Big Data in Manufacturing. Benefits and Use Cases

Improving the entire manufacturing process. Enhancing quality assurance. Managing the supply chain more effectively. Customizing and personalizing production. All of that becomes possible as soon as you introduce data analytics into your manufacturing company. There has even been coined a new term–digital manufacturing. It refers to the modern manufacturing process, enhanced and supported by Industry 4.0 technologies (including IoT, AI, digital twins, etc.).

In this article, we want to take a closer look at how big data and related technologies transform the modern manufacturing industry. We invite you to take this fascinating trip with us!

It all started with the industry 4.0 revolution around 2011. The goal was straightforward–to improve diverse manufacturing-related technologies and processes. Today, manufacturing companies all over the world benefit greatly from this revolution.

It’s industry 4.0 that popularized technologies like:

• Artificial intelligence and machine learning
• The Internet of Things
• Automation
• Personalization and customization
• Digital twins
• Computer vision, and many more

These technologies have already been introduced to various manufacturing and production sectors and industries. You name it! We bet that industry 4.0 technologies are extensively used in almost every single one of them.

Why? Primarily because they work very well. Industry 4.0 is called a revolution for a reason. This new trend in technology and production has become a true game-changer that completely altered the way modern production companies work. But first things first, what should you know about big data in manufacturing industry?

The role of big data in manufacturing industry

Today, big data is everywhere. Every person, every device, every company–we all produce mind-boggling amounts of data. When data becomes so voluminous, it can be processed and analyzed only with the use of advanced technologies and analytics algorithms–we start talking about big data.

However, big data itself is not useful in any way. It’s just an asset that can be utilized or not. It’s the analytics part that’s crucial. Modern companies, including manufacturing ones, try to:

• Gather
• Organize
• Process
• And analyze information they possess

That’s because it’s the only way to make the most of it business-wise. In order to do so, manufacturing companies use specific software and IT solutions, as manual analysis in the case of big data is not just ineffective but also close to impossible. But that’s a story for a whole different article.

Now, once your big data is analyzed, your goal is to draw useful conclusions from it.

Here, solutions like data visualization step into the game.

Visualization tools

The vast majority of people process information based on what they can see. According to the Social Science Research Network[1] study published by Forbes, as much as 65% of us are visual learners. That’s why we need a solution that helps us comprehend big data and make it more digestible. At Addepto, we truly believe that big data visualization does this job perfectly.

You see, visualization, as long as it’s well done, removes the noise from data and highlights useful information. In other words, it clarifies the picture, makes conclusions obvious and transparent. Modern visualization tools make you understand what you see, even if you’re not a data scientist. That’s why today, almost every big data analytics in manufacturing comes with the visualization feature.

Benefits of data analytics

Now, suppose you run a manufacturing company. Thanks to big data in manufacturing, you have instant access to thorough information concerning:

• Your products
• Every step of the production process
• Entire supply chain and vendors
• Quality assurance

The natural next step is to take this vital insight and turn it into action by implementing beneficial changes. And that’s exactly what manufacturing companies have been doing for some time now. Let’s examine some examples:

How manufacturing companies use big data

We’ve made a list of five essential aspects of big data in manufacturing industry. Here’s what we can share with you:

Predictive maintenance (PM) and digital twins

That’s by far one of the most exciting aspects of big data use cases in manufacturing. Thanks to PM, manufacturing companies can predict production delays, potential complications, but also necessary repairs and maintenance procedures. You see, every machine and every device in your company needs appropriate maintenance so they can remain fully functional and safe for your employees.

This is what PM is all about: Predicting potential glitches and repairs before they become a problem. Predictive maintenance is frequently supported by another industry 4.0 technology–digital twins.

As you may know, a digital twin is an exact digital replica of a specific machine or device. Companies working with digital twins can replicate the exact state of the physical object and create a digital version of it. This gives them the necessary insight into each machine and assesses when specific repairs and maintenance procedures are required. It all happens based on the device’s (and its critical components) condition.

In fact, both PM and digital twins offer a great deal of work improvement. They optimize the wear of every device and save your money. That’s because, with PM, unpredicted system malfunctions are far less likely.

Quality assurance thanks to machine vision

A word of explanation first: Machine vision is a subset of computer vision that is applicable primarily in the manufacturing sector. In fact, both these solutions have a lot in common. Just like computer vision, machine vision algorithms are based on cameras and sensors, but this technology is tailored specifically to the needs of the production sector. It’s frequently used in quality control and assurance. For example, machine vision algorithms are capable of:

• Detecting deviations and problems with the given end product
• Detecting quality defects
• Processing tons of visual information simultaneously

As a result, quality assurance is enhanced and more accurate. This solution also allows you to decrease the workforce devoted to this part of your everyday work. In addition, quality assurance is frequently backed by other big-data-related technologies such as machine learning and deep learning.

Simply put, QA applications thoroughly analyze final products and compare them with the model example. In case of any deviations, glitches, or other differences, they alert the QA team to verify the problem. Simple and effective!

