As an American child growing up on a military base in Germany, I learned many important life lessons. One of the most important was about language and communication. I realized at age 12 that, even though I could speak German, I thought in English -- and it led me to wonder, how do they think? The more languages I learned, the more flexible my thinking became. If I couldn't solve a problem in English, working it out in American Sign Language often gave me a kinesthetic jolt that allowed me to come to a surprising solution. Sometimes simply reading about an issue in German was enough to give my brain an unexpectedly fresh jolt of creativity.

The challenge I find in manufacturing is that, in many cases, people appear to be speaking the same language, but they’re not. I will never forget sitting in an architecture meeting with one engineer from the operational technology (OT) side of the organization talking about Ethernet I/P and another one from the IT side of the house talking about Ethernet, neither of them realizing that they weren’t talking about the same thing.  They talked past each other and many unnecessary misunderstandings ensued.

Two new languages operational leaders must learn

In this world of digital transformation, operations leaders who wish to remain competitive will have to learn at least two new languages: The language of cloud and the language of analytics. And as with that Ethernet/IP example, there’s a pretty strong risk that many people will incorrectly assume that they already know what those things are.

In my experience most operations personnel are so consumed with the day-to-day tasks of hitting production targets, improving quality while reducing costs, maximizing uptime while maintaining a solid commitment to safety protocols and attracting and developing talent, that they have limited time to explore new worlds and new opportunities. But taking a few moments to ask – how do they think? - can pay off enormously in the long run, as manufacturing operations leaders learn to speak this new language of opportunity.

Thinking, working differently, thanks to SAS, Microsoft partnership

I have the immense good fortune to work for SAS, the world leader in massively parallel analytics and AI. SAS recently partnered with Microsoft, and thus I’ve had ample opportunity to ask myself, how do they think? And perhaps most importantly, how can they help me to think differently? 

 Here are three areas where the combined offerings of these two powerhouses can help manufacturers change their way of thinking:

1. Low cost and high value of investigating enormous data sets to solve quality issues

Historically “huge data sets” was equivalent to “huge expense” when it came to diverse manufacturing data. Cloud has changed all that. No longer do you have to procure capital-intensive servers, beg IT for staff and wait months to install hardware to look at all of your data in context.

With the power of Microsoft Azure, you can get precisely the amount of data storage that you need, when you need it, and for as long as you need it. Such an approach has helped some manufacturers comb through terabytes of data quickly to hone in on only those variables that actually impact their outcome measures – no more guessing. And once those variables are identified, they can continue to be captured in a cost-effective and secure way using robust cloud.

2. Quality-centric data models

“Models” is one of those words that causes manufacturers and analytics experts to talk past each other. The manufacturers are typically referring to an actual, physical model of a product, like a car. But the analytics experts are talking about a way of representing data in an analytic system. Most vendors are used to simple, clean data like banking data where a credit card number is a credit card number. But when dealing with industrial machines, which have high frequency, noisy sensor data from systems as disparate as automation systems, LIMS systems, manufacturing execution systems (MES), ERP systems, and reams of sensor data, it’s helpful to have a prebuilt data model designed to seamlessly capture, integrate, and display that data in a usable form.  SAS’ Production Quality Analytics solution has just such a data model built in.

3. A workspace for advanced and predictive analytics – accessible to everyone

Many manufacturers assume that to take advantage of the benefits of artificial intelligence, machine learning and other advanced analytics, it will be necessary to hire full-time PhD level statisticians to run endless analyses and reports.  Fortunately, SAS has “baked in” the most common advanced analyses necessary to improve manufacturing quality into its packaged solution, and Microsoft makes it easy to stand up that solution and deploy it enterprisewide.

Imagine what you can do to improve your yields and reduce scrap if everyone from the line engineers to the executive suite has access to precisely the data that they need, when they need it, and in a format that makes sense to them for their line of work.

When it comes to digital transformation, Microsoft Azure has turned the world of data and analytics into a veritable playground for manufacturers.  I encourage operations leaders to start with curiosity and learning the vocabulary of analytics and cloud. To learn more, visit:


About Author

Marcia Elaine Walker

Global Industry Principal, Manufacturing

Marcia Elaine Walker is a principal consultant in the industry practice of SAS, where she helps clients apply data management and analytics technologies to make better decisions, faster. Walker’s career has revolved around the industrial sector including energy, manufacturing, and mining. She holds eight issued and multiple pending patents related to analytics in the industrial space and was named one of the most influential women in IoT by the IoT Institute. Ms. Walker was a Fulbright scholar at the University of Heidelberg and in addition to her academic studies has hundreds of hours of training in martial arts. She is also an accomplished improv actress and an enthusiastic though very off-key singer. She lives in North Carolina with her husband and their excessively furry orange cat and loves to write.

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