Digital Twin Workplace Modeling for Pandemic Return Planning
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Solution Overview
Problem
Large organizations face challenges in ensuring business continuity and safe return to workplace amidst Covid-19 pandemic uncertainties, with existing methods failing to accurately account for organizational heterogeneity and employee-specific factors, leading to inaccurate infection trend estimates and neglect of equity, empathy, and employee wellbeing.
Innovation Solution
A digital twin-based system and method that models stock and flow dynamics using a Susceptible-Exposed-Infected-Recovered (SEIR) model, incorporating employee and office-specific data to simulate infection risks, leveraging agent-based models for detailed simulations, and applying statistical decay functions to predict organization-specific susceptible to exposure (s2e) rates, ensuring precise infection predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital twin based systems with detailed employee-specific modeling are implemented, then measurement precision of infection trends is improved, but device complexity increases
Solution Approach 1:
The system segments the organization into multiple digital twins representing different levels (organization-level, office-level, employee-level). Each digital twin models specific aspects of the hierarchy, allowing detailed individual tracking while maintaining overall system manageability through modular structure.
Solution Approach 2:
The system creates digital copies (digital twins) of employees, offices, and organizational structures that mirror real-world entities. These virtual replicas enable accurate simulation and prediction of infection dynamics without requiring physical intervention, achieving high measurement precision through virtual modeling.
2Adaptability or versatility
If location-specific stock and flow models are created for each office, then adaptability to local pandemic conditions is improved, but device complexity increases
Solution Approach 1:
The system employs a universal stock and flow model framework that can be instantiated at multiple levels (organization, office, location). This multi-functional approach allows the same modeling methodology to serve different purposes across hierarchical levels, achieving adaptability without proportionally increasing complexity.
Solution Approach 2:
The modeling system uses a nested structure where location-specific models are contained within office-level models, which are in turn contained within organization-level models. This hierarchical nesting allows detailed local adaptation while maintaining overall system coherence and reducing the burden of managing independent models at each level.
3Reliability
If employee demographic and health profile data are integrated into digital twins, then reliability of infection risk assessment is improved, but loss of information privacy increases
Solution Approach 1:
The system creates digital copies of employee data in the form of digital twins that contain demographic and health profile information. These virtual replicas enable accurate risk assessment by processing sensitive data in a controlled virtual environment, potentially reducing actual data exposure while maintaining assessment reliability.
Solution Approach 2:
The digital twin acts as an intermediary layer between raw employee data and infection risk assessment processes. This intermediary structure allows demographic and health information to be integrated for reliable risk evaluation while providing a protective barrier that manages data privacy concerns through controlled access and virtual processing.
Data Source
AI summary
Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of pandemic related uncertainties which exists along multiple dimensions effected by authorities. This uncertainty plays out in a non-uniform manner thus leading to highly heterogeneous evolution of pandemic. Present disclosure provides digital twin based systems and methods for business continuity plan and safe return to workplace wherein a simulation-based data-driven evidence-backed approach is implemented that captures details pertaining to virus, individualistic characteristics of employees and their dependents, offices, locations of the employees and offices, and various pandemic control measures that are in effect and need to be explored using a hybrid modelling and simulation approach that combines fine-grained actor/agent model and coarse-grained stock-and-flow model. The present disclosure further leverages past macro-level data pertaining to pandemic evolution of relevant cities, states, and countries to make available information amenable for collective analysis and infection prediction.