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Uncertainty-aware decision-support tools for next-generation pharmaceutical supply chains

Year: 2024
Type: article
Abstract: Supply chain resilience and sustainability are becoming growing priorities in the pharmaceutical sector. In recent years, the industry has seen a market boom of next generation therapies allowing treatment of otherwise incurable diseases and platform vaccine technologies. Manufacturers catering for these markets re- ported shortages and delays due to unforeseen demand growth combined with uncertainty in manufacturing capabilities of platforms still under development. At the same time, regulatory and industrial initiatives are urging manufacturers to quantify and reduce supply chain environmental footprints. This highlights an opportunity for computer-aided modelling and optimisation to support invest- ment planning and help quantify trade-offs between cost, environmental, social and resilience impact indicators. This thesis presents decision-support tools that assess resilience and sustain- ability a priori for supply chain networks require rapid scale-up and operate under supply-demand uncertainty. Firstly, the cost optimisation problem un- der process uncertainty is considered. In this context, techno-economic mod- elling and uncertainty analysis are used to quantify manufacturing uncertainties necessary to mitigate against when proceeding in investment planning through stochastic optimisation. Leveraging on the acquired knowledge on process uncer- tainty, a feasibility problem is formulated which helps quantify an uncertainty- aware flexibility space for a given design. Secondly, the cost optimisation problem under demand and process uncertainty is considered to identify planning strate- gies that flexibly adapt to time-dependent uncertainties. Finally, the integration of sustainability-related metrics in the investment planning problem is proposed. It is demonstrated that given a target demand, cost minimisation reduces envi- ronmental footprint for the case study considered. To conclude, key findings are discussed with an outlook on emerging research directions to combine capabilities in integrated decision-support platforms.
DOI: 10.25560/122334
Language: English
Open Access status: gold
OpenAlex ID: W6945519700
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