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分配的不对称，在这种情况下，供应链负责人——通常是零售商等终端买家——主导着供应商关系。因此，风险被推到产业链下游的较低层次公司，这些公司必须应对日益增加的不确定性，因此，工人不得不承担所需灵活性的负担。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，在那里，较低层次的公司能够通过诉诸强迫劳动的做法来降低成本结构。权力关系和全球供应链的碎片化要求我们采取责任共担的方式，让利益攸关方参与并发挥作用。英国《现代奴隶制法案》正式赋予消费者在消除现代奴隶制方面的关键角色，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]。反奴隶制实践的需求方管理仍然是一个研究不足的学术领域。它强调需要调查消费者行动的背景障碍（例如行为偏见和社会影响）和心理障碍（例如中立化和合法化），评估消费者群体的特征，确定影响因素，并制定针对特定群体的战略，以推动行为走向一个无奴隶制的世界。这些策略的实际实施高度依赖于供应链网络不同层次需求预测的准确性，这些层次具有复杂的层次结构和不同的聚合水平。人工智能，特别是机器学习方法在解决现代奴隶制问题中的应用，为理论和实证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。它们也证明了开发利用多变量数据的好处的准确分层预测模型的前景。

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.