A comparison of three simulation methodologies for a Lean Six Sigma manufacturing project – a business case study

Abstract

Purpose This paper aims to compare the prognostic and visualisation capabilities of all the three simulation paradigms to identify their suitability and rigor in eliminating weaknesses and bottlenecks in a Lean Six Sigma (LSS) project. Design/methodology/approach The paper uses an light-emitting diode (LED) factory as a business case to show the differences and advantages of using three different simulation techniques to solve a manufacturing problem. Findings Even though this is only one business case that shows how system dynamics (SD) can be effective in a Six Sigma manufacturing project, more examples are needed to validate this hypothesis within Six Sigma and Lean manufacturing fields. Even though, discrete-events (DE) and agent-based (AB) models are both equally well suited to develop the manufacturing processes and the choice of what to use may be arbitrarily dependent on the available software or the preference of the modeller, hybrid models seem to become extremely powerful. Therefore, more hybrid models need to be constructed within LSS (especially when a flowchart and a SIPOC ((Suppliers, Inputs, Process, Outputs and Customers) table are combined to develop a hybrid model) to achieve the most accurate results with accurate representation of reality. Originality/value There are three commonly used simulation techniques, DE, AB and SD, but choosing the right simulation methodology has often been a challenge.

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“…Six Sigma (Ali et al, 2019;Ali et al, 2020). In their work, Standridge and Marvel (2006) draw similar conclusions when applying M&S techniques to assess three different cases of LM integration in inherently different situations.…”

“…Regarding the integration of LM concepts, the main problem arising is the inability to evaluate the performance changes that stem from transforming parts of the manufacturing line or even the whole line in some cases. To combat this deficiency, M&S becomes a natural part of this integration process in terms of both LM (Marvel and Standridge, 2009) and lean Six Sigma (Ali et al , 2019; Ali et al , 2020). In their work, Standridge and Marvel (2006) draw similar conclusions when applying M&S techniques to assess three different cases of LM integration in inherently different situations.…”

Application of six sigma through deep learning in the production of fasteners

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