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This paper aims to investigate Lean Six Sigma (LSS) in the Brazilian context, seeking to identify its main characteristics and opportunities for future research.

This study focuses on a literature review in the area of production engineering and operations management, where 104 relevant scientific publications were identified.

The results show that the most important critical success factor for integrating LSS in Brazil is top management support and commitment. LSS integration occurs predominantly in large industrial companies, being incipient in small and medium-sized enterprises. In general, there is no structured way of applying LSS. A standard framework for LSS is still lacking.

This study is limited to a sample that only comprises Brazilian scientific studies.

Professionals and practitioners can understand the evolution of LSS through practical applications and the main LSS tools used in both the industrial and services sectors. It also points out the critical success factor for the implementation of LSS. The study highlights several roadmaps for LSS implementation adopted by large and modern Brazilian automotive industries based on a robust technological base. This study also contributes to expanding the evidence base of LSS application, both in Brazil and in other countries.

Some suggestions are proposed to clarify the structure and complexity of integrated implementation of LP and SS as well as expand the LSS application in small and medium-sized enterprises. This study is the first to discuss the current situation of LSS in Brazil and provide suggestions to expand LSS in the country. Comparisons of Brazilian LSS literature review with researches of others countries are also presented.

The purpose of this paper is to present a case study the application of lean six sigma combined with mining transportation overall vehicle effectiveness (MTOVE) to improve mining transportation performance. MTOVE is a newly developed model to measure the overall effectiveness of mining transportation.

The method used is case study combines the MTOVE and LSS methodologies. Data were collected from the hauling operation during a three-month period. Various lean six-sigma tools, such as the Pareto chart, ANOVA, two sample t-tests, one sample t-test, cause-and-effect analysis and time study, have been used.

The case study resulted in improvement of vehicle overall effectiveness; a 35% increase in MTOVE value, a 17% improvement in productivity and a 9% increment in truck utilization. Statistical tests confirmed the significance of reducing the mean and variation in the hauling process cycle time, which led to productivity improvement.

This study provides practitioners with additional quantitative evidence of the potential benefits of LSS methods in the coal mining industry.

This paper practically and unquestionably has contributed to the LSS body of knowledge focused on the mining sector, which is recently still far behind the manufacturing sector. The study has demonstrated that some challenges in the mining environment can be solved through the effective implementation of LSS tools. Hence, this paper could be used as a reference for both researchers and practitioners.

The study contributes in the field of LSS spread in mining industries using a case study. This study shows practical evidence of improving overall vehicle effectiveness using LSS. Practitioners can refer to this study to understand the benefits of LSS in mining sector. Since the mining industry should also adopt the LSS principle into the mining business process due to its ability to improve business performance (Valente et al., 2020; Tupamahu et al., 2019; Zanon et al., 2021).

There has been little scientific study of the LSS implementation in the mining industry. This research provides detailed evidence of LSS implementation in the mining sector. The main contribution is the implementation framework, which shows the combination of newly developed indicators (MTOVE and LSS) to enhance hauling operation effectiveness. This paper demonstrates how LSS tools and methods can be applied in the mining transportation industry.