DMAIC is an acronym that discusses the five phases of a project. The five-phase involves defining the problem, measuring the process efficiency, analyzing the root cause of defects, improving process output, and controlling. For example, Valles et al. (2009) implemented Six Sigma to enhance the manufacturing process. Their research involved the use of Six Sigma in the manufacture of circuit cartridges by a semiconductor company. The DMAIC roadmap ensures that the project team establishes the main problem, actions, and causes to lower defects. The paper describes the various tools used in every DMAIC phase throughout the project.
Tools and Techniques Used in the Case Study
During phase one of the DMAIC model, the team used tools such as the Critical to Quality matrix, SIPOC diagram, bar chart, pie chart, and Pareto diagram (Valles et al., 2009). Besides, some analytical tools were used, such as brainstorming. The definition tools revealed that about 3.12% of the materials were faulty. Critical to Quality matrix was used to represent the portion of good parts from electrical testing. The cost caused by waste material during the process was represented by the Critical to Cost matrix. The tools used in the measurement phase include statistical process control, process map, gauge R & R, control charts, and Pareto diagram (Valles et al., 2009). The measurement tools were used to validate the accuracy of the equipment. The analysis phase included evaluating some potential components that may affect the electrical functioning of the equipment. The team used a process map, cause and effect diagram, brainstorming, analysis of variance, hypothesis testing, histogram, and Pareto diagram to analyze the process.
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The next phase after analysis is the improvement phase, which involves identifying a potential solution, implementing them, and verifying them to ensure the improvement is according to the experimental designs. The tools used in this phase include brainstorming, design of experiments, analysis of variance, bar chart, and pie chart (Valles et al., 2009). Besides, an evaluation of the potential benefits of the project was recommended during this stage. The improvements implemented were meant to lower the number of defective products. In the control phase, the tools used include descriptive statistics, flow charts, and control charts. The control phase enabled the company to achieve a stable process and determine some the pressure parameters.
Table 1: Tools Used in DMAIC Phases
| DMAIC Phase | Tools Used |
| Define | Critical to Quality matrix, SIPOC diagram, Pie, bar chart, Pareto diagram, and brainstorming. |
| Measure | Statistical process control, process map, Gauge R & R, control charts, and Pareto diagram. |
| Analyze | Process map, Cause and Effect diagram, Brainstorming, analysis of variance, hypothesis testing, histogram, and Pareto diagram. |
| Improve | Brainstorming, design of experiments, analysis of variance, bar chart, and pie chart. |
| Control | Descriptive statistics, flow chart, and control chart. |
Some redundant tools include brainstorming, Pareto diagram, bar charts, and pie chart since they were used in more than one phase. However, I would not consider it a redundancy since they were used to address different issues in various DMAIC phases. More tools that can be used in the define phase include the project charter, as it provides a framework for the whole project (Patel & Desai, 2018). Check sheet and Scatter Plot also helps in the measure phase of the project (Bhowmik et al., 2018). The use of the Six Sigma tool saved costs in the company equivalent to INR 4.366. Besides, it reduced the Defect per Million Outputs (DPMO) to 1667 from 28357. As a result, DMAIC improved the sigma level of the company to 5.02 and reduced electrical failures by about 50%.
Conclusion
Conclusively, the case study revealed that Six Sigma could enhance efficiency in the manufacturing process. It is indeed a business strategy that can improve the company's performance in the competitive world. However, the success of the process depends on the ability of the company to follow the correct methodology and apply the correct techniques and tools. The company achieves maximum benefits when it applies a proper combination of tools in each phase.
References
Bhowmik, C., et al. (2018). Integrating Six-Sigma and theory of constraint for manufacturing process: a case study. In Soft computing: theories and applications (pp. 607-617). Springer, Singapore.
Patel, M., & Desai, D. A. (2018). Critical review and analysis of measuring the success of Six Sigma implementation in manufacturing sector. International Journal of Quality & Reliability Management . https://doi.org/10.1108/IJQRM-04-2017-0081
Valles, A., et al. (2009). Implementation of Six Sigma in a manufacturing process: A case study. International Journal of Industrial Engineering , 16 (3), 171-181.
