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What Manufacturers Need to Know About Big Data Engineering

Big data has transformed the way modern businesses approach their daily operations — and this holds true for many manufacturing firms, as well. While the Lean methodology (Kaizen, TPM, etc.) and Six Sigma principles have played their part in optimizing efficiency across several manufacturing sectors, many industries (such as energy and construction) still endure extreme variability in production processes. 

The reality is, manufacturing environments yield an overwhelming number of internal challenges, ranging from machine failures to siloed quality control systems. Many manufacturing firms require a highly granular approach to identifying, analyzing, and adjusting fault detection processes. In that vein, big data engineering can provide a workable solution for companies in this predicament.

Big Data vs Data Engineering

It’s important to note that data engineering goes beyond what big data offers a manufacturing firm in its “raw” form. But, what’s the difference between big data and data engineering?

Big data refers, of course, to extremely large data sets that may yield patterns and trends across millions or billions of computational data points. A big data architect will design the technical framework for such a system. However, a data engineer will “build” what the big data solutions architect has already conceptualized.

In short, big data engineers develop, maintain, audit, analyze, and implement big data solutions within organizations. They have experience in working with data storage solutions, and are experts at building and utilizing big data processing systems. Moreover, big data engineers have an in-depth understanding of the design, functionality, and use cases of high-performance algorithms that cover huge data sets. Thus, data engineering is a natural result of the advent of big data; in essence, it’s big data’s logical, practical endpoint.

Use Cases for Data Engineering

Big data engineering has countless possible use cases in the manufacturing industry. For example, data engineers can:

  • Identify bottlenecks in the manufacturing process and recommend potential solutions
  • Design and/or adjust operational workflow to yield increased productivity
  • Analyze big data for manufacturing interdependencies to inform targeted process changes

As an example of data engineering in action, consider the following case study:

Biopharmaceutical production teams must often monitor over 200 variables within the manufacturing workflow to ensure that end products remain completely free of harmful contaminants. The very nature of this production process often leads to high variances in quality from one batch to the next. 

One biopharmaceutical company decided to leverage big data engineering to increase its yield in vaccine production without taking on additional budgetary demands. A project team segmented the company’s entire manufacturing process into granular clusters of correlating production activities. Then, for each cluster, the team compiled a database of highly detailed information about process steps, source materials, and other key factors.

Subsequently, the team of data engineers audited the data set for the most influential interdependent parameters throughout the entire process. They found that nine parameters were most influential, including time to cell inoculation. Armed with this data, they recommended targeted process changes to those nine parameters to increase production efficiency. In the end, the company was able to increase its vaccine yield by over 50% and save over $5 million dollars a year.

Leveraging Big Data Engineering to Increase Manufacturing Productivity

Manufacturing companies that leverage big data solutions will see an increase in productivity, a decrease in downtime, and the generation of larger revenue streams. Of course, such results won’t come about overnight. Developing and implementing advanced analytics platforms takes time and effort. However, with the right partner in hand, manufacturing companies can remain focused on their core competencies. And, they can do so while reaping the benefits of big data engineering.

At Entrance Consulting, our analytics experts can handle the entire process of complex data analytics and management for your business. If you’d like to learn more, reach out to us today.

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