Uncertainty-aware decision-support tools for next-generation pharmaceutical supply chains
File(s)
Author(s)
Sarkis, Miriam
Type
Thesis
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.
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.
Version
Open Access
Date Issued
2024-09-16
Date Awarded
01/12/2024
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Papathanasiou, Maria
Shah, Nilay
Publisher Department
Chemical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)