(44a) Supply Chain Logistics for Personalized Medicines

Traditional supply chains emphasize the need to be efficient, fast and continuously stable. Strategic supply chain optimization for the pharmaceutical industries is always investigated through product management, capacity management, and trading structure to select a product development and introduction strategy and a capacity planning and investment strategy [1]. However, in the targeted therapy supply as the foundation of precision medicine which is becoming a trend, the individually treated end-users and precise manufacturing process have increased the complexity of optimization. The product demand should be classified into multiple sub-categories, instead of being modelled as random parameters that follow normal distribution with a mean value and standard deviation range, according to the central limit theorem, in normal supply chain design [2].

Modern supply chains are oriented to responsiveness, flexible and configurable to custom needs. When applied to cell and gene therapies, novel concepts are required that streamline and de-risk manufacturing and delivery to patient activities in a personalized model in economic fashions [3]. In this paper we demonstrates typical processes included in a supply chain for cell therapies, encompassing end-to-end flow of information, materials, and money. Multi-objective stochastic supply chain optimization models are developed can fit into the scope to obtain a pareto-optimal curve that considers criteria including both the supply and demand side.

The results depict how the optimized supply chain logistics guarantee personalized medicines realizable in real-world bio-manufacturing and delivery to patients. Furthermore, the cognitive technology is able to gather and correlate information from various resources such as historical clinic data, environmental variables forecast and social media information, to track and predict supply chain disruptions and tread. Machine-learning technology can also be applied to new product introduction into the supply chain through studying user profiles and behaviors. Future work is also discussed about how to enhance the supply chain theories with lessons from these emerging healthcare domains.

[1] Papageorgiou, L., Rotstein, G., & Shah, N. (2001). Strategic supply chain optimization for the pharmaceutical industries. Industrial Engineering Chemical Research, 40(1), 275–286.

[2] You, F., & Grossmann, E. (2008). Design of responsive supply chain under demand uncertainty. Computers & Chemical Engineering, 32, 3090–3111.

[3] Waldman, S. A., & Terzic, A. (2013). Information Hierarchies Optimize Patient-Centered Solutions. Clinical Pharmacology & Therapeutics, 93(1), 3–7.