Cognitive Factors Affecting the Manufacturing Optimization Skills of Rural Indian BPO Workers

Gokula Vasantha, Jonathan Corney, Chandra Kant Upadhyay
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Abstract

Crowdsourcing offers on-demand access to large numbers of human workers to implement new forms of human–computer collaborative functionalities that can be seamlessly integrated into advanced software and algorithms. However, crowdsourcing tasks are primarily undertaken by urban rather than rural workers. To enable the development of skilled rural employment, this research aims to assess rural crowdsourcing workers’ spatial reasoning and creative abilities and their abilities to solve irregular strip packing problems associated with the manufacture of sheet materials. The study conducted experiments and data collection with 140 rural Business Processing Outsourcing (BPO) workers located in six states of India. The statistical analyses of the data collected from seven rural BPO firms (140 rural workers) reveal that rural workers can achieve a 2D packing efficiency that is up to 8% higher than that of commercial algorithm outcomes. The results suggest that rural crowdsourcing can lead to effective job creation, skill development, and, for a modest cost, it can support industries that employ CAD/CAM systems to generate geometric data for common manufacturing processes.
影响印度农村BPO工人制造优化技能的认知因素
众包为大量人力提供了按需访问,以实现新型人机协作功能,这些功能可以无缝集成到先进的软件和算法中。然而,众包任务主要由城市工人承担,而不是农村工人。为了促进农村技术就业的发展,本研究旨在评估农村众包工人的空间推理和创造能力,以及他们解决与薄板材料制造相关的不规则条形包装问题的能力。该研究对印度六个邦的140名农村业务流程外包(BPO)工人进行了实验和数据收集。对7家农村BPO公司(140名农村工人)收集的数据进行统计分析表明,农村工人可以实现二维包装效率,比商业算法结果高出8%。结果表明,农村众包可以有效地创造就业机会,发展技能,并且以适度的成本,它可以支持采用CAD/CAM系统为普通制造过程生成几何数据的行业。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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