Not Enough Pixels
Consider a photo taken with an early digital camera. Taking one 100×100 pixel image took a full 23 seconds. The low number of pixels meant the image came out blurry and zooming in revealed squares of solid color where details were sacrificed to the most prevalent color value. Taking an action shot was out of the question. Zooming or cropping to provide a particular perspective was a recipe for disappointment. For its time, the technology was groundbreaking and paved the way for a whole new era of image capturing. But today’s standards demand speed, clarity, and storage that was unimaginable at the outset of digital cameras.
Many of today’s manufacturers try to improve data visibility with dated technology akin to an early digital camera. Data collection can be relatively slow, incomplete, and laborious to analyze. It’s digital, so it’s better than paper-gathering practices, but many important details are lost and some data is entirely left out due to unique machines being incompatible with connectivity to data collection methods. When issues come up, operators cannot zoom in or target a specific detail, wasting hours and days of labor pinpointing a process issue or bug fix. A malfunctioning machine may go undetected while its impact on an entire operation blurs into the background. Utilizing low-resolution data collection and visualization methods means manufacturers lose time, money, and materials trying to catch up on issues that are days, weeks, months, or even years old.
21st Century Images
Digital cameras have made enormous strides. Now, even the camera on most of our phones can take a burst of photos that crisply captures an action shot. This high-resolution technology is in the hands of many consumers today and contains sophisticated tools, capable not only of editing and cropping but also analyzing for color, light, and focus optimization. Most cameras in even consumer-grade phones zoom in to a degree that far exceeds that of the human eye.
At today’s leading edge of digital cameras and data collection, is NASA’s James Webb 122-megapixel telescope. This marvel has the infrared sensitivity to detect the heat signature of a bumblebee as far away as the moon and can visualize details on an object as small as a penny from about 40 km away. With its high-resolution data on the universe, astronomers can uncover previously undetectable details and unexplained space oddities. This technology is changing our view of the universe, replacing educated guesses with concrete expert data.
While manufacturing experts don’t need an intergalactic view of their factory processes (yet), nimble, high-resolution data plays a key role in manufacturers making informed optimization decisions at a global scale. Many important details in the operations of large-scale manufacturing organizations are lost with blurry, low-resolution data. Such constraints not only keep solutions to a comparative minimum, they often remain isolated to the factory or plant in which they originated, never benefitting the rest of the organization.
Manufacturers are leaving opportunities on the factory floor for optimizing production and capturing lost uptime by not utilizing updated data collection methods. In the era of the James Webb telescope, manufacturers can do better than a 100×100 pixel image. And with new data technologies, high-resolution data can become a reality. And it can be done in real-time with zero context loss from the data.
The old saying is that a picture is worth a thousand words. Little wonder the technology behind it has continued to improve and remain in demand. For today’s manufacturers, that same concept absolutely applies. They need data solutions that not only get the richest, granular data from their operations, but simultaneously show them what happened (or didn’t), and then take it to the next level by creating alerts in process issues, even before they occur, and automatically trigger recommendations to resolving them– saving them precious production time. A complete event-based picture of their operations is simply not possible via the majority of data practices and solutions today. This level of detail allows problems to be both discoverable and scalable. A solution anywhere becomes a solution everywhere in the organization.
Global Transparency
High-resolution, expert data is not a distant, futuristic ideal for manufacturing. It’s here and its results are compelling. Technological advancements in data collection and analysis are changing manufacturing organizations’ view of their operations by bringing global transparency to their operations. New advancements in data analytics mean manufacturers can easily identify and resolve operational inefficiencies across multiple lines, factories, and even continents with high-resolution data, rendering it incredibly powerful to every level of the organization. Line operators and managers benefit from detailed insights surfaced at the line level, guiding rapid improvements and increasing active production time while decision-makers zoom all the way out and look at aggregated data delivering powerful insights into utilization and capacity analysis, guiding buying decisions.
Given the availability of these new technologies, manufacturers no longer need to settle for spotty, low-resolution data to make global operational decisions. They now can rely on analyzed, detailed information from across the globe, bringing the whole landscape into focus for manufacturing decision-makers.
Fortunately, getting started with enterprise-scale data automation is more on the order of starting up a new smartphone than launching a telescope. Technologies specialized for manufacturing put increasing global data resolution in manufacturers’ reach and into more hands across the organization. The game of too many factory workers waiting for guidance from IT or other organizational data experts to guide real improvements in the organization is over. The technology to expertly guide them in their day-to-day operations is now within reach, all within projects measured in days and weeks, not months and years.