A Systematic Literature Review on the Application of Industry 4.0 Technologies in Manufacturing Supply Chain Planning Phase

Document Type : Review Paper

Authors

1 Operations Management ,Faculty of Management Sciences, Tshwane University of Technology

2 Department of Operations Management, Faculty of Management Sciences, Tshwane University of Technology

3 Department of Quality and Operations Management, Faculty of Engineering and the Built Environment, University of Johannesburg

Abstract

Supply chain systems have become crucial in today’s highly competitive and ever-changing industrial environment; hence, effective management of supply chains is required to achieve operational excellence. Planning is the first stage of supply chain systems and is crucial to ensure overall optimization of the supply chain and business sustainability. With various business processes adopting industry 4.0 technologies for optimization, the supply chain system is not an exception. Also noting that, organizations based in the least developed and developing countries need more support in adopting 4IR technologies. Thus, the objective of the study is to investigate, through literature, the key 4IR technologies that have been applied within the supply chain planning phase of manufacturing organizations. A five-step systematic literature review was adopted to carry out the research. The steps included the identification and selection of a database, keywords development, and selection of inclusion and exclusion criteria through database filters and search categories. The findings of this systematic review revealed Industry 4.0 technologies that have been deployed in the supply chain planning phase of manufacturing organizations. These include Simulation, Machine Learning, Digital Twin, and Internet-of-things. While manufacturing supply chain planning departments are faced with myriads of operational challenges and constraints, the deployment of Industry 4.0 technologies serves as a potential solution towards promoting effective supply chain operations, thereby stimulating sustainable supply chain management. The results of this study serve as a revelation to Supply Chain Managers to identify the latest trends and gain insights into appropriate Industry 4.0 technologies that could be deployed to ensure effective supply chain planning.

