Effects of trust-based decision making in disrupted supply chains

Abstract

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.

Fig 1. Interaction between decision makers in a pharmaceutical supply chain.

Fig 2. Comparison between different disruption profiles.

Disruption A, dashed red line; disruption B, solid blue line; (a) disruption A has larger breadth compared to disruption B; (b) severity of disruption A is larger than severity of disruption B.

Fig 3. The network structure of the supply chain.

The network consists of two manufacturers (Not-Disrupted MN and Disrupted MN), two distributors (Not-Disrupted DS and Disrupted DS) and two healthcenters (Trust HC and Equal HC).

Fig 4. Results for S2.

(a) Trust HC’s order amounts to distributors; (b) Trust HC’s attributed trustworthiness to the distributors with δ = 0.5.

Fig 5. Effect of sensitivity factor δ on healthcenters’ costs when experiencing a Short disruption.

Fig 6. (a). Trust HC’s attributed trustworthiness to Disrupted DS with different sensitivity factors; (b) Trust HC’s order amount to both distributors when δ = 0.05.

Fig 7. Effect of sensitivity factor δ on healthcenters cost when experiencing a Long disruption.

Fig 8. Order amount of Not-Disrupted DS.

When (a) disruption is Short; b) disruption is Long.

Fig 9. Overall supply chain cost under different types of disruptions.

Fig 10. Comparing order amounts and trustworthiness with Moderate and Long disruptions.

a) Trust HC’s order amounts to distributors with a Moderate disruption; b) Trust HC’s attributed trustworthiness to the distributors with δ = 0.5 and a Moderate disruption; c) Trust HC’s order amounts to distributors with a Long disruption; d) Trust HC’s attributed trustworthiness to the distributors with δ = 0.5 and a Long disruption.

Fig 11. Order amount of Not-Disrupted DS in face of Moderate disruption.