us to unlock the full potential of the knowledge that data can provide. Now that we know how to build control systems that know how to use it, it would be a detriment not to.
Applications of Industrial Data Analytics in Automation
When you are able to understand the full picture of what is happening on the floor of your facility in real time, you ensure you truly do have control and oversight covering every detail of your operations. And with the hardware, software and automation services available, any industrial entity can transform their business with these analytical keys to success. Industrial analytics can be applied to virtually every facet of a company’s operations with functions like:
- Data-driven insights: Data analytics replaces guesswork with factual insights, enabling manufacturers to make informed decisions about production processes, resource allocation and investments.
- Identifying bottlenecks: Industrial analytics can pinpoint inefficiencies in production processes, allowing manufacturers to streamline operations and maximize output.
- Improved safety: By analyzing data on machine performance and worker activity, manufacturers can identify potential safety hazards and implement preventive measures.
- Improved scheduling: Data insights can help optimize production scheduling, ensuring on-time delivery and minimizing downtime.
- Innovation: Data analytics can provide valuable insights for developing new products and processes, fostering innovation and keeping manufacturers ahead of the curve.
- Optimized inventory management: Industrial analytics can help manufacturers optimize inventory levels, reducing storage costs and the risk of stockouts.
- Optimized resource allocation: By analyzing data on resource usage, manufacturers can allocate materials, labor, and equipment more effectively, reducing waste and cost.
- Predictive maintenance: By analyzing sensor data, manufacturers can predict equipment failures before they occur, allowing for proactive maintenance and preventing costly downtime.
- Real-time quality control: Data from sensors on the production line can be used to identify defects early in the process, improving overall product quality and reducing waste.
- Reduced downtime: Predictive maintenance and real-time quality control minimize unplanned equipment failures and production disruptions, leading to cost savings.
And the benefits don’t stop there. Consider how improved efficiency can reduce energy waste; how increased consistency can lead to better quality; how better visibility can improve network security. It’s the technological incarnation of the old adage, “The more you know”.
E Tech Group: Helping Clients Reach Industry 4.0 & Beyond
Is there such a thing as too much data? Not if you know how to handle it. E Tech Group partners closely with our clients to create comprehensive automation systems that optimize the present and prepare for the future. Our advanced control system solutions utilize automation software that constantly collects, organizes, analyzes and translates usable raw data into information that’s easy to understand and also applicable. Let us help you transform your business with automation system design that won’t leave valuable data resources to waste when they could be used for actionable insights.