Apparel Products Research Articles

Investigating the role of relational factors on buyer-supplier relationship commitment to enhance firm performance through supply chain integration

PurposeThis study assesses how relational factors and buyer-supplier relationship commitment (BSRC) influence supply chain integration (SCI) and firm performance in Bangladesh's apparel manufacturing sector. Firm performance includes operational performance and innovation performance.Design/methodology/approachGrounded in the social exchange theory, a survey data-based structural equation modeling (SEM) approach is applied. Based on two experts and four executives' opinions and an in-depth literature review, 28 measurement items were identified in the close-ended questionnaire design. Further, 144 valid questionnaires from the manufacturer-supplier dyads in Bangladesh were collected and used for SEM analysis.FindingsOur study reveals that relational factors positively influence BSRC. BSRC directly impacts SCI, operational, and innovation performance, whereas SCI is significantly related to operational and innovation performance. Besides, SCI mediates the two relationships: BSRC and operational performance; and BSRC and innovation performance.Originality/valueOur results contribute to the literature and offer a new way to understand relationships that connect relational factors of BSRC, BSRC, and outcomes not only by examining the focal firm but also by examining its dyadic supplier partner separately. Separate assessment in the dyad displays some similar and dissimilar results. Moreover, we suggest practical implications for managers to enhance firm performance by focusing on the significance of linking relational factors, BSRC, and SCI.

The global apparel industry is a significant yet overlooked source of plastic leakage

Plastic pollution is a global environmental threat with potentially irreversible impacts on aquatic life, ecosystems, and human health. This study is a comprehensive assessment of the global apparel industry’s contribution to plastic pollution. It includes plastic leakage of packaging and end-of-life apparel waste in addition to fiber emissions during apparel production and use. We estimate that the apparel industry generated 8.3 [4.8–12.3] million tons (Mt) of plastic pollution in 2019, corresponding to 14% [5.5%–30%] of the estimated 60 Mt from all sectors. In this study, we demonstrate that the main source of plastic pollution from the apparel supply chain is synthetic clothing as mismanaged waste either in the country of its original use or in the countries receiving used apparel exports. A fundamental transformation of the apparel economy towards a circular framework and decreased synthetic apparel consumption is needed to tackle apparel-related plastic pollution.

Mathematical Model to Upcycle End-of-Roll Leftover Fabrics in Apparel Manufacturing

This study addresses the problem of end-of-roll leftover fabrics originating after the production of baby/child apparel. The ineffective management of these leftovers results in excess inventory, occupies storage space, and imposes economic and environmental loads. To address this challenge, a novel mathematical modeling approach is proposed. The model maximizes the upcycling of leftovers by incorporating these into the manufacturing of garments while adhering to marketing, production, and ordering constraints. This model also introduces the feasibility of ordering new fabrics with a penalty, as defined by the decision makers, to increase utilization. The model was tested using actual end-of-roll leftover data. The upcycling utilization of leftovers was calculated to be between 57% and 87%. Notably, at an upcycling rate of 58%, 96% of the utilized fabrics were sourced from leftovers. The case study results validate the model efficacy and provide insights into leftover-fabric management.

Benefits of Using Data-Driven Lean Manufacturing in Textile and Apparel Manufacturing Units in Pakistan

The objective of the present study is to investigate the advantages of lean manufacturing in textile and apparel manufacturing facilities in Pakistan. This study examines the influence of lean manufacturing on the time-saving, process efficiency, and industry improvement. Modern organizations are employing lean manufacturing as a critical concept in their operations. There are numerous advantages to employing lean manufacturing; consequently, the textile industry is also transitioning to this methodology. The quantitative methodology employed in this investigation is employed to investigate the current subject matter. The study's data is gathered from the customers and employees of textile and apparel manufacturing units in Pakistan. In order to accumulate data from the respondents of the investigation, questionnaires were implemented. The study's findings suggest that lean manufacturing is significantly positively correlated with industry improvement, process efficiency, and time savings. These three factors are also essential for the success of an organization. This investigation also encompasses practical and managerial implications, future direction, and limitations.

Supply Shocks in Global Value Chains and Export Performance: Evidence from Bangladesh Using Triple Difference-in-Differences

In this paper, we evaluate how supply shocks in global value chains, such as the lockdown in China during the coronavirus pandemic, affected firm-product-level export performance in Bangladesh, which is a least-developed country where domestic inputs are inadequate due to capacity constraints. Transaction-level customs data on the universe of Bangladeshi apparel manufacturing firms are used in the analysis. We apply a triple difference-in-differences approach to compare the woven apparel exports of the firms that relied on China for inputs before and after the lockdown with those of knit apparel and firms that depended on the rest of the world as a comparison group. The results show that firms exposed to the Chinese input supply shock experienced a 36.5% decline in woven apparel exports after the lockdown. A plausible explanation for the decline in exports is the reduced shipment size driven by the constrained production capacity due to the shortage of inputs from China. We also find that exposed firms whose exports were highly concentrated in the European Union and the United States incurred an additional 18% decline compared to other firms.

