Data Science in Manufacturing

The Promise of Big Data
Big data is paving the way for U.S. manufacturers to stay competitive in a global economy.

Companies like Ford and GM are integrating huge quantities of data – from internal and external sources, from sensors and processors – to reduce energy costs, improve production times and boost profits.

Some of the benefits?

Raytheon learned this when they implemented MES (manufacturing execution systems), a software solution that collects and analyzes factory-floor data.

By studying their data, Raytheon was able to determine that a screw in one of the components must be turned thirteen times. If it is turned only twelve times, an error message flashes and installation shuts down.

Like every other manufacturer, Raytheon is benefiting from the fact that complex robotics and automation have replaced humans on the factory floor. Machines embedded with sensors are constantly conveying high-quality data.

When properly parsed by data scientists, this information can be used to:

For Example, FleetPride saved costs by using IBM decision optimization to optimize warehouse locations as a way to minimize delivery time..

And that’s just the internal data. Imagine, if you will, a world where machines bypass humans and speak directly to each other.

This is called the Internet of Things, a concept that is already a reality. RFID readers, tags and sensors have become an integral part of manufactured objects, able to relay data to each other at the drop of a hat.

GE is particularly interested in the possibilities and continues to promote the value of the Internet of Things. They predict that 46% of the world economy can benefit and is also one of the founders of the Industrial Internet Consortium!.

The goal is to reduce the amount of unplanned downtime for industrial equipment (e.g., wind turbines) and avoid potential problems (e.g., power grid outages). The trick is going to be ensuring that all of these objects are speaking the same language.

Having suffered right along with manufacturers during the Great Recession, the government is doing its best to help. Back in 2012, the Obama administration proposed National Network for Manufacturing Innovation (NNMI), modeled after the Fraunhofer Institutes in Germany.

The proposal called for a series of public/private partnerships between U.S. industry, universities and federal government agencies. These partnerships would be focused on developing and commercializing new manufacturing technologies.

NNMI’s 2012 pilot institute was the National Additive Manufacturing Innovation Institute (NAMII), led by the National Center for Defense Manufacturing and Machining. In 2013, it announced the establishment of three new IMIs, each with a separate focus:

In 2014, the Obama administration signed the Revitalize American Manufacturing and Innovation Act as a way to connect manufacturing innovation institutes. In 2018, the National Science and Technology Council, Subcommittee on Advanced Manufacturing (NSTC SAM) started developing a National Strategic Plan for Advanced Manufacturing.

But the manufacturing sector isn’t breaking out the champagne just yet. Though supply chain efforts continue to go digital with Industry/Supply Chain 4.0, there are still some challenges when it comes to the data:

With so many data points available, it’s possible to “discover” correlations and connections that aren’t really there at all.

Big data has raised a number of red flags amongst watch dogs. Unlike the EU, the U.S. does not have a single data-protection law. Instead, there’s a hodgepodge of legislation, regulations and self-regulations. As sensors proliferate and the role of big data in manufacturing grows, the questions surrounding information will only grow louder:

Manufacturers must also contend with the reality of post 9/11 industry. The USA Patriot Act gave the government sweeping powers of law enforcement and investigation. At some point, a manufacturer may find itself subject to a higher authority.

For example, picture a scenario in which a criminal has stolen a Ferrari:

It is at this point that the police discover that the manufacturer’s data error has targeted the wrong Ferrari.

This hypothetical case could be expanded to include complicated regulations surrounding the import and export of goods; articles and services related to the U.S. Defense Department; and practically any interaction with the financial industry. Whatever manufacturers do with big data, they must be aware of the consequences.

“The factory environment is a data scientist’s paradise: both highly multivariate and relatively quantifiable.” – Travis Korte, Data Scientists Should Be New Factory Workers

The U.S. industrial revolution gave birth to a few things: mass production, environmental degradation, the push for workers’ rights… and data science.

No sooner had the first factories gone up than owners were looking for ways to squeeze more efficiency from the production process. It didn’t take long for smart entrepreneurs to realize the power of quantifiable analysis.

In the mid-19th century, the young Frederick Winslow Taylor had a problem. Despite his Quaker wealth and considerable brains, his eyesight was poor. Following in his father’s legal footsteps seemed impossible.

But instead of frittering away his inheritance like any self-respecting dilettante, Taylor joined the masses. He became an apprentice pattern-maker and machinist at Philadelphia’s Enterprise Hydraulic Works.

After his promotion to laborer and machinist at Midvale Steel, Taylor began to notice that the machines – and the men who handled them – weren’t working efficiently.

He realized he’d need to back up his observations with hard data. One thing led to another, and the result was – scientific management.

Take Taylor’s famous time studies. With a stopwatch in hand, Taylor would:

Was the lack of breaks impacting your productivity? Were your shovels the wrong size? Were your workers loafing? Thanks to his data observations, Taylor could tell you which tasks to tweak.

So could Frank and Lillian Gilbreth, two of the first management consultants in manufacturing. In their early 20th century, their goal was to help workers (and their employers) increase output and decrease time-wasting tasks.

It was Frank Gilbreth who first proposed that a surgeon be given a “caddy” – someone to fetch the scalpel and sponges – and Gilbreth who came up with the technique that military recruits use for assembling and disassembling their weapons in the dark. He even used a motion picture camera to observe and time a worker’s tiniest motions.

The Gilbreths believed in a never-ending story. Every aspect of the workplace should be constantly questioned, new improvements sought every hour. Their legacy, as it has become known in boardrooms, is continuous quality improvement.

These lessons were not lost on automobile manufacturers. One of the most famous early pioneers was, of course, Henry Ford.

Ford was a believer in the quantifiable:

And, because black paint was the fastest-drying color on the assembly line, he famously stated:

“Any customer can have a car painted any color that he wants so long as it is black.”

In Japan, similar developments were afoot. In the 1930s, Kiichiro Toyoda, founder of Toyota, discovered issues with the company’s engine manufacturing process. Too much money was being wasted in repairing poor quality work. Like Taylor, he turned to analysis, meticulously studying each step to identify the hiccups.

Taiichi Ohno, an executive at the company, took this systematic approach even further. With Ford’s work in mind, he developed the Toyota Production System (TPS), the forerunner of lean manufacturing.

His main objectives were to:

After visiting U.S. supermarkets, Ohno realized that actual sales – not sales targets – should be driving Toyota’s production line. Toyota began a policy of Pull (build-to-order) rather Push (target-driven) manufacturing. Their profits soared.

The year was 1981. MTV went on the air with “Video Killed the Radio Star.” AIDS was first identified. And at Motorola, employees were developing a strategy to become known as Six Sigma.

Like Lean Manufacturing, Six Sigma was aimed at eliminating errors, minimizing variability and improving overall quality. Its basis is the idea that every aspect of manufacturing and business processes can be:

What’s more, every Six Sigma project has a quantifiable target. Motorola could develop one to:

In 1995, Jack Welch made Six Sigma a central part of his business strategy at General Electric, prompting a surge in adoption by other companies. The term is now ubiquitous in business and industry.

Last updated: June 2020