One way to be sure to eliminate a problem for good is to identify the root cause and eliminate it. In the world of quality, we have this easy to use tool for getting to the root cause of a problem.

The “Five Whys”, simply put, means you state the problem and keep asking why until you’ve identified the root cause. However, using the Five Whys means the problem has occurred. Isn’t it better to prevent the problem from happening than correct it after the fact? Preventive action is infinitely preferable to corrective action.

I’ll give you a “for instance”. Someone I know recently left a wireless provider she’d been with for several years. What upset her most was that when she canceled, the customer service rep (CSR) didn’t ask why she was leaving. She might have reacted positively if the CSR had offered her an incentive to stay but he didn’t, and she’d pretty much made up her mind by then that they weren’t worthy.

If you can’t give somebody a reason to stay all along, your problems aren’t going to be magically solved by root cause analysis or any other corrective action tools. A root cause analysis may help you solve your problem, but why let the problem happen in the first place? Why not head off the problem? Take an active interest in your customers, rather than sit back and wait for things to happen.

Most customers will walk away from you without complaining. They don’t announce that they’re taking their business elsewhere: they just do it. They don’t give you a chance to explain yourself because they feel like they’ve been let down all along.

Dissatisfaction isn’t the result of a one-time occurrence. It happens over a period of time.  If, from the outset, communication is poor or nonexistent, the foundation for customer dissatisfaction is being laid. If you don’t continue to make your customer feel valued and welcome, the relationship that might have been never is.

Next, I’ll be looking for an answer to the question, “Why don’t customers complain?”, and I’m asking for your help. Are you more likely to complain to your vendors, or do you keep quiet and look for an alternative right away? What if you don’t have an alternative? What do you do then?

Thanks for your insights, and best wishes.

We all know what “corrective action” is, right? If you don’t, it’s really easy. It’s an action you take to eliminate the root cause of a problem (or nonconformance), thereby preventing — or reducing the likelihood of — the problem’s recurrence.

So, define the problem. (Well, see, it’s like this. Our skater was ahead — I mean “way ahead” — in the longest of the long-distance races. It’s, like, six miles. And with nearly three-quarters of the race gone, his opponent’s nowhere near him. He might as well be in another building…or another country.)

Doesn’t sound like a problem to me. (I was about to get to that. It’s at that point that our guy’s supposed to switch lanes to the outside. Only our coach says, “INSIDE!”, and our guy GOES inside, like he’s told. And because he didn’t switch lanes, our man’s DQ-ed.)

DQ-ed? (Disqualified. He had the best time, but didn’t win the race. We had the best man, the best coaches, the best training, best nutrition, best staff, the fastest track…and we have nothing to show for all that. No winner, no medal, no endorsements…nothing.)

And why was your man disqualified? (Like I said, the coach said “go inside” and he went inside. The coach made a mistake. So did our skater, I guess.)

Why did the coach tell your skater to go inside? (He wasn’t paying close attention…he was distracted…he was confused, somehow.)

Why did the skater do what the coach said? (He trusted the coach. He wasn’t paying attention, either.)

Why weren’t they paying attention? (I can’t say for sure. Maybe they were so far ahead, they got a little careless.)

See what we did? Recognize the “Five Whys”? We got down to a possible root cause. I say ”possible” because we rely on an individual’s focus, memory and biases. If we subject several people, including the skater and coach, to the “Five Whys”, we get a somewhat balanced result.

Now that we’ve identified a root cause, how do we eliminate it? Better yet, “What does this have to do with MY business?” For the answer to these and other questions…

…stay tuned.

There are seven common Quality Tools you can use to understand and improve processes during a process improvement event.   Each tool helps you identify sources of variation and aids in the analysis, documentation, and organization of the information, which leads to process improvement. 

