Ishikawa Diagrams

Ishikawa Diagrams - Advanced Analytical Techniques, Winter 2010 - Mercyhurst College, Erie PA

Kenda Puchalski - http://intl520-aat-puchalski.wikispaces.com/

Thursday, January 20, 2011

Source Critique 4

Cause and Effect Diagram
Ruhm, K. (2005). Cause and Effect Diagram. Retrieved from http://www.mmm.ethz.ch/dok01/d0000538.pdf


Ishikawa Diagram

Purpose

This article is from a series of training modules on measurement science and technology topics made available by the Institute of Machine Tools and Manufacturing (IWF), Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland.

Strengths and Weaknesses

Strengths
>  Developing cause-and-effect diagrams is not time-consuming.
>  Cause-and-effect diagrams effectively illustrate relationships in a process.
>  Users can start with minimal data and build on the group knowledge base by going through the process of developing a cause-and-effect diagram.
Weaknesses
>  Causes cannot be weighted for evaluation.
>  Diagram is limited to describing a single effect.
>  Result is only qualitative; user must apply other unrelated techniques to derive any quantitative data needed to support decision making.

Description

Ruhm provides a very brief narrative about creating cause-and-effect diagrams:
1.  Identify the effect to be analyzed.
2.  Identify potential causes and add them to the graphical model as arrows that point to the effect.
3.  Review completed diagram to identify qualitative links and relationships. 

Uses
Ruhm recommends cause-and-effect diagrams as a graphic tool to map processes and illustrate complex relationships. He stresses that it is effective for deriving only first-level, qualitative results.


Sources Cited

This article includes no outside citations or references. Citations would improve the integrity of this document as a reference source; however, as it stands, it is a well-developed essay from a subject matter expert on the qualitative functions of cause-and-effect diagrams.

Most Informative

This article provides a technical perspective on Ishikawa diagrams that complements the descriptions found in many project management and quality control resources. It is also very frank about some of the limitations of this analytical method, including the fact that that the results are uniquely qualitative – not quantitative.   

Source Autho
r
Karl Ruhm is a Senior Lecturer with the Institute of Machine Tools and Manufacturing (IWF), Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland. He is a frequent presenter at international conferences on metrology issues and measurement science.
Comparison

This article differs from previously reviewed sources because the target audience is scientists and engineers, and the focus is very narrow, specifically describing the qualitative applications of cause-and-effect diagrams. 

Source Reliability
High credibility

Critique Author

Kenda Puchalski, kenda.puchalski@gmail.com, “Mercyhurst College, Erie PA, Advanced Analytic Techniques Course,” 20 January 2011.

2 comments:

  1. It seems like as the source critiques continue, we are all moving into the more technical works regarding our methods. It is probably due to our increased personal understanding and desire to learn more advanced principles and techniques within our research. Based on what I've read with your critiques so far, I would agree that a major limitation is the qualitative versus quantitative results.
    Interestingly, this is almost the opposite of much of the literature about Cost Utility Analysis (my project's method), in that it is criticized as not giving enough consideration to qualitative factors within the analysis.
    Good review Kenda.

    -Mark

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  2. I think that this source provides some very strong strengths and weaknesses. While these diagrams provide mainly qualitative information and conclusions, is there any way you could set the diagram up to produce a quantitative result? Every time I’ve used a fishbone diagram, it was for qualitative information, so I was just wondering if you have found a way to work quantitative data into the mix.

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