{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:25:09Z","timestamp":1775856309553,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["825631"],"award-info":[{"award-number":["825631"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["958205"],"award-info":[{"award-number":["958205"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MCIN\/AEI\/ 10.13039\/501100011033","award":["RTI2018-101344-B-I00"],"award-info":[{"award-number":["RTI2018-101344-B-I00"]}]},{"name":"&quot;ERDF A way of making Europe&quot;","award":["RTI2018-101344-B-I00"],"award-info":[{"award-number":["RTI2018-101344-B-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.<\/jats:p>","DOI":"10.3390\/computers10120156","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T02:46:08Z","timestamp":1637721968000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Smart Master Production Schedule for the Supply Chain: A Conceptual Framework"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6671-7077","authenticated-orcid":false,"given":"Julio C.","family":"Serrano-Ruiz","sequence":"first","affiliation":[{"name":"Research Centre on Production Management and Engineering (CIGIP), Universitat Polit\u00e8cnica de Val\u00e8ncia Escuela Polit\u00e9cnica Superior de Alcoy, C\/Alarc\u00f3n 1, Alcoy, 03801 Alicante, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8447-3387","authenticated-orcid":false,"given":"Josefa","family":"Mula","sequence":"additional","affiliation":[{"name":"Research Centre on Production Management and Engineering (CIGIP), Universitat Polit\u00e8cnica de Val\u00e8ncia Escuela Polit\u00e9cnica Superior de Alcoy, C\/Alarc\u00f3n 1, Alcoy, 03801 Alicante, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4475-6371","authenticated-orcid":false,"given":"Ra\u00fal","family":"Poler","sequence":"additional","affiliation":[{"name":"Research Centre on Production Management and Engineering (CIGIP), Universitat Polit\u00e8cnica de Val\u00e8ncia Escuela Polit\u00e9cnica Superior de Alcoy, C\/Alarc\u00f3n 1, Alcoy, 03801 Alicante, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","first-page":"103","article-title":"Understanding Supply Chain 4.0 and Its Potential Impact on Global Value Chains","volume":"2019","author":"Ferrantino","year":"2019","journal-title":"Glob. Value Chain. Dev. Rep."},{"key":"ref_2","first-page":"e8","article-title":"Trends in Digitization of the Supply Chain: A Brief Literature Review","volume":"7","author":"Hartmann","year":"2020","journal-title":"EAI Endorsed Trans. Energy Web"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.compind.2018.02.010","article-title":"Digital Supply Chain: Literature Review and a Proposed Framework for Future Research","volume":"97","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.promfg.2018.10.069","article-title":"Impact of Sustainability on the Supply Chain 4.0 Performance","volume":"17","author":"Dossou","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/00207543.2019.1612964","article-title":"Logistics 4.0: A Systematic Review towards a New Logistics System","volume":"58","author":"Winkelhaus","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.procir.2020.05.033","article-title":"Digital Twin: Revealing Potentials of Real-Time Autonomous Decisions at a Manufacturing Company","volume":"88","author":"Feldt","year":"2020","journal-title":"Procedia CIRP"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.jmsy.2021.09.011","article-title":"Smart Manufacturing Scheduling: A Literature Review","volume":"61","author":"Mula","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_8","unstructured":"John, H., and Blackstone, P.C. (2014). Association for Supply Chain Management (APICS) APICS Dictionary, APICS. [14th ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.jmsy.2020.06.017","article-title":"Review of Digital Twin about Concepts, Technologies, and Industrial Applications","volume":"58","author":"Liu","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.procir.2020.04.154","article-title":"Digital Twin-Driven Supply Chain Planning","volume":"93","author":"Wang","year":"2020","journal-title":"Procedia CIRP"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cirpj.2020.02.002","article-title":"Characterising the Digital Twin: A Systematic Literature Review","volume":"29","author":"Jones","year":"2020","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_12","unstructured":"Vasant, P., Zelinka, I., and Weber, G.