Many prominent figures have emerged within the quality field, but some have stood out as key figures of quality.  Most have passed away, but their memory still lives on in the ideas, concepts, and methods that permeate our quality thinking today.  In no particular order, they are:

• Dr. Walter Shewhart developed the Plan, Do, Check, Act (PDCA) cycle (known as “Plan-Do-Study-Act” in some circles, as well as theories of process control and the Shewart transformation process.
• Dr. W. Edwards Deming developed his complete philosophy of management, which he encapsulated into his “fourteen points” and the “seven deadly diseases of management”.  He advanced the state of quality, originally based on work done by Shewhart with his explanations of variation, use of control charts, and his theories on knowledge, psychology and variation.  Deming greatly helped to focus the responsibility of quality on management and popularized the PDCA cycle, which led to it being referred to as the “Deming Cycle”.
• Dr. Joseph M. Juran developed the quality trilogy – quality planning, quality improvement, and quality control.  Quality management plans quality improvements that raise the level of performance, which then must be controlled or sustained at that level in order to start the cycle again.
• Armand V. Feigenbaum developed the idea of total quality control based on three steps to quality consisting of quality leadership, modern quality technology, and an organizational commitment to quality.
• Dr. Kaoru Ishikawa developed the Ishikawa diagram and was known for popularizing the seven basic tools of quality and the philosophy of total quality.
• Dr. Genichi Taguchi developed the “Taguchi methodology” of robust design, also known as “designing in quality”, which focused on making the design less sensitive to variation in the manufacturing process instead of trying to control manufacturing variation.
• Shigeo Shingo developed lean concepts such as Single Minute Exchange of Die (SMED) or reduced set-up times instead of increased batch sizes as well as Poka-Yoke (mistake proofing) to eliminate obvious opportunities for mistakes.  He also worked with Taiichi Ohno to refine Just-In-Time (JIT) manufacturing into an integrated manufacturing strategy, which is widely used to define the lean manufacturing used in the Toyota production system (TPS).
• Philip B. Crosby developed the idea of “quality is free” which asserts that implementing quality improvement pays for itself through the savings from the improvement, increased revenue from greater customer satisfaction, and the improved competitive advantage that results. His popularized “zero defects” to define the goal of a quality program as the elimination of all defects and not the reduction of defects to an acceptable quality level.
• Dr. Eliyahu M. Goldratt developed the Theory of Constraints which focuses on a single element in a process chain as having the greatest leverage for improvement (i.e., “1% can have a 99% impact”). This compares to the Pareto principle which states that 20% of the factors have an 80% effect on the process.
• Taiichi Ohno developed the seven wastes (muda), which are used in lean to describe non-value-added activity. He developed various manufacturing improvements with Shigeo Shingo that evolved into the Toyota Production System.

Top Ten Quality Gurus

1. Dr. Walter Shewhart
2. Dr. W. Edwards Deming
3. Dr. Joseph M. Juran
4. Armand V. Feigenbaum
5. Dr. Kaoru Ishikawa
6. Dr. Genichi Taguchi
7. Shigeo Shingo
8. Philip B. Crosby
9. Dr. Eliyahu M. Goldratt
10. Taiichi Ohno

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.

At Bizmanualz, process improvement — internal and external — is one of our main objectives.

Many of us in the USA and elsewhere are aware of the need for significant improvement in many aspects of the health care process — providing and insuring, for example.  In a recent blog post about the US Healthcare Problem, we presented a case for using the ISO 9001 standard to drive health care process improvement.  Now, we’ll look at ”lean” and how it pertains health care.

The concept of “lean” was developed for production environments (see the Toyota Production System) but with a few modifications, it applies to services as well.  In either case, Lean considers the use of resources for goals other than “creating value for the customer” to be waste and such wastes should be eliminated.

From the customer’s perspective, value describes an item or a service they’re willing to pay for.  Lean is sometimes said to be about “creating more value with less work”; in reality, it’s about “maximizing value while minimizing waste”.  And though people can’t seem to agree on much of anything in the health care “debate”, one thing we should all be able to agree on is that there’s plenty of inefficiency throughout the health care system.

Bicheno and Holweg (in their book, “Lean Toolbox”), describe seven service wastes:

1. Delay – customers waiting for a service;
2. Duplication — having to reenter data, repeat details on forms, copy information across, or answer queries from several sources within the same organization;
3. Unnecessary Movement — having to get in line several times, lack of a “one-stop” service encounter, etc.;
4. Unclear Communication – wastes of seeking clarification, confusion over product or service use, wasting time finding a location that may result in misuse or duplication;
5. Incorrect inventory — being out-of-stock, unable to get exactly what was required, substitute products or services, or not having the right provider available;
6. Opportunity lost to retain or win customers – failure to establish rapport, ignoring customers, unfriendliness, and rudeness; and
7. Errors in the service transaction — product defects in the product-service bundle, lost or damaged goods (famously, the airman who was supposed to have his gallbladder removed but had his lower limbs amputated).

As providers and as customers, we’ve seen these wastes…far too many times.  We need to remove as many of these wastes as possible and improve the process.  That’s where Lean can help, and many health care providers are already implementing Lean and other process improvement tools and techniques.

We need to take Lean, ISO 9001, and other tools deeper into the entire process of providing health care — more providers and insurers — if we’re going to make things better and make the improvements last.  The answer is certainly not going to be found in new legislation (see #4, above).

Now, shall we – at long last — begin?

Information Technology Infrastructure Library (ITIL) has been growing in popularity because of its universal suitability as a framework for managing information technology (IT) services, including the infrastructure, development, and operations of an IT department.

In its fullest implementation, ITIL is a perfect complement to – and is perfectly complemented by – Six Sigma and Lean to create more agile and higher quality IT operations.  Using Six Sigma techniques like the DMAIC process introduces a more structured engineering approach to ITIL’s framework.  Lean thinking promotes continuous improvement and waste reduction into ITIL’s best practices.

ITIL itself does not provide methods to identify and target waste, document value streams (as is usually done with Lean), or measure customer satisfaction.  Nor is ITIL itself a transformation method used for change management.  But ITIL does provide the vocabulary and framework we think of as the process approach advocated by Deming, which is where all process improvements start.

Implementing an ITIL framework is an excellent starting point for IT organizations looking to evolve toward a more process-oriented state.  Six Sigma and Lean can be added to the ITIL framework to help your IT organization achieve continuous improvement and organizational agility.

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.

I will be leaving to speak at and attend the ASQ Lean Six Sigma Conference on Mar 2-3, 2009 in Phoenix, AZ.  If you are in the area then stop by to hear about Setting Goals with Lean Thinking.  You will learn the importance of position goals for lean thinking, finding your lean goals/metrics or what success will look like. Many people wonder about what lean tools are used to determine where to start your lean journey. I will be talking about using Value stream, Visual space, and Material flow analysis to create your lean improvement opportunities plan.  Stop by the Lean Six Sigma conference to learn about Setting Goals with Lean Thinking.