Manufacturing supply chains are undergoing change as geopolitical uncertainties and tensions rise, supplier failures grow, and demand volatility increases — making robust risk modeling and scenario simulations indispensable. The year-to-quantum (Y2Q) era, with practical quantum computing solutions, promises to transform manufacturing supply chain problem-solving.
Unlike classical computing, which struggles with high-dimensional, interdependent variables, quantum systems promise potential for exponential compute processing power capable of modeling complex risks and simulating scenarios with unprecedented precision – revolutionizing global manufacturing over the next decade. I explore some possibilities below.
Historical risk modeling
Many enterprise manufacturers currently rely on statistical models like Monte Carlo simulations (computer-based mathematical technique using repeated random sampling to model complex systems to predict a range of possible outcomes for uncertain events), and machine learning to assess risks like supply chain disruptions or shifting product markets. But classical computing falters when processing vast, nonlinear datasets associated with global supply chains.
