Automated commissioning, set-up and operation of a novel fabric inspection system delivering objective quality control and a fully remote customer journey

由于新冠肺炎危机，纺织品和服装供应链正在遭受中断。零售商无法通过实体店销售产品。供应链是复杂和全球分散的。供应链内的生产订单已被取消或削减，许多地理区域无法为其工厂提供政府支持。制造业和零售业被Statista列为2020年受Covid-19影响最大的第一和第三大行业\[Statista-June-24th-2020\]。我们的生态系统结合了这两个部分。零售品牌创造并控制供应链。主要的供应链驱动因素是数字化和可持续性。零售品牌经常访问他们的供应链工厂，以监控合规性、质量、交货时间表、数字化进展和可持续发展过程。数字化使远程监控成为可能，大大降低了成本和旅行碳足迹。然而，该行业仍然非常传统，劳动密集型，因此一直存在变革阻力。2019冠状病毒病是变革的催化剂，数字化正在迅速加速。第二个变化驱动因素是环境可持续性。大多数供应链都远离最终消费者，这意味着它们相对来说是在视线之外的。社会意识和压力有限，但这种情况正在改变。可持续性现在是所有零售品牌的关键。质量控制（称为供应链中的织物检验）不仅是数字化的关键因素，也是减少浪费（可持续性）和提高产量（可持续性）的关键因素。传统上，质量控制是非常劳动密集型的，并且执行不一致（主观）.c-tex和Shelton是自动化在线颜色变化监测和缺陷检测的全球领导者。这些都是织物检测的核心功能。自2014年以来，我们的技术在领先的纺织品生产商和服装厂中获得了广泛的关注，使他们能够实现自动化（数字化）织物检测。InnovateUK已经进一步提升了两家公司在该领域的技术领导地位，以开发他们的技术，用于图案织物和纯色织物的自动质量检查。因此，工厂现在可以使用c-tex和Shelton对其纯色和图案织物进行数字质量检查。这项技术是c-tex和Shelton独有的。然而，由于2019冠状病毒病，我们现在需要将我们的商业模式（客户旅程）自动化（数字化）。我们的传统商业模式是面对面的销售流程和长时间的现场用户入职（安装、调试、培训）。我们现在需要一个非旅行的自动化客户旅程，这样我们的自动化技术就可以被购买、实施和使用。对于我们的新客户旅程，我们将在适用的情况下使用现成的远程工作技术，但我们需要开发我们的专有技术来实现远程安装、调试和机器学习。实现这一目标的关键是加快我们在机器学习和传感器技术自我调试方面的人工智能能力。我们最近开始在领导层面进行合作，但现在我们需要将其转移到技术团队之间的合作上。结合我们两种技术的单一界面的开发对于我们的新客户旅程也是至关重要的。

Textiles and garment supply chains are enduring disruption due to the Covid-19 crises. Retailers have not been able to sell products through their bricks and mortar outlets. Supply chains are complex and globally dispersed. Production orders within the supply chains have been cancelled or curtailed, many of the geographic regions have not been able to offer government support for their factories. Manufacturing and Retail have been ranked by Statista as the 1st and 3rd most impacted sectors by Covid-19 in 2020 \[Statista-June-24th-2020\]. Our ecosystem combines both these sectors.Retail Brands create and control the supply chains. The dominant supply chain drivers are digitisation and sustainability. Retail Brands constantly visit their supply chain factories to monitor compliance, quality, delivery schedules, digitisation progress and sustainability processes. Digitisation enables remote monitoring, significantly decreasing their costs and travel carbon footprint. However the sector remains very traditional and labour intensive, so there has been resistance to change. Covid-19 is a catalyst for change, digitisation is accelerating rapidly.The second change driver is environmental sustainability. Most the supply chains are far from the end consumers meaning they are relatively out of sight. Societal awareness and pressure has been limited, however this is changing. Sustainability is now key for all retail brands.Quality Control (termed fabric inspection within the supply chain) is a key factor not only for digitisation, but also decreasing waste (sustainability) and improving yields (sustainability). Traditionally quality control has been very labour intensive and executed inconsistently (subjectively).c-tex and Shelton are global leaders in automated inline colour variation monitoring and defect detection. These are the core functions of fabric inspection. Since 2014 our technologies have been gaining traction among the pioneering textile producers and garment factories, enabling them to automate (digitise) fabric inspection. The sector technical leadership of both companies has already been furthered by InnovateUK to develop their technologies for automated quality checking of patterned fabrics in addition to solid colour fabrics. Therefore factories can now use c-tex and Shelton to digitally quality check their solid colour and patterned fabric. This technological offering is unique to c-tex and Shelton.However due to Covid-19 we now need to automate (digitise) our business model (customer journey). We have had a traditional business model of a face-to-face sales process and long duration onsite user on-boarding (installation, commissioning, training). We now need a non-travel automated customer journey so that our automated technology can be purchased, implemented and used.For our new customer journey we will use readily available remote working technologies where applicable, but we need to develop our proprietary technologies to enable remote installation, commissioning and machine learning. Key to this is to accelerate our AI capabilities for machine learning and self-commissioning of our sensor technologies. We recently started to collaborate at leadership level but we now need to move this onto collaboration between our technical teams. The development of a single interface combining both our technologies is also essential for our new customer journey.