Small modular reactors (SMRs) are presented as an opportunity to implement nuclear power at lower capital cost through factory manufacture, and thereby overcome a significant challenge facing current large nuclear construction projects. Cost reductions are intended to be realised through the implementation of three principles: modularisation, giving a greater degree of factory manufacturing and assembly; standardisation of plant design and construction processes; and series production of multiple units. These principles would potentially deliver both one-off productivity savings for the first SMR plant and on-going cost reduction through production learning of a standard design, as well as underpin the much shorter build times claimed by SMR designers. While past studies have shown the necessity for production learning to make SMRs cost competitive, the causal factors of learning and consequent requirements for SMR production have not been sufficiently explored. The structure of the supply chain determines the distribution of manufacturing and construction activities, which will directly affect the learning cost reductions that can be achieved.

This paper presents an SMR cost modelling approach to estimate overnight capital costs (OCCs) that accounts for the structure of the supply chain. In this model, the supply chain structure is defined by the number of module assembly facilities, the number of component suppliers, and their relationship to the reactor vendor. Supplier relationships can affect learning in two ways: firstly through the division of production volume, and consequent effect on production rate and learning; secondly due to the degree of supplier bargaining power, causing the possible retention of cost savings realised from learning. Factory made components are assigned one of three supplier types: integrated, strategic, and competing suppliers. Furthermore, the occurrences of learning are modelled separately within supplier firms and within SMR module assembly facilities.

To demonstrate the impact of this cost modelling approach, case studies of possible SMR production scenarios are presented. The effect on learning of a consolidated supply chain producing all SMR plants is considered, compared to spreading component production amongst competing suppliers. In all cases the effect of varying the production rate is shown, thereby highlighting the important influence of overall market demand; this in turn depends on SMRs achieving competitive costs.