Digital Twins Meet ESG: New Framework Aims to Make Smart Factories Predictive and Sustainable

Smart manufacturing has spent the last decade collecting data at a staggering rate, yet most factories still treat their digital tools as isolated islands. A digital twin might simulate a production line, an industrial IoT network might shuttle sensor readings to the cloud, and a sustainability dashboard might track emissions, but rarely do these systems talk to each other in a coordinated, decision-ready way. A new conceptual framework published in Mobile Networks and Applications argues that this fragmentation is precisely what prevents Industry 5.0 ambitions from becoming operational reality, and it proposes a detailed architecture for knitting the pieces together.

The framework, developed by researchers at the Technical University of Košice in the Slovak Republic, integrates six distinct concerns that the literature has typically handled separately: heterogeneous industrial data, a discrete-event simulation-based digital twin, MQTT and OPC UA communication protocols, external predictive analytics, operational ESG-oriented evaluation, and planner decision support. Rather than treating each as a standalone project, the authors arrange them into a six-layer, event-driven architecture in which information flows between layers as discrete events, triggering analysis, prediction, and ultimately human-approved decisions on the factory floor.

At the heart of the proposal is the idea that events, not periodic reports, should drive production planning and control. In an event-driven design, every meaningful occurrence on the shop floor, such as a machine fault, a material shortage, or a completed batch, becomes a message that propagates through the architecture. Lightweight publish-subscribe protocols like MQTT can carry high-frequency telemetry from sensors to cloud services, while OPC UA provides the standardized, semantically rich interface that industrial equipment uses to expose its state. Prior performance studies of these protocols, cited in the paper, show they are well suited to exchanging data between industrial plants and cloud servers, which makes them natural candidates for the communication backbone of a digital twin.

The digital twin itself is built on discrete-event simulation, a modeling technique that represents a production system as a chronological sequence of events and state changes. This choice matters because discrete-event simulation can answer the questions production planners actually ask: what happens to throughput if a machine goes down for two hours, how a rush order reshapes the schedule, or where buffers will overflow. By coupling the simulation model to live industrial data streams, the twin becomes a predictive instrument rather than a static mirror, allowing planners to test candidate schedules and interventions in silico before committing resources on the floor.

The second major contribution is what the authors call a four-layer transformation logic, and it is here that the framework makes its most distinctive claim. Digital technologies, the authors argue, do not directly improve environmental, social, or governance outcomes. Instead, they influence ESG-relevant results only through changes in production planning and control processes and in managerial evaluation. A sensor network by itself reduces nothing; it is the rescheduled batch, the avoided machine failure, or the reweighted performance indicator, approved by a human planner, that translates digital capability into measurable sustainability impact. This mediating logic is intended to correct what the authors see as a common weakness in the literature, where digitalization and sustainability are linked loosely without specifying the causal pathway.

To make the framework concrete, the paper illustrates it with a hypothetical manufacturing scenario, a complete event trace showing how messages move through the six layers, and formal definitions of measurable indicators. The indicator definitions are designed to be operational, meaning they specify exactly what is measured, from what data, and how the resulting values feed into ESG-oriented evaluation. This level of specification is what separates a genuine decision-support architecture from a conceptual diagram: planners can, in principle, implement the indicators directly and audit how each recommendation was derived from underlying events.

Human oversight is built into the architecture rather than bolted on. The framework explicitly positions planner decision support as the final layer, so that predictive outputs and ESG evaluations arrive as recommendations that a human decision-maker reviews and approves before any change to production planning takes effect. This design reflects the broader Industry 5.0 movement, which, as the European policy literature cited in the paper emphasizes, seeks a sustainable, human-centric, and resilient industry rather than full autonomy. The authors position their work in this tradition, drawing on recent scholarship that frames Industry 5.0 as a corrective to the technology-first ethos of Industry 4.0.

The intellectual lineage of the framework is broad. It builds on established digital twin reference models and six-layer architectural patterns from the manufacturing literature, on systematic reviews of machine learning applications in production lines and predictive quality, and on a growing body of work connecting intelligent manufacturing to ESG performance, including empirical studies of Chinese manufacturing firms and recent analyses asking whether smarter production is also greener. By synthesizing these strands, the authors aim to provide what they describe as an explicit integration of technical event flows with human-approved, ESG-oriented production planning and control decisions, something they argue no single existing framework delivers.

The authors are candid about the limits of the current study. The framework is derived through a structured conceptual synthesis, and the paper does not report implemented or empirically validated performance results. No datasets were generated or analyzed, and the hypothetical scenario exists to illustrate the architecture, not to prove it. The authors list as future work a set of concrete validation steps: discrete-event simulation experiments, construction of an MQTT and OPC UA testbed, expert assessment, and industrial validation in real manufacturing settings. Until those studies are complete, the framework should be read as a rigorous design proposal and a research agenda rather than a demonstrated solution.

Even so, the timing of the proposal is significant. Manufacturers worldwide face simultaneous pressure to digitize operations and to report credible environmental, social, and governance performance, and regulators and investors increasingly demand that sustainability claims rest on verifiable operational data. A framework that traces a straight line from a sensor event, through a predictive simulation, to a planner-approved scheduling decision and a defined ESG indicator offers exactly the kind of auditable causal chain that both engineers and sustainability officers need. If the planned testbed and industrial validation bear out the design, event-driven digital twins could become the connective tissue that finally unifies the smart factory’s many brains, turning streams of industrial telemetry into decisions that are simultaneously faster, more predictive, and demonstrably more sustainable.

Subject of Research: An event-driven digital twin framework for predictive production planning and ESG-oriented decision support in smart manufacturing

Article Title: A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing

Article References: Karakai, M., & Bobko, D. (2026). A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02535-3

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02535-3

Keywords: digital twin, smart manufacturing, Industry 5.0, discrete-event simulation, MQTT, OPC UA, predictive production planning, ESG, decision support, industrial IoT, sustainability, conceptual framework