How to Calculate the Value of Big Data in a Manufacturing Environment

Now that the connection between managing data and higher performance has taken hold in leading organizations, businesses are looking into how to measure the effectiveness of their Big Data strategies.

According to a March 2013 study from the Aberdeen Group, top performers are building up their Big Data and advanced analytics activities as a priority to help them take control of manufacturing complexity. Managers have built their data capabilities as an integral part of corporate strategy because Big Data aggregates operational knowledge that can scale from individual departments to company-wide initiatives.

"Earlier we were dealing with the idea of whether Big Data is a challenge or an opportunity," Mariela Koenig, a research director from Aberdeen Group, told IMT. "Now we are thinking about how we can take advantage of these new technologies to improve the way we work."

One way manufacturers are sensing the value of Big Data is the response time: time to action, time to value, or time to decisions. Managers are able to trust the fact-based actions of their employees.

Gloria Rios-Monarrez, a quality engineer from Enterasys Networks, told IMT that until three years ago, "the data from suppliers was coming piecemeal and only for the few key parts about which I was asking -- that was all I could manage."

Enterasys Networks is located in Salem, N.H., and outsources 100 percent of its manufacturing of security, networking, and Wi-Fi communications products, hence its need for data transparency and rationalization.

Enterasys implemented a Big Data cloud technology system that provided cradle-to-grave information integrating all its suppliers, parts, and in-process data.

"This system gave us the support to make proactive, not reactive decisions," said Rios-Monarrez. The company also utilized a product calibration and management tool for rapid actionable communications throughout the company. "We are able to prevent issues before going out to customers. We are able to monitor production and prevent re-work in the assembly process," she noted.

Rios-Monarrez said customer satisfaction rose over the past three years from 70 percent to 97.5 percent and revenue has grown steadily for the last eight of nine quarters.

In February, Forbes cited a Nucleus Research study showing that a 241 percent ROI can be generated by applying data to business decisions. But businesses are not advised to dive in too quickly. "Getting started with big data requires baby steps. The process of data collection, analysis, and application to business decisions is not a sprint; it's a marathon," reported Forbes.

Manufacturers know that not everybody in their organizations speaks numbers. So it is really important to them that the data is synthesized, distributed, and digested in ways that can produce value for both executives and operators.

"When we talk about data, we are not just talking about numbers anymore," said Koenig. "We're talking about training materials, practical experiences, exchanges between technicians and managers on instant messaging, videos -- content management and tools that have content reach."

The data has to find the right users. Problem-solving is a high priority, so Big Data solutions providers are adding analytics tools targeted for specific areas. Structured and unstructured data can be mixed with different kinds of data and scaled from the individual to company-wide, depending on the problem that is trying to be solved.

If performance is the goal, for example, the system might provide information via dashboards. Other applications might include supplier assessments, such as scorecards evaluating preparedness for a new product launch or to brace for new regulations.

Big Data systems are also multipurpose. Over the past couple of years, collaborative enterprise or cross-functional electronic exchanges are being employed. Searches across functions like safety, quality, and supply chain management, make it easier to distribute and propagate information across an operation.

Manufacturers are also taking advantage of Big Data in order to predict and adjust to change. These could be changes in the economy, technology, customer behavior, or regulations. In all cases, the data allows them to adapt more quickly.

Another handy area is behavioral analysis. Manufactures have been applying this approach to process controls and automation in their production environments. The goal might entail characterizing a quality, safety, sustainability, or productivity behavior to gain an understanding of how it might be improved.

Still, manufacturers are pragmatic and fast learners. Once they understand that Big Data can solve a specific problem, they seize the opportunity to implement it.


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