Big Data in Manufacturing: The Digital Evolution Yields Information

The digital revolution has brought many benefits, from better customer experiences to streamlined industrial processes.

But one new frontier — big data — has been largely untouched.

Big data projects, which analyze large amounts of data to find new insights, are not usually a priority for manufacturers. But that is changing, thanks to changes in technologies in manufacturing and the pressing need for manufacturers to become more efficient.

The manufacturing market is one of the most data-intensive industries out there, generating an average of 1.9 petabytes per year according to the McKinsey’s Global Institute. That’s enough data to fill about 2 million DVDs or 3 billion ebooks.

Data generated by supply chains sourcing, factory ops, and the phases of compliance and quality management generate most data, according to Forbes. And it will only grow as technologies in manufacturing gets more sophisticated and involved in every stage of the manufacturing process.

This article will look at what exactly big data means and how it’s used by manufacturing businesses today.

What is Big Data?

Before we start talking about how big data can be applied to manufacturing, it’s worth explaining what exactly big data actually is.

As its name suggests, big data refers to large amounts of information that come from different sources, which are then combined and analyzed using tools to extract knowledge and insights.

The goal is to gain insights that can be used to improve business decisions, products, or services. 

What Does Big Data Mean for Manufacturers?

Well, it could potentially provide answers to questions like “How long would it take us to produce our next batch if we had access to all the raw materials we needed?” Or “Can we find out if there’s any demand for our product and from where?”

shutterstock_633278762 copyQuestions like this can be answered through the analysis of big data. 

The uses of big data analytics in manufacturing are many. 

Asset performance and operations optimizations

Manufacturers are always looking to improve their efficiency. Asset performance is an important factor.

Some of the best ways to maintain an asset are through log analysis. Logs contain data about the health of the asset. This data can help manufacturers to plan ahead.

Predictive analytics enable manufacturers to schedule preventive maintenance before unexpected downtime happens. 

According to a report by Pwc and Mainnovation, the adoption of predictive maintenance using data could:

* Reduce costs by 12%
* Improve uptime by 9%
* Cut safety, health, environment, and quality risks by 14%
* Extend equipment lifetime by 20%


One of the biggest advantages of using big data in manufacturing is that it enables manufacturers to understand and measure everything that happens within their factory. With this information, they can spot areas of improvement and focus their efforts accordingly.

A chemical manufacturer in Europe used an advanced form of operational analytics to look for deeper insights about their manufacturing processes. Looking at various factors like temperature, CO2 flow, coolant pressure and other things, the manufacturing company determined which factors had a significant impact on its overall production yield. After analyzing data, the company was able to cut its material waste by 20% and reduced energy costs by up to 15%. Source

Manufacture Better Products

With the right amount of data, manufacturing businesses can create better products.

For instance, Volkswagen created an algorithm which enabled it to make cars lighter than ever before. In fact, it is18% lighter. It used a combination of data, and with the help of AI, they made “adaptive decisions along the way” to figure out where the car could be improved. It then tested different designs until it found one that worked best. As a result, they were able to reduce weight while improving fuel economy.

Increase Production Efficiency

Another advantage of using big data in the manufacturing analytics is that it helps manufacturing companies to better manage their resources.

For example, they can easily predict when certain materials will run low and order more while still having enough time left to fulfill orders. They can also track the cycle time for different parts of the assembly process and analyze which part takes the longest. This allows them to avoid wasting time and energy by focusing attention on the most important tasks. It also reduces the chances of errors during production and improves overall productivity.

Discover Opportunity Gaps

shutterstock_196477304 copyAdvanced analytics may reveal new opportunities for yield improvement, even within an established best in-class operation.

A mining company analyzed all available data about their industrial processes and then used that information to determine what changes should be made to improve efficiency. Longs story short, the mining company made minor adjustments to its leach-recovery processes and increased its average production by 3.7 percent. Without making any major investment or implementing a major change in any existing technology and production processes, the company was able to generate a sustainable $10 million per year profit improvement.

Faster Decision

The biggest advantage of big data is that it allows smart manufacturing companies to gather an incredible amount of information very quickly.

These facilities can use real-time data to create manufacturing intelligence that supports accurate and timely decision-making with a positive impact across the whole organization.

For example, let’s say you wanted to analyze whether you need to increase production capacity. If you were to do this manually, it would require a lot of effort and cost money. But, if you used big data, you could do this automatically and get results in a fraction of the time and cost. 

The Bottom Line

With big data, you have access to an infinite amount of information. And it’s easy to see why. With just a click of a button, this tool can collect vast quantities of data from various sources. All this information comes together to create insights that help you improve your manufacturing processes and increase profits. 



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