Wyniki wyszukiwania - BazTech

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Research opportunities in textile reverse logistics: a systematic review

Sumo Peter Davis, Alimo Philip Kofi, Agyeman Stephen, Sumo Dorris Zayzay, Cai Liling, Ji Xiaofen

LogForum

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2023

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Vol. 19, no. 2

303--315

EN

Background: The textile industry generates a large volume of waste due to the increasing demand for clothing for daily use and fashion. To reduce waste, reverse logistics (RL) has been proposed to ensure the recycling and reuse of waste textiles in the value chain. RL has been broadly examined in several manufacturing supply chains but less explored in the textile industry. The absence of a systematic review on textile reverse logistics (TRL) makes it difficult to identify existing knowledge gaps and research opportunities.Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this paper contributes a systematic literature review of 28 relevant papers published on TRL between 1999 and August 2022. Results: Overall, there is a shortage of recycling facilities in developing economies. There is a need for quantitative models that assess the location and potential disruptions and aversion of the resulting risks of TRL. Investigating consumers’ perspectives on the desire to sort and transport old textiles to collection sites would be helpful to manufacturers. Additionally, system optimization to reduce emissions that emerge through the TRL production line would help reduce costs. It is also found that incentivizing clothing businesses that adhere to TRL practices would encourage more participation.

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Building Self-Confidence by Using a Sports Bra as an Everyday Bra - a Study Based on Pressure Analysis

Ocran Francisca Margarita, Ji Xiaofen, Zhai Lina

Fibres & Textiles in Eastern Europe

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2023

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Vol. 31, no. 4

1--9

EN

Women often use sports bras when doing physical activities, but most women have recently shown interest in using sports bras as everyday bras. Therefore, this study used pressure analysis to identify a suitable sports bra that generates less pressure at the shoulder strap for all breast sizes. Among fashion design and engineering students, an interview was conducted at Zhejiang Sci-Tech University. Based on this random survey, women found more comfort in using sports bras as everyday bras, but the shoulder strap and waistband prevented them from doing it regularly. Therefore, this study emphasized only the shoulder straps because it was the most uncomfortable feature among others. More attention needs to be made to the strap width and neckline when selecting a sports bra because when it comes to females with breast sizes 34C and 36C, encapsulation types with cross-back designs are suitable. However, we noticed that the shoulder straps should be widened based on breast size. The encapsulation type with a racer back is recommended for women with breast size 38C.

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SWOT Framework Based on Fuzzy Logic, AHP, and Fuzzy TOPSIS for Sustainable Retail Second-hand Clothing in Liberia

Sumo Peter Davis, Ji Xiaofen, Cai Liling

Fibres & Textiles in Eastern Europe

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2022

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Vol. 30, no. 6

27--44

EN

The fast-fashion business model is marred by high resource consumption and enormous emission of greenhouse gases. It is based on inaccurate forecasts, resulting in excess supply than demand. Globally, 85% of two-week-old garments end up as unfashionable or worn-out items that must be discarded as waste, disposed of for recycling, or donated to charities. With this colossal increase in textile waste, resource efficiency is one of the biggest challenges facing the fashion industry, which now calls for a swift implementation of a new sustainable business and consumption model to extend product life cycles. This demand for sustainable consumption encourages consumers to reuse, recycle and resell. The resell campaign known as second-hand clothing is a growing market worldwide. Current global forecasts predict a 185% increase over the next ten years, compared to FF, which will expand by just 20%. Africa is a top destination, with more than 80% of its population wearing SHCs. We contribute to this literature by assessing the significance of SHC trade in Liberia. We extend this assessment by developing a hybrid MCDM tool incorporating AHP, fuzzy logic, Ensemble, and TOPSIS to build a SWOT framework to identify criteria and sub-criteria for prioritizing SHC retailing in Liberia and Africa. Data for this study were gathered from a survey involving 100 SHC retailers from the Red-Light, Waterside, Duala, and Omega markets in Monrovia, Liberia. We identified several important factors in implementing sustainable SHC and recommended strategic directions towards their successful implementation.

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Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization

Shao Yibing, Ji Xiaofen, Zheng Menglin, Chen Caiya

Autex Research Journal

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2022

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Vol. 22, no. 3

290--297

EN

Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.

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Prediction of Garment Production Cycle Time Based on a Neural Network

Cao Huaqing, Ji Xiaofen

Fibres & Textiles in Eastern Europe

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2021

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Vol. 29, no. 1 (145)

8--12

EN

The process of garment production has always been a black box. The production time of different clothing is different and has great changes, thus managers cannot make a production plan accurately. With the world entering the era of industry 4.0 and the accumulation of big data, machine learning can provide services for the garment manufacturing industry. The production cycle time is the key to control the production process. In order to predict the production cycle time more accurately and master the production process in the garment manufacturing process, a neural network model of production cycle time prediction is established in this paper. Using a trained neural network to predict the production cycle time, the overall error of 6 groups is within 5%, and that of 3 groups is between 5% and 10%. Therefore, this neural network can be used to predict the future production cycle time and predict the overall production time of clothing.

PL

Czas produkcji różnych ubrań jest inny i podlega dużym zmianom, dlatego menedżerowie nie mogą dokładnie zaplanować produkcji. Wraz z wkroczeniem świata w erę przemysłu 4.0 i gromadzeniem dużych zbiorów danych dobrym rozwiązaniem dla przemysłu odzieżowego jest zastosowanie maszyn uczących się. Czas cyklu produkcyjnego jest kluczem do kontroli procesu produkcyjnego. W celu dokładniejszego przewidywania czasu cyklu produkcyjnego i opanowania procesu produkcyjnego w procesie produkcji odzieży, w artykule opracowano model sieci neuronowej do przewidywania czasu cyklu produkcyjnego. Do przewidywania czasu cyklu produkcyjnego użyto sieci neuronowej, ogólny błąd 6 grup mieścił się w granicach 5%, a 3 grup – między 5% a 10%. W związku z tym zaprezentowana sieć neuronowa może znaleźć zastosowanie w przewidywaniu czasu cyklu produkcyjnego i całkowitego czasu produkcji odzieży.