Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States of America.
SRI International, Menlo Park, CA, United States of America.
PLoS One. 2020 Feb 18;15(2):e0224761. doi: 10.1371/journal.pone.0224761. eCollection 2020.
The United States has experienced prolonged severe shortages of vital medications over the past two decades. The causes underlying the severity and prolongation of these shortages are complex, in part due to the complexity of the underlying supply chain networks, which involve supplier-buyer interactions across multiple entities with competitive and cooperative goals. This leads to interesting challenges in maintaining consistent interactions and trust among the entities. Furthermore, disruptions in supply chains influence trust by inducing over-reactive behaviors across the network, thereby impacting the ability to consistently meet the resulting fluctuating demand. To explore these issues, we model a pharmaceutical supply chain with boundedly rational artificial decision makers capable of reasoning about the motivations and behaviors of others. We use multiagent simulations where each agent represents a key decision maker in a pharmaceutical supply chain. The agents possess a Theory-of-Mind capability to reason about the beliefs, and past and future behaviors of other agents, which allows them to assess other agents' trustworthiness. Further, each agent has beliefs about others' perceptions of its own trustworthiness that, in turn, impact its behavior. Our experiments reveal several counter-intuitive results showing how small, local disruptions can have cascading global consequences that persist over time. For example, a buyer, to protect itself from disruptions, may dynamically shift to ordering from suppliers with a higher perceived trustworthiness, while the supplier may prefer buyers with more stable ordering behavior. This asymmetry can put the trust-sensitive buyer at a disadvantage during shortages. Further, we demonstrate how the timing and scale of disruptions interact with a buyer's sensitivity to trustworthiness. This interaction can engender different behaviors and impact the overall supply chain performance, either prolonging and exacerbating even small local disruptions, or mitigating a disruption's effects. Additionally, we discuss the implications of these results for supply chain operations.
在过去的二十年里，美国一直经历着严重的关键药物短缺。这些短缺的严重程度和持续时间的原因是复杂的，部分原因是潜在供应链网络的复杂性，其中涉及多个实体之间的供应商-买家互动，这些实体具有竞争和合作的目标。这导致了在维持实体之间的一致互动和信任方面的有趣挑战。此外，供应链中断通过在网络中引起过度反应的行为，从而影响了一致满足由此产生的波动需求的能力，从而影响了信任。为了探讨这些问题，我们使用有限理性的人工决策者来建模药物供应链，这些决策者能够推理其他人的动机和行为。我们使用多智能体模拟，其中每个智能体代表药物供应链中的一个关键决策者。智能体具有心智理论能力，可以推理其他智能体的信念、过去和未来的行为，从而能够评估其他智能体的可信度。此外，每个智能体都有关于其他智能体对其自身可信度的看法的信念，这些信念反过来又会影响其行为。我们的实验揭示了一些违反直觉的结果，表明小的、局部的中断如何会产生持续时间较长的级联全球后果。例如，为了保护自己免受中断的影响，买家可能会动态地转向从被认为更可信的供应商处订购，而供应商可能更喜欢订单更稳定的买家。这种不对称性可能会使对信任敏感的买家在短缺期间处于不利地位。此外，我们展示了中断的时间和规模如何与买家对可信度的敏感性相互作用。这种相互作用可以产生不同的行为，并影响整个供应链的绩效，无论是延长和加剧甚至是小的局部中断，还是减轻中断的影响。此外，我们还讨论了这些结果对供应链运营的影响。
