Co-Designing an AI Factory Flow Manager for On-demand Apparel Manufacturing with Garment Workers

**业务需求**模式项目专注于将全球时尚业过渡到按需无库存模式。*我们的硬件和软件流水线是为“拉动式”动态模式设计的，在这种模式下，生产基于客户需求和自动化工厂流程。服装是个性化的或定制的，员工们接受的是五件一件的小批量培训。*最困难的挑战是管理机械师每周按需生产的工作流程。我们目前为五个品牌生产12种款式，每周欢迎新品牌。每种款式都有9-30个单独的步骤，使用2-5种不同的机器。每周批量生产，每种款式的数量可以在1-50台之间变化。*我们根据订单队列手动安排生产花名册，并确定给定的项目及其数量的列表，并涉及机械师最少的切换时间。**新想法-AI Factory Flow Manager**我们想为按需服装制造创建一个人工智能工厂流程管理器，可以优化各种风格的生产流程。这一人工智能将能够针对以下变量进行优化：*物流因素-服装批次的大小、期望的批次交付日期、机器设置和切换时间*服装风格因素-缝纫加工的各种风格的协同效应*服装工人因素-机械师当前的技能，个别机械师的学习阶梯，机械师的相对速度，机械师当前的产品技能，关于生产线上的弹性，生活质量和有意义的工作，对挑战的响应**好处*通过提高整体质量和生产率以及使按需生产具有成本效益来服务于管理。*服装工人通过让他们参与工作的设计和控制而受益。提高他们的积极性和工作满意度。*符合整个组织的利益-通过创建一个正式的系统来鼓励学习，捕捉和交流方法的改进。灵感-[NUMMI工厂转型][0]。**EDI挑战**妇女占全球制衣工人总数的80%。尽管如此，男性仍然主导着制造技术/系统的发明和设计。这一点令人担忧，因为生产力软件、自动化和机器人技术的使用直接影响制衣工人的生活体验和就业前景。**电子数据交换通过充满活力和合作的文化，将培训整合到日常工作中，影响*服装工人的生活质量。*按需定制生产可以使劳动密集型行业的利润率提高一倍--保护和创造更好的就业机会。[0]：https://sloanreview.mit.edu/article/how-to-change-a-culture-lessons-from-nummi/

**Business Need*** Pattern Project is focused on transitioning the global fashion industry to an on-demand no-stock model.* Our hardware and software pipeline is designed for the "pull" dynamic model, in which production is based on customer demand with an automated factory flow. Garments are personalised or custom-fit, and staff is trained on micro-batches of five.* The most difficult challenge is managing the workflow of machinists for weekly on-demand production. We currently produce 12 styles for five brands and welcome new brands each week. Each style has between 9 - 30 individual steps and employs 2 - 5 different machines. Production is batched weekly, and quantity per style can vary between 1 - 50 pcs.* We manually schedule a production roster based on the order queue and determine, given a list of items and their quantities and involving the least switchover time for machinists.**New idea - AI Factory Flow Manager**We want to create an artificial intelligence factory flow manager for on-demand apparel manufacturing that can optimise the production process for various styles. This AI will be capable of optimising for the following variables:* Logistic factors - Size of garment batch, Desired date of delivery of the batch, Machine set up and switchover times* Garment style factors - Synergies across styles for seam finishes* Garment Workers factors - The current skillset of the machinist, The learning ladder of individual machinists, Relative speeds of machinists, Machinists' current product skillset concerning resilience in the production line, Quality of life and meaningful work, Response to Challenge**Benefits*** Serves management by improving overall quality and productivity and making on-demand production cost-effective.* Benefits garment workers by involving them in the design and control of their work, increasing their motivation and job satisfaction.* Serves the interests of the entire organisation - by creating a formal system to encourage learning and capture and communicate improvements in methods. Inspiration -[NUMMI plant transition][0].**EDI Challenge**Women account for 80% of all garment workers worldwide. Still, men continue to dominate the invention and design of manufacturing technologies/systems. This is concerning since productivity software, automation, and the use of robotics directly impact the lived experience of garment workers and their employment prospects.**EDI Impact*** Better quality of life for garment workers through a dynamic and cooperative culture, training integrated into daily work.* On-demand custom-fit production can double margins for the labour-intensive industry - safeguarding and creating better jobs.[0]: https://sloanreview.mit.edu/article/how-to-change-a-culture-lessons-from-nummi/