Responsive Manufacturing: Maximising Value Through Life

想象一下，你负责一个生产下一代电动汽车的制造系统的运营。制造系统精简，生产出最先进的创新汽车，正好满足消费者需求，浪费最少，向生产线提供材料和产品的供应链是绿色的，生产质量很高。每个人都很开心。然而，突然之间，汽车制造中使用的核心材料的供应现在相当稀缺，即供应有限。不幸的是，我们的制造系统不再像它应该的那样工作了！制造系统没有生产足够的汽车，生产率已经跌至谷底。遗憾的是，有迹象表明，这些材料正变得越来越稀缺--供应商一直在发出警告，但警告被遗漏了，没有人意识到这会产生什么影响。因此，我们现在的制造系统效率低下，汽车不能再以适当的速度制造，制造商即将破产！如果我们有一个反应迅速的制造系统，即适应变化、可持续和有弹性，这些都是可以避免的。这项研究的输出旨在通过提供信息来使制造系统适应内部和外部因素，即使制造系统能够响应。我们的研究将使用通过数字方法自动获取的数据、信息和知识，使制造系统的大脑(控制中心)能够持续评估其当前状态并预测未来状态。我们将促进制造系统真正做出反应的能力，同时保持其整个生命价值。尽管说起来容易--要做到这一点却极具挑战性。然而，鉴于目前新冠肺炎等重大中断对制造业的影响，制造商强烈希望并愿意确保其系统能够响应。因此，这一呼吁和我们提出的解决方案非常及时。与这种需求并行的是，技术和流程的进步，如数字化、5G和工业4.0，已经达到了我们可以创建一种手段，通过这种手段，制造系统可以自动评估它是否需要改变并预测最合适的行动。我们建议的解决方案以价值建模为基础(价值模型用于评估任何建议的解决方案在成本、质量、交付、环境方面的影响)，以评估任何建议的应对制造系统内部/外部的变化的影响。我们将通过调查和分析一些现实生活中的制造案例研究来实现这一点，以确定与制造系统的特征相关的适当的自治水平。我们将确定建立价值模型所需的核心数据信息和知识，使用数据分析技术(如集群/网络建模)自动分析制造系统，并创建一个实用和可用的循序渐进的过程，以确保研究结果的影响。总而言之，我们的愿景是创建一个自动化的实时制造系统支持工具包，以实现当前和未来制造系统的全生命周期价值，通过其生命周期实现价值最大化，即响应、可持续、适应性和弹性。

Imagine you are responsible for the operation of a manufacturing system that is producing the next generation of electric cars. The manufacturing system is streamlined and producing leading-edge innovative cars just in time to meet the consumer needs, has minimum waste, the supply chain providing materials and products to the manufacturing line is green and quality of production is high. Everyone is happy. However, suddenly the supply of a core material used in the manufacture of the car is now quite scarce i.e. there is limited availability. Unfortunately, our manufacturing system is no longer working as it should! The manufacturing system is not producing enough cars and the productivity has hit rock bottom. Sadly, there were indications that the material was becoming scarce - the supplier had been issuing warnings, but the warnings were missed and no-one realised the impact this would have. So, we now have a manufacturing system that is not efficient, the cars can no longer be manufactured at an appropriate rate, and the manufacturer is about to be bankrupt! This could have all been avoided if we had a manufacturing system that was responsive i.e. adapt to change, be sustainable and resilient. The outputs from this research are geared to avoid such occurrences by providing the information to enable the manufacturing system to adapt to both internal and external factors i.e. enable the manufacturing system to be responsive.Our research will use Data, Information and Knowledge, automatically accessed via digital methods to enable the brain (the control centre) of the manufacturing system to continually assess its current status and predict future states. We will facilitate the ability of a manufacturing system to be truly responsive, whilst sustaining its whole life value. Although easy to say - achieving this is extremely challenging. However, with the current impacts of major disruptions such as COVID-19 on manufacturing there is a strong desire and willingness from manufacturers to ensure their systems can be responsive. Hence, the call and our proposed solution is very timely. In parallel to this need, the advancements in the technology and processes, such as digitalisation, 5G and Industry 4.0 have reached the stage that we can create a means by which a manufacturing system can automatically assess whether it needs to change and predict the most appropriate action.Our proposed solution has its foundations in value modelling (a value model is used to assess the impact of any proposed solution in terms of e.g. cost, quality, delivery, environment) to evaluate and assess the impact of any proposed response to changes within/external to the manufacturing system. We will achieve this via the investigation and analysis of a number of real-life manufacturing case studies to identify the level of autonomy that is appropriate in relation to the characteristics of the manufacturing system. We will identify the core Data Information and Knowledge required to create the value model, use data analytic techniques such as clustering/network modelling to automatically analyse the manufacturing system and create a pragmatic and useable step-by-step process to ensure impact from the outputs of the research.In summary, our Vision is to create an automated real-time manufacturing system support toolkit to achieve whole life value from current and future Manufacturing Systems, maximising value through their lifetime i.e. being responsive, sustainable, adaptable and resilient.