Management science did not appear suddenly, nor was it much of a success at first. The initial ideas emerged in the late 1800s when management pioneers such as Fred Taylor sought to improve production by pursuing standards and scientific methods that could boost productivity. Today we are used to factories with layers of employees and clear divisions of work. But a century ago these were very radical concepts, and not many understood them, let alone used them.
Taylor and his management evangelists almost didn’t change minds either. Their initial projects were disasters, mainly because their customers weren’t willing to adapt to see results. But then, two remarkable things happened. First, Louis Brandeis hired Taylor to help sort out the U.S.’ ailing railway services - Brandeis also coined the phrase ‘management science’. Not long after, Henry Ford published an article that revealed his company’s incredible manufacturing processes. It didn’t credit Taylor, but the symmetries were apparent.
Manufacturing 1.0
Manufacturers were the first to adopt these practices, and they have been served well as a result. Today, most companies make use of systems that centre themselves around the results of management science’s methodologies, such as time & motion studies.
“The word for manufacturing before Taylor was ‘chaos’,” says Vincent Maila, APS Consultant at nCoded Solutions. “Everyone did their own thing. Processes and equipment were very inefficient and not improving. Standards didn’t exist. It would be unimaginable now.”
Manufacturers are not stuck in the past. The sector was the first to adopt management science practices. It had since kept that appreciation for new ideas, even using Internet-of-Things technologies decades before they were known by that name.
“A manufacturer uses many technology services, including ERP systems, maintenance systems, shop floor control systems, and quality management systems,” says Lance Zikalala, MD of nCoded. “They are often used to chase the outcomes of scientific management. Most companies have developed applications and programs that assist in the determination of time standards. It’s also reflected in global standards and frameworks. The new ISO 9000 listing has a section that talks about the significance and availability of time standards, so does Six Sigma training.”
Time standards are determined through time & motion studies, which are cornerstones of scientific management practices. They are thus an excellent example of why current digital modernisation represents the new wave in manufacturing practices.
Digitally-powered management science
Before such studies, it was anyone’s guess how a manufacturing line performed. As the likes of Taylor and Ford tightened their theories into pragmatism, metrics such as equipment and personnel performance became essential measures. Soon enough, time officers - employees armed with clipboards and stopwatches - roamed floors, noting the time a specific line would take to accomplish its task. Method and fatigue studies, as well as Piece Wage Systems, also emerged.
These were very effective when they became accepted and remain so to this day, when every manufacturer adheres to them. Yet they are not agile enough: as you can imagine, one person with a stopwatch hardly captures the full picture across all machines in a factory, the observations are normally constrained to a singular machine at a time. Discrete manufacturers, in particular, need more specific and contextual data to reach their next level of efficiency. It’s where digital makes its most profound difference, said Maila:
“Digital systems can capture data. This capturing can be automated, but that is still very advanced and therefore, expensive. But it can also be done manually and still focus on more than one area of the floor. An operator can report to a shop floor tracking system, entering starts and stops as well as other events. You don’t need dozens of time officers running around - the operators do that job for them, and they do it so they are not blamed for other failures on the line.”
The captured data feeds a wide variety of efficiency improvements. It supports management science activities, such as determining the capacity of a line.
This data can inform schedulers and salespeople of the capacity, cost and delivery of a line. It can establish and improve standards - metrics used by the entire business to accomplish different tasks. Overall business performance can be gauged at a granular level by this data, since manufacturing operations are core to the business model.
Discrete manufacturers can put these improvements to work and develop new, predictable production lines as customers require it. Improving management science is the essential promise of manufacturing 2.0: integrated, efficient and agile operations created by capturing and using data effectively. Though such ideas seem overwhelming from a technology perspective, they make perfect sense through the prism of management science.
“Digital technologies are improving the management sciences that have served us well over the previous century,” says Zikalala. “If you focus on these as catalysts of change, then the opportunities in digital systems become obvious.”
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