IEKV-Based Model of Organizational Manageability

This white paper introduces an IEKV-based model of organizational manageability, a decision-centric framework for analyzing, simulating, and comparing governance structures in complex socio-technical systems. Traditional enterprise systems and management methodologies focus on optimizing operational performance while implicitly assuming unlimited or idealized managerial capacity. This work addresses a critical gap by modeling management itself as a constrained system, characterized by finite decision throughput, delays, escalation dynamics, and nonlinear saturation effects. The proposed framework represents managerial activity as a flow of decision cases processed by organizational roles under capacity constraints. It introduces IEKV (Integrated Energetic-Cognitive Value) as a computable, system-level index that integrates decision overload, managerial work-in-progress, decision entropy, and agenticity. IEKV is derived from observable event data and is designed for comparative analysis rather than individual evaluation. A simulation-based methodology is presented to explore: nonlinear degradation of manageability under increasing decision load, emergence of controllability boundaries and regime transitions, structural effects of governance policies such as delegation, escalation, and WIP limits. Experimental results demonstrate that organizations with similar operational throughput may exhibit radically different levels of manageability and resilience. Governance policies are shown to shift controllability boundaries more strongly than marginal improvements in decision speed or optimization depth. The framework is explicitly designed for incremental deployment on top of existing ERP, MES, and APS systems, requires minimal initial instrumentation, and avoids individual-level performance assessment. It provides a foundation for decision-centric digital twins of organizations and supports responsible integration of AI as a capacity-augmenting element rather than a substitutive controller. This work is intended for researchers and practitioners in systems engineering, organizational design, digital transformation, and decision science.