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.
JMASM 51: Bayesian Reliability Analysis of Binomial Model – Application to Success/Failure Data