This paper first enlists the generic problems ofalloy wheel machining and subsequently details on theprocess improvement of the identified critical-to-qualitymachining characteristic of A356 aluminum alloy wheelmachining process. The causal factors are traced using theIshikawa diagram and prioritization of corrective actions isdone through process failure modes and effects analysis. Process monitoring charts are employed for improving theprocess capability index of the process, at the industrialbenchmark of four sigma level, which is equal to the valueof 1. 33. The procedure adopted for improving the processcapability levels is the define-measure-analyze-improvecontrol(DMAIC) approach. By following the DMAICapproach, the Cp, Cpk and Cpm showed signs of improvementfrom an initial value of 0. 66,-0. 24 and 0. 27, to afinal value of 4. 19, 3. 24 and 1. 41, respectively.

The define–measure–analyze–improve–control (DMAIC) approach is a five-strata approach, namely DMAIC. This approach is the scientific approach for reducing the deviations and improving the capability levels of the manufacturing processes. The present work elaborates on DMAIC approach applied in reducing the process variations of the stub-end-hole boring operation of the manufacture of crankshaft. This statistical process control study starts with selection of the critical-to-quality (CTQ) characteristic in the define stratum. The next stratum constitutes the collection of dimensional measurement data of the CTQ characteristic identified. This is followed by the analysis and improvement strata where the various quality control tools like Ishikawa diagram, physical mechanism analysis, failure modes effects analysis and analysis of variance are applied. Finally, the process monitoring charts are deployed at the workplace for regular monitoring and control of the concerned CTQ characteristic. By adopting DMAIC approach, standard deviation is reduced from 0.003 to 0.002. The process potential capability index (CP) values improved from 1.29 to 2.02 and the process performance capability index (CPK) values improved from 0.32 to 1.45, respectively.

The 6 Sigma methodology is widely used in different types of industries, from manufacturing type industries to service industries. The objective of the methodology is based on reducing variability in the production system, that is, achieving that the standard deviation is as many times as possible within the mean of the process being measured. The Six Sigma methodology is based on the DMAIC process which is to define, measure, analyze, implement, and control, with these stages, it is possible to identify quality critics and understand their root cause, therefore, if it is known Why a problem occurs, it can be fixed. With DMAIC methodology, the reduction of the variation that arises from the types of problems that were mentioned within the 6 Sigma methodology is sought. In the other hand, 6 sigma and DMAIC process possess more that 40 quality tools to improve the critical of quality and in these manners increase the company efficiency. In such a way, the objective of this research is the presentation of the most effective tools and simply to apply in companies of the methodology, also how this quality tools can be applied in any type of problem to increase efficiency and productivity, this with the necessity of companies’ quality improvement and beginners’ practitioners can used as a simple way of the methodology implementation.

The aim of this paper is to propose a Six Sigma-DMAIC and food waste hierarchy-based framework that can be successfully implemented by Ethiopian University canteen managers to reduce food waste in a sustainable way. The importance of this research is twofold. First, the Six Sigma-DMAIC methodology is used to evaluate and improve the process to prevent food surplus and waste in the canteen. Second, other waste hierarchy options will be used to mitigate food waste problem in a sustainable way. This has a positive social, environmental, and economic implications.

One of the goals of the manufacturing industry in the globalisation era is to reduce defects. Due to a variety of factors, the products manufactured in the industry may not be defect-free. Six Sigma is one of the most effective methods for reducing defects. This paper focuses on implementing Six Sigma in the automobile industry's stator motor shaft assembly. The high decibel noise produced by the stator motor is regarded as a rejected piece. Six Sigma focuses on continuous improvement and aids in process optimization by identifying the source of the defect. In the Six Sigma process, the problem is measured and analysed using various tools and techniques. Before beginning this case study, its impact on the company in terms of internal and external customer cost savings is assessed. This case study was discovered to be in a high-impact area. The issue was discovered during the Core and Shaft pressing process. Further research leads to dimensional tolerance, which reduces the defect percentage from 16. 5 percent to 0. 5 percent.

Today’s highly flexible and dynamic customer demand for the best quality of products with lower selling price and short delivery time makes garment manufacturing industries globally stiff to survive and to compete in the world market. Lean concentrates on the elimination of production wastes to create a production system highly adaptive to meet market demand. Six sigma, on the other hand, is a continuous improvement plan that is intended to reduce variability. However, the scale of implementation of lean and six sigma in developed and developing countries varies based on the technologies’ own manufacturing. In this paper, a customized lean and six sigma is implemented through DMAIC (Define, Measure, Analyze, Improve, and Control)-based methodology for the emerging garment industry in Ethiopia. In addition, the efficiency of the customized lean and six sigma in reducing the quality problem and enhancing the productivity is proven through a real case study. The customized lean and six sigma helps the case company to lower the lead time by 36.05%, increased the sigma level by 21.03%, and increase labor productivity by 25.54%.