Digital Twins and Business Analytics: A Review of Predictive Modelling for Strategic Planning and Risk

Author(s)	Ms. Saloni Shokeen
Country	India
Abstract	This review examines the integration of digital twins with business analytics and predictive modeling to enhance strategic planning and risk management under deep uncertainty. Digital twins, which are virtual representations mirroring physical assets through bidirectional IoT data flows, ML-driven simulations, and hybrid physics-data models, enable real-time anomaly detection, scenario forecasting, and prescriptive optimisation across manufacturing, supply chains, healthcare, and finance. Addressing core research questions, findings reveal their efficacy in embedding predictive frameworks for "what-if" analyses and DMDU paradigms (monitor-adapt over predict-act), yielding cost savings via prototyping efficiencies, PHM, and resilient decision-making amid nonlinear risks. Despite transformative benefits like shorter design cycles and proactive interventions, challenges persist: high deployment costs, legacy integration barriers, data standardisation gaps, cybersecurity vulnerabilities, and AI opacity. Future research must prioritise interoperable ontologies, edge-cloud architectures, and empirical validations to realise self-evolving twins, fostering agile enterprises in volatile contexts.
Keywords	Digital twins, Business analytics, Predictive modeling
Field	Business Administration
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-12
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.83525

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