A Goal Programming Decision Model for Assessing Pharmaceutical Supply Chain Network Strategies
Open AccessLike other industries, the pharmaceutical supply chain networks are global systems. This global footprint increases the vulnerability of supply chain networks to unforeseen disruptions, which exacerbate drug shortages in the United States. Additionally, pharmaceutical companies that produce drug products for the general public are obligated to manufacture those items in accordance with good manufacturing practices. Lapses in quality levels of the manufacturing areas can contribute to recalls and supply chain disruptions at best and could lead to adverse events and even the death of patients. This research proposes a goal-programming decision model to evaluate pharmaceutical supply chain networks. The research investigated a conditional value at risk model used in the financial sector and applied it to the pharmaceutical supply chain to estimate the risk associated with potential disruptions. The research quantifies supplier quality risk and applies the conditional value at risk model to the quality level data. The goal programming model is compared to existing conditional value at risk models that optimize cost and service level in a pharmaceutical supply chain network. The practical application of the proposed goal programming decision model is to assist pharmaceutical supply chain managers in selecting a supply chain network architecture that maximizes the quality level of the suppliers selected for the supply chain network. The optimized supply chain network minimizes the risks associated with deviations and unforeseen disruptions to alleviate drug shortages in the United States. The optimization of the supplier quality level demonstrated that the conditional value at risk model can be used to design a robust pharmaceutical supply chain model. Increasing the confidence level in the conditional value at risk model leads to a more risk-averse posture and the inclusion of more suppliers, which decreases the potential impact of supplier disruptions. The quality level optimization model was consistent with the service level conditional value at risk models' solutions and had an expected cost 22% higher than the cost optimization model. The quality optimization model did not recommend the same suppliers as the cost optimization model to be included in the supply chain network. Managers can choose the most qualified suppliers for their supply chain and decide how to allocate demand orders, and perform inspections under mean risk decision-making strategies presented by the results of the models and statistical analysis.
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