Prescriptive Decision-Making for Sustainable Production Management An Overall Sustainable Equipment Effectiveness (OSEE) Framework Using Causal AI
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Nov 11, 2025
Theresa Madreiter
Fazel Ansari
Abstract
In response to the growing challenges posed by climate change and demographic shifts, industrial operations must move beyond traditional productivity metrics such as Overall Equipment Effectiveness (OEE). While OEE is a valuable key performance indicator, it fails to account for the ecological, social, and economic dimensions essential for long-term sustainability. This paper introduces an Overall Sustainable Equipment Effectiveness (OSEE) framework, designed to integrate sustainability factors into operational performance measurement, enabling a holistic assessment and optimization approach. Key sustainability factors and their interrelationships are identified through an extensive literature review and subsequently validated by industry experts to ensure practical relevance and applicability to real-world operational settings. To address the complexity of these interconnected factors, causal AI methods, in particular Dynamic Bayesian Networks (DBN) are employed. DBN allow a qualitative understanding of sustainability interrelationships (cause-effects) and enable a quantitative optimization of sustainability impacts on operational efficiency. The proposed OSEE framework offers a structured approach for balancing productivity with environmental and social factors, equipping decision-makers with insights for informed sustainable operational strategies. This research contributes to the broader agenda of twin transformation, aligning digitalization and sustainability, and provides a foundation for building resilient, future-ready industrial operations.
##plugins.themes.bootstrap3.article.details##
Keywords
Sustainability, Operational Sustinability, Causal AI, Key Performance Indicators, Overall Equipment Effectiveness
References
Abreu, M.F., Alves, A.C. and Moreira, F. (2024), “Business Overall Performance and Sustainability Effectiveness: An Indicator to Measure Companies’ Lean–Green Compliance”, Sustainability, Vol. 16 No. 11, p. 4508.
Acemoglu, D. and Restrepo, P. (2018), “Demographics and Automation”, SSRN Electronic Journal.
Ademujimi, T. and Prabhu, V. (2024), “Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience”, Sustainability, Vol. 16.
Afum, E., Sun, Z., Agyabeng-Mensah, Y. and Baah, C. (2023), “Lean production systems, social sustainability performance and green competitiveness: the mediating roles of green technology adoption and green product innovation”, Journal of Engineering, Design and Technology, Vol. 21 No. 1, pp. 206–227.
Ansari, F., Glawar, R. and Nemeth, T. (2019), “PriMa: a prescriptive maintenance model for cyber-physical production systems”, International Journal of Computer Integrated Manufacturing, Vol. 32 4-5, pp. 482–503.
Ansari, F., Glawar, R. and Sihn, W. (2020), “Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks”, in Beyerer, J., Maier, A. and Niggemann, O. (Eds.), Machine Learning for Cyber Physical Systems, Technologien für die intelligente Automation, Vol. 11, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1–8.
Ben‐Gal, I. (2007), “Bayesian Networks”, in Ruggeri, F., Kenett, R.S. and Faltin, F.W. (Eds.), Encyclopedia of Statistics in Quality and Reliability, Wiley.
Chang, J., Bai, Y., Xue, J., Gong, L., Zeng, F., Sun, H., Hu, Y., Huang, H. and Ma, Y. (2023), “Dynamic Bayesian networks with application in environmental modeling and management: A review”, Environmental Modelling & Software, Vol. 170, p. 105835.
Core Writing Team, H. Lee and J. Romero (eds.) (2023),: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change.
Da Costa, D.D., Mehl, V.O. and Aguiar, F.R.T. (2024), “Real-time assessment of the overall effectiveness of legacy machine tools”, Production Engineering.
Durán, O., Capaldo, A. and Duran Acevedo, P. (2018), “Sustainable Overall Throughputability Effectiveness (S.O.T.E.) as a Metric for Production Systems”, Sustainability, Vol. 10 No. 2, p. 362.
Elkington John (1996), Cannibals with Forks: The Triple Bottom Line of 21st century business.
European Comission (2019), The European Green Deal.
Fouquet, R. and Hippe, R. (2022), “Twin transitions of decarbonisation and digitalisation: A historical perspective on energy and information in European economies”, Energy Research & Social Science.
Franciosi, C., Voisin, A., Miranda, S., Riemma, S. and Iung, B. (2020), “Measuring maintenance impacts on sustainability of manufacturing industries: from a systematic literature review to a framework proposal”, Journal of Cleaner Production, Vol. 260, p. 121065.
Ghafoorpoor Yazdi, P., Azizi, A. and Hashemipour, M. (2018), “An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach”, Sustainability, Vol. 10 No. 9, p. 3031.
Göçoğlu, V., Düzsöz, E. and Demirkol, A. (2025), “Challenges and Priorities in Achieving the Sustainable Development Goals: A Multi‐Scaled Global Analysis”, Sustainable Development.
Han, X., He, Y., Wang, Z., Cai, Y. and Dai, W. (2022), “Remaining useful life prediction of manufacturing system based on fuzzy Quality State Task Network”, Journal of Manufacturing Systems, Vol. 65.