-W. (2020, January 17\u201318). Digital Twins in Supply Chain Management: A Brief Literature Review. Proceedings of the Intelligent Computing and Optimization ICO 2020, Koh Samui, Thailand."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ivanov, D., Dolgui, A., and Sokolov, B. (2019). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. Handbook of Ripple Effects in the Supply Chain, Springer International Publishing.","DOI":"10.1007\/978-3-030-14302-2_15"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Angione, G., Cristalli, C., Barbosa, J., and Leit\u00e3o, P. (2019, January 22\u201325). Integration Challenges for the Deployment of a Multi-Stage Zero-Defect Manufacturing Architecture. Proceedings of the IEEE 17th International Conference on Industrial Informatics INDIN 2019, Helsinki, Finland.","DOI":"10.1109\/INDIN41052.2019.8972259"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Psarommatis, F., and Kiritsis, D. (2021). A Hybrid Decision Support System for Automating Decision Making in the Event of Defects in the Era of Zero Defect Manufacturing. J. Ind. Inf. Integr., 100263.","DOI":"10.1016\/j.jii.2021.100263"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lindstr\u00f6m, J., Ky\u00f6sti, P., Birk, W., and Lejon, E. (2020). An Initial Model for Zero Defect Manufacturing. Appl. Sci., 10.","DOI":"10.3390\/app10134570"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00207543.2019.1605228","article-title":"Zero Defect Manufacturing: State-of-the-Art Review, Shortcomings and Future Directions in Research","volume":"58","author":"Psarommatis","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_18","unstructured":"Camarinha-Matos, L.M., Ferreira, P., and Brito, G. (2021). Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing BT\u2014Technological Innovation for Applied AI Systems, Springer International Publishing."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Psarommatis, F., Sousa, J., Mendon\u00e7a, J.P., and Kiritsis, D. (2021). Zero-Defect Manufacturing the Approach for Higher Manufacturing Sustainability in the Era of Industry 4.0: A Position Paper. Int. J. Prod. Res., 1\u201319.","DOI":"10.1080\/00207543.2021.1987551"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5184","DOI":"10.1166\/jctn.2017.6729","article-title":"Solution for Multi-Objective Optimisation Master Production Scheduling Problems Based on Swarm Intelligence Algorithms","volume":"14","author":"Bakar","year":"2017","journal-title":"J. Comput. Theor. Nanosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s00521-017-3159-5","article-title":"A New Hybrid Algorithm of Simulated Annealing and Simplex Downhill for Solving Multiple-Objective Aggregate Production Planning on Fuzzy Environment","volume":"31","author":"Zaidan","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_22","unstructured":"Wu, Z.-J., Wang, W., Zhou, J., Ren, F.-F., and Zhang, C. (2010, January 11\u201314). Research on Double Objective Optimization of Master Production Schedule Based on Ant Colony Algorithm. Proceedings of the 2010 International Conference on Computational Intelligence and Security, CIS 2010, Nanning, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s10845-019-01531-7","article-title":"Machine Learning Applied in Production Planning and Control: A State-of-the-Art in the Era of Industry 4.0","volume":"31","author":"Lamouri","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.ifacol.2019.11.155","article-title":"Machine Learning in Production Planning and Control: A Review of Empirical Literature","volume":"52","author":"Cadavid","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0925-5273(00)00180-8","article-title":"A model for supply planning under lead time uncertainty","volume":"78","author":"Dolgui","year":"2002","journal-title":"Int. J. Prod. Econ."},{"key":"ref_26","unstructured":"G\u00e9han, M., Castanier, B., and Lemoine, D. (2013, January 28\u201330). Joint Optimization of a Master Production Schedule and a Preventive Maintenance Policy. Proceedings of the 2013 International Conference on Industrial Engineering and Systems Management (IESM), Agdal, Morocco."},{"key":"ref_27","unstructured":"Vasant, P., Weber, G., and Dieu, V.N. (2016). Stochastic Optimization of Manufacture Systems by Using Markov Decision Processes. Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, IGI Global. Chapter 7."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.promfg.2018.02.034","article-title":"Industry 4.0\u2014A Glimpse","volume":"20","author":"Vaidya","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.ifacol.2018.08.474","article-title":"Digital Twin in Manufacturing: A Categorical Literature Review and Classification","volume":"51","author":"Kritzinger","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MIE.2014.2312079","article-title":"Industrie 4.0: Hit or Hype? [Industry Forum]","volume":"8","author":"Drath","year":"2014","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1108\/SCM-09-2018-0339","article-title":"Supply Chain 4.0: Concepts, Maturity and Research Agenda","volume":"25","author":"Frederico","year":"2020","journal-title":"Supply Chain. Manag. An. Int. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1108\/BIJ-04-2020-0156","article-title":"Supply Chain Management 4.0: A Literature Review and Research Framework","volume":"28","author":"Zekhnini","year":"2021","journal-title":"Benchmarking An. Int. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0925-5273(00)00100-6","article-title":"Planning and Replanning the Master Production Schedule under Demand Uncertainty","volume":"78","author":"Tang","year":"2002","journal-title":"Int. J. Prod. Econ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1111\/j.1937-5956.2001.tb00067.x","article-title":"Lot-sizing Rule and Freezing the Master Production Schedule under Capacity Constraint and Deterministic Demand","volume":"10","author":"Zhao","year":"2001","journal-title":"Prod. Oper. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00170-018-1617-6","article-title":"Digital Twin-Based Smart Production Management and Control Framework for the Complex Product Assembly Shop-Floor","volume":"96","author":"Zhuang","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1080\/17517575.2018.1526324","article-title":"The Modelling and Operations for the Digital Twin in the Context of Manufacturing","volume":"13","author":"Bao","year":"2019","journal-title":"Enterp. Inf. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.promfg.2017.07.198","article-title":"A Review of the Roles of Digital Twin in CPS-Based Production Systems","volume":"11","author":"Negri","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_38","unstructured":"Mitchell, T.M. (1997). Machine Learning, The McGraw-Hill Companies."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"El Naqa, I., Li, R., and Murphy, M.J. (2015). What Is Machine Learning?. Machine Learning in Radiation Oncology: Theory and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-319-18305-3"},{"key":"ref_40","unstructured":"Karabegovi\u0107, I. (2021, January 24\u201326). The Importance of Machine Learning in Intelligent Systems. Proceedings of the New Technologies, Development and Application IV, Sarajevo, Bosnia and Herzegovina."},{"key":"ref_41","unstructured":"Halpin, J.F. (1966). Zero Defects: A New Dimension in Quality Assurance, McGraw-Hill."},{"key":"ref_42","first-page":"271","article-title":"A Scheduling Tool for Achieving Zero Defect Manufacturing (ZDM): A Conceptual Framework","volume":"536","author":"Psarommatis","year":"2018","journal-title":"IFIP Adv. Inf. Commun. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/48.551","article-title":"Artificial Intelligence-Definition and Practice","volume":"13","author":"Simmons","year":"1988","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1108\/JMTM-10-2019-0368","article-title":"The Impact of Industry 4.0 Implementation on Supply Chains","volume":"31","author":"Ghadge","year":"2020","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.techfore.2019.05.021","article-title":"Driving Forces and Barriers of Industry 4.0: Do Multinational and Small and Medium-Sized Companies Have Equal Opportunities?","volume":"146","year":"2019","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1080\/02684527.2012.699285","article-title":"A New Definition of Intelligence","volume":"28","author":"Breakspear","year":"2013","journal-title":"Intell. Natl. Secur."