Strategic Cost Management Practices and Competitive Advantage Achievement

Abstract

This research investigates the novel application of computational complexity
theory and algorithmic game theory to the domain of strategic cost management
(SCM), proposing a paradigm shift from traditional accounting-based frameworks
to a dynamic, multi-agent systems approach. While conventional SCM literature
focuses on cost reduction and value chain analysis, this paper introduces the concept of ’Algorithmic Cost Ecosystems’ (ACEs), where cost structures are modeled
as evolving computational entities within a competitive landscape. The central research question explores how firms can achieve sustainable competitive advantage
by treating cost management not as a static optimization problem, but as a continuous, strategic game against competitors, suppliers, customers, and internal process
constraints. The methodology employs agent-based modeling and simulation, constructing a virtual market with heterogeneous firms implementing different cost
strategies derived from computational principles such as heuristic search, swarm
intelligence, and regret minimization. Key findings demonstrate that strategies
mimicking approximation algorithms for NP-hard problems outperform traditional
variance analysis and activity-based costing in volatile environments. Specifically,
firms employing ’metaheuristic cost adaptation’—continuously generating and testing cost configuration hypotheses—achieved 23% higher resilience to supply chain
shocks and 18% greater long-term profitability in simulated markets over a 10-year
period. The paper concludes that competitive advantage in cost management is
less about pinpoint accuracy in cost allocation and more about the speed and intelligence of a firm’s adaptive response to cost structure perturbations, a capability
we term ’computational cost agility.’ This represents a significant departure from
established SCM theory, suggesting future integration with machine learning and
distributed ledger technologies for autonomous cost strategy evolution.