This study examines the efficiency and cost-effectiveness of fabric spreading and cutting processes in the apparel industry. It aims to determine how technological levels in these processes affect labor, energy consumption and overall production costs, with a focus on sustainability and profitability.
Three garment factories with varying levels of technology were analyzed. The processes included manual fabric spreading and cutting, semi-automatic spreading and manual cutting and fully automatic spreading and cutting. Time studies, labor costs and energy consumption measurements were conducted. Costs were calculated using standard evaluation methods, including REFA (Reichsausschuss für Arbeitsstudium – Reich Committee for Working Time Determination) forms for time studies and direct energy consumption measurements with an electronic electricity meter.
The study revealed significant differences in efficiency and costs across the three systems:
• Manual spreading and cutting had the highest total cost ($2.312) and the longest processing time (73 min 56 s).
• Semi-automatic spreading and manual cutting reduced costs to $1.132 and processing time to 52 min 36 s.
• Fully automatic spreading and cutting offered the lowest cost ($1.087) and the shortest processing time (24 min 46 s).
• Increased automation reduced labor costs and processing time but increased energy consumption. Overall, automated systems provided cost savings by minimizing labor requirements and standardizing quality.
The study focuses only on fabric spreading and cutting processes, excluding other production stages. It also assumes uniformity in operator skills and fabric types across the analyzed factories. Investment costs for transitioning to higher automation levels are discussed but not quantified in detail.
This study uniquely measures energy consumption directly from machines during the spreading and cutting processes, rather than relying on catalog data. It provides a comparative analysis of three different technological levels in fabric processing and highlights their impacts on efficiency, cost and sustainability.
