Manufacturing is one of the oldest industries, and rapid technological advances occurring in recent years have introduced an entirely new set of capabilities to manufacturing processes. Automated data collection, in particular, has significantly impacted the industry, as manufacturers can access wide-ranging data and insight to make smarter business decisions and constant improvements. In a recent article for The Wall Street Journal, contributor John Koten explored what he referred to as a "revolution" in manufacturing that could revive American industrialism.
"Welcome to the New Industrial Revolution - a wave of technologies and ideas that are creating a computer-driven manufacturing environment that bears little resemblance to the gritty shop floors of the past," Koten wrote. "The revolution threatens to shatter long-standing business models, upend global trade patterns and revive American industry."
Emerging Data Capture Software Produces Big Change
Today, manufacturers can leverage new tools such as data collection software to make smarter decisions. As more companies discover the power of big data, Koten noted that manufacturers will be able to build "smarter, leaner factories" as well as explore the implementation of innovating new products, techniques and materials. In addition, affordable pricing allows smaller companies to utilizing data collection solutions to compete against larger enterprises. Big data and other new technology has contributed largely to the revolution now underway, and this trend has caused additional cost decreases for items like electronic sensors and microprocessors. The main advantage of increased accessibility to these tools is that they are connected to the Internet, allowing advanced data collection software to analyze existing information instantaneously.
"Manufacturing is undergoing a change that is every bit as significant as the introduction of interchangeable parts or the production line, maybe even more so," Michael Idelchik, head of advanced technologies at GE's global research lab, said to the source. "The future is not going to be about stretched-out global supply chains connected to a web of distant giant factories. It's about small, nimble manufacturing operations using highly sophisticated new tools and new materials."
Koten shared the example of a General Electric factory in Schenectady, N.Y., which utilized tiny embedded sensors in a series of its machines as part of its data capture system. When a large and powerful storm hit the area, the sensors captured data and sent information to GE manufacturing engineer Ken Hislop, alerting him to a power outage at the factory. Thanks to the valuable information collected about plant operations, Hislop was able to quickly restart the machinery in the correct sequence so that sensitive battery material wasn't damaged. This allowed the company to suffer minimal, if any, disruption to business operations.
With affordable data capture devices, companies can automate data collection in a way that allows manufacturing engineers like Hislop to effectively perform their job duties without having to rely on additional human capital. The alerts that managers receive then enable them to improve performance and efficiency, two criteria for enhanced business success.
Valuable Metrics for Collecting Data
Steve Wise, an authority on real-time manufacturing intelligence, also explored the industry's new age in a recent article for Manufacturing Business Technology. He noted that in order to fully take advantage of new capabilities, businesses need a strong understanding of what parts of operations are the most valuable. These are the areas where data collection will prove most useful. In order to accurately identify which business elements these are, Wise suggested seeking input from each organizational level.
"Metrics must be meaningful, and there are good metrics and bad metrics," Wise wrote. "Good metrics must be bi-directional, and server customers both upstream and downstream. They should also be objective, customer-specific and assist decision makers with real-time and historical data. Also, don't worry about getting the perfect, high-end set of metrics right away - collect data, but make sure you challenge it. Identify where specific metrics fit in the process; if they are deemed no longer useful, there is no need to continue to measure them. Remember that just because something can be measured doesn't mean it is important."
Wise argued that by taking these steps in integrating people, process and technology, businesses can most effectively adjust operations and supply chain management processes, implement quality system controls and more easily realize a return on investment.