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Dr Genichi Taguchi

04 Jul

Dr Genichi Taguchi

1 January 1924, to 2 June 2012

To understand Dr Genichi Taguchi, I sought out a little background information. An article published in Production and Operations Management (POMS) highlights his association with the other quality directed people mention in the textbook (Genichi). He built upon the work of Sir R. A. Fisher, P. C. Mahalanobis, C. R. Rao, Walter A. Shewhart, and others to advance the statistical analysis of quality assurance that we studied in this class. I believe he has a more striking influence on current methodologies because of this more recent arrival in this genre of scientific review. The textbook, POMS, and other sources discuss his involvement with such organizations as Bell Labs, Ford Motor Company, Nippon Telegraph and Telephone Corporation, and various scientific societal organizations, such as Japanese Union of Scientists and Engineers. Dr Taguchi received numerous commendations, including, but limited to, the Walter A Shewhart Medal, Willard F Rockwell Medal, and the Blue Ribbon Award from the Emperor of Japan.

If these achievements are not enough to highlight his significance, the application of his method to my day-to-day work also adds significant value to his techniques. As a computer programmer and data analyst, I have used, without foreknowledge, the methodologies discussed in the textbook, and especially Dr Taguchi’s methods.

“The Taguchi method is a valuable tool for achieving Six Sigma quality by helping to develop robust designs that are insensitive to variation” (Foster, 302). The simplicity behind Taguchi’s concept of robust design strikes to the heart of quality assurance. Table 2–7 of the textbook categorization demonstrates this. Taguchi shared a narrower focus on the intended purpose. The textbook highlights the key factors of the approaches. For Taguchi the focus of the variables is on quality assurance of products and services, philosophy driven, and project/team-based improvement (Foster, 44).

The software development process falls right in line with the Taguchi Process. In programming, we begin by identifying a problem through customers (computer users), data quality tests, or software needs. Brainstorming is used to identify a direction for finding potential solutions. Developers have to understand whether the information about the problem is caused by coding or other influences. This could be an existing software bug, data acquisition problem, data entry problem, or a problem with the analysis. Taguchi categorizes these elements as control factors and noise factors.

Software programming follows the Taguchi process with experimental design, experimentation, and analysis. In these steps, the developer will create an initial design, and run various predetermined tests to evaluate if it is achieving the desired output. Finally, in the confirming experiment step, the software is delivered to the end user who decides if further adjustments are necessary.

Further, concept design highlights a key point in the process of software development. Information technology professionals, in the software design work, manage the development process from acquiring consumer information and applying that knowledge to the product design and implementation. Taguchi focuses strictly on managing controllable variables. This is key in computer programming; it necessitates considering all possible inputs and determining how to manage those to achieve the desired outputs.

Works Cited

Foster, S. Thomas. Managing Quality: Integrating the Supply Chain, Fifth Edition. Pearson, 2013. eText.

“Genichi Taguchi.” Production and Operations Management 17.5 (2008): 2,I,II. ProQuest. Web. 29 June 2013. Retrieved from http://search.proquest.com/docview/228786159.

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2 Comments

Posted by on July 4, 2013 in Class Writing

 

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2 responses to “Dr Genichi Taguchi

  1. saginawrobin

    July 10, 2013 at 9:59 am

    To an extreme lay person, could you give an example of an ‘uncontrollable variable’?

     
    • n0dakbud

      July 11, 2013 at 9:18 am

      In short, uncontrolled variables are random occurrences; these have no cause within the defined process being examined. However, those variables do influence the final product.

      To explain this more clearly, allow me to provide examples of both. First, take as an example the cookie experiment that my class conducted. We were given an oatmeal chocolate chip cookie recipe that specified the use of Hershey’s chocolate bar. We modified this recipe by adding or subtracting ingredients; these are controlled variables. If the Hershey’s brand chocolate bar is used, then the uncontrollable variable would be any product variances Hershey’s allows in the production process. In this case, the recipe attempts to eliminate variances in the final product by sole-sourcing the chocolate bar, but it instills a uncontrollable variable.

      The uncontrollable, or random, variable is the manufacturing process within Hershey’s. You, as a purchaser of Hershey’s bar, have not control over its production process. Alternatively, you, as the manufacturer, could grow your own cocoa beans and process these into chocolate bars; this would be controlled.

      Another example, the auto manufacturing industry outsourced parts manufacturing to hundreds of suppliers. This allowed the manufacturers to significantly reduce their production costs. However, it moved the quality control to the suppliers. As the suppliers tried to maximize their profits, the quality of the parts declined. With the help of Taguchi, Ford identified this and took tighter control of its parts manufacturing and quality control specifications.

      As a third and final example, and very simple one, look at farming. The farmer (manufacturer) has control variables such as where to purchase land, which seed to plant, and when to harvest. However, weather is one uncontrollable variable for the farmer; early spring, early snow, excessively dry summer, etc. The farmer cannot accurately or effectively predict and manage the weather-caused changes to the yield of the crop, or harvest timeframes.

       

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