“There’s an almost universal lack of data into the activities that people perform in the factory,” says Hu. “This analytical gap severely limits manufacturers’ ability to make informed decisions on capacity planning, workforce management, process engineering and many other strategic domains. And, it suggests that manufacturers may overprioritize automation due to an inability to quantify investments in the human workforce that would result in greater efficiencies.

“Despite the prominence of people on the factory floor, digital transformation strategies for even the most well-known, progressive manufacturers in the world remain largely focused on machines,” explains Hu. “This massive imbalance in the analytics footprint leaves manufacturers around the globe with a human-shaped blind spot that prevents them from realizing the full potential of Industry 4.0.”

While manufacturing technology has seen increasing innovation for decades, the standard practices for gathering and analyzing tasks done by humans—and the foundation of holistic manufacturing practices like lean and Six Sigma—are time-and-motion study methodologies.

“They can be directly traced back to the time of Henry Ford and have not been updated for the digital age,” says Hu.

“The principles underlying these 100-year-old measurement techniques are still valid, but they are too manual to scale, return incomplete datasets and are subject to observation biases,” adds Prasad Akella, CEO of Drishti. “In the age of Industry 4.0, manufacturers need larger and more complete datasets from human activities to help empower operators to contribute value to their fullest potential.

“This data will benefit everyone in the assembly ecosystem: plant managers, supervisors, engineers and, most importantly, the operators themselves,” says Akella.

According to Akella, engineers typically spend up to 37 percent of their time gathering analytics data manually. In addition, he says 73 percent of variability on the factory floor stems from humans and 68 percent of defects are caused by human activities.

“Perhaps as a result, 39 percent of engineering time is spent on root cause investigations to trace defects,” claims Akella. “[That is] another manual expenditure of time that could be greatly reduced with better data.

“Humans are the most valuable asset in the factory, and manufacturers should leverage new technology to extend the capabilities of both direct and indirect labor,” notes Akella. “Instead of spending so many hours collecting data, [engineers should focus] their attention and capabilities on the most critical decisions and tasks.”

“The bottom line is that better data can help both manufacturers and human operators across the board,” adds Hu. “Data illuminates opportunities for productivity and quality improvements; simplifies traceability; mitigates variability; and creates new opportunities for operators to add even greater value.

“Humans are going to be the backbone of manufacturing for the foreseeable future,” claims Hu. “The companies that improve their human factory analytics are the ones that will be best positioned to compete in Industry 4.0.”