Towards cellular agriculture: An exploratory supply chain model

Cellular agriculture, which uses biotechnology to produce animal-derived products, has been identified as a possible solution to reduce the negative environmental impacts associated with traditional meat and dairy production. However, available life cycle and techno-economic assessments for cultured meat production suggest that additional environmental and cost improvements are needed to compete with traditional meat production methods. The adoption of circular supply chains has been found to improve the economic and environmental outcomes of production processes. The use of agricultural and food byproducts, such as hydrolyzed soymeal, as a source of amino acids has been identified as a way to reduce cost and environmental impacts. However, the impact of these undefined sources on cell production efficiency is largely unknown. The aim of this paper is to develop a novel exploratory supply chain model for a viable large-scale cellular agriculture network that considers facility location, ingredient blending, capacity design and technology selection problems. A bi-objective mixed integer linear programming model is developed to investigate the dynamics between demand, capacity design, location, ingredient blending and technology selection decisions as well as trade offs when optimizing for cost versus carbon emissions. Useful managerial insights are developed through various computational experiments, including modeling supply chain network design under deterministic and stochastic demand and the development of iso-cost curves to help decision makers design the optimal blending of chemically undefined byproduct ingredients with pure pharmaceutically sourced ingredients. • Cellular agriculture makes meat and dairy products via biotechnological processes. • Cell Ag has potential to reduce the environmental impact of meat/dairy production. • An exploratory supply chain model for a large-scale Cell Ag network is developed. • Addresses location–allocation, blending, capacity, tech selection/sizing decisions. • Dynamics and trade-offs of design factors, costs and carbon emissions are studied.