Keywords

References

Abalkina, A. (2024). Challenges posed by hijacked journals in Scopus. Journal of the Association for Information Science and Technology, 75(4), 395-422. https://doi.org/10.1002/asi.24855
Alexopoulos, K., Nikolakis, N., Bakopoulos, E., Siatras, V., & Mavrothalassitis, P. (2023). Machine learning agents augmented by digital twinning for smart production scheduling. IFAC-PapersOnLine, 56(2), 2963-2968. https://doi.org/10.1016/j.ifacol.2023.10.1420
Anaba, D. C., Kess-Momoh, A. J., & Ayodeji, S. A. (2024). Digital transformation in oil and gas production: Enhancing efficiency and reducing costs. International Journal of Management & Entrepreneurship Research, 6(7), 2153-2161. https://doi.org/10.51594/ijmer.v6i7.1263
Andrés, M., Álvaro, G., & Julián, M. (2019, March). Advantages of learning factories for production planning based on shop floor simulation: A step towards smart factories in industry 4.0. In 2019 IEEE World Conference on Engineering Education (EDUNINE) (pp. 1-5). IEEE. https://doi.org/10.1109/EDUNINE.2019.8875782
Atibodhi, N., Toprasert, P., Thienthanukit, W., Supha-Kitti, D., Pongsripian, W., Wang, F., ... & Mijares, G. (2023, October). Application of a Digital Twin for Asset-Wide Production Planning and Optimization–A Case Study. In Abu Dhabi International Petroleum Exhibition and Conference (p. D031S108R007). SPE. https://doi.org/10.2118/216698-MS
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative science studies, 1(1), 377-386. https://doi.org/10.1162/qss_a_00019.
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International journal of production economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776.
Behrendt, S., Wurster, M., May, M. C., & Lanza, G. (2023). Extended Production Planning of Reconfigurable Manufacturing Systems by Means of Simulation-based Optimization. ESSN: 2701-6277, 210-220. https://doi.org/10.15488/13440
Belil, S., Tchernev, N., & Kemmoé-Tchomté, S. (2019). Simulation based optimization decision support tool for production planning. IFAC-PapersOnLine, 52(13), 2402-2407.  https://doi.org/10.1016/j.ifacol.2019.11.566
Berlak, J., Berg, J., Stangl, M., & Baumgart, L. (2019, February). Real-Time IoT-Based Production Planning and Control of Industrial Robots in an Automated Cyber-Physical Production System Under Dynamic Conditions: Lessons Learned from a Make-to-Order Usage Case. In International Conference on Computer Aided Systems Theory (pp. 485-492). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-45096-0_59
Biesinger, F., Meike, D., Kraß, B., & Weyrich, M. (2019). A digital twin for production planning based on cyber-physical systems: A Case Study for a Cyber-Physical System-Based Creation of a Digital Twin. procedia CIRP, 79, 355-360. https://doi.org/10.1016/j.procir.2019.02.087
Bojic, S., Maslaric, M., Mircetic, D., Nikolicic, S., & Todorovic, S. (2023). Simulation and Genetic Algorithm-based approach for multi-objective optimization of production planning: A case study in industry. Advances in Production Engineering & Management, 18(2), 250-262. https://doi.org/10.14743/apem2023.2.471
Bueno, A., Godinho Filho, M., & Frank, A. G. (2020). Smart production planning and control in the Industry 4.0 context: A systematic literature review. Computers & industrial engineering, 149, 106774. https://doi.org/10.1016/j.cie.2020.106774.
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895.
Chai, X., Zhao, L., & Li, X. (2020). Production scheduling and management of industrial product based on computer simulation technology. Academic Journal of Manufacturing Engineering, 18(2), 124-129.
Chen, J., Shi, H., Sivakumar, B., & Peart, M. R. (2016). Population, water, food, energy and dams. Renewable and Sustainable Energy Reviews, 56, 18-28. http://dx.doi.org/10.1016/j.rser.2015.11.043
Chen, W. (2020). Intelligent manufacturing production line data monitoring system for industrial internet of things. Computer communications, 151, 31-41.https://doi.org/10.1016/j.comcom.2019.12.035.
Chen, Y., Zhou, Y., & Zhang, Y. (2020, November). Collaborative production planning with unknown parameters using model predictive control and machine learning. In 2020 Chinese Automation Congress (CAC) (pp. 2185-2190). IEEE. https://doi.org/10.1109/CAC51589.2020.9326614.
Chen, Y., Zhou, Y., & Zhang, Y. (2021). Machine learning-based model predictive control for collaborative production planning problem with unknown information. Electronics, 10(15), 1818. https://doi.org/10.3390/electronics10151818
Chiadamrong, N., & Tangchaisuk, C. (2021). Hybrid simulation-based optimization for production planning of a dedicated remanufacturing system. International Journal of Knowledge and Systems Science (IJKSS), 12(3), 53-79. https://doi.org/10.4018/IJKSS.2021070103
