Seven quality tools can help supervisors roll a winner.

Think of dice ... consider the probability of rolling a 7. You have six possible combinations: 1 and 6, 2 and 5, 3 and 4, 4 and 3, 5 and 2, 6 and 1. So the probability is 6/36, or 16.7 percent, that you will roll a 7.

In manufacturing, if this is the percentage produced within specification, you'll be in trouble fast--which is why it's a good thing there are Seven Quality Tools to help us.

When we think of rolling dice or playing cards, we think of chance playing a key role in the outcome.

But being able to predict the likelihood of future events is not always enough in manufacturing. A wise man once said that in order to control the future, we must master the lessons of the past. Through the use of probability statistics, and by knowing something about recent past, we can predict and respond to control the likelihood of future events.

The discipline of total quality control uses Seven Quality Tools to identify focus for continuous improvement efforts in manufacturing. Over the years, quality experts realized that many quality-related problems could be solved with these tools.

1) Check Sheet

Think of a check sheet as similar to a grocery list. A check sheet is a list of all necessary key settings, tools, or raw materials are needed to make the product. A check sheet can also be used to capture pertinent data.

2) Cause & Effect Diagram

A man by the name of Kaoru Ishikawa developed a diagram that is commonly referred to as a cause and effect fishbone diagram. Much like when our normally dependable car stops working, if our manufacturing process exhibits a problem, we need to ask questions to determine what causes contribute to the effect.

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By assessing possible causes related to the 5Ms--materials, machines, measurements, man, and methods--we can seek effect relationships to understand today's problem.

3) Pareto chart

The Pareto chart was named for Vilfredo Pareto, whose principles allow us to identify the few truly important cause factors.

In a Pareto chart, the reasons are plotted with the greatest reason first and in descending order of importance. At a glance, the greatest contributing reasons are apparent.

4) Flowchart

The intent of a flowchart is to describe the various steps and decisions in a process. Consider a chart of getting in our car: step one, open door; step two, get in; step three, insert and turn key; step four, "Did the car start?" Depending on this answer, there are other steps outlined graphically.

5) Histogram

Consider a checksheet that records temperature readings. Consider that we make a tick mark for each reading within a five-degree temperature category.

For the histogram, we plot the data as adjacent bars with categories across the bottom X axis and the frequency of readings on the vertical Y axis. The result is the histogram showing data proportions.

6) Control Chart

A control chart helps the control of the process based upon knowing something about the normal in-specification manufacturing process. The process average or in-specification mean is plotted as the centerline. Additionally upper and lower control limits can be calculated and plotted.

Think of this process average as the centerline of the road and the control limits are the edges of the road that we must stay between. By plotting all new data within these limits, we can monitor and respond to trends or out-of-control conditions.

7) Scatter diagram

The scatter diagram has the ability to show nonlinear relationships between variables by plotting variables against each other. Variables with no relationships will result in scattered data plots. A plotting of variables that have relationships will show clear correlations.

If your intent is to master dice in the game of craps, consult the applied mathematics of game theory. If your intent is to master manufacturing processes, consult more information on the Seven Quality Tools to ensure process total quality control.

Elizabeth Maze-Emery is a quality professional from Dayton, OH. For information about these quality tools, related topics, or an extended version of this article that includes sample charts, e-mail her at emazeemery@jiu.edu.