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1108\/IMDS-08-2016-0331","article-title":"IoT-Based Framework for Performance Measurement","volume":"117","author":"Rezaei","year":"2017","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1111\/jbl.12271","article-title":"Two Perspectives on Supply Chain Resilience","volume":"42","author":"Wieland","year":"2021","journal-title":"J. Bus. Logist."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5592","DOI":"10.1080\/00207543.2015.1037934","article-title":"Supply Chain Resilience: Definition, Review and Theoretical Foundations for Further Study","volume":"53","author":"Tukamuhabwa","year":"2015","journal-title":"Int. J. Prod. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1108\/09574090910954873","article-title":"Understanding the Concept of Supply Chain Resilience","volume":"20","author":"Ponomarov","year":"2009","journal-title":"Int. J. Logist. Manag."},{"key":"ref_51","unstructured":"Sisco, C., Chorn, B., and Pruzan-Jorgensen, P.M. (2015). Supply Chain Sustainability. A Practical Guide for Continuous Improvement, United Nations Global Compact and BSR."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.ijpe.2015.06.032","article-title":"Supply Chain Sustainability: A Risk Management Approach","volume":"171","author":"Giannakis","year":"2016","journal-title":"Int. J. Prod. Econ."},{"key":"ref_53","unstructured":"Boone, T., Jayaraman, V., and Ganeshan, R. (2012). Models, Methods, and Public Policy Implications. International Series in Operations Research & Management Science, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lopata, A., Butkien\u0117, R., Gudonien\u0117, D., and Sukack\u0117, V. (2020, January 15\u201317). Diffusion of Knowledge in the Supply Chain over Thirty Years\u2014Thematic Areas and Sources of Publications. Proceedings of the Information and Software Technologies ICIST 2020, Kaunas, Lithuania.","DOI":"10.1007\/978-3-030-59506-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10845-012-0667-0","article-title":"Solving a Multi-Objective Master Planning Problem with Substitution and a Recycling Process for a Capacitated Multi-Commodity Supply Chain Network","volume":"25","author":"Chern","year":"2014","journal-title":"J. Intell. Manuf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1504\/IJBIC.2015.069557","article-title":"Application of Particle Swarm Optimisation with Backward Calculation to Solve a Fuzzy Multi-Objective Supply Chain Master Planning Model","volume":"7","author":"Grillo","year":"2015","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sutthibutr, N., and Chiadamrong, N. (2019, January 24\u201326). Applied Fuzzy Multi-Objective with \u03b1-Cut Analysis for Optimizing Supply Chain Master Planning Problem. Proceedings of the 2019 International Conference on Management Science and Industrial Engineering, Phuket, Thailand.","DOI":"10.1145\/3335550.3335571"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ins.2017.09.045","article-title":"Integrated Material-Financial Supply Chain Master Planning under Mixed Uncertainty","volume":"423","author":"Arani","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"103715","DOI":"10.1016\/j.engappai.2020.103715","article-title":"Robust Master Planning of a Socially Responsible Supply Chain under Fuzzy-Stochastic Uncertainty (A Case Study of Clothing Industry)","volume":"94","author":"Sarlak","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s10100-019-00607-2","article-title":"Master Production Schedule Using Robust Optimization Approaches in an Automobile Second-Tier Supplier","volume":"28","author":"Mula","year":"2020","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1080\/00207543.2011.588267","article-title":"Fuzzy Multi-Objective Optimisation for Master Planning in a Ceramic Supply Chain","volume":"50","author":"Peidro","year":"2012","journal-title":"Int. J. Prod. Res."},{"key":"ref_62","unstructured":"Vasant, P., Zelinka, I., and Weber, G.-W. (2020, January 17\u201318). The Use of Agent-Based Models Boosted by Digital Twins in the Supply Chain: A Literature Review. Proceedings of the Intelligent Computing and Optimization, ICO 2020, Koh Samui, Thailand."