Determination of drivers in embedding environmental sustainability practices in the apparel sector of Sri Lanka

Apparel is the second most polluted industry in the world after oil. Therefore, implementing environmental sustainability practices in the apparel sector is a buzzing topic discussed worldwide. The apparel industry is the prominent export revenue generator of Sri Lanka which contributes to more than 40% of its export income. This research was done to determine the drivers in embedding environmental sustainability practices in the apparel sector of Sri Lanka. Data was collected using a mixed method approach using quantitative data collected through an online questionnaire survey (205 respondents) and qualitative data gathered via semi-structured interviews (30 interviews). Sample size and data analyzing methods were decided using globally accepted scientific methods. The researcher didn’t find any previous scholarly work of a similar study about the apparel sector of Sri Lanka even though many studies were found on the same topic related to other major apparel manufacturing countries in Asia and Africa. Thus, five drivers namely pressure from brands and retailers, enhancing organizational reputation, reduction of operational cost, government regulations, and safety of workers were extracted through the extensive literature survey conducted using previous scholarly work of other countries which were further evaluated for Sri Lankan context using above mentioned quantitative and qualitative data. It was found that all these drivers were relevant to the apparel sector of Sri Lanka while pressure from brands was the most influential and safety of workers was the least influential driver. No other drivers were mentioned by any of the respondents apart from the above-mentioned factors.

Presentation-order effect of product images on consumers’ mental imagery processing and purchase intentions

PurposeThis study aims to investigate how image-presentation-order influences mental imagery (MI) processing and purchase intentions. This study also examines the moderating effect of a series of images on the relationship between image-presentation-order and MI processing.Design/methodology/approachThis research conducted two studies using an experimental approach.FindingsTwo studies showed that MI processing was higher, when an apparel product image worn by a model with a background was shown after rather than before a simple product image (SPI), indicating the recency effect. In contrast, examining a series of images, consumers were more engaged in MI processing, when product image(s) worn by a model with a background were presented first, followed by the four SPIs, than the reversed order (primacy effect). The level of MI in two studies subsequently increased purchase intentions.Research limitations/implicationsResults of this study have the potential to provide guidance to online retailers for how to best order their product images on a website to help consumers form elaborated MI about the product and thus increase purchasing intentions.Originality/valueAlthough past research has examined presentation-order effect using textual information, very limited studies have explored presentation-order effect of pictorial information. To the best of the authors’ knowledge, this research is in the forefront of investigations about the joint effect of image-presentation-order and the number of images on individuals’ perceptions.

The role of developing countries in the global fashion industry sustainability discourse: Ghana in perspective - A narrative review

Fashion Industry practices globally are considered to have major negative impacts on both the environment and society. Production and consumption have increasingly moved towards the industry’s model of take-create-dispose. As this phenomenon is observed to take center stage in more industrialized and advanced nations, developing countries including Ghana, also contribute directly or indirectly to the negative impacts. Fashion production in Ghana is predominantly centered on small to medium-scale enterprises, and their mode of operation allows for customized small runs tailored to specific end-customers, thereby providing an avenue for exploring sustainable strategies that may have a positive impact on the environment and society. This paper takes a narrative review approach to how the fashion industry in Ghana impacts sustainability and the opportunities to make positive impacts on sustainability at a global level. It demonstrates how Ghana, as a developing country, imports raw materials for both textile and apparel production from counterparts in developing and developed countries and contributes to negative environmental impacts associated with sourcing countries. However, with the emergence of sustainable strategies, local contemporary fashion designers can embrace culturally friendly sustainable models to enhance their practice toward industry sustainability.

The role of marketing as a determining factor in the production and sale of fashion apparel

The role of marketing as a determining factor in the production and sale of fashion apparel

Optimization Model of Clothing Production Plan using Branch and Bound Method

The increasing number of clothing activities in Indonesia has resulted in companies having to increase their marketing and production activities in order to obtain maximum profit. One of the factors influencing the growth of basic clothing is the price of the middle class in Indonesia. This can be a strong foundation for a long-term clothing consumer base. One of the businesses engaged in the production of clothing is a garment production business. The problem that is often faced by companies is limited resources such as raw materials, capital, time, etc. Production planning is a process to produce goods in a certain period in accordance with those predicted through the organization of resources. Therefore, production optimization is one way to optimize profit and apparel production. This study aims to apply the Integer Linear Programming model and is completed using the Branch and Bound method to obtain the optimal number of clothing production in order to obtain maximum profit. The conclusion obtained from the research is the application of the Branch and Bound method to the problem of optimizing production planning at PT. GM Tactical to obtain more optimal results compared to the actual production carried out by the company.

