The Relevancy of the Five Whys

Operations

Even in the time of Big Data, is it important to ask “why”

By Sundeep Sanghavi

The “Five Whys” is an iterative concept for problem solving that was developed almost two centuries ago. Once used to find solutions in a previous era, the technique is as relevant as it has ever been as Big Data marches on in its supremacy.

For those who don’t already know, the Five Whys are an interrogation technique designed to explore cause-and-effect relationships as related to specific problems. It was developed by Sakichi Toyoda, founder of the Toyota car company and hero of the Japanese industrial revolution.

The number of whys (five, obviously), is taken from the anecdotal observation that - more often than not - five iterations of “why” were needed to find the root cause of any given problem. The idea behind this is that asking “why” of a situation five times would be sufficient to find a way to solve problems with machinery and processes.

One example of the Five Whys goes as follows: a robot stops working and engineers are trying to fix it. "Why did the robot stop?” asks engineer number one, with number two replying that the circuit overloaded and blew a fuse. “Why is the circuit overloaded?” number one continues, only to be informed that the bearings were insufficiently lubricated. After asking why there was insufficient lubrication, number one learns from number two that the robot’s oil pump “is not circulating enough oil”. “Why is that?” number one asks, “because the pump intake is clogged with metal shavings.”

“And why is it clogged?”

“There is no filter on the pump.”

These five questions, asked in succession, have identified the root cause of the problem, which now puts engineers in a position to fix it. Originally intended for an earlier industrial age, the technique of the Five Whys is as relevant now as it has ever been. Moreover, it is also applicable to new fields and industries, such as Big Data and machine learning.

Why is the technique still pertinent?

One big reason as to why the Five Whys remain crucial is recalls. Recalls remain a constant and ever-present problem within manufacturing, resulting in high costs and - at times - shattered consumer confidence.

The Five Whys - which were formally developed in the 1950s, a century after Toyoda’s death - can be employed alongside data to stop and prevent the prevalence of recalls. Sadly, this is yet to be properly implemented. Just last year companies like Samsung and Takata, an airbag manufacturer, were caught up in high-profile recalls that led to injuries and deaths.

Getting to the bottom of recalls requires a lot of pertinent question asking. Effective questions, asked - and answered - quickly, can restore consumer trust in a brand that has just had to issue a very serious warning about a product that it has sold. It can, in the case of recalls like Takata, save lives.

Machines are an aid, not a replacement

The fact is that, even with factory floors - as well as vehicle fleets - being filled with sensors, questions still need to be asked. Even in the age of Industrial Internet of Things (IIoT) - that is, factory equipment that can communicate with sensors and computers - this does not render human overseers obsolete. Not yet at least.

IIoT can and will be used to speed up factory processes, making production more manageable and more scalable. This also means finding the solutions to problems that cause machines to breakdown or otherwise fail in their primary function.

However, while the impact of this speed cannot be denied, the machines will require human workers to at least ask the Five Whys when considering any problem. The sensors may detect and report faults, but they will not be asking their own questions. Rather, the humans on the factory floor will be able to ask machines this vital series of questions, and will be able to expect faster responses that more accurately report what is causing problems.

The future marches on

But, of course, while sensors can help humans interact with machines, the rise of cognitive predictive (and preventative) maintenance and the need to have a workforce to ask the Five Whys will also come to pass. New machines with their thinking capabilities can employ this interrogation technique across thousands of factories and millions of sensors. This is, of course, not possible with a human workforce.

Machines can also come up with an answer that requires brief and non-technical approval from a human supervisor, so that maintenance teams know what questions are being asked to reach the end conclusion. For example, General Electric has hundreds of factories across the globe and for the company to be able to ask all the questions it needs to ask, there’s just no physical way that it could have human teams achieve this. Now machines can do that for GE, providing a brief and actionable solutions in the process.

There are, of course, some limitations to the Five Whys; no system is perfect, nothing is flawless. In using this system, it can, at times, be difficult to distinguish between causal factors and root causes. At times, there is also a lack of rigor in deducting, where advocates of the system are not required - and subsequently do not - test for the sufficiency of root causes generated by this method of industrial soul searching. The trick to solving this problem, and ensuring the Five Whys remains relevant, is to keep it grounded in observation, not deduction. This, combined with skillful use of the system, will keep the method a viable one to aid technology now and in the future.

As sensor use continues to grow and proliferate, the benefits of machines using the Five Whys will be felt across industry the whole world over. Sensors and factory equipment will combine over the IIoT, making advanced warnings and predictive maintenance better and more advanced. This will result in safer factories, and more protected workforces.

Here’s to asking why.

CEO Sundeep Sanghavi co-founded DataRPM with the goal of providing a platform that delivers hyper-fast cognitive data products to organizations challenged by the volume, velocity and variety of their big data and machine learning. Learn more at DataRPM.com. Sanghavi can be reached at sundeep@datarpm.com.

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