Assessing Resilience of Medicine Supply Chain Networks to Disruptions: a Proposed Hybrid Simulation Modeling Framework

Medicine supply chains are complex systems, as they are long fragmented, interconnected global systems, involving many actors, which can result in medicine shortages, due to a number of reasons including manufacturing issues, commercial withdrawal, medicine recalls and quality issues, availability of raw ingredients, increased demand, distribution problems.

This paper introduces a simulation study of interventions to ensure a stable supply of a generic medicine in Norway. A hybrid simulation modeling framework is proposed to evaluate the effect of alternative supply chain shortage interventions in response to various disruptions to support national decision making with respect to preparedness planning and emergency response.

Simulation framework

The key components of the HS modeling framework and the interaction between them is summarized on the image below.

An overview of the hybrid simulation modeling framework.
An overview of the hybrid simulation modeling framework.

The case model consists of following agents:

  • SalePoint (Community and Hospital Pharmacies), where demand for the packages of medicine is received and satisfied if sufficient inventory is available.
  • Wholesaler, which serves a cross-docking function ensuring that their sale points have sufficient stock to meet demand.
  • Warehouse, which are operated by wholesalers in a similar way as sale points.
  • Supplier, who produces the case medicine, process orders received by and deliver weekly to warehouses.
  • Active Pharmaceutical Ingredients (API) producer, receives orders from, and delivers shipments to suppliers weekly. API producers record their inventory level, production rates, supplier orders backlog, costs, and supplier order history.
  • Demand
  • Order
  • Ranking
  • Shipment

The last four agents are passive entities that are passed between the other active agents, to facilitate interaction between the model components.

Two disruption scenarios have been investigated.

In the demand increase scenario, if no intervention is activated, the aggregate sale points inventory dropped to zero and took considerable time to recover to pre disruption levels (Demand 0 – Sale Points) due to production capacity constraints at the single supplier.

In the supply decrease scenario, if no intervention is activated, all levels of the system are affected apart from the preposition stock. The aggregate sale points inventory drops to zero and the aggregate wholesale inventory drops substantially, as the main single supplier is running at reduced capacity and this ripples through the system.


The model results suggest that the modeled SC is capable of absorbing and recovering from the examined disruptions, when interventions are implemented. Prepositioned stock is effective, but expensive given the associated costs, e.g., storage and wastage. The model suggests that it would take considerable time to replenish prepositioned stock following both disruptions, leaving the system vulnerable to subsequent disruptions.

There are several areas for future work that would improve the case model including, obtaining historical quantitative data to validate the model with stakeholders and to verify the model’s design. Extensive sensitivity analysis is planned to validate the case model and identify the key parametric and structural parameters in the model.

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