Using simulation with Design For Six Sigma in a server manufacturing environment

Ramakrishnan,; Цай,; Drayer,; Srihari,

doi:10.1109/wsc.2008.4736282

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…The first three stages are greatly connected with the customer requirements (Seifert, 2005) or product characteristics that are critical-toquality (CTQ). At the Define stage the problem is stated, sponsors are identified, the scopes of the project are settled and the metrics are chosen (Ramakrishnan & Tsai & Drayer, & Srihari, 2008). The measure stage is associated with the measurement of factors influencing the CTQ product with the usage of statistical tools.…”

Section: Six Sigmamentioning

confidence: 99%

“…DFSS is a method for radical changes and introduction of new processes. It usually deals with the defects at the early stages of production (Ramakrishnan & Tsai & Drayer, & Srihari, 2008). There are several methods accepted in DFSS that may vary depending on the company and/or company strategy: DMADV (Define, Measure, Analyze, Design, Verify), IDOV (Identify, Design, Optimize, Validate), DIDES (Define, Initiate, Design, Execute, Sustain), IIDOV (Invent, Innovate, Develop, Optimize, Verify), CDOV (Concept development, Design Development, Optimization, Verify, certification), DCOV (Define, Characterize, Optimize, Verify), IDEAS (Identify, Design, Evaluate, Assure, Scale-up) (Shahin, 2008).…”

Section: Six Sigmamentioning

confidence: 99%

“…Simulation was also found to be favorable in combination with Six Sigma in Software development (Bubevski, 2010), as it helps to reduce costs, discard defects and advance reliability (Mahanti & Antony, 2005). Another application of Six Sigma DFSS was found to be for server manufacturing, as this manufacturing a complex process, the conjunction with Six Sigma was favorable (Ramakrishnan, Tsai, Drayer & Srihari, 2008). That combination might be of great use in the technological era.…”

Section: Six Sigma and Simulationmentioning

confidence: 99%

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A Bibliometric Analysis of the Evolution of Six Sigma in the Context of Industry 4.0

Efimova

Briš

Ефимов

2021

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Contemporary world with constantly increasing speed of change and rapid development of technologies is challenging companies to accustom to existing complexity. This led to the fact that various methodologies change accordingly. Practitioners and researchers are seeking the ways to ameliorate processes using emerging technologies. One of the methodologies is Six Sigma that has always been connected with technologies necessary for data collection and analysis. The emergence of new technologies might benefit or challenge Six Sigma. In this paper an attempt has been performed to analyze the trends in research output of the conjunction of Industry 4.0 technologies and Six Sigma methodology. This paper is based on the bibliometric analysis. In the process of analysis, it was found that the combination of Six Sigma methodology and Industry 4.0 technology has positive potential, however, not all of the technologies have been analyzed.

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Section: Six Sigmamentioning

confidence: 99%

Section: Six Sigmamentioning

confidence: 99%

Section: Six Sigma and Simulationmentioning

confidence: 99%

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A Bibliometric Analysis of the Evolution of Six Sigma in the Context of Industry 4.0

Efimova

Briš

Ефимов

2021

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“…Other researchers discuss the integration of Six Sigma and computer simulation to improve outcomes, such as improving a patient's experience in hospital (e.g., . Ramakrishnan et al (2008) mention that approaches of LSS have been effective in term of improving performance by reducing waste in the design and operational processes. Aft (2001), suggested that "Lean Sigma is a customer focused, well defined, problem solving methodology supported by a handful of powerful statistical tools."…”

