Understanding and modelling the impact of consumer purchasing behaviour on the global supply chains' decisions in adapting anti-slavery practices

全球供应链的增长造成了供应链内部风险和利润的不对称分配，供应链负责人-通常是零售商等最终买家-主导着供应商关系。因此，风险被推到了链条的下游，低层公司不得不应对增加的不确定性，因此，工人不得不承担必要的灵活性。由比较优势原则驱动的全球供应链找到了通往网络的道路，在网络中，下层公司能够通过诉诸强迫劳动的做法来降低成本结构。全球供应链的权力关系和碎片化要求采取共同责任的方法，涉及利益相关者的参与和杠杆作用。英国现代奴隶制法案正式指定消费者在消除现代奴隶制方面发挥关键作用，不仅通过实际需求，而且作为社会政治场景中代表其他利益相关者的关键利益相关者[5，6]。反奴隶制做法的需求方管理仍然是一个研究不足的学术领域。它强调需要调查的上下文（如行为偏见和社会影响）和心理（如中立化和合法化）的障碍，消费者的行动，评估消费者细分的特点，确定有影响力的因素，并制定细分的具体战略，推动行为走向奴隶制自由的世界。这些战略的实际实施在很大程度上取决于供应链网络不同层次需求预测的准确性，其特点是具有不同聚合层次的复杂层次结构。人工智能，特别是机器学习方法在解决现代奴隶制问题上的应用为理论和经验途径提供了许多机会。这些方法允许通过使用大量的观察和属性将行为考虑纳入决策。他们也证明有前途的发展，利用多变量数据的好处，准确的层次预测模型。

The growth of global supply chains has caused an asymmetrical distribution of risk and profit within the chains where chain captains - often end buyers, such as retailers - dominate supplier relationships. As a result, risk is pushed down the chain to lower-tier firms, which have to address increased uncertainties and consequently, to workers who have to shoulder the burden of required flexibility. Global supply chains driven by the principle of comparative advantage find their way to networks where lower-tier firms are able to reduce their cost structure by resorting to forced labor practices. Power relationships and fragmentation of global supply chains call for adopting a shared responsibility approach, involving participation and leverage of stakeholders.The UK Modern Slavery Act formally assigns consumer a critical role in eradicating modern slavery not only through actual demand but also as a key stakeholder who represents other stakeholders in the socio-political scene [5, 6]. The demand-side management of anti-slavery practices is still an under-researched academic field. It highlights the need to investigate the contextual (e.g. behavioral biases and social influence) and psychological (e.g. neutralization and legitimation) barriers to consumer action, assess characteristics of consumer segments, identify influential factors, and develop segment-specific strategies to nudge behavior towards a slavery free world. Practical implementation of these strategies depends highly on the accuracy of demand forecasts at different levels of the supply chain network characterized by complex hierarchies with different levels of aggregation.The application of AI, especially machine learning methods, in tackling modern slavery offers numerous opportunities for theoretical and empirical pathways. These methods allow for the incorporation of behavioral considerations into decision-making by using a large number of observations and attributes. They also prove promising for the development of accurate hierarchical forecasting models exploiting the benefit of multi-variate data.