The Ramifications of “Big Data”

We seem to be inundated with the term “Big Data.” What is Big Data and what does it have to do with manufacturing and distribution?

Big Data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term "big data" often refers to the use of predictive analytics, user behavior analytics or certain other advanced data analytics methods that extract value from data.Accuracy in Big Data may lead to more confident decision-making and better decisions can result in greater operational efficiency, cost reduction and reduced risk.

Manufacturers are investing in Big Data. According to the Tech Target 2015 IT Priorities Survey, 31 percent said their organizations plan deployments of BI, analytics or data warehousing tools in 2015. A quarter of respondents expect to invest in Big Data analytics and 21 percent expect to invest in Big Data processing and management.

Big Data is quickly becoming an important element of ERP. In “5 Big Data Technology Predictions for 2015,” Thor Olavsrud, senior technology writer for CIO magazine wrote, “In just a few short years, Big Data technologies have gone from the realm of hype to one of the core disruptors of the new digital age. 2014 saw Big Data initiatives inside the enterprise increasingly move from test to production. In 2015, Big Data will push further into the enterprise with even more use cases—specifically real-time use cases.”

The opportunities to access to Big Data are mind-boggling…the standard customer questionnaire…who clicks what on the website. Compared to Big Data, these are small examples. It’s on the factory floor itself where the biggest purveyor of data is now becoming available and growing. The connected factory, with its Internet of Things (IoT) and smart sensors, smart tools, smart diagnostics and smart machines – all running together on the Internal Internet or Industrial Control System – provide more data that the most enthusiastic statistician could ever hope for. Using this data, manufacturers can access real-time data to analyze just about anything they might want from warranty claims to preventative maintenance to manufacturing inventory control to scheduling and capacity planning. The problem is that the amount of data can be overwhelming.

For example, the installed base for Internet-connected devices already exceeded 14 billion by early 2015 and is forecast to mushroom to nearly 50 billion by 2020. To how many of these is your Internal Internet connected? How many data points per second are transmitted? 10? 100? 1,000? Pick a number. Multiply it times the number of connected devices you have and, then, multiply that by 28,800, the number of seconds in an eight-hour shift. Whatever numbers you use, you will find out that, per day, you’re collecting Big Data.

Let’s Look at a Medium-Sized Plant

Let’s consider a battery plant that has installed over 10,000 sensors in its plant. The sensors are connected to an internal Ethernet and track materials usage, the temperatures used to bake high-tech ceramics that are used in the batteries and ambient air pressure. The data are readily available to factory floor employees’ iPads through the factory’s Wi-Fi. Big Data.

To take another example, wind turbines contain some 20,000 sensors that produce 400 data points per second, which is analyzed in near real-time to optimize turbine performance. The data is stored and used for predictive analytics to improve maintenance and parts replacement. That calculation would be 20,000 times 400 times 28,800 equals 8 million pieces of data per day. Big Data

PWC, the large consulting firm reports that small and mid-sized manufacturers are looking for ways to leverage their Big Data. It is suggested that one way companies can enter the IoT world at the ground floor is by gaining a realtime situational awareness of how one’s assets are performing. And cloud computing is a catalyst. According to PWC, cloud computing has lowered computer processing and storage costs, while hardware costs are also coming down for certain devices.  MEMS accelerators, for example, which once cost in the hundreds of dollars, now cost in the tens. All this helps the smaller manufacturer access Big Data.

Big Data...Big Results

As PWC’s survey of manufacturers found, manufacturers are making impressive strides in leveraging more data in their operations. They’re responding to customer needs by embedding intelligence into their products to increase functionality. Furthermore, manufacturers are connecting products to the IoT to track the performance of products over their life cycles to satisfy customer expectations for smarter products and to overlay services upon those products.

All of these initiatives produce data – Big Data - that can be analyzed. For manufacturers, Big Data typically leads into a discussion on predictive analysis. That’s what we will cover in our next blog.