Although many companies have abandoned SPC tools for the more powerful DOE techniques, there are exceptions. Major exception is they have trained their entire direct labor force in elementary SPC tools so they can tackle low-grade quality problems. The result is instead having a few professionals tackle these problems, they now have a whole host of problem solvers.

Elementary SPC Techniques

The “Elementary SPC Techniques” figure lists the “seven tools of SPC”. These are tools that every production line worker should be learning and using. They are listed here because every R&D professional should be also familiar with them. Because they’re both limited and value as is compared to the design of experience methodology, only the objectives and methodology are outlined here. The one exception is the control chart, which will be discussed in some detail the next section. That said a brief commentary on each of these tools is in order.

PDCA (Plan, Do, Check, Act) is a variant of the traditional problem-solving approach of “observe, think, try, explain”. As a problem-solving tool, it has the same poor effectiveness as brainstorming and Kepneff-Tragoe techniques for solving technical problems.

Data Collection and Analysis is the first step in the long road to variation identification and reduction. Some planning is the key to effective data collection. The “why, what, when, where, who, and how” of data collection must be established a priori, that is, before the fact. This avoids teams and plants drowning in meaningless and useless data. Common pitfalls include: not defining the objective; not knowing what parameter to measure how to measure; not having sufficiently accurate equipment for the measurement; not randomizing; and poor stratification of data. Similarly, the analysis of data should be undertaken only with proven approaches, rather than with hit and miss approaches, such as PDCA, brainstorming, cause-and-effect diagrams, etc.

Graphs/Charts are tools for the organization, summarization, and statistical display of data. As in the case of data collection and analysis, the purpose of using graphs and charts should be clearly established and the usefulness and longevity periodically reexamined.

Checks Sheets/ Tally Sheets/ Histograms/ Frequency Distributions are tools whose main function is to simplify data gathering and to arrange data for statistical interpretation and analysis. There are several types of check sheets: for process distribution; for defective items/ causes/ defect locations (sometime referred to as measles charts); and memory joggers for inspectors, quality control, and servicers in checking product.

Tally sheets are special forms of check sheets to record data, keeps score of privacy process in operation, and divide data into distinct groups to facilitate statistical interpretation.

Histograms and frequency distributions provide a graphical portrayal of variability. Their shape often gives clues about the process measured, such as mixed lots (binomial distribution); screened lots (truncated distribution); amount of spread relative to specifications; nonstandard spread relative to the specification’s, etc. There are two general characteristics of frequencies distributions that can be quantified, central tendency and dispersion. Central tendency is the bunching up effective observations of a particular quality characteristic at the center and is measured by the average of all the observations, mode (the value of a quality characteristic with the largest number of observations), and median (the value that divides the number of observations into two equal parts). Dispersion is the spread of the observations and can be measured by range, the highest observation minus the lowest, and the standard deviation, which is approximately 1/6 of the range (but only for a normal distribution).

Pareto’s Law was put forth by Alfredo Pareto who was a 19th-century Italian economist who studied the distribution of income in Italy and concluded that a very limited number of people have most of its wealth. The study produced the famous Pareto Lorentz mal-distribution law, which states that the cause and effect are not linearly related; that a few causes produce most of the given effect, and, more specifically, that 20% of the causes produce 80% or more of the effects.

Juran, however, is credited with converting Pareto’s law into a versatile, universal industrial tool applicable in diverse areas, such as quality, manufacturing, suppliers, materials, inventory control, cycle time, value engineering, sales and marketing. In fact it can be applied to almost any industrial situation, blue-collar or white color. By separating the few important causes of any industrial phenomenon away from the trivial many, work can be prioritized to focus on these few important causes.

Brainstorming/Cause-and-Effect Diagrams/CEDAC when they have been applied to process control, sometimes become good examples of beautiful techniques applied wrongly. Brainstorming for instance in the social sciences, and even white-collar industrial work, is a marvelous tool for generating the maximum number of ideas and utilizing group synergy. However, its effectiveness in quality problem-solving is highly overrated. Even though group ideas are generally better than individual ones, guessing of problems is a kindergarten approach to finding root causes of variation.

Cause-and-effect diagrams were developed by Dr. Ishikawa, one of the foremost authorities on quality control in Japan. As a result, is often called the Ishikawa diagram or by reason of its shape, a fishbone diagram. It is probably the most widely used quality control tool for problem-solving among line workers. However its effectiveness is poor. Because only one cause is varied at a time, interaction affects are missed, which results in partial solutions and marginal improvements in quality.

CEDAC is the acronym for cause-and-effect diagram with the addition of cards. Developed by Fuduka, the technique is explained in detail in his book “Managerial Engineering”. CEDAC represents an improvement over the cause-and-effect diagram, with workers free to change any branch or tweak costs in the diagram as they observe new phenomena in a process and thereby gain new insights. Use of cards, under their own control, facilitate such instant updating of causes. Worker participation is enhanced and raw, unqualified information is captured before it evaporates. All this said, CEDAC still suffers from the same judgment weaknesses as the cause-and-effect diagrams contain.