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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.

The purpose of this paper is to propose a methodology to use complaints, claims and company innovation as an internal data source of customer needs for product development using the quality function deployment (QFD)-Kano approach instead of an ordinary customer survey.

This paper confirms that the customer complaints and claims and company innovations from the internal data source are equivalent to the Kano model’s product attributes. Data were selected from the company’s documents. To investigate the data category, a Kano questionnaire was designed and tested with 100 random respondents. Based on their answers, categories for the quality characteristics were determined and compared with the initial data categories. A second survey using professional customer respondents was conducted to increase the results’ reliability.

The approach was shown to be effective in employing complaints, claims and innovations as an alternative source of customer needs in the QFD-Kano approach.

It is assumed that companies document their customer complaints and claims, as well as their strategic innovation plans. The complaint and claim data need to be extracted to reveal their quality characteristics. For future research, data extraction using text or data mining may be useful to bridge this gap.

The product development team may ascertain customer needs as duly classified. This voice of the customer is more accurate and requires less time.

The paper may be of value to researchers and practitioners involved in product design and development, since it offers a new source of customer need data obtained internally as an alternative to customer surveys.