Application of Taguchi method to optimise the characteristics of green sand in a foundry

Gijo, E.V.; Scaria, Johny

doi:10.1504/ijbex.2011.038788

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Ultimately, the enhanced process performance will be maintained to apply effective control plans. The framework of Six Sigma (DMAIC) uses different non-statistical and statistical tools and techniques to reduce the variation in the process in a controlled manner (Gijo et al, 2011a(Gijo et al, , 2011b.…”

Section: Six Sigmamentioning

confidence: 99%

Process improvement based on an integrated approach to DMAIC and multi-method simulation

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Many companies have used simulation techniques to improve their operations and generate process improvements for many years. Six Sigma has been developed as a disciplined approach and has introduced an alternative way of thinking about product or process improvement. The concept of combining the advantages of simulation techniques and Six Sigma's methodology has recently led companies and academics to investigate and implement this strategy. The Six Sigma method, composed by the Define, Measure, Analyze, Improve and Control stages, is a problem-solving method. However, it also identifies problems for which the approach may be ineffective. The objective of this study is to improve and control processes, reduce non-value-added activities and support decision-making by using Six Sigma's methodology alongside AnyLogic; using agent-based, discrete event, and system dynamics models. Moreover, the paper explores a multi-method simulation as a guided tool to assist organizations with the decision to implement a Six Sigma approach and pinpoints the strategies to be adjusted and monitor the process' parameters to be improved as well as reduce bottlenecks and weaknesses in the entire process by setting up management guidelines of such complex and dynamic process. This ensures that existing systems and proposed improvements account for any short and long-term outcomes. This paper begins with an overview of Six Sigma, followed by a description and the benefits of using the AnyLogic simulation package for implementing this methodology. This paper also shows the fundamental relationships between the Six Sigma methodology and AnyLogic simulation displaying a framework in which they can be integrated and a business case where this framework is used. The improvement and the strengths from this combination between Six Sigma and simulations are represented as preferable and capable enhancements to Six Sigma to deal with defects in many aspects.

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Section: Six Sigmamentioning

confidence: 99%

Process improvement based on an integrated approach to DMAIC and multi-method simulation

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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“…The goal of the measure phase of the DMAIC project is to gather information from the existing process to evaluate the baseline status of the process. This entails the following key tasks: identifying the characteristics were data to be collected, studying the accuracy of the measurement system, collecting and recording the data and establishing a baseline performance of the process (Gijo et al, 2011a).…”

Section: The Measure Phasementioning

confidence: 99%

“…Having identified the root causes, implemented the solutions for them and observed the results, the project team moved on to the control phase of the project. The critical components of this phase include standardization and documentation of the improved Note: * Significant at 5 per cent level of significance process and creating a plan for monitoring the process (Gijo et al, 2011a). To ensure that the proposed methods of improvement are sustained, the team implemented a set of control mechanisms.…”

Section: Control Phasementioning

confidence: 99%

“…Even after Box-Cox and Johnson transformations, the data were not transformed to normality (Montgomery and Runger, 2007). Because none of the distributions were fitting to these data, the "observed parts per million (PPM) total" of 270,000 from Figure 4 was considered as an estimate of baseline performance of the process (Gijo et al, 2011a).…”

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confidence: 99%

“…From the ANOVA table, it was found that the p-values for factors "quenching time" and "reservoir volume" and the interaction of "quenching time" with "quenching oil" and "oil temperature" were found to be less than 0.05, leading to the conclusion that these factors and interactions significantly affect the hardness. To identify the optimum level for these factors, the main effect and interaction plots for the S/N ratio values were prepared and are presented in Figures 6 and 7 (Gijo et al, 2011a, Gijo andScaria, 2012). The best level for any factor corresponds to the level with the highest S/N ratio (Phadke, 1989;Wu and Hamada, 2011).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Application of Six Sigma methodology in a small-scale foundry industry

Gijo

Bhat²,

Jnanesh

2014

International Journal of Lean Six Sigma

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Purpose – The purpose of this article is to illustrate how the Six Sigma methodology was applied to a small-scale foundry industry to reduce the rejections and rework in one of its processes. Design/methodology/approach – The research reported in this paper is based on a case study carried out in an industry using the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) approach and its application in improving the leaf spring manufacturing process of a foundry shop. Findings – The root causes for the problem of rejection and rework were identified through data-based analysis at different stages in the project. The process parameters were optimized and measures for sustainability of the results were incorporated in the process. As a result of this study, the overall rejection was reduced from 48.33 to 0.79 per cent, which was a remarkable achievement for this small-scale industry. This was leading to improvement in on-time delivery to the customer. The finance department of the company estimated the annualized savings due to the reduction in rejection and was to the tune of USD8,000 per year. Research limitations/implications – The paper is based on a single case study executed in a company, and hence, there is limitation in generalizing the specific results from the study. However, the approach adopted and the learning from this study can be generalized. Originality/value – This article illustrates the step-by-step application of Six Sigma DMAIC methodology in a small-scale foundry industry to solve an age-old problem in the organization. Thus, this article will be helpful for those professionals who are interested in implementing Six Sigma to such industries.

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Process improvement through Six Sigma with Beta correction: a case study of manufacturing company

Gijo

Scaria

2013

Int J Adv Manuf Technol

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Application of Taguchi method to optimise the characteristics of green sand in a foundry

Cited by 6 publications

References 27 publications

Process improvement based on an integrated approach to DMAIC and multi-method simulation

Process improvement based on an integrated approach to DMAIC and multi-method simulation

Application of Six Sigma methodology in a small-scale foundry industry

Process improvement through Six Sigma with Beta correction: a case study of manufacturing company

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