Hoyos, E., Serna, M.C., Backer, J. de and Martin, J. (2023), “Sustainability Score Comparison of Welding Strategies for the Manufacturing of Electric Transportation Components”, Sustainability, Vol. 15 No. 11, p. 8650.
Jena, M.C., Mishra, S.K. and Moharana, H.S. (2024), “Integration of Industry 4.0 with reliability centered maintenance to enhance sustainable manufacturing”, Environmental Progress & Sustainable Energy, Vol. 43 No. 2.
Koller, D. and Friedman, N. (2009), Probabilistic Graphical Models: Principles and Techniques, Adaptive Computation and Machine Learning, MIT Press, Cambridge.
Madreiter, T. and Ansari, F. (2024), “From OEE to OSEE: How to reinforce Production and Maintenance Management Indicator Systems for Sustainability?”, IFAC-PapersOnLine, Vol. 58 No. 8, pp. 204–209.
Nakajima, S. (1988), Introduction to TPM: total productive maintenance, Productivity Press.
Nannapaneni, S., Mahadevan, S., Dubey, A. and Lee, Y.-T.T. (2020), “Online monitoring and control of a cyber-physical manufacturing process under uncertainty”, Journal of intelligent manufacturing, Vol. 195.
Nannapaneni, S., Mahadevan, S. and Rachuri, S. (2016), “Performance evaluation of a manufacturing process under uncertainty using Bayesian networks”, Journal of Cleaner Production, Vol. 113, pp. 947–959.
Pearl, J. (2009), “Causal inference in statistics: An overview”, Statistics Surveys, Vol. 3.
Pearl, J. (2010), “An Introduction to Causal Inference*”, The International Journal of Biostatistics, Vol. 6 No. 2.
Seyed Hosseini, S.M., Shahanaghi, K. and Shasfand, S. (2024), “Functional Model of Integrated Maintenance (Reliability, Overall Equipment Effectiveness, Safety, and Cost) in Petrochemical Industries”, International Journal of Engineering, Vol. 37 No. 11, pp. 2392–2404.
Thiede, S. (2023), “Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing”, Procedia CIRP, Vol. 116, pp. 438–443.
United Nations (2023), “Sustainable Development Goals”, available at: https://sdgs.un.org/goals#icons (accessed 29 April 2025).
Wadood, S.A., Sadiq Jajja, M.S., Chatha, K.A. and Farooq, S. (2023), “Lean, sustainability and the triple bottom line performance: a systems perspective-based empirical examination”, International Journal of Productivity and Performance Management, Vol. 72 No. 6.
World Economic Forum (2023), Net-Zero Industry Tracker: 2023 Edition, Insight Report.
Zackrisson, M., Kurdve, M., Shahbazi, S., Wiktorsson, M., Winroth, M., Landström, A., Almström, P., Andersson, C., Windmark, C., Öberg, A.E. and Myrelid, A. (2017), “Sustainability Performance Indicators at Shop Floor Level in Large Manufacturing Companies”, Procedia CIRP, Vol. 61, pp. 457–462.
Zehra, K., Mirjat, N.H., Shakih, S.A., Harijan, K., Kumar, L. and El Haj Assad, M. (2024), “Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability”, Sustainability, Vol. 16 No. 7, p. 2973.
Zimmermann, H.-J. (2010), “Fuzzy set theory”, WIREs Computational Statistics, Vol. 2 No. 3, pp. 317–332.
Acemoglu, D. and Restrepo, P. (2018), “Demographics and Automation”, SSRN Electronic Journal.
Ademujimi, T. and Prabhu, V. (2024), “Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience”, Sustainability, Vol. 16.
Afum, E., Sun, Z., Agyabeng-Mensah, Y. and Baah, C. (2023), “Lean production systems, social sustainability performance and green competitiveness: the mediating roles of green technology adoption and green product innovation”, Journal of Engineering, Design and Technology, Vol. 21 No. 1, pp. 206–227.
Ansari, F., Glawar, R. and Nemeth, T. (2019), “PriMa: a prescriptive maintenance model for cyber-physical production systems”, International Journal of Computer Integrated Manufacturing, Vol. 32 4-5, pp. 482–503.
Ansari, F., Glawar, R. and Sihn, W. (2020), “Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks”, in Beyerer, J., Maier, A. and Niggemann, O. (Eds.), Machine Learning for Cyber Physical Systems, Technologien für die intelligente Automation, Vol. 11, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1–8.
Ben‐Gal, I. (2007), “Bayesian Networks”, in Ruggeri, F., Kenett, R.S. and Faltin, F.W. (Eds.), Encyclopedia of Statistics in Quality and Reliability, Wiley.
Chang, J., Bai, Y., Xue, J., Gong, L., Zeng, F., Sun, H., Hu, Y., Huang, H. and Ma, Y. (2023), “Dynamic Bayesian networks with application in environmental modeling and management: A review”, Environmental Modelling & Software, Vol. 170, p. 105835.
Core Writing Team, H. Lee and J. Romero (eds.) (2023),: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change.
