Resilient remanufacturing networks: forecasting, informatics and holons

据估计，再制造对英国经济的价值为24亿英镑，在不久的将来可能会增加到56亿英镑。整个过程依赖于退货项目（核心）的时间、数量和质量，但迄今为止还没有研究关注退货预测以及如何将这种预测与库存和生产优化（IPO）程序系统地集成在一起。这些程序是实现财政、环境和社会可持续性的垫脚石。如果得到支持，这是第一个研究这些问题的研究，因此将对英国再制造的理论和实践做出相当大的贡献。我们的愿景是创造一个可持续和有弹性的世界，在这个世界中，再制造商及其闭环供应网络对产品退货具有“可见性”，并将这些信息反映到与循环经济（CE）兼容的IPO中，以提高可持续性和弹性。在再制造环境中，物料清单失去了其原有的意义，并在很大程度上取决于回收使用过的物品的状态。这不仅需要预测退货的时间和数量，还需要预测退货的质量，以便决定：i)需要更换哪些部件才能将商品恢复到预期状态？Ii)当修复项目在经济上或实际上不可行时，哪些可用部件可以反馈到制造过程中？预期回报率与使用中的物品数量和物品生命周期的阶段密切相关。使用中的产品数据、服务信息和判断输入也应具有解释力，同时也可能存在时间序列效应，例如季节性。上述情况使得经典的需求预测方法无法利用，需要新的估计方法。尽管回报预测在CE背景下具有明显的重要性，但相关文献极其有限。此外，与回报相关的不确定性并不意味着（再）制造产品的经典需求不确定性不存在，这可能导致所谓的“双尾不确定性”！至关重要的是，上述预测问题转化为系统性IPO挑战。越来越多的文献关注闭环供应链中的库存和/或生产问题。然而，有趣的是，所有这些工作都是在回报方面没有不确定性的条件下进行的，因此不需要预测它们，这显然削弱了这些解决方案的实际效用。将回报和需求预测与IPO相结合需要一种前所未有的整体方法。整体既是一个整体，也是一个更广泛系统的一部分——例如，在任何组织中，每个部门都可能建立自己的战略重点，但如果没有总体的更高层次的组织战略方向来优化它们的相互作用，它们可能会相互冲突。因此，每种不同的预测协议、库存控制器和生产排序规则都有自己的动态特性，但当它们以不同的组合方式集成时，就会形成一个新的整体，而这个整体可能不是不同部分的简单相加。因此，我们将开发适当的预测方案，并通过系统建模将其集成到IPO中。CE的库存和生产优化是实现以下目标的基石：i)大量减少库存和释放空间，从而降低供应链成本，并在市场上提供更便宜、更实惠的产品（财务可持续性）；Ii)降低材料、零件和成品的过时风险，对环境可持续性有巨大影响；Iii)提供更多的再制造产品，为消费者创造更合乎道德的营销渠道（社会可持续性）。

The value of remanufacturing is estimated at £2.4B to the UK economy, potentially increasing to £5.6B in the near future. The entire process relies upon the timing, quantity and quality of the returned items (cores), and yet there have been no studies to-date that look at returns forecasting and how such forecasts can be integrated in a systemic way with inventory and production optimisation (IPO) procedures. Such procedures are stepping-stones towards financial, environmental and societal sustainability. If supported, this is the first study to look at these issues and therefore would make a considerable contribution to the theory and practice of remanufacturing in the UK.Our vision is to create a sustainable and resilient world where remanufacturers and their closed-loop supply networks have 'visibility' of product returns and reflect such information into circular economy (CE) compatible IPO to improve sustainability and resilience. In a remanufacturing context, the bill of materials loses its original meaning, and greatly depends on the state of the returned-used items. This introduces a need to forecast not only the timing and volume of the returns, but also their quality, in order to decide: i) what parts need to be replaced for the item to be restored to the desired state? ii) which usable parts can be fed back into the manufacturing process when restoring the item is not economically or practically viable? Rate of returns is expected to strongly correlate to the number of items in use and the stage in the item's life cycle. In-use product data, service information and judgmental inputs should also have explanatory power while time series effects, e.g. seasonality, may also be present. The above make the utilisation of classic demand forecasting methods impossible, calling for novel estimation approaches. Despite the obvious importance of returns forecasting in a CE context, the relevant literature is extremely limited. Further, the uncertainty associated with returns does not imply that the classic demand uncertainty for (re)manufactured products is not present, leading to what may be termed a 'two-tailed uncertainty'! Critically, the foregoing forecasting problems translate into systemic IPO challenges. A growing body of literature looks at inventory and/or production problems in closed-loop supply chains. Interestingly though, all these works are conditioned to no uncertainty with regard to returns and thus no need to forecast them, obviously diminishing the practical utility of these solutions. Integrating returns and demand forecasting with IPO requires a holonic approach, not previously attempted. A holon is an element that is both a whole in its own right but also part of a wider system - for example, in any organisation each department may establish its own strategic priorities but potentially they could act in conflict with each other if there is no general higher level organisation strategic direction to optimise their interactions. Hence, each different forecasting protocol, inventory controller and production ordering rule has its own dynamic properties but which, when integrated in different combinations, creates a new whole that may not be the simple addition of the different parts. Therefore, we will develop appropriate forecasting protocols and integrate them into IPO through systems modelling.Inventory and production optimisation in the CE are stepping-stones towards: i) immense inventory reductions and space liberation, resulting in reduced supply chain costs and cheaper, more affordable products in the market (financial sustainability); ii) reduced obsolescence risk for materials, parts and finished items, with huge implications for environmental sustainability; iii) greater availability of remanufactured products, creating a more ethical marketing channel to consumers (societal sustainability).