Real-Time Goal Optimization Under Demand Volatility: A Behavioral Operations Approach
Keywords:
real-time optimization, demand volatility, behavioral operations, goal setting, algorithmic management, gig economy, platform governance, resilienceAbstract
Contemporary digital platforms and service supply chains operate under conditions of extreme demand volatility, rendering traditional static goal-setting mechanisms increasingly inadequate. This paper develops a behavioral operations framework for real-time goal optimization that integrates continuous data streams, algorithmic nudges, and systematic consideration of human cognitive biases. We argue that effective dynamic goal management requires a layered socio-technical architecture in which adaptive target computation, behavioral feedback loops, and transparent governance mechanisms co-evolve. Drawing on evidence from the gig economy, automated supply chains, and intelligent transportation systems, the analysis examines how loss aversion, reference dependence, and bounded rationality shape worker and system responses to dynamically adjusted goals. The paper elaborates a system-level architecture that couples event-driven microservices with behavioral model updates to deliver personalized, context-aware targets at sub-second latencies. Critical structural trade-offs involving fairness, algorithmic opacity, worker autonomy, and system resilience are unpacked in depth. Governance and policy implications are foregrounded, including the need for participatory algorithm design, enforceable audit trails, and regulatory frameworks that address the new power asymmetries introduced by real-time behavioral optimization. The analysis contributes an interdisciplinary synthesis that bridges behavioral operations, systems engineering, and platform governance, offering a roadmap for sustainable, fair, and resilient goal-optimization infrastructures.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