Supply chain management

That aspect suddenly became crucial at the beginning of 2020, when the COVID-19 pandemic started all over the world. Today, manufacturers have to keep their supply chains optimized and effective. And this is where big data analytics makes a case for itself because there are lots of vital data sources, including GPS, traffic, and IoT data (e.g., radio frequency identification sensors) to use.

All that information can be used to track semi-finished products or delivery vehicles and optimize their routes by integrating live traffic information. As a result, companies can manage deliveries more effectively and avoid potential delays.

Production forecasting

The dead stock issue is a serious problem in many sectors. It’s caused by excess production, which in turn results from bad production planning. With big data analytics in manufacturing, you have access not just to the internal company data but also to external data sources, comprising i.a. market and traffic data. By combining historical selling, pricing, and buying data and comparing them with other indexes (i.a. number of customers, traffic on the website), you can get accurate production predictions.

And it goes further! For instance, you can integrate your AI and machine learning algorithms with your CRM app and sales management software to make these forecasts even more accurate!

Product lifecycle management (PLM)

Generally speaking, PLM refers to handling a product as it goes through all the stages of its life. PLM refers primarily to your product’s development and production, and the manufacturing part is critical here. The product life cycle concept helps inform business decision-making, from pricing and promotion to expansion or cost-cutting.

With big data in manufacturing, you can optimize literally every element of your product, making its PLM improve altogether. Manufacturing companies also take into account data concerning their previous products, which enables making current ones even better.

Big Data Use Cases in Manufacturing

Intouch

It is a China-based quality control and assurance company specializing in product inspection, factory audits, and sourcing. Their services range from full-scale production monitoring and supplier verification up to lab testing and sample reviews. They also build predictive models in order to provide production and plant managers with a better understanding and insight into their equipment.

Machine metrics

It’s a US-based company. Their Industrial IoT Platform is plug-and-play with no on-site setup required, enabling consumable machine data and insights. In addition, their platform increases productivity through real-time visibility, deep analytics, and AI-driven predictive notifications. Today, Machine Metrics support a number of the top manufacturers in the world.

Intrastage

It’s another US company that uses real-time big data in manufacturing to build a production quality monitoring platform that’s suitable for both simple components and complex assemblies. The intrastage platform detects any deviation in the components’ parameters before they arrive for production. Intrastage offers failure analysis that allows manufacturers to investigate and resolve issues as they occur.

Ecolibrium energy

Ecolibrium is a company with offices in North America and Asia that develops a system helping production companies improve their asset efficiency with IoT-based predictive analytics. Their so-called Digital Twin Plant and Creation platform makes use of a multitude of diverse sensors and data inputs, which allows companies to perform automated fault detection and diagnostics. According to their data, Ecolibrium can lower unplanned downtime by 70%, decrease maintenance costs by 30%, and even reduce energy consumption by 10%.

To sum up, big data in manufacturing is a true game-changer, the next milestone that allows production and manufacturing companies to enter a whole new level of production optimization and quality. If you’d like to find out something more, we invite you to reach out. Addepto is an AI consulting company.

Every month, we work with manufacturing companies and help them achieve new levels of improvement. We are happy to help you with big data services as well. Fill in the contact form and let us know about your project and company. We can’t wait to hear from you and share more of our big data use cases in manufacturing!

Big Data in Manufacturing. Benefits and Use Cases – FAQ

What is big data analytics in the telecom industry?

Big data analytics in the telecom industry refers to the process of analyzing large volumes of data generated by telecommunications networks and services to extract valuable insights. These insights can be used for various purposes such as improving network performance, enhancing customer experience, and optimizing business operations.

What are some use cases of big data analytics in the telecom industry?

Some common use cases of big data analytics in the telecom industry include predictive maintenance of network infrastructure, churn prediction to retain customers, personalized marketing campaigns based on customer behavior analysis, network optimization to improve quality of service, fraud detection to prevent unauthorized usage, and real-time monitoring of network performance.

How does big data analytics benefit the telecom industry?

Big data analytics offers several benefits to the telecom industry, including improved operational efficiency, better decision-making based on data-driven insights, enhanced customer experience through personalized services, increased revenue through targeted marketing strategies, and proactive identification and resolution of network issues.

What technologies are used in big data analytics for the telecom industry?

Technologies commonly used in big data analytics for the telecom industry include data mining, machine learning algorithms for predictive analytics, real-time data processing frameworks such as Apache Kafka and Apache Flink, distributed computing platforms like Apache Hadoop and Spark, and data visualization tools for presenting insights in a comprehensible manner.

How does big data analytics contribute to the future of the telecom industry?

Big data analytics plays a crucial role in shaping the future of the telecom industry by enabling innovation, driving digital transformation, and creating new opportunities for growth. By harnessing the power of big data, telecom companies can stay competitive in a rapidly evolving market landscape and meet the evolving needs of their customers effectively.

This article is an updated version of the publication from Jun 25, 2021.

References

[1] Forbes.com. Why Infographics Rule. URL:
https://www.forbes.com/sites/tjmccue/2013/01/08/what-is-an-infographic-and-ways-to-make-it-go-viral/?sh=356bc8527272. Accessed Jun 15, 2021.