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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.

Automation and the new buzzword, “Industry 4.0”, have dominated the media headlines in recent months. In this scenario, apparel manufacturers should not only install automatic machines but also standardise them based on specific industry requirements, and precise measures are required for daily target demands.

This study demonstrates the application of Predetermined Motion and Time System (PMTS) tools in various automatic and semiautomatic machines to obtain higher productivity and the highest utilisation percentage of operator and automats between the 1:1 and 1:2 man vs machine configuration models. In this study, timeSSD® was used to calculate the micro motions of humans. In addition, a video annotation and modelling software Tracker was used to calculate high-speed machine movements with loading frames of 30 FPS.

After the implementation of PMTS tools, it was found that for a 1:1 man vs machine configuration, the operator utilisation is 75% per shift and the operator idle time is 50% per cycle time, and the operator is sitting idle for 2 h per 8 h of shift. So, there is scope to improve the utilisation and idle time of operator.

With the PMTS software, an industrial engineer professional with knowledge of the micromotion economy can only calculate micromotion.

Exploring the first time in the world to establish standard allowed minute (SAM) of a partly automated single-unit sewing machine with partial human intervention and a semiautomatic machine. Theoretical underpinnings indicate that manufacturers use the experience to determine the SAM of any operation over time, necessitating this work to calculate standard minutes automatically.

SINCE the world is so replete with institutes of various kinds it is unlikely to welcome another without a close scrutiny of its intentions, even when launched with all the eclat of a House of Commons dinner attended by distinguished figures from the industrial and organizational fields.

The problem of assembly line balancing is to assign different tasks to individual workstations for ensuring the sum of task times at any station not exceeding the station time. Standard minute time is generally used in the clothing industry as a predictor of sewing speed and production efficiency. In the clothing industry, the standard minute time derived from the work study methods is generally assumed as a constant for line balancing. However, a lot of factors cause variations on operational time of the same task such as the fabrics and sub‐materials, performance of the machinery, working environment and quality level of the product. With the aid of an illustrating example selected from a men’s shirt manufacturing factory, the effect of time variations for assembly line balancing has been studied in this paper.

The paper discusses the design, implementation and validation of FLEAS, a flowline environment for automated supervision of clothing manufacturing systems. The paper argues for a mixed initiative approach to system control which incorporates both a scheduling component, based on local search, and a simulation component which dynamically tests the validity of the supervisory decisions made by the scheduler.

Shorter lead time with low price and quality product demand is pivotal in the garment industry. Pressure on production lead time stresses the importance of reducing style changeover time in manufacturing factories, and this paper aims to contribute to solving the challenge by showing how the single minute exchange of die (SMED) methodology in practice can be adapted to garment factories in developing countries.

The paper investigates three cases of SMED implementation integrated with responsible, accountable, consulted, informed (RACI) matrices in garment factories in an action research approach. Both quantitative and qualitative methods are applied.

The study shows a reduction of 50% to 64% of changeover time with SMED implementation measured with two key indicators – throughout time and time to reach peak production. Moreover, the implementation depends on the application of the RACI matrix for the distribution of responsibility as well as integration with the basic production flow before and after the application of SMED.

The study can guide better SMED implementation in garment factories with limited investment by stressing the need to adapt to the specifics of the garment industry, secure the division of responsibility and integrate SMED in the production flow before and after the changeover.

Limited research on the application of SMED in the garment industry. This paper contributes to understanding the specific conditions for successful implementation in the garment industry in developing countries and addresses additional activities that help secure a sustainable implementation process.

This study aims to investigate how buyer-assisted lean intervention in garment supplier factories affects garment suppliers' productivity and production capability development.

Employing a qualitative research approach and a lean intervention design, a multiple case study method was adopted for this study. Quantitative data on productivity performance and qualitative data on production capability development were collected from a Danish buyer and their four corresponding garment suppliers. Collected data were analysed using standard lean measurement tools and qualitative data analysis techniques.

This study demonstrates that buyer-assisted lean intervention is a useful strategy for garment suppliers to enhance their productivity and production capability. However, suppliers need to select the right lean tools, ensure seriousness and commitment to lean initiatives, substantial involvement of top management and workers, arrange formal and informal training, provide performance-based financial/non-financial incentives and nurture a learning culture to facilitate suppliers' production capability development.

This study implemented few lean tools in a single sewing line in four supplier factories for a short intervention duration. Thus, there is a scope for future studies to investigate the impact of the lean intervention on a large scale.

The findings of this study might bring new insights to the management of buyer and supplier firms concerning how buyers could involve in suppliers' lean intervention initiatives and what suppliers need to ensure to develop production capability.

For the first time, this study engaged a buyer in suppliers' lean intervention initiatives to improve productivity and production capability in the garment industry of a developing country.

This study aims to propose a new design value, based on experimental and numerical studies, for surface emissivity of zinc hot-dip galvanized members exposed to fire.

The paper sums up experiments, used specimens and also shows results. Four experiments were performed in a horizontal furnace and one test in a fire compartment of the experimental building. Several tests were carried out for determination of the surface emissivity of galvanized steel structures in fire. The experimental and numerical studies were used for preparation of new generation of the structural steel fire standard Eurocode EN 1993-1-2:2025.

Hot-dip galvanizing is one of the most widely used processes for corrosion protection of steel products. The new design value for surface emissivity of zinc hot-dip galvanized members exposed to fire is determined using experimental results as 0.35. The value is proposed for next generation of EN 1993-1-2:2025. If hot-dip galvanization additionally can contribute beneficially to the fire resistance of unprotected steel members, it would be a huge economic advantage.

Experimental studies in the past years have indicated the influence of hot-dip galvanizing on the heating of steel members. This study suggests 50% reduction of the surface emissivity of a carbon steel member. This amendment will be incorporated in future versions of Eurocodes 3 and 4 and has already been implemented in some fire design tools for steel members in order to consider the beneficial contribution of hot-dip galvanized for fire-resistance requirements of less than 60 min.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.