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This study aims to address the gap in integrating ergonomic principles with lean principles in the shoe product manufacturing process. The objective is to develop a customized model that effectively combines and synergizes ergonomics and lean principles.

Primary data was collected through on-site observations, interviews and assessment of whole-body discomfort to evaluate the implementation of lean and ergonomic principles in each section of the shoe production process. The collected data were analysed using descriptive statistical tools, content analysis and software such as Microsoft Excel.

The findings of the study indicate that the case company’s underperformance, achieving only 26.96% of its designed capacity and 50.19% of its planned capacity, is primarily attributed to the poor integration of ergonomic principles with the lean philosophy model. The high prevalence of work-related musculoskeletal disorders (WMSDs) in the shoe production process significantly contributes to a loss of productivity due to increased absenteeism. The factory experiences a labour absenteeism rate of 5.59%, resulting in overtime and additional costs. To address these issues, the study proposes the adoption of 8S principles and an ergo-lean production system model. This model, conceptualized as a building construction structure, effectively eliminates waste in the shoe production process.

The study’s findings will greatly enhance productivity in the shoe manufacturing sector by establishing a work environment that prioritizes employee needs and well-being. This will have significant practical implications for improving overall productivity in the industry.

This study stands out as it investigates the proactive integration of ergonomics and lean principles in the shoe manufacturing industry, an area that has not been previously explored. By bridging the gap between these two principles, the research contributes to the existing knowledge base.

The study presented healthcare service quality, lean thinking and Six Sigma to enhance patient satisfaction. Moreover, the notion of machine learning is combined with lean service quality to bring about the fundamental benefits of predicting patient waiting time and non-value-added activities to enhance patient satisfaction.

The study applied the define, measure, analyze, improve and control (DMAIC) method. In the define phase, patient expectation and perception were collected to measure service quality gaps, whereas in the measure phase, quality function deployment (QFD) was employed to measure the high-weighted score from the patient's voice. The root causes of the high weighted score were identified using a cause-and-effect diagram in the analysis phase.

The study employed a random forest, neural network and support vector machine to predict the healthcare patient waiting time to enhance patient satisfaction. Performance comparison metrics such as root-mean-square error (RMSE), mean absolute error (MAE) and R2 were accessed to identify the predictive model accuracy. From the three models, the prediction performance accuracy of the support vector machine model is better than that of the neural network and random forest models to predict the actual data.

Lean service quality improvement using DMAIC, QFD and machine learning techniques can be generalized to predict patient waiting times. This study provides better realistic insights into patient expectations by announcing waiting times to enable data-driven service quality deliveries.

Prior studies lack lean service quality, Six Sigma and waiting time prediction to reduce healthcare waste. This study proposes lean service quality improvement through lean Six Sigma (LSS), i.e. DMAIC and machine learning techniques, along with QFD and cause-and-effect diagram.

This study aims to improve productivity and customer satisfaction through lean manufacturing for metals and engineering industries. Its aims also to understand the concept of lean manufacturing, various tools and techniques of lean, lean implementation benefits and barrier toward its implementation. Then, on the basis of the result, a conceptual frame work was developed to reduce the existing gaps.

Lean thinking is one of the methods that can bring productivity and customer’s demand improvement for manufacturing and service giving industries. To arrive at the lean thinking productivity improvement and customer satisfaction of the sector (MEIs), intensive literature review and secondary data investigation were conducted.

Articles and secondary data related to the case were reviewed and found the existing gaps. The gaps identified such as missing energy waste, space waste and material waste, waste of knowledge or talents. In addition to the 5 S of kaizen, this study added safety as the sixth on the existing Kaizen’s strategies. In lean practice, managers give priority to waste reduction and ignore the product quality aspect, which lead to dissatisfaction among customers. Fragmented implementation of lean manufacturing and the conflict between human resource waste and unemployment were reconciled in this study. A model that can improve productivity and increase customer satisfaction was developed. Solutions to alleviate the problems and speed up development were forwarded.

This study focused solely on the manufacturing industries of developing countries, specifically deals with basic metals and engineering industries. In addition to this, the research didn’t take a case study on a specific firm as it is a literature review.

The findings of this study emphasized that lean manufacturing is the key for wise resource utilization, which enables a firm for cost, lead time and waste reductions on one hand and productivity and flexibility improvements on the other. To the end, lean can bring sustainable development and bright images to firms, and wellbeing life to workers together with customer satisfaction.

The gaps that have not been identified by other researchers were clearly discussed, and on the basis of the gaps, a new conceptual model was developed. This is useful to basic metals and engineering industries in overcoming resource-limitation problems by eliminating wastes.

This research aims to improve the performance and productivity of low-level technology organizations using lean tools – value stream mapping (VSM). In order to investigate the application of VSM in low-level technology organizations, this study takes footwear manufacturing organizations as a case study.

Identifying a suitable organization was the first step for conducting the case study, followed by product family identification. Time and motion studies are used to determine the cycle time and identify the value-adding and non-value-adding activities, respectively. After making necessary observations and calculations, the current state map was developed. Different improvement proposals were identified, and the future state map was constructed.

As a result, 56.3% cycle time reduction and 69.7% reduction in lead time were obtained, confirming its application in low-level technology organizations to improve their performance and productivity. This promising result indicates that a significant improvement can be achieved if VSM is applied in low-level organizations other than footwear industries, such as the clothing and furniture industries. Speed is investigated to be one of the parameters in motion study.

This study focuses on low-level technology organizations, specifically leather shoe manufacturing industries. But experiences gained from implementing this study are realistic and applicable in similar organizations.

Performance and productivity improvement are critical issues to be addressed in low-level technology organizations, especially in the leather footwear manufacturing sector. This can be achieved through VSM by identifying and removing the wastes. VSM can be applied to low-level organizations as well. VSM is a powerful tool that helps managers and practitioners in identifying wastes and opportunities for improvement.

The paper addresses applicability of VSM in the production process of low-level technology organizations with an original industrial case study in Ethiopia.