How can digitalization of scheduling improve aggregate planning?

The digitalisation of scheduling transforms aggregate planning in production systems, providing enhanced capabilities for aligning supply with demand through optimised scheduling and resource allocation. This shift is particularly relevant within the smart metering organisation iSwitch as it enables real-time data utilisation, improved forecasting, and heightened adaptability to uncertainties in dynamic production environments. A major digital tool facilitating this shift is the digital twin, which generates a virtual version of physical production processes, enabling ongoing monitoring and scheduling adjustments based on real-time data. Wang and Wu (2020) underscore the role of digital twins in managing uncertain factors that typically disrupt traditional scheduling, enhancing the accuracy of production planning. Additionally, Gao et al. (2022) demonstrate that digital twins support dynamic adjustments to production plans, mitigating the impact of unforeseen disturbances. Integrating advanced data analytics and machine learning into digital scheduling frameworks fosters more sophisticated decision-making processes. The emergence of Industry 4.0, encompassing IoT and cloud computing, has substantially enhanced data collecting and analysis capabilities, facilitating optimum scheduling models that adapt to intricate production situations (Deb & Gupta, 2023). Furthermore, digital twin-oriented models improve the predictability and efficiency of aggregate planning, as Chen et al. (2022) indicate, promoting intelligent operations within manufacturing by allowing real-time feedback adjustments in response to demand fluctuationsFinally, the literature suggests that digitalisation allows for a multi-objective approach to aggregate planning, incorporating economic, social, and environmental considerations (Rasmi et al., 2019). This paper adopts a systems perspective to explore how a digitalised approach to aggregate planning can address these challenges, guided by the following research question: “How can integrating digital technologies, such as digital twins and advanced analytics, optimise scheduling and resource allocation to improve aggregate planning outcomes within iSwitch’s production systems?”