Chiurco, A., Elbasheer, M., Longo, F., Nicoletti, L., & Solina, V. (2023). Data modelling and ML practice for enabling intelligent digital twins in adaptive production planning and control. Procedia Computer Science, 217, 1908-1917. https://doi.org/10.1016/j.procs.2022.12.391
Chu, C. H., & Baroroh, D. K. (2024). Production planning and simulation in mixed reality for human work performance variations. Computers & Industrial Engineering, 193, 110327 .https://doi.org/10.1016/j.cie.2024.110327.
Cruz, J. A. D., Salles-Neto, L. L. D., & Schenekemberg, C. M. (2024). An integrated production planning and inventory management problem for a perishable product: optimization and Monte Carlo simulation as a tool for planning in scenarios with uncertain demands. Top, 32(2), 263-303.https://doi.org/10.1007/s11750-024-00667-x
Barreto, B. D. S., Sagawa, J. K., & Silva, M. S. (2021). Use of workload control in production planning and control: modeling on a simulation software. Gestão & Produção, 28(2), e5744. https://doi.org/10.1590/1806-9649-2021v28e5744
Dcoutho, J. F., Eisenbart, B., & Kulkarni, A. (2023, November). Hierarchy Priortization and Dynamic Simulation for Low Volume Production Planning. In 2023 IEEE Engineering Informatics (pp. 1-10). IEEE. https://doi.org/10.1109/IEEECONF58110.2023.10520413
Dimoudis, D., Vafeiadis, T., Nizamis, A., Musiari, E., Ziliotti, L., Ioannidis, D., & Tzovaras, D. (2023, June). A holistic framework for production scheduling in Industry 4.0. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) (pp. 269-276). IEEE. https://doi.org/10.1109/DCOSS-IoT58021.2023.00053
Ding, J., Liu, Y., Zeng, F., Huang, Y., & Li, X. (2023, June). Simulation of Production Scheduling System in Automated Manufacturing Workshop Based on Markov Model. In 2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII) (pp. 621-626). IEEE. https://doi.ieeecomputersociety.org/10.1109/ICMIII58949.2023.00130
Elbasheer, M., Longo, F., Mirabelli, G., Padovano, A., Solina, V., & Talarico, S. (2022). Integrated prescriptive maintenance and production planning: a machine learning approach for the development of an autonomous decision support agent. IFAC-PapersOnLine, 55(10), 2605-2610. https://doi.org/10.1016/j.ifacol.2022.10.102
El-Garaihy, W. H. (2021). Analysis of supply chain operations reference (SCOR) and balanced scorecard (BSC) in measuring supply chains efficiency using DEMATEL and DEA techniques. Journal of Global Operations and Strategic Sourcing, 14(4), 680-700. https://doi.org/10.1108/JGOSS-07-2020-0034
El Jaouhari, A., Alhilali, Z., Arif, J., Fellaki, S., Amejwal, M., & Azzouz, K. (2022). Demand forecasting application with regression and iot based inventory management system: a case study of a semiconductor manufacturing company. International journal of engineering research in Africa, 60, 189-210. https://doi.org/10.4028/p-8ntq24. 
Fabianova, J., Kacmary, P., & Janekova, J. (2019). Operative production planning utilising quantitative forecasting and Monte Carlo simulations. Open Engineering, 9(1), 613-622. https://doi.org/10.1515/eng-2019-0071.
Fani, V., Antomarioni, S., Bandinelli, R., & Bevilacqua, M. (2023). Data-driven decision support tool for production planning: a framework combining association rules and simulation. Computers in Industry, 144, 103800. https://doi.org/10.1016/j.compind.2022.103800
Fülöp, M. T., Gubán, M., Gubán, Á., & Avornicului, M. (2022). Application research of soft computing based on machine learning production scheduling. Processes, 10(3), 520. https://doi.org/10.3390/pr10030520.
Garcia-Arismendiz, J., Huertas-Zúñiga, S., Lizárraga-Portugal, C. A., Quiroz-Flores, J. C., & Garcia-Lopez, Y. J. (2023). Improving demand forecasting by implementing machine learning in poultry production company. Learning, 71(2), 39-45. https://doi.org/10.14445/22315381/IJETT-V71I2P205
Garg, K., Goswami, C., Chhatrawat, R. S., Dhakar, S. K., & Kumar, G. (2022). Internet of things in manufacturing: A review. Materials Today: Proceedings, 51, 286-288. https://doi.org/10.1016/j.matpr.2021.05.321
Ghaleb, M., Zolfagharinia, H., & Taghipour, S. (2020). Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns. Computers & Operations Research, 123, 105031. https://doi.org/10.1016/j.cor.2020.10503
Ghasemi, A., Yeganeh, Y. T., Matta, A., Kabak, K. E., & Heavey, C. (2023, December). Deep Learning Enabling Digital Twin Applications in Production Scheduling: Case of Flexible Job Shop Manufacturing Environment. In 2023 Winter Simulation Conference (WSC) (pp. 2148-2159). IEEE. https://doi.org/10.1109/WSC60868.2023.10407811
González Rodríguez, G., Gonzalez-Cava, J. M., & Méndez Pérez, J. A. (2020). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing, 31(5), 1257-1273. https://doi.org/10.1007/s10845-019-01510-y