Design heuristics for cut order planning problems in the apparel industry

PurposeIn recent years, the demand for garments has significantly increased, requiring manufacturers to speed up their production to attract customers. Cut order planning (COP) is one of the most important processes in the apparel manufacturing industry. The appropriate stencil arrangement can reduce costs and fabric waste. The COP problem focuses on determining the size combination for a pattern, which is determined by the length of the cutting table, width, demand order, and height of the cutting equipment.Design/methodology/approachThis study proposes new heuristics: genetic algorithm (GA), symbiotic organism search, and divide-and-search-based Lite heuristic and a One-by-One (ObO) heuristic to address the COP problem. The objective of the COP problem is to determine the optimal combination of stencils to meet demand requirements and minimize the total fabric length.FindingsA comparison between our proposed heuristics and other simulated annealing and GA-based heuristics, and a hybrid approach (conventional algorithm + GA) was conducted to demonstrate the effectiveness and efficiency of the proposed heuristics. The test results show that the ObO heuristic can significantly improve the solution efficiency and find the near optimal solution for extreme demands.Originality/valueThis paper proposes a new heuristic, the One-by-One (ObO) heuristic, to solve the COP problem. The results show that the proposed approaches overcome the long operation time required to determine the fitting arrangement of stencils. In particular, our proposed ObO heuristic can significantly improve the solution efficiency, i.e. finding the near optimal solution for extreme demands within a very short time.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

The Impact of Access to Intermediate Inputs on Export Margins: Firm-Level Evidence from the Regression Decomposition Approach

This paper analyzes how export margins responded to an intermediate input supply shock caused by the 2020 lockdown in China. We use regression decomposition with triple and quadruple difference-in-differences models to identify causal impacts and mitigate potential heterogeneity in transaction-level customs data from the Bangladesh apparel manufacturing industry. The triple difference estimate shows that the average export value per firm–product–destination combination declined by approximately 65%, leading to a decrease in overall exports of woven apparel from Bangladesh. The input supply shock also adversely affected the subgroups of firms across various firm-level characteristics along the intensive margin. Moreover, the export market share decomposition reveals that the shock significantly affected intensive margins by decreasing incumbents’ market allocation by 9%. An equivalent increase in extensive margins led to a readjustment in the market allocation, leading to fewer market leavers and slightly more new market entrants. Our results indicate that Bangladesh’s exports mostly decreased due to the smaller quantities of products exported rather than there being fewer firms, destinations, or products involved in export trade. There were significant market share reallocations that occurred after the Chinese input supply shock. An appropriate policy stance is required for sustainable export sector growth strategies, which will enhance the country’s defense against potential future shocks and foster the achievement of sustainable development goals (SDGs) in Bangladesh.

Phase change materials in textiles: synthesis, properties, types and applications – a critical review

Phase change properties of clothing gain attention of the researcher now for their significant unique ability to absorb, release, store, and deposit temperature during phase transition. Phase change materials have been widely used and utilized now in various fields, as well as in textiles. In apparel products, phase change materials are used to introduce various special features, especially its make garments smart or have technical functions. Various researchers pay attention to this area for its large organic and inorganic resources. This review paper summarizes the road map of phase change materials in textiles, including the way of synthesis, the characteristics of phase change materials, and their applications in smart textiles. In addition, the diverse usage of phase change materials in different textiles is discussed. It also tries to cover the principles of phase change behavior, phase change material types, and their thermal properties. After that, the paper will try to mention the potential benefits and challenges associated with utilizing phase change materials in various applications that have been discussed here. Finally, this paper concludes with the mass acceptance of phase change materials in various technological advancements, along with a short note about future research opportunities.

Examination of Digitalized Non-Heritage Apparel Design Using CLO3D: Case Study of Ningbo Gold and Silver Color Embroidery

Driven by the wave of digitalization and fashion, traditional handicrafts in intangible cultural heritage (ICH) have experienced a remarkable transformation in the existing social value coordinate system. On the one hand, as a key carrier of the historical and cultural memory of the nation and the state, intangible cultural heritage is faced with many challenges of preservation and inheritance; on the other hand, the rapid development of digital technology provides innovative solutions for the modernization of traditional arts. This paper uses Ningbo gold, silver and color embroidery as a case study to explore the application of CLO3D software technology in the design of nonheritage apparel products and shows nonheritage crafts through digital simulation technology, which is not only the future development direction of the apparel industry but also the reconstruction of the apparel form and the use of function, aiming to assess its impact on nonheritage protection and innovation and to find a new path for the digital inheritance of nonheritage.