1. Flowcharts, or Process Maps, visually represent relationships among the activities and tasks that make up a process.   They are typically used at the beginning of a process improvement event; you describe process events, timing, and frequencies at the highest level and work downward.  At high levels, process maps help you understand process complexity.  At lower levels, they help you analyze and improve the process.
2. Ishikawa, Fishbone, or Cause & Effect Diagrams visually represent the causes of a problem – or effect – and help you determine the ultimate source of the problem — the root cause.  (This tool is called a “fishbone” diagram because of its appearance; Ishikawa was its inventor.)   The cause-and-effect diagram is used at the beginning of root cause analysis, to organize the causes of a problem (people, methods, equipment, materials, measurement, and environment) and prioritize them.
3. Data Checklists, check sheets, or recording tables are matrices designed to assist in the tallying, recording, and analysis of test results or event occurrences.  They are utilized in production to count defects and collect process data, which you analyze to identify opportunities for improvement.
4. The Pareto chart is named after Vilfredo Pareto, who came up with the Pareto Principle (or the “80/20 rule”), which says that 20% of the factors account for 80% of potential problems.  The Pareto chart ranks defects, causes, or data from the most significant to the least significant, in descending order.  Pareto charts help you separate the “vital few” from the “trivial many”.  They are typically used during process improvement analysis, to understand where to focus improvement for the greatest impact.
5. Histograms consist of vertical bars, side-by-side, that depict frequency distributions within tables of numbers and can help you understand data relationships over time (e.g., the familiar “bell curve”).  Histograms are generally used during process improvement analysis.
6. Scatter charts display relationships between dependent (predicted) and independent (prediction) variables.  They are used during hypothesis testing, to determine if there is a correlation between two variables and how strong the correlation is.  Less scattering indicates stronger correlation.
7. The control chart is a type of statistical process control tool.  Process performance is plotted over time against upper and lower control limits; this helps you readily identify process variations and enables determination of special cause and common cause variation.  Control charts are used during production, or after process improvement implementations, to ensure that processes are within control limits, or “in control”.

To achieve the best results, start by (1) drawing up a process map, so you understand the process flow.  Next, (2) analyze the process flows for the primary causes of problems and develop your cause-effect diagram.  Then, (3) collect data using check sheets and (4) plot your data using a Pareto chart and/or (5) a histogram.  Next, (6) determine the relationship of various variables in your cause-effect chain using a scatter chart.  Once you have solved your problem, (7) use a control chart to ensure that the process is staying within process control limits — demonstrate process control.

The Seven Quality Tools

To summarize, using these seven quality tools:

1. Flowcharts or Process Maps;
2. Ishikawa, Fishbone, or Cause & Effect Diagrams;
3. Data Checklists, check sheets, or recording tables;
4. Pareto Charts;
5. Histograms;
6. Scatter plots; and
7. Control Charts (SPC)…

…especially in combination, will help you improve your processes and achieve your objectives.

For those who work with processes, we know that variability is the key factor.  The desired state is more consistency and less variability.  When processes have wide variability and inconsistent results, we call the process out of control.  When processes operate within established limits, the process is considered in control.

Typically, we attribute process variability to two causes—common cause and special cause.  Common cause variation is expected.  It is a result of the process design, machinery, and activities.  For example, I walk to the train station every day after work, and it takes six to 10 minutes.  The variation is due to factors like how long I have to wait for the elevator, how many times the elevator stops, and how long I have to wait at crosswalk lights.  These variations occur every day, and they are expected.  They are common cause variations.

Then one day it took 12 minutes to walk to the train station.  It took longer because someone approached me on the sidewalk and asked for directions.  They were lost, so I took a few minutes to explain to them where they are and how to get to where they are going, plus exchange a few pleasantries.  But that doesn’t happen very often.  In fact, it hardly ever happens.  The next day I return to the six to 10 minute window for my walk to the station.  It was a special cause of variation.

When addressing variation in a process, you have to understand if the variation is due to common cause or special cause.  The type of variation determines the activities we need to take to reduce variation.

To reduce common cause variation, it usually takes experimentation and/or statistical analysis to optimize the process.  Experimentation means changing something and measuring the results over time.  Statistical analysis means looking at results in different ways—stratifying and categorizing data in diverse manners and employing varying statistical methods like Pareto charts.

For example, I might experiment and collect data and find that if I leave at 4:45 instead of 5:00, the elevators are much less busy, and variation in the time to reach the station is reduced.

Special cause variation is typically discovered using root cause analysis.  In my example it was easy to identify why it took extra time to reach the station, but frequently the cause of unexpected variation is not so easy to see.  It takes an investigation using quality tools like 5 whys or fishbone charts to understand what happened.  Then, you can take action to prevent the unexpected cause of variation or simply ignore it because you realize that it happens rarely and the consequences are acceptable (as in my example).  I don’t mind missing a train to help someone out.