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Specific Literature Requests Related to Lean Six Sigma

Six Sigma – iSixSigma Forums General Forums General Specific Literature Requests Related to Lean Six Sigma

This topic contains 5 replies, has 3 voices, and was last updated by  HumbleData 2 months, 1 week ago.

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  • #237165

    HumbleData
    Participant

    I am a recent Bachelor’s Degree college graduate that is training to become a Data Scientist. I am planning to seriously adopt the Lean Six Sigma and TRIZ Innovation mindsets over the next three to five years as I improve my proficiency in software development and data analysis.

    I have two questions for Lean Six Sigma:

    1) Can a professional please provide link(s) to good books or peer reviewed literature on using Machine Learning and rigorous advanced methodologies from Data Science and Statistics during the analysis stage of Six Sigma? Of course, these techniques would be used after the current standard methodologies.

    2) Can a professional please provide link(s) to good books or peer reviewed literature on experimental design and analysis to optimize waste and process variance to maximize business revenue / business expansion instead of minimizing defect rates?

    For question 2), maximizing business growth and minimizing defect rates may reach the same goal or they may not. For example, a business may not want to minimize over-production if it means increasing innovation (I’m not sure if this specific example is accurate, but the general idea is important).

    Thus, a business may have the desire to increase some of its process variation to improve overall revenue and shareholder support. Yet, at what magnitude and direction should the individual process component variances change? How do you design experiments to hypothesize and determine the ideal direction and magnitude of changes to process variance to maximize corporation growth and desired qualities like innovation?

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    #237204

    shideler14
    Participant

    Here’s a research paper that you may find helpful: “Lean Six Sigma meets data science: Integrating
    two approaches based on three case studies”. See attached.

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    #237205

    shideler14
    Participant
    #237245

    HumbleData
    Participant

    Thank you so much for providing access to this article; it looks great!

    I’m also downloading or requesting access for:

    Achieving Aggressive Goals through Lean Six Sigma: A Case Study to Improve Revenue Collection

    Using a Lean Six Sigma approach to drive innovation

    Creativity, innovation and lean sigma: a controversial combination?

    Applying Lean Six Sigma and TRIZ methodology in banking services

    Leading holistic improvement with lean six sigma 2.0 (book)

    Assessment of critical failure factors (CFFs) of Lean Six Sigma in real life scenario: evidence from manufacturing and service industries

    Critical failure factors of Lean Six Sigma: a systematic literature review

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    #237254

    Robert Butler
    Participant

    I would like to offer a few comments and make a few recommendations with respect to your initial post.

    Statistical methods, machine learning, advanced methods of data analytics and statistics are all tools one can/does use in the analysis phase of six sigma. In this light, the observation “Of course, these techniques would be used after the current standard methodologies.” does not make sense.

    At first I thought your statement in your second question “… experimental design and analysis to optimize waste and process variance to maximize business revenue / business expansion instead of minimizing defect rates?” Meant you were talking about waste and variance reduction in a process but then later you said, “…a business may have the desire to increase some of its process variation to improve overall revenue and shareholder support.” Assuming this was not a typo, the only business of which I’m aware that would view increased process variation as a good thing is the field of cryptography where increased variability means increased difficultly in code breaking – in every other instance the focus is on variance reduction.

    The overall impression I have of your initial post is you want to gain some basic understanding of analytical methods and their underpinnings. If this is the case then you might want to consider the following:

    1. With respect to connections between machine learning and statistics I think your best bet would be a Google search. I plugged those terms into their engine and a number of peer reviewed papers came up in the first pass. I can’t vouch for the quality/relevance to your question but it would be a start.

    2. For a basic understanding of statistical principles I’d recommend reading Gonick and Smith’s book The Cartoon Guide to Statistics.

    3. For a basic understanding of regression methods (I’m citing the 2nd edition) try Draper and Smith Applied Regression Analysis. For a first pass, read and understand Chapter 1, in particular the very well written description of just what linear regression is and the basic assumptions of the method. You could skip Chapter 2 because that is just Chapter 1 in matrix notation form and then read and memorize Chapter 3 – residual analysis. You might want to augment Chapter 3 with Shayle Searle’s August 1988 paper in The American Statistician concerning data that results in banded plots of residuals. You could follow this with a reading of Chatterjee and Price’s book Regression Analysis by Example.

    4. Experimental design is experimental design and it can be used to address changes in the mean, changes in the variance, or both. To the best of my knowledge there are no special requirements/restriction if one wishes to employ experimental design methods in conjunction with machine learning or data analytics – indeed I have built experimental designs to explicitly guide machine learning efforts.

    A good overview of design methods would be Schmidt and Launsby’s book Understanding Industrial Designed Experiments. It covers all of the types and provides a good discussion of when and where you might want to use the various forms. It also contains an excellent discussion of the Box-Meyers method for using the data from an experimental design to identify sources of process variation.

    5. Data quality: A book that is worth reading in its entirety, particularly in this age of big data, is Belsley, Kuh and Welsch’s book Regression Diagnostics – with the subtitle “Identifying Influential Data and Sources of Collinearity.” The book is not an easy read and you will need some knowledge of matrix algebra/symbol manipulation in order to understand what it is saying.

    The issues it discusses and evaluates are particularly important given the somewhat prevalent attitude which can be summarized as; if I have a big data set I have everything and I don’t need to concern myself with issues that are related to “small” data sets. The attitude has no foundation in anything save blissful ignorance – big data has the same problems as small data and, to keep yourself from going wrong with great assurance, you need to keep this in mind and know what questions to ask. A good recent book which discusses some of these issues in a non-technical manner is Weapons of Math Destruction by Cathy O’Neil. She is a mathematician who has worked with big data and I think her book is a balanced look at the promise and pitfalls of big data analysis.

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    #237271

    HumbleData
    Participant

    Thank you. This was very helpful.

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