Building A Better Mousetrap: The Emerging Role Of Big Data And Visualization In Manufacturing

Productivity is down. According to the U.S. Bureau of Labor Statistics, labor productivity fell 3.1 percent in early 2015, while unit labor costs increased 6.7 percent. The numbers are striking, especially considering the wealth of technology now used by companies to enhance automation and improve production times.

While there’s no simple answer to the problem of suboptimal production, it’s possible to leverage alternatives beyond typical tech investment or staffing scenarios here’s a look at the emerging role of big data and visualization in manufacturing.

Supply Chain Connections

Fifty years ago, manufacturing was like a game of chicken: Companies could either overstock and hope suppliers received enough orders that inventory didn’t go to waste or underproduce based on current trends and accept the risks of not meeting deadlines if demand exceeded supply. The emergence of agile and lean manufacturing principles, however, combined with advanced inventory tracking technologies made it possible to adopt just-in-time strategies that provided a much higher correlation between the drive to supply and the pull of demand.

As noted by Forbes, big data and visualization solutions now take this process a step further by allowing manufacturers to view product quantities and delivery accuracy in real time. This shifts the focus from simply meeting deadlines and anticipating demands to increasing overall quality; in effect, data-enabled supply clarity allows producers to deliver better results end-to-end.

Sources of Conflict

Big data and visualization tools can also help determine the source of production delays or problems with specific components. Because these issues may have multiple root causes, action taken based on observed symptoms may not provide the ideal response while immediate concerns are addressed, underlying issues remain and impact the next production cycle. According to recent data from McKinsey & Company, however, the use of advanced analytics such as data visualization, correlation analysis, significance testing and artificial neural networks can shed light on the specific drivers of production delays, allowing companies to target and correct these issues at the source.

Employee Empowerment

It’s also possible to improve the manufacturing process by providing more actionable data to on-floor staff. While high-level results that allow engineers or IT experts to diagnose and solve a problem help reduce time wasted, what if it were possible to empower other workers to do the same?

It starts with digital signage; monitors and displays posted around common work areas or in break rooms that provide real-time updates on production status, the progress of specific orders, or the efficiency of various departments. By fine-tuning the mix of data presented, it’s possible to encourage improved work ethic without the need for reprimands or lectures.

Consider the case of Fujitsu: The company installed displays on the shop floor that provide at-a-glance information about production efficiency and the number of errors in each process. The result? Errors that took six experts six hours to resolve can now be corrected in an hour by anyone on staff.

Productivity is down, but the amount of available production data is rapidly increasing. In combination with effective data analytics, it’s possible to improve supply chain connections, identify root causes, and improve employee efficiency and morale.