Big Data For Manufacturing: An Intro To Concepts & Applications

As lean manufacturing methodologies become more widely adopted as we progress deeper into the digital era, there are more opportunities than ever to turn routine production runs into data that makes a difference.

This is largely because of the maturation of big data–a catchall term for a suite of storage, organization, and analysis techniques developed for massive data sets.

In this post, we’ll introduce you to some key big data concepts, as well as the most important use cases and applications for big data analysis in manufacturing.

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Big Data is defined as exceptionally large data sets, potentially numbering into billions of rows and parameters. In manufacturing, big data can include data collected at every stage of production, including data from machines, devices, and operators.

This data can be either structured or unstructured.

The sheer volume and complexity of large data sets and the number of specific tools, techniques, and best practices for working with them have led to the maturation of the field of data science and big data analytics in and around manufacturing.

While it’s possible to understand how the growth of big data will revolutionize manufacturing data analytics without understanding how it works “beneath the hood,” so to speak, familiarity with a few key concepts can go a long way.

For one, it’s important to understand that big data analysis isn’t just a matter of software.

There’s a tremendous amount of hardware and infrastructure necessary to support AI, machine learning, and deep-learning algorithms.

In many cases, manufacturing data is stored in data lakes via the cloud and processed on GPU clusters rather than with traditional CPU processors. All of this is a jargon-y way of saying the quantity of data generated by the modern factory requires updated storage and processing tools to support it.

In terms of data analysis, there are few fundamental ways outcomes and processes behind even the most sophisticated techniques.

Separating Correlation From Causality with Certainty

Especially in manufacturing, understanding root causes is absolutely essential to continuous improvement.

It should be no surprise then that tools designed to determine whether or not two variables are correlated or infer which variables are causal are so important. While standard techniques like linear regression have been used to great effect for decades, machine learning algorithms make it possible to find correlation and covariance in larger, noisier data sets.

Isolating Outliers and Inliers

When working with a large data set, it’s critical to understand which data points can be grouped into a trend, and which are outliers.

This is important not only because better data means cleaner results, but because outlier detection is important for programs like predictive maintenance, which rely on detecting anomalies and correlating them with machine failure or part degradation.

With enough data, neural networks and machine learning analysis (random forest, isolation forest) can help detect, classify, and measure the significance of data points.

Creating Novel Classifications

One of the most exciting outcomes of machine learning is the production of novel classification structures and hierarchies of an organization that could easily elude human efforts. Usually referred to as “unsupervised learning” or “cluster analysis,” these algorithms parse and classify the information in a data set by detecting patterns inherent in the data. For manufacturing, an application for classification algorithms could be to find novel information about machine efficiency in data collected as part of a machine monitoring program.

Ultimately, these techniques distinguish themselves in their ability to “train” on a given data set to produce more reliable outputs with each new input; on the size of the data set they can accommodate; and in the reliability of their classification, prediction, and forecasting capabilities.

Predictive Maintenance

Most manufacturers follow some schedule of preventative maintenance (PM). With PM, supervisors schedule downtime at regular (or not so regular) intervals to repair assets before an unexpected breakdown leads to costly unplanned downtime.

Predictive Quality

The concept here is similar to predictive maintenance. There are dozens of variables that contribute to quality outcomes. For manufacturers that track these variables, big data analysis can help determine root causes and identify factors that lead to non-conformances.

Anomaly Detection

Whether it’s a small deviation from norms in the quality of a milled part or the amount of heat generated by the mill itself, big data analytics makes it possible to separate signal from noise. Modern algorithms make it possible to identify anomalies with a high degree of statistical significance.

Computer Vision

Computer vision is a tool for analyzing dynamic human action in real-time. Advances in AI and machine learning have made it possible for computers to observe, classify, and respond to human events as they unfold.

Tool Life-cycle Optimization

While there are few tricks to extend tool life, it can be tricky. This is because there are many variables that impact how a tool will wear over time. Big data analytics make it possible to isolate the root cause with greater certainty.

Supply Chain Management

Timing is everything. Big data makes it possible to predict with greater certainty whether or not a supplier will deliver as agreed, and makes it possible to optimize supply chains to reduce risk.

Production Forecasting

Anticipating demand is critical for optimizing production. The data you collect about your operations, business, and suppliers can help you prepare better for the future.

Improving Throughput and Yield

There are innumerable factors that impact production yield. Big data can help you find hidden patterns in your processes, enabling you to pursue continuous improvement initiatives with greater certainty.

Work Cell Optimization

How a work cell is structured is critical to efficiency. AI can find patterns in human-environment interactions that enable you to design the most efficient manufacturing systems possible.

Product Lifecycle Management (PLM)

In some industries (pharma and biotech), every month on the market multiplies a product’s lifetime value. AI pulls insights from previous products and critical market factors to help you optimize the value your products create over time.

The innovations here are just a quick survey. There are countless other applications and use cases for big data in manufacturing.

One thing, however, unites all of them. You need data to realize them. The sooner you get started collecting data about your manufacturing operations systems, the sooner you’ll be able to apply the latest innovations in data science.

Ready to learn more? Check out our guide to machine monitoring to learn how to start collecting the data you need.