Simulation-optimization approach to clinical trial supply chain management with demand scenario forecast

• Published in: Computers & chemical engineering 2012-05, Vol.40, p.82-96
• Main Authors: Chen, Ye, Mockus, Linas, Orcun, Seza, Reklaitis, Gintaras V.
• Format: Article
• Language: English
• Subjects: Applied sciences; Biological and medical sciences; Clinical trial; Decision theory. Utility theory; Demand scenario; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects; General pharmacology; Logistics; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical sciences; MILP; Operational research and scientific management; Operational research. Management science; Optimization; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Simulation; Supply chain
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2012.01.007

► Clinical trials supply chain has explicit termination point and leftovers at the end of clinical trials constitute an expensive cost. ► We present a simulation-optimization approach to improve clinical trial supply chain management problem. ► We built a simulation model to forecast the stochastic drug demands and generate discrete time demand scenarios. ► Three case studies with different demand types are reported and compared to demonstrate the utility of the proposed approach. In the pharmaceutical industry, the development activities that are required to bring a new drug to market involve considerable expense (upwards of $1 Billion) and can take in excess of 10 years. Clinical trials constitute a critically important and very expensive part of this development process as the associated supply chain encompasses producing, distributing and administering the candidate therapy to volunteer patients located in different geographic regions. A number of different approaches are being pursued to reduce clinical trial costs, including innovations in trial organization and patient pool selection. In this work, we focus our attention on improved management of the clinical supply chain. A simulation-optimization approach is presented, including patient demand simulation and demand scenario forecast, mathematical programming based planning, and discrete event simulation of the entire supply chain. Three case studies with different demand types are reported and compared to demonstrate the utility of the proposed approach.