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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.

Ethiopia’s economy is primarily based on agriculture, but starting form 2010, the government has been determined to diversify the exports with a priority set for strategic sectors like light manufacturing in which textile and apparel manufacturing industries are included. The purpose of this paper is to measure the comparative advantages of the Ethiopian textile and apparel industry using the revealed comparative advantage (RCA) in the period from 2007 to 2016.

For the purpose of the paper, secondary data were collected from the UN comtrade site, and related data sources were cited in the literature review for the purpose of triangulation (cross-checking of the analysis with theoretical background). From the theoretical background, the two indices of RCA (Balassa index and Lafay index) were used for the evaluation of the industries’ competitive advantage and to identify which industry (textiles or apparel) was of more importance in the country.

The findings of the study showed that Ethiopia was more competitive in the textile sector. However, and with reference to the Lafay index, the country has been focusing on apparel sector, because of the opportunities for job creation.

For the purpose of this study, secondary data were used and the general conclusions are limited to the corresponding sources of data.

Because of the labor-intensive nature, the textile and apparel sector has been one of the areas promoted by the Ethiopian Government in its industrialization policy. The finding of this paper can be used by policy makers to evaluate the competitiveness of the country.

The findings can be used to assess social upgrading issues in the textile and apparel sector.

The work is the first of its kind in the sector as well as the country.

This study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T² chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.

Multivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T² statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.

The finding of this paper is that the points and lines in the classic Hotelling T² chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T² control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.

Modifying the classical Hotelling T² control chart by integrating with the concept of graph theory and neural network is new of its kind.

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 research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.

Technological outsourcing requires possessing the technological capability level by enterprises taking the outsourced activity and further mandates build-up capabilities. Small and medium enterprises (SMEs) in developing nations such as Ethiopia are usually equipped with low level of technological capability and could benefit from government-supported or government-initiated outsourcing networks. The current study aims to preliminarily assess performance of outsourcing initiative taken by the Hibret Manufacturing and Machine Building Industry, a subsidiary of a national corporation, in developing technological capability of SMEs in Ethiopia.

The study used a qualitative research approach through interviews with the parent company officials and owners of SMEs and site visit to these SMEs. Findings are organized in a way to draw lessons to be learned from technological outsourcing examined.

Technological learning, acquisition of new technologies, market access and process innovation are few capabilities achieved by the involved SMEs. To facilitate and harness these opportunities and further assist in policy ratification, a conceptual framework has been presented and elaborated.

Further investigation into outsourcing procedure and biases are expected to shed further light onto the outsourcing initiative by the parent company. This study drew results from investigation of the SMEs involved. Additional investigation of other SMEs is expected to reveal additional insights.

There is a dearth of literature focusing on exploration of technological outsourcing in low-income developing countries, such as Ethiopia, to build SMEs’ technological capabilities. This research presents insightful contribution to strategic outsourcing to build local technological capability in developing economies.

This research focuses on the implementation of Just-in-Time (JIT) inventory management, drawing on a case study of a commercial livestock farm located in a swampy area of southern Nigeria.

The research adopts a qualitative approach. Interviews and workshops were used for data collection.

Findings from the study reveal that the commitment on the internal organisational members and skilful collaboration with supply chain partners are required for effective use of JIT, especially in an odd contextual situation such as the case in this study. This also justifies the embraced of additional cost of securing JIT inventory management practices such as the situation in the case study organisation that could not allow conventional inventory management.

It is suggested for further research to consider the topic from a mixed method approach as well as extend the focus on the possibility of legal regulations and government support to exceptional operational practices among organisations, especially those in the context of the food production sector, where this research was based.