Energy consumption determinants for apparel sewing operations: an approach to environmental sustainability

Doctor of PhilosophyDepartment of Apparel, Textiles, and Interior DesignMelody L. A. LeHewFashion is the second most polluting industry and accounts for 10% of global carbon emissions. Consuming fossil fuel based electricity, the primary source of energy in the apparel production process, causes a great deal of greenhouse gas (GHG) emissions. Due to ever-increasing apparel demand and population growth, this industry’s carbon footprint will only grow bigger. As attention on sustainability issues in our world intensifies, research on environmental sustainability in the apparel manufacturing industry is needed. The purpose of this exploratory study was to investigate energy consumption (EC) of the apparel sewing process. The objectives are to (a) identify the most influential EC factors and develop a model to capture EC levels, (b) determine factor interrelationships, (c) identify steps to reduce EC, and (d) explore experts' level of concern regarding EC of the apparel manufacturing and its contribution to greenhouse gas emissions and climate change. A mixed method research study was employed in this study: a qualitative method was utilized to assess expert perceptions and a quantitative method was used to measure EC and build a regression model. This study determined dominant EC and GHG emissions factors from sewing process so that apparel manufacturers can understand which factors need to be controlled to reduce environmental damage. Findings from the study indicated sewing machine motor capacity, sewing speed, and standard allocated minute (SAM) were the most influential EC factors, and shortening the sewing time was found as the best solution to reduce energy consumption in the apparel sewing process. The energy consumption model was found as: Log (EC) = 9.283 + 0.771* log (SAM) + 0.386*knit fabric type + 0.260*sportswear fabric type + 0.080*SPI - 0.008*capacity + 0.004*seam length - 0.001* speed + 0.495 The EC model along with GHG calculator (a tool to convert GHG from EC) will help the industry to determine their EC and GHG emissions level to boost their awareness and to encourage greater impetus for environmental actions. Finally, this study will help designers, retailers, and consumers to pursue environmentally friendly actions in terms of decisions regarding apparel design, sourcing, and purchasing

Energy Consumption Model for Apparel Sewing Process: An Approach to Environmental Sustainability

Fossil fuel based electricity, the primary source of energy in the apparel production process, causes a great deal of greenhouse gas (GHG) emissions. Due to ever-increasing apparel demand and population growth, this industry\u27s carbon footprint only grows bigger. The purpose of this exploratory study were to (a) identify the most influential energy consumption (EC) factors and (b) develop a model that capture EC levels from the apparel sewing process. A multiple regression analysis using STATA version 12.0 was used to analyze EC factors to determine their explanatory power over EC. A total of 98 observations from 98 different sewing operations from three apparel factories were collected. Findings from the study indicated sewing machine motor capacity, sewing speed and standard allocated minute were the most influential EC factors. The EC model was found as: Log (EC) = 9.68 + 0.739*log (SAM) + 0.084*SPI - 0.008*motor_capacity + 0.004*seam_length - 0.001*speed + 0.51

The foundations of relationship marketing

Vol. No.2, Issue No.10, October, 201

Investigate the fabric performance of Tencel-cotton blended denim in terms of the percentage change of Tencel

In terms of sustainability & wearing comfort, denim manufacturers are entering a new era of product variety. Tencel's regenerative nature and unique mechanical qualities usher in a new era for the denim industry. In this work, denim fabrics were manufactured using Tencel Cotton blended yarn using very fi ne yarn (20 Tex or 30 Ne), and fabric performance was examined following factors like tensile strength and other relevant metrics. For better evaluation, All the structures were 2/1 RHT (right-hand twill) that contained 115 EPI & 70 PPI and also indigo blue dyed. According to the result of the investigation, 100% Tencel Fabric (both the warp and the weft yarn were 100% Tencel) demonstrated the highest quality of fabric performance in terms of tensile strength, tearing strength, stiff ness, air permeability, and water vapor permeability than any other cotton or cotton Tencel blended fabric. However, a downward trend of abrasion resistance was observed in Tencel or cotton Tencel blended fabric concerning the percentage change of Tencel. Additionally, the performance of the fabric was significantly improved by the percentage addition of Tencel fiber in the warp and weft directions. In addition, a denim fabric made entirely of cotton performed the least well when compared to fabrics made entirely of Tencel or a blend of Tencel and cotton

Predicting Total Assembling Time for Different Apparel Products Utilizing Learning Curve and Time Study Approaches: A Comparative Case Study

Many articles have investigated operator performance, predicting standard cycle time by learning curve approach (e.g., Bevis, Finnlear, & Towill, 1970; De Jong, 1957). Surprisingly, no previous research has been found where time is predicted by learning curve approach in the apparel assembling process. In this comparative study, both learning curve and time study approach were used in the apparel assembling process to determine which method better predicts the total assembling time.</p

Exploring weft knit fabric defects based on their presence and quality impact: A case study

While addressing grey fabric quality in a renowned circular weft knitting mill of Bangladesh, the authors experienced some questionable approach practiced by knitters. The subjective nature of defect detection by knitters/inspectors often time causes wrong emphasizing on frequently occurring defect(s) instead of focusing on influential defect(s) and subsequently, employing wrong quality control approach to minimize the grey fabric defects. Knit fabric defects (e.g., hole, stain, press-off, gout, miss knit, barrè, tucking, etc.) should be assessed by type, fault coverage, gravity and the frequency of occurrence instead of focusing only on frequency of occurrence in the fabric. In this study, grey weft-knitted fabric quality is investigated based on influential defects instead of frequently occurring defects. Quality data of single jersey, fleece and 1X1 rib were gathered and analyzed from an established knitting factory in Bangladesh over three months duration. A fabric inspection machine and 4-point inspection method were employed in this study. Gout was found as the most frequently occurring defect for each fabric type but not influential for rib fabric. For a significant amount of knitted fabrics, totaling of 55,524.91 m2 inspected fabric, the most occurring defects were ranked as gout, press-off, hole, miss knit, stain, and tucking and influential defects (based on inspection points) were ranked as gout, press-off, hole, stain, miss knit, and tucking (highest to lowest). In the inspection report, the knitter/inspector mistakenly categorized gout as the most occurring as well as the most influential defect for 1X1 rib fabrics and suggested remedies accordingly