Spindell cited a March Aberdeen Group study, which found that advanced big data analytics are being pursued by manufacturers as a means of managing complexity. This is because data capture software enables them to more effectively gather knowledge about processes being used within individual departments and across the company.
"Earlier we were dealing with the idea of whether big data is a challenge or an opportunity," Mariela Koenig, research director at Aberdeen Group, told the source. "Now we are thinking about how we can take advantage of these new technologies to improve the way we work."
Specifically in regard to manufacturing, big data is proving particularly valuable for improving response time, Spindell reported. By enabling employees with insight derived from effective data collection and analytics, managers have observed a significant improvement in the amount of time it takes to respond to issues, recognize value and make decisions. The source also noted that this is largely due to managers trusting employees' actions more knowing they are based off of collected data and facts.
Spindell also stressed the importance that collected data being synthesized and distributed to organizational authorities in a manner that is easily comprehensible for business level executives and plant operators alike. One of the main reasons companies engage in data collection and analytics is to solve problems. In order for this to happen, the right data needs to reach the right people. Furthermore, that data should also be displayed in specific ways to suit the needs of the individual or department looking to utilize it.
"Manufacturers are also taking advantage of big data in order to predict and adjust to change," Spindell wrote. "These could be changes in the economy, technology, customer behavior or regulations. In all cases, the data allows them to adapt more quickly. Another hand area is behavioral analysis. Manufacturers 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."
Big Data's Value in the Supply Chain
As Spindell pointed out, manufacturers were quick to adopt data collection systems once they discovered how big data analytics could be used to solve a variety of specific problems. Chuck Fuerst, a product strategy expert for supply chain companies, explored how big data can be utilized for supply chains, noting that data analytics seem to factor into nearly every discussion these days.
While integrating and analyzing large quantities of data can seem like a big project, Fuerst recommended that companies start small and scale out for best results. For supply chain companies, valuable data collection solutions can include anything from customer emails to fulfillment and inventory management.
"With the exponential growth of data and the increasing complexity of decisions in the extended supply chain, companies can no longer rely on manual analysis in decision making," Fuerst wrote. "One of the significant barriers to succeeding with big data will be the ability to ask the right questions and use the right technologies to get the answers … You must have a very clear understanding of what you are trying to accomplish or what problems you are trying to solve."