Da Costa, D.D., Mehl, V.O. and Aguiar, F.R.T. (2024), “Real-time assessment of the overall effectiveness of legacy machine tools”, Production Engineering.
Durán, O., Capaldo, A. and Duran Acevedo, P. (2018), “Sustainable Overall Throughputability Effectiveness (S.O.T.E.) as a Metric for Production Systems”, Sustainability, Vol. 10 No. 2, p. 362.
Elkington John (1996), Cannibals with Forks: The Triple Bottom Line of 21st century business.
European Comission (2019), The European Green Deal.
Fouquet, R. and Hippe, R. (2022), “Twin transitions of decarbonisation and digitalisation: A historical perspective on energy and information in European economies”, Energy Research & Social Science.
Franciosi, C., Voisin, A., Miranda, S., Riemma, S. and Iung, B. (2020), “Measuring maintenance impacts on sustainability of manufacturing industries: from a systematic literature review to a framework proposal”, Journal of Cleaner Production, Vol. 260, p. 121065.
Ghafoorpoor Yazdi, P., Azizi, A. and Hashemipour, M. (2018), “An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach”, Sustainability, Vol. 10 No. 9, p. 3031.
Göçoğlu, V., Düzsöz, E. and Demirkol, A. (2025), “Challenges and Priorities in Achieving the Sustainable Development Goals: A Multi‐Scaled Global Analysis”, Sustainable Development.
Han, X., He, Y., Wang, Z., Cai, Y. and Dai, W. (2022), “Remaining useful life prediction of manufacturing system based on fuzzy Quality State Task Network”, Journal of Manufacturing Systems, Vol. 65.
Hoyos, E., Serna, M.C., Backer, J. de and Martin, J. (2023), “Sustainability Score Comparison of Welding Strategies for the Manufacturing of Electric Transportation Components”, Sustainability, Vol. 15 No. 11, p. 8650.
Jena, M.C., Mishra, S.K. and Moharana, H.S. (2024), “Integration of Industry 4.0 with reliability centered maintenance to enhance sustainable manufacturing”, Environmental Progress & Sustainable Energy, Vol. 43 No. 2.
Koller, D. and Friedman, N. (2009), Probabilistic Graphical Models: Principles and Techniques, Adaptive Computation and Machine Learning, MIT Press, Cambridge.
Madreiter, T. and Ansari, F. (2024), “From OEE to OSEE: How to reinforce Production and Maintenance Management Indicator Systems for Sustainability?”, IFAC-PapersOnLine, Vol. 58 No. 8, pp. 204–209.
Nakajima, S. (1988), Introduction to TPM: total productive maintenance, Productivity Press.
Nannapaneni, S., Mahadevan, S., Dubey, A. and Lee, Y.-T.T. (2020), “Online monitoring and control of a cyber-physical manufacturing process under uncertainty”, Journal of intelligent manufacturing, Vol. 195.
Nannapaneni, S., Mahadevan, S. and Rachuri, S. (2016), “Performance evaluation of a manufacturing process under uncertainty using Bayesian networks”, Journal of Cleaner Production, Vol. 113, pp. 947–959.
Pearl, J. (2009), “Causal inference in statistics: An overview”, Statistics Surveys, Vol. 3.
Pearl, J. (2010), “An Introduction to Causal Inference*”, The International Journal of Biostatistics, Vol. 6 No. 2.
Seyed Hosseini, S.M., Shahanaghi, K. and Shasfand, S. (2024), “Functional Model of Integrated Maintenance (Reliability, Overall Equipment Effectiveness, Safety, and Cost) in Petrochemical Industries”, International Journal of Engineering, Vol. 37 No. 11, pp. 2392–2404.
Thiede, S. (2023), “Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing”, Procedia CIRP, Vol. 116, pp. 438–443.
United Nations (2023), “Sustainable Development Goals”, available at: https://sdgs.un.org/goals#icons (accessed 29 April 2025).
Wadood, S.A., Sadiq Jajja, M.S., Chatha, K.A. and Farooq, S. (2023), “Lean, sustainability and the triple bottom line performance: a systems perspective-based empirical examination”, International Journal of Productivity and Performance Management, Vol. 72 No. 6.
World Economic Forum (2023), Net-Zero Industry Tracker: 2023 Edition, Insight Report.
Zackrisson, M., Kurdve, M., Shahbazi, S., Wiktorsson, M., Winroth, M., Landström, A., Almström, P., Andersson, C., Windmark, C., Öberg, A.E. and Myrelid, A. (2017), “Sustainability Performance Indicators at Shop Floor Level in Large Manufacturing Companies”, Procedia CIRP, Vol. 61, pp. 457–462.
Zehra, K., Mirjat, N.H., Shakih, S.A., Harijan, K., Kumar, L. and El Haj Assad, M. (2024), “Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability”, Sustainability, Vol. 16 No. 7, p. 2973.
Zimmermann, H.-J. (2010), “Fuzzy set theory”, WIREs Computational Statistics, Vol. 2 No. 3, pp. 317–332.
Section
Technical Papers