scispace - formally typeset
Search or ask a question
Journal ArticleDOI

The integration of lean management and Six Sigma

01 Feb 2005-The Tqm Magazine (Emerald Group Publishing Limited)-Vol. 17, Iss: 1, pp 5-18
TL;DR: A thorough analysis of the two programs provides some likely reasons why the programs alone may fail to achieve absolute perfection, and a lean, Six Sigma (LSS) organization would capitalize on the strengths of both lean management and Six Sigma.
Abstract: Purpose – To eliminate many misconceptions regarding Six Sigma and lean management by describing each system and the key concepts and techniques that underlie their implementation. This discussion is followed by a description of what lean organizations can gain from Six Sigma and what Six Sigma organizations can gain from lean management.Design/methodology/approach – Comparative study of Six Sigma and lean management using available literature, critical analysis, and knowledge and professional experience of the authors.Findings – The joint implementation of the programs will result in a lean, Six Sigma (LSS) organization, overcoming the limitations of each program when implemented in isolation. A thorough analysis of the two programs provides some likely reasons why the programs alone may fail to achieve absolute perfection.Practical implications – A lean, Six Sigma (LSS) organization would capitalize on the strengths of both lean management and Six Sigma. An LSS organization would include three primary t...
Citations
More filters
Journal ArticleDOI
TL;DR: A review of Lean Manufacturing (LM) literature can be found in this paper, where the authors highlight the divergent definitions, scopes, objectives, and tools/techniques/methodologies.
Abstract: Purpose – The advent of recession at the beginning of twenty-first century forced many organizations worldwide to reduce cost and to be more responsive to customer demands. Lean Manufacturing (LM) has been widely perceived by industry as an answer to these requirements because LM reduces waste without additional requirements of resources. This led to a spurt in LM research across the globe mostly through empirical and exploratory studies which resulted in a plethora of LM definitions with divergent scopes, objectives, performance indicators, tools/techniques/methodologies, and concepts/elements. The purpose of this paper is to review LM literature and report these divergent definitions, scopes, objectives, and tools/techniques/methodologies. Design/methodology/approach – This paper highlights various definitions by various researchers and practitioners. A total of 209 research papers have been reviewed for the research contribution, research methodology adopted, tools/techniques/methodologies used, type of industry, author profile, country of research, and year of publication. Findings – There are plethora of LM definitions with divergent objectives and scope. Theory verification through empirical and exploratory studies has been the focus of research in LM. Automotive industry has been the focus of LM research but LM has also been adopted by other types of industries also. One of the critical implementation factors of LM is simultaneous adoption of leanness in supply chain. LM has become an integrated system composed of highly integrated elements and a wide variety of management practices. There is lack of standard LM implementation process/framework. Originality/value – The paper reviews 209 research papers for their research contribution, research methodology, author profile, type of industry, and tools/techniques/methodology used. Various characteristics of LM definitions are also reviewed.

665 citations

Journal ArticleDOI
TL;DR: In this paper, the integration of lean principles with Six Sigma methodology as a coherent approach to continuous improvement is examined, and a conceptual model for their successful integration is provided. But, although research has been undertaken on the implementation of lean within various industries, the many tools and techniques that form the “tool box”, and its integration with six Sigma (mainly through case studies and action research), there has been little written on the journey towards integration of the two approaches.
Abstract: Purpose – Although research has been undertaken on the implementation of lean within various industries, the many tools and techniques that form the “tool box”, and its integration with Six Sigma (mainly through case studies and action research), there has been little written on the journey towards the integration of the two approaches. This paper aims to examine the integration of lean principles with Six Sigma methodology as a coherent approach to continuous improvement, and provides a conceptual model for their successful integration.Design/methodology/approach – Desk research and a literature review of each separate approach is provided, followed by a view of the literature of the integrated approach.Findings – No standard framework for lean Six Sigma or its implementation exists. A systematic approach needs to be adopted, which optimises systems as a whole, focusing the right strategies in the correct places.Originality/value – This paper contributes to knowledge by providing an insight into the evol...

500 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a Lean-Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process, which integrates Lean tools (current state map, 5S system, and total productive maintenance) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty.
Abstract: Lean and Six Sigma are two widely acknowledged business process improvement strategies available to organisations today for achieving dramatic results in cost, quality and time by focusing on process performance. Lately, Lean and Six Sigma practitioners are integrating the two strategies into a more powerful and effective hybrid, addressing many of the weaknesses and retaining most of the strengths of each strategy. Lean Sigma combines the variability reduction tools and techniques from Six Sigma with the waste and non-value added elimination tools and techniques from Lean Manufacturing, to generate savings to the bottom-line of an organisation. This paper proposes a Lean Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process. The proposed framework integrates Lean tools (current state map, 5S System, and Total Productive Maintenance (TPM)) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty. Implementation of the proposed framework shows dramatic improvement in the key metrics (defect per unit (DPU), process capability index, mean and standard deviation of casting density, yield, and overall equipment effectiveness (OEE)) and a substantial financial savings is generated by the organisation.

411 citations


Cites background from "The integration of lean management ..."

