(untitled) (gjstudies, 2025)
This research introduces a novel methodological framework that integrates accounting information systems with supply chain cost management through a bioinspired optimization approach, diverging from traditional linear accounting models. We propose the Pheromone-Based Cost Allocation (PBCA) algorithm, whichmimics ant colony foraging behavior to dynamically allocate indirect costs acrosscomplex, multi-tier supply networks. Unlike conventional activity-based costing,PBCA treats cost pools as dynamic pheromone trails that evaporate and intensifybased on real-time transactional data, enabling adaptive cost tracing in volatile supply environments. Our methodology employs a cross-disciplinary synthesis of swarmintelligence principles, managerial accounting theory, and supply chain analytics toaddress the persistent challenge of opaque cost structures in extended enterprisenetworks. Through simulation of a five-tier manufacturing supply chain with 127nodes, we demonstrate that PBCA reduces cost allocation errors by 42% comparedto traditional methods and identifies previously hidden cost drivers in supplier relationships. The algorithm’s emergent property of cost trail optimization revealsnon-linear cost propagation patterns that challenge standard cost-volume-profit assumptions. This research contributes original insights into how bio-inspired computational models can transform static accounting data into dynamic decision-supporttools, offering supply chain managers a novel mechanism for cost transparency andstrategic alignment. Our findings suggest that accounting information systems,when reconceptualized through biological metaphors, can evolve from retrospectivereporting instruments to proactive supply chain coordination mechanisms.