An adaptive automated market maker (AMM) for industrial capacity sharing: efficiency and resilience in stochastic environments

Abstract Industrial manufacturing networks frequently experience temporary mismatches between installed capacity and stochastic demand, creating simultaneous excess capacity and shortages across firms. This study develops an Adaptive Automated Market Maker (AMM) for the short-term allocation of pre-qualified production capacity, complementing conventional negotiation and request-for-quote mechanisms with algorithmic pricing and allocation. The proposed mechanism combines a hybrid linear–quadratic liquidity constant, an exponential moving average of available capacity, buyer-repetition damping, and a correction based on the coefficient of variation of suppliers’ costs. The model is evaluated through Java-based stochastic simulation across four scenarios representing homogeneous conditions, cost heterogeneity, demand heterogeneity, and combined cost–demand heterogeneity. Simulation results show that capacity sharing increases total network profit relative to the no-sharing benchmark across all investigated scenarios. Damping has a limited effect on aggregate profit but substantially reduces transaction-price escalation and price dispersion and mitigates extreme value transfers between recurrent buyers and sellers. Under the investigated stochastic conditions, the results indicate that the proposed Adaptive AMM can preserve the efficiency benefits of capacity sharing while improving the economic sustainability of decentralized industrial exchange.