Analisis Pengukuran Beban Kerja Mental Pekerja Produksi CV. Hikmah Jaya Garment Menggunakan Metode NASA-TLX dan RSME

Hikmah Jaya Garment is a company that produces various clothing equipment and apparel manufacturing. With the number of production requests that continue to increase every time as well as the application of the make-to-order system which causes an uncertain production schedule, it causes the emergence of its own work demands for workers. These conditions can affect the quality of the production results they are working on, so this study aims to analyze the mental workload experienced by employees at CV. Hikmah Jaya Garment is a reference for providing improvement solutions to increase work productivity. This research uses the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Rating Scale Mental Effort (RSME) methods through a questionnaire containing factors or indicators of mental workload to workers. Based on the results of calculations and analysis, the average NASA-TLX score is 74.9% and the average RSME score is 83.1%, which means that the mental workload experienced by workers is in a high category with the amount of effort expended included in a large enough category. These high results cause less than optimal worker productivity so there are several recommendations for improvement in the aspects of workload indicators. Keywords: Mental Workload, NASA-TLX, Production, RSME, 5S

A Study on Impact of Green Supply Chain Management Practices on the Environmental Performance of Apparel Industry in Sri Lanka

Setting new strategies has become necessary due to increased attention to environmental concerns and a rising tendency among nations and organizations toward environmental sustainability. This study explores the impact of Green Supply Chain Management Practices on environmental performance in the Sri Lankan apparel industry. As a result, the study's objectives are to identify the impact of green supply chain management practices on environmental performance and provide recommendations for improving the environmental performance of the apparel industry. This study mainly focused on investigating the level of green supply chain management practices (GSCMP) including Internal Environment Management (IE), Eco Design (ED), Green Manufacturing (GM), Green Purchasing (GP), and Cooperation with Customers (CC). By adopting the convenience sampling technique and quantitative survey method and using a self-administered questionnaire, the study collects data from 60 employees, including managers and executives, from three leading apparel manufacturing companies located in the Western and North Western provinces of Sri Lanka. These findings imply that GSCMP are more likely to impact environmental performance, as evidenced by correlations and regression analyses. The study will analyze the data to identify the extent to which green supply chain management practices are implemented in the industry and provide insights into the potential benefits of green supply chain management practices for the apparel industry in Sri Lanka. Furthermore, the study's limitations are discussed, and potential areas for further research based on the study's findings are highlighted.An exploratory study can also examine the influence of GSCMP practices on organizational performance based on the other service sectors of Sri Lanka.

Tailoring Garment Fit for Personalized Body Image Enhancement: Insights from Digital Fitting Research

In the context of the Fashion Apparel Industry 4.0, a transformative evolution is directed towards the Online Apparel Mass Customization (OAMC) strategy, which provides efficient and personalized apparel product solutions to consumers. A critical challenge within this customization process is the determination of sizes. While existing research addresses comfort evaluation in relation to wearer and garment fit, little attention has been given to how garment fit influences the wearer’s body image, which is also an important purchase consideration. This study investigates the impact of garment fit on the wearer’s body scale perception using quantitative research design. A digital dataset of avatars, clothed in varying sizes of T-shirts, were created for the body scale perception experiment, and an Artificial Neural Network (ANN) model was developed to predict the effect of T-shirt fit on body image. With only a small number of garments and body measurements as inputs, the ANN model can accurately predict the body scales of the clothed persons. It was found that the effect of apparel fit on body image varies depending on the wearer’s gender, body size, and shape. This model can be applied to enhance the online garment shopping experience with respect to personalized body image enhancement.

Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Evaluation of a new artificial intelligence-based textile digitization using fabric drape

Three-dimensional (3D) textile-based garment prototyping, widely adopted in the apparel and textile industry, enhances cost efficiency, work productivity, and seamless communication via visual prototyping. Neural network-based 3D textile digitization has the potential to streamline manufacturing processes by negating the need for traditional physical property (PT) measurements. However, a research gap exists concerning the accuracy of the technology and its applicability to advanced functional apparel manufacturing. The primary research question is to investigate how variations in digitized physical properties obtained from PT measurements and artificial intelligence (AI)-based textile digitization impact the accuracy of a fabric’s mechanical representation. In this study, we aimed to evaluate AI-based textile digitization accuracy using a drape test method. The drape coefficient (DC) analysis revealed that the PT-based simulated DC exhibited a normalized mean absolute error (NMAE) ranging from 2% to 11%, while the AI-based simulated DC showed a range of 3–51%. Notably, for the samples, except those with very limp or very stiff fabric samples, the AI-based simulation exhibited a NMAE within 3–15%.