  • ...2002, Sharma 2003, Arnheiter and Maleyeff 2005). Shah and Ward (2003) accentuated the importance of plant size, plant age, and union status on the likelihood of implementing 22 manufacturing practices that are key facets of the Lean production system....

    [...]

  • ...…Danaher Corporation, General Electric, Motorola, Honeywell, and many others, have achieved dramatic results by implementing either Lean or Six Sigma methodologies in their organisation (Womack and Jones 1996, Harry 1998, Basu 2001, Murman et al. 2002, Sharma 2003, Arnheiter and Maleyeff 2005)....

    [...]

Journal ArticleDOI
06 Jun 2008
TL;DR: If six sigma and lean are new methods, or if they are repackaged versions of previously popular methods – total quality management (TQM) and just‐in‐time (JIT) – is explored.
Abstract: Purpose – The purpose of this paper is to explore if six sigma and lean are new methods, or if they are repackaged versions of previously popular methods – total quality management (TQM) and just‐in‐time (JIT).Design/methodology/approach – The study is based on a critical comparison of lean with JIT and six sigma with TQM, a study of the measure of the publication frequency – the number of academic articles published every year of the previous 30 years – for each topic, and a review of critical success factors (CSF) for change efforts.Findings – The more recent concepts of lean and six sigma have mainly replaced – but not necessarily added to – the concepts of JIT and TQM. lean and six sigma are essentially repackaged versions of the former, and the methods seem to follow the fad (product) life cycle. The literature offers fairly similar and rather general CSF for these methods, e.g. top management support and the importance of communication and information. What seems to be missing, however, is the need ...

342 citations


Cites background from "The integration of lean management ..."

  • ...Similarly, six sigma has been promoted as a new organizational change and improvement method (Hoerl et al., 2004; Arnheiter and Maleyeff, 2005)....

    [...]

  • ...” Recently, there have also been efforts to promote lean six sigma (George et al., 2004; Arnheiter and Maleyeff, 2005; Brett and Queen, 2005; Caldwell et al., 2005)....

    [...]

  • ...Proponents claim that it is more than just a quality system (Arnheiter and Maleyeff, 2005; Spector, 2006)....

    [...]

  • ...Motorola, the company usually recognized as one of the original developers of six sigma, decided in the 1980s that the traditional quality levels, measuring defects in thousands of opportunities, were not satisfactory (Arnheiter and Maleyeff, 2005)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the use of Lean and Six Sigma methodologies increased OR efficiency and financial performance across an entire operating suite across three surgical specialties, which resulted in substantial improvements in on-time starts and reduction in number of cases past 5 PM.
Abstract: in 3 domains, ie, personnel, information processed, and time. Multidisciplinary teams addressed 5 work streams to increase value at each step: minimizing volume variation; streamlining the preoperative process; reducing nonoperative time; eliminating redundant information; and promoting employee engagement. Process improvements were implemented sequentially insurgicalspecialties.Keyperformancemetricswerecollectedbeforeandafterimplementation. RESULTS: Across 3 surgical specialties, process redesign resulted in substantial improvements in on-time starts and reduction in number of cases past 5 PM. Substantial gains were achieved in nonoperative time, staff overtime, and ORs saved.These changes resulted in substantial increases in margin/OR/day. CONCLUSIONS: Use of Lean and Six Sigma methodologies increased OR efficiency and financial performance across an entire operating suite. Process mapping, leadership support, staff engagement, and sharing performance metrics are keys to enhancing OR efficiency.The performance gains were substantial, sustainable, positive financially, and transferrable to other specialties. (J Am Coll Surg 2011;213:83–94. © 2011 by the American College of Surgeons)

334 citations

References
More filters
Book
01 Jan 1982
TL;DR: In this article, the authors present a comprehensive overview of the statistical properties of point estimates and their relationship with the probability of a given point in a single-sample set of data.
Abstract: 1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. 3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution. 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots. 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population. 8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean. The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances. 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA 11. 4 2p Factorial Experiments. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter ss1. Inferences Concerning Y*x* and the Prediction of Future Y Values. Correlation. 13. NONLINEAR AND MULTIPLE REGRESSION. Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. 14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling.

2,313 citations

Journal ArticleDOI
TL;DR: This paper presents a meta-modelling framework for estimating the probabilities of different types of population sizes using a simple linear Regression model, and some examples show how this model can be modified to accommodate diverse population sizes.

1,406 citations


"The integration of lean management ..." refers background in this paper

  • ...997) to the power of 1,000, and is based on the binomial probability distribution ( Devore, 2000...

    [...]

Book
01 Jan 1986

425 citations


"The integration of lean management ..." refers methods in this paper

  • ...A ZQC system includes mistake proofing (poka-yoke), source inspection (operators checking their own work), automated 100 percent inspection, stopping operations instantly when a mistake is made, and ensuring setup quality (Shingo, 1986)....

    [...]

Book
01 Jan 1975

95 citations

Journal ArticleDOI

74 citations


"The integration of lean management ..." refers background or methods in this paper

  • ...The calculation used to obtain this probability requires raising the fraction conforming (0.997) to the power of 1,000, and is based on the binomial probability distribution (Devore, 2000)....

    [...]

  • ...997) to the power of 1,000, and is based on the binomial probability distribution (Devore, 2000)....

    [...]