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This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

This paper aims to present investigations of the influence of sewing and adhesive bonding technology on the aesthetic, mechanical and conductive properties of the e-textile package. Commercially available conductive textiles are tested for the production of e-textile package by most common cut-and-sewn clothing production technologies.

Sewing, adhesive bonding and seam sealing technologies used to obtain e-textile packages with woven and knitted conductive textiles. Produced e-textile packages described in terms of thickness, bending rigidity and general appearance. Exploitation properties of prepared samples tested by cycle tensile experiment and discussed on the basis of variation of linear electrical resistance property.

Research has shown that a reliable e-textile package can be obtained by applying cut-and-sew technology for conductive tracks of silver coated woven and knitted material. Seam sealing by thermoplastic polymer layer has an impact on the electrical and deformation properties of the samples. To create attractive smart clothing design, the appropriate joining method and its technological parameters must be chosen to ensure the durability and safety of e-textile packages.

The findings of the research are of substantial value for the production of e-textiles by cut-and-sewn technologies. The required shape of the conductive textile element for various applications can be cut and joined to the garment parts using traditional sewing or adhesive bonding techniques.

The aim of this review is to present together the studies on textile-based moisture sensors developed using innovative technologies in recent years.

The integration levels of the sensors studied with the textile materials are changing. Some research teams have used a combination of printing and textile technologies to produce sensors, while a group of researchers have used traditional technologies such as weaving and embroidery. Others have taken advantage of new technologies such as electro-spinning, polymerization and other techniques. In this way, they tried to combine the good working efficiency of the sensors and the flexibility of the textile. All these approaches are presented in this article.

The presentation of the latest technologies used to develop textile sensors together will give researchers an idea about new studies that can be done on highly sensitive and efficient textile-based moisture sensor systems.

In this paper humidity sensors have been explained in terms of measuring principle as capacitive and resistive. Then, studies conducted in the last 20 years on the textile-based humidity sensors have been presented in detail. This is a comprehensive review study that presents the latest developments together in this area for researchers.

The garment factories focus on improving their production systems by involving innovative and advanced production methods and/or techniques to cope with fast-changing trends. Accordingly, this study aims to establish the standard allowed minutes (SAMs) and sewing efficiencies for Tanzania’s sewing industry, thus improving the production processes.

The research deployed a quantitative method. A stopwatch measured each operation for shirts and trousers to compute SAMs and efficiency. The shirt manufacturing processes involved 40 operations. Ten measurements were taken from different SL and LL industries operators for each operation. The trouser comprised 42 operations with 10 measurements taken from a different operator at the same garment factories for each operation.

SAMs for shirts at SL and LL factories were 29 and 31 min, respectively, while trousers were 30 and 34 min. The sewing efficiencies for shirts at both SL and LL factories were 83.98% and 81.93%, respectively. Similarly, the sewing efficiencies for trousers at both SL and LL factories were 81.25% and 80.95%, respectively.

Since SAMs results are not established through literature rather a quantitative approach, the findings thus place crucial information for similar factories to benchmark from. Such information are crucial as factories could increase productivity and operational efficiency, reduce costs and non-value adding activities and estimate lead times. Notwithstanding the findings gathered, the study only established SAMs for two garments.

Although the garment industry has been developing over the years, this study was probably among the first studies in Tanzania that established SAMs. Theoretical underpinnings indicate that the factories use the experience to assemble garments, thus the need for this study.

Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

The purpose of this paper is to present a fit prediction case study of virtual twins of women jacket. For this reason, several basic principles of clothes fit prediction were developed and verified.

To develop the principles of fit prediction, the women's jacket sleeve was selected as the study object. The study objects were categorized into three types: the patterns and two virtual sleeves generating on full avatar from Clo3D and on dummy (arm partly removed). Through series of subjective and objective evaluation experiments, the relationship between the similar indexes of the patterns and the virtual sleeves was built, including fit criteria range, the categorization of the indexes in terms of its sensitiveness, and the linear regressions to predict several indexes of virtual sleeves after its pattern parameterization. The results obtained were verified in case study by the virtual and real sleeves generating.

The proposed principles of clothing fit prediction based on parallel research of the flat patterns and its virtual 3D shapes. The principles include the choosing of virtual twins of human body for virtual try-on, the establishing of indexes common schedule for patterns and virtual sleeves, the creation of criteria and its ranges for perfect fit and poor fit evaluation, and the application of existing relations between the patterns and the sleeves for predicting indexes responsible for fit.

The authors propose and verify the validity of the principles to predict several parameters of virtual 3D sleeve of women's jacket which are forming the level of fit. The result of this study can be used to convenient fit prediction and to find the misfit reasons.

This study developed basic principles for predicting the fit of the clothing through the virtual simulation and the statistical analysis. Through studying the jacket sleeve, the several related ranges, the row of more sensitive indexes, and the equations were presented and verified, which certified the validity of proposed principles.

The results can effectively predict the sleeve fit before sewing, which reduce the time and materials cost and the operator's skill requirements.

The authors propose and verify the validity of the principles to predict several parameters of virtual 3D sleeve of women's jacket which form the level of fit. The result of this study can be used for convenient fit prediction and to find the misfit reasons.

The purpose of this paper is to analyze the evolution of the rules of origin (RoO) of Regional Comprehensive Economic Partnership (RCEP).

This analysis is done by comparing them with those of existing FTAs/EPAs of ASEAN with other RCEP member countries, and also examining the impact of recent mega-FTAs/EPAs, such as TPP11 and Japan-EU EPA, in which some of the member countries participated.

RCEP holds great significance in that it connects Japan and China and Japan and South Korea, which previously have not had any EPAs/FTAs, transforms this massive economic sphere from one with minutely divided and differing RoO under ASEAN plus FTAs to one that is seamlessly connected with those of having evolved into the unified RoO under RCEP, and realizes ideal production networks in Asia.

This paper makes it clear that RCEP, while based on ASEAN plus FTAs, reflects progressive provisions of recent mega-FTAs/EPAs, and adopts simpler and more systematic rules. These provisions limit the distortive effect on trade, realize ideal production networks in Asia, and are appropriate as uniform RoO connecting networks across this massive economic sphere. It also points out that there are provisions that have not been introduced and those that are considered to have been incomplete as a result of negotiations, and the possibility of evolving into more ideal RoO by utilizing the system for revisions established under the agreement.

The complexity of succession in family firms is multifaceted and can sometimes lead to turbulence. While structured succession strategies offer a roadmap for smoother transitions, intergenerational differences in family small and medium-sized enterprises (SMEs) can lead to varied interpretations of an effective succession blueprint. This study synergizes the strategic entrepreneurship framework with the socioemotional wealth (SEW) perspective to probe into how formalized succession planning impacts performance in family SMEs. Furthermore, it delves into the mediating role of succession satisfaction, especially in family firms characterized by pronounced SEW and helmed by CEOs from different generational cohorts.

Employing a comprehensive dataset from 1,833 global family businesses, this research utilizes bootstrapping regression models to discern the intertwined effects of mediator and moderator variables and their statistical significance.

The main findings suggest that succession satisfaction does matter for a good succession process and that succession plans work only in family firms with a high degree of SEW and that are led by older family CEOs (e.g. baby boomers).

The results offer fresh perspectives on succession processes, with a particular focus on how to improve the satisfaction of millennial family CEOs.

The study uniquely combines strategic entrepreneurship and SEW to offer a holistic view of succession planning, highlighting satisfaction’s mediating role and SEW’s moderating influence. Additionally, it pioneers the incorporation of generational cohorts into the succession discourse.