Section: Lean Six Sigma and Simulationmentioning

confidence: 99%

Adopting Lean Six Sigma to AnyLogic Simulation in a manufacturing environment

Ahmed

Olsen²

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

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The aim of this paper is to illustrate the use of the Lean Six Sigma (LSS) methodology in a manufacturing environment by adopting the simulation software Anylogic®. In doing so, we present an innovative approach that implements simulation as a decision-supporting tool in the management of Six Sigma quality improvement projects. We intend to demonstrate how the LSS improvement process, "Define, Measure, Analyse, Improve & Control" (or DMAIC) can be applied in the Anylogic® environment so as to enable us to validate a manufacturing system's existing state and its subsequent improvement. LSS has become the de-facto method for process performance improvement in manufacturing sectors and LSS has been progressively increasing its acceptance in industrial manufacturing that confronts challenges, such as, how to decrease waiting time, maximise capacity, best use resources etc. Determining what alterations and changes to make is not simple and it is not easy to forecast the effect of proposed changes precisely. Coping with complicated systems composed of several interacting components poses the risk of generating s unacceptable decisions. This paper explains how the simulation program Anylogic®, with its powerful predictive and visualization capabilities reduces the risk of making undesirable process improvement decisions and assists in ensuring the best possible solutions are found and executed. Consequently, the quality of any process may be enhanced by utilising Lean Six Sigma in an Anylogic Simulation environment and it has the potential to offer a useful approach in term of improving quality and reducing costs. This approach enables evaluation of the performance of the manufacturing process after the proposed and recommended solutions for waste reduction have been implemented. Anylogic® can confirm improvements in yield, Defects Per Million Opportunities (DPMO) and six sigma levels.

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“…As suggested by Ramakrishnan et al (2008), simulation techniques are recommended within Six Sigma Methodology for: quantifying different performance metrics to make better decisions before the implementation phase; studying alternatives and perform ‘what-if’ analyses; identify interactions between sub-systems; provide data analysis to understand better the system’s nature; and accounting both structural and random variability. …”

Section: Introductionmentioning

confidence: 99%

Enhancing Six Sigma methodology using simulation techniques

Ahmed

Page

Olsen

2020

IJLSS

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Purpose In recent times, the idea of taking advantage of the benefits of simulation techniques and Six Sigma discipline altogether has led various organizations towards implementation of simulation tools within Six Sigma methodology. The purpose of this study is to provide a more comprehensive literature review on the topic exploring how this amalgamation could work both in theory and practice. This precisely entailed finding dependable studies that shows how Six Sigma (DMAIC) Methodology can be enhanced by the three prevalent simulation techniques; Agent-Based (AB), Discrete-Events (DE), System Dynamics (SD). Design/methodology/approach A systematic literature review was considered more fitting in research because it involves rigorous and well-defined approach compared to other forms of literature review. In this case, the literature was comprehensive, well-encompassing and involved finding Six Sigma and Simulations literatures from reputable scholarly databases. The outcome of these reviews was the identification of a set of key finding compiled and classified by topics. The study follows an inductive approach and utilises a meta-synthesis review technique. Findings As numerous studies assert, simulation techniques including AB, DE and SD are applicable tools in almost every stage of DMAIC, especially the Analyse, Improve and Control phases, because of their capability to test and identify potential bottlenecks and improvement areas. Findings show that the simulation tools such as CLDs, Group Model Building, Dynamic Balance Scorecards and Cost of poor quality all have the potential to add value to a Six Sigma methodology. Originality/value The findings of this study highlight the importance of further inquiry in this area of study. The finding of this study suggests that although the study on the integration of Six Sigma and simulations is increasing, empirical evidence on its effectiveness is still limited. Therefore, this study suggests more roadmaps and investigations aimed at merging Six Sigma methodology and various simulation technique. Moreover, studies that centre on hybrid or multi-method simulations within Six Sigma are also urgently necessitated.

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Using simulation with Design For Six Sigma in a server manufacturing environment

Cited by 6 publications

References 12 publications

A Bibliometric Analysis of the Evolution of Six Sigma in the Context of Industry 4.0

A Bibliometric Analysis of the Evolution of Six Sigma in the Context of Industry 4.0

Adopting Lean Six Sigma to AnyLogic Simulation in a manufacturing environment

Enhancing Six Sigma methodology using simulation techniques

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