},{"key":"ref_63","first-page":"1498","article-title":"Concept for a Supply Chain Digital Twin","volume":"5","author":"Barykin","year":"2020","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1504\/IJISM.2020.107780","article-title":"Coronavirus (COVID-19\/SARS-CoV-2) and Supply Chain Resilience: A Research Note","volume":"13","author":"Ivanov","year":"2020","journal-title":"Int. J. Integr. Supply Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4138","DOI":"10.1080\/00207543.2020.1774679","article-title":"Reconfigurable Supply Chain: The X-Network","volume":"58","author":"Dolgui","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1080\/00207543.2020.1788738","article-title":"do The Architectural Framework of a Cyber Physical Logistics System for Digital-Twin-Based Supply Chain Control","volume":"59","author":"Park","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lalla-Ruiz, E., Mes, M., and Vo\u00df, S. (2020, January 28\u201330). Deep Reinforcement Learning and Optimization Approach for Multi-Echelon Supply Chain with Uncertain Demands. Proceedings of the Computational Logistics ICCL 2020, Enschede, The Netherlands.","DOI":"10.1007\/978-3-030-59747-4"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Peng, Z., Zhang, Y., Feng, Y., Zhang, T., Wu, Z., and Su, H. (2019, January 22\u201324). Deep Reinforcement Learning Approach for Capacitated Supply Chain Optimization under Demand Uncertainty. Proceedings of the 2019 Chinese Automation Congress, CAC 2019, Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8997498"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Boute, R.N., Gijsbrechts, J., van Jaarsveld, W., and Vanvuchelen, N. (2021). Deep Reinforcement Learning for Inventory Control: A Roadmap. Eur. J. Oper. Res.","DOI":"10.2139\/ssrn.3861821"},{"key":"ref_70","unstructured":"Bae, K.-H., Feng, B., Kim, S., Lazarova-Molnar, S., Zheng, Z., Roeder, T., and Thiesing, R. (2020, January 14\u201318). A Deep Reinforcement Learning Approach for Optimal Replenishment Policy in A Vendor Managed Inventory Setting for Semiconductors. Proceedings of the 2020 Winter Simulation Conference, WSC 2020, Orlando, FL, USA."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kegenbekov, Z., and Jackson, I. (2021). Adaptive Supply Chain: Demand-Supply Synchronization Using Deep Reinforcement Learning. Algorithms, 14.","DOI":"10.3390\/a14080240"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Siddh, M.M., Soni, G., Gadekar, G., and Jain, R. (2014, January 9\u201311). Integrating Lean Six Sigma and Supply Chain Approach for Quality and Business Performance. Proceedings of the 2014 2nd International Conference on Business and Information Management (ICBIM), Durgapur, India.","DOI":"10.1109\/ICBIM.2014.6970949"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"012089","DOI":"10.1088\/1757-899X\/528\/1\/012089","article-title":"A Framework for the Impact of Lean Six Sigma on Supply Chain Performance in Manufacturing Companies","volume":"528","author":"Wibisono","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"012034","DOI":"10.1088\/1757-899X\/923\/1\/012034","article-title":"Quality Transformation to Improve Customer Satisfaction: Using Product, Process, System and Behaviour Model","volume":"923","author":"Poornachandrika","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.jclepro.2018.12.201","article-title":"Change Management for Sustainability: Evaluating the Role of Human, Operational and Technological Factors in Leading Indian Firms in Home Appliances Sector","volume":"213","author":"Thakur","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_76","first-page":"93","article-title":"The Bullwhip Effect in Supply Chains","volume":"38","author":"Lee","year":"1997","journal-title":"Sloan Manag. Rev."},{"key":"ref_77","unstructured":"M\u00fcller, J.M., Schmidt, M.-C., R\u00fccker, M., Veile, J.W., Birkel, H., and Voigt, K.-I. (2021, January 12\u201313). Pitfalls, Sticks and Stones: Understanding Challenges Industry 4.0 Poses For Inter-Company Logistics. Proceedings of the International Symposium on Logistics (ISL 2021), Seoul, Korea."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1108\/BIJ-12-2018-0435","article-title":"Industry 4.0 and Digital Supply Chain Capabilities","volume":"28","author":"Queiroz","year":"2021","journal-title":"Benchmarking Int. J."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"107379","DOI":"10.1016\/j.cie.2021.107379","article-title":"Implementing Industry 4.0 Principles","volume":"158","author":"Mula","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hermann, M., Pentek, T., and Otto, B. (2016, January 5\u20138). Design Principles for Industrie 4.0 Scenarios. Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA.","DOI":"10.1109\/HICSS.2016.488"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1108\/JMTM-08-2018-0238","article-title":"Industry 4.0: Coherent Definition Framework with Technological and Organizational Interdependencies","volume":"31","author":"Nosalska","year":"2020","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1108\/JMTM-02-2018-0057","article-title":"The Future of Manufacturing Industry: A Strategic Roadmap toward Industry 4.0","volume":"29","author":"Ghobakhloo","year":"2018","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1080\/00207543.2020.1798035","article-title":"Researchers\u2019 Perspectives on Industry 4.0: Multi-Disciplinary Analysis and Opportunities for Operations Management","volume":"59","author":"Ivanov","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Habib, M.K., and Chimsom, C. (2019, January 23\u201324). Industry 4.0: Sustainability and Design Principles. Proceedings of the 2019 20th International Conference on Research and Education in Mechatronics (REM), Wels, Austria.","DOI":"10.1109\/REM.2019.8744120"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.compind.2018.04.006","article-title":"Extracting and Mapping Industry 4.0 Technologies Using Wikipedia","volume":"100","author":"Chiarello","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"32030","DOI":"10.1109\/ACCESS.2021.3060863","article-title":"The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities","volume":"9","author":"Rathore","year":"2021","journal-title":"IEEE Access"},{"key":"ref_87","unstructured":"Serrano-Ruiz, J.C., Mula, J., and Poler Escoto, R. A metamodel for digital planning in the supply chain 4.0. J. Ind. Inf. Integr., Under review."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Serrano-Ruiz, J.C., Mula, J., and Poler Escoto, R. (2021, January 7\u20139). Smart Digital Twin for ZDM-Based Job-Shop Scheduling. Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488473"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s00170-020-05977-5","article-title":"A Digital Twin-Driven Production Management System for Production Workshop","volume":"110","author":"Ma","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"107781","DOI":"10.1109\/ACCESS.2020.3000437","article-title":"A Requirements Driven Digital Twin Framework: Specification and Opportunities","volume":"8","author":"Moyne","year":"2020","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_92","first-page":"1","article-title":"Scalable Multi-Product Inventory Control with Lead Time Constraints Using Reinforcement Learning","volume":"1","author":"Meisheri","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3389\/fcomp.2020.00026","article-title":"Product Quality Improvement Policies in Industry 4.0: Characteristics, Enabling Factors, Barriers, and Evolution Toward Zero Defect Manufacturing","volume":"2","author":"Psarommatis","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.procir.2019.03.218","article-title":"Towards Intelligent and Sustainable Production Systems with a Zero-Defect Manufacturing Approach in an Industry 4.0 Context","volume":"81","author":"Lejon","year":"2019","journal-title":"Procedia CIRP"},{"key":"ref_95","first-page":"100214","article-title":"Analysis of Relevant Standards for Industrial Systems to Support Zero Defects Manufacturing Process","volume":"23","author":"Nazarenko","year":"2021","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.procir.2020.05.221","article-title":"A Two-Layer Criteria Evaluation Approach for Re-Scheduling Efficiently Semi-Automated Assembly Lines with High Number of Rush Orders","volume":"97","author":"Psarommatis","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s00502-021-00912-2","article-title":"An Adaptive System-of-Systems Approach for Resilient Manufacturing","volume":"138","author":"Weichhart","year":"2021","journal-title":"Elektrotechnik Und Inf."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/12\/156\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:35Z","timestamp":1760168075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/12\/156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,23]]},"references-count":97,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["computers10120156"],"URL":"https:\/\/doi.org\/10.3390\/computers10120156","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,23]]}}}