The lean and green imperative of manufacturing data - Patsavellas, John; Haddad, Yousef; Salonitis, Konstantinos (Springer, 2024)
This study introduces a stochastic model-based framework for the prediction and measurement of the environmental impact of manufacturing systems’ digitalization. Utilising a Monte Carlo simulation experimental framework, this paper forecasts the CO2e emissions from the entire lifecycle of manufacturing data over long-term time horizon under different scenarios. The analysis proceeds to estimate the maximum, average, and minimum potential CO2e emissions, under different growth models. Findings reveal that, with the current exponential growth of data that exceeds data centres’ efficiency improvements and carbon intensity decay rates, the environmental footprint associated with the entire lifecycle of data can have a potential adverse impact on the realisation of net-zero goals. The proposed approach provides a viable pathway for manufacturing enterprises aiming to align their data management practices with environmental sustainability and operational efficiency.
Keywords: Digitalization; Industry 4.0; Monte Carlo Simulation; Sustainable Data