The growth of advanced pharmaceutical products paves the way to a step change in manufacturing network design and planning, with an increasing pressure to meet uncertain demands. In this space, the ability to ramp up manufacturing capacity becomes a crucial metric to consider during planning. This work presents an optimization-based approach for network design and scalability assessment. Firstly, techno-economic and life cycle analyses are used to quantify input optimization parameters for candidate technologies. Secondly, a multi-product design and planning problem is solved to obtain optimized network designs. Thirdly, the scalability of given designs is quantified through a solution space assessment methodology, whereby uncertain demand and adjustable production yield parameters are sampled simultaneously and the size of the feasible solution region is quantified through α-shape methods. This becomes an indicator of the capacity of the network design to withstand demand variations through process manipulation, intensification, and scale-out, thus quantifying a space of operation versus a single optimal point. Comparative results are presented for multi-use (MU) equipment-based manufacturing and a single-use (SU) counterpart, which enables a modular manufacturing approach. Costs and footprints of the SU-based network are lower compared to MU, whilst scalability significantly increases. This highlights the potential of modular and flexible manufacturing in improving resource use and overall supply chain responsiveness.

The growth of advanced pharmaceutical products paves the way to a step change in manufacturing network design and planning, with an increasing pressure to meet uncertain demands. In this space, the ability to ramp up manufacturing capacity becomes a crucial metric to consider during planning. This work presents an optimization-based approach for network design and scalability assessment. Firstly, techno-economic and life cycle analyses are used to quantify input optimization parameters for candidate technologies. Secondly, a multi-product design and planning problem is solved to obtain optimized network designs. Thirdly, the scalability of given designs is quantified through a solution space assessment methodology, whereby uncertain demand and adjustable production yield parameters are sampled simultaneously and the size of the feasible solution region is quantified through 𝛼-shape methods. This becomes an indicator of the capacity of the network design to withstand demand variations through process manipulation, intensification, and scale-out, thus quantifying a space of operation versus a single optimal point. Comparative results are presented for multi-use (MU) equipment-based manufacturing and a single-use (SU) counterpart, which enables a modular manufacturing approach. Costs and footprints of the SU-based network are lower compared to MU, whilst scalability significantly increases. This highlights the potential of modular and flexible manufacturing in improving resource use and overall supply chain responsiveness.