Guo, K., Yang, M., & Zhu, H. (2020). Application research of improved genetic algorithm based on machine learning in production scheduling. Neural Computing and Applications, 32, 1857-1868. https://doi.org/10.1007/s00521-019-04571-5.
Hatami-Marbini, A., Sajadi, S. M., & Malekpour, H. (2020). Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Computers & Industrial Engineering, 146, 106614. https://doi.org/10.1016/j.cie.2020.106614
Heesch, R., Ehrhardt, J., Niggemann, O. (2024). Integrating Machine Learning into an SMT-Based Planning Approach for Production Planning in Cyber-Physical Production Systems. Communications in Computer and Information Science, 318-331.  https://doi.org/10.1007/978-3-031-50485-3_33.
Hong, H., Zhang, X., Ren, K., Bei, H., Deng, C., & Wang, H. (2023, September). Modelling and Simulation of Ship Block Production Scheduling Optimization Problem Considering the Multiple Resources Constraints. In 2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS) (pp. 1379-1384). IEEE. https://doi.ieeecomputersociety.org/10.1109/EIECS59936.2023.10435431
Ikeziri, L. M., Souza, F. B. D., Gupta, M. C., & de Camargo Fiorini, P. (2019). Theory of constraints: review and bibliometric analysis. International Journal of Production Research, 57(15-16), 5068-5102. https://doi.org/10.1080/00207543.2018.1518602.
Irawan, E., Rosjanuardi, R., & Prabawanto, S. (2024). Research trends of computational thinking in mathematics learning: A bibliometric analysis from 2009 to 2023. EURASIA Journal of Mathematics, Science and Technology Education, 20(3), em2417. https://doi.org/10.29333/ejmste/14343.
Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725
Jayant, A., Agarwal, A., Gupta, V. (2021). Application of Machine Learning Technique for Demand Forecasting: A Case Study of the Manufacturing Industry. In: Pandey, P.M., Kumar, P., Sharma, V. (eds) Advances in Production and Industrial Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5519-0_31
Kim, B., & Maravelias, C. T. (2022). Supervised machine learning for understanding and improving the computational performance of chemical production scheduling MIP models. Industrial & Engineering Chemistry Research, 61(46), 17124-17136. https://doi.org/10.1021/acs.iecr.2c02734.
Kocsi, B., Matonya, M. M., Pusztai, L. P., & Budai, I. (2020). Real-time decision-support system for high-mix low-volume production scheduling in industry 4.0. Processes, 8(8), 912. https://doi.org/10.3390/pr8080912
Kuo, H. A., Hong, T. Y., & Chien, C. F. A Deep Reinforcement Learning Based Digital Twin Framework for Resilient Production Planning in Semiconductor Wafer Fabrication. (2023).  International Conference on Computers and Industrial Engineering, CIE, (1),287-296.
Lan, X., & Chen, H. (2023). Simulation analysis of production scheduling algorithm for intelligent manufacturing cell based on artificial intelligence technology. Soft Computing, 27(9), 6007-6017.  https://doi.org/10.1007/s00500-023-08074-3
Lanzini, M., Adami, N., Benini, S., Ferretti, I., Schieppati, G., Spoto, C., & Zanoni, S. (2023). Implementation and integration of a Digital Twin for production planning in manufacturing. In Proceedings of the 35th European Modeling & Simulation Symposium, EMSS. CALTEK srl. https://doi.org/10.46354/i3m  
Laroque, C., Leißau, M., Scholl, W., & Schneider, G. (2021, December). Backward Simulation for Production Planning–Recent Advances in a Real-World Use-Case. In 2021 Winter Simulation Conference (WSC) (pp. 1-10). IEEE. https://doi.org/10.1109/WSC52266.2021.9715490
Li, Y., Tao, Z., Wang, L., Du, B., Guo, J., & Pang, S. (2023). Digital twin-based job shop anomaly detection and dynamic scheduling. Robotics and Computer-Integrated Manufacturing, 79, 102443. https://doi.org/10.1016/j.rcim.2022.102443
Liu, J., Yu, D., Hu, Y., Yu, H., He, W., & Zhang, L. (2021, December). Digital Twin-driven Multi-objective Optimization Production Scheduling with Restraint Tool Resources. In 2021 7th International Conference on Computer and Communications (ICCC) (pp. 1228-1233). IEEE. https://doi.org/10.1109/ICCC54389.2021.9674414
Liu, Y., Ma, C., & Huang, Y. (2024). An Internet of Things-Based Production Scheduling for Distributed Two-Stage Assembly Manufacturing with Mold Sharing. Machines, 12(5), 310. https://doi.org/10.3390/machines12050310
Macchi, M., Ragazzini, L., & Negri, E. (2023). A conceptual framework for Digital Twins in production scheduling and control. IFAC-PapersOnLine, 56(2), 5661-5666. https://doi.org/10.1016/j.ifacol.2023.10.491.
Magnanini, M. C., Melnychuk, O., Yemane, A., Strandberg, H., Ricondo, I., Borzi, G., & Colledani, M. (2021). A Digital Twin-based approach for multi-objective optimization of short-term production planning. IFAC-PapersOnLine, 54(1), 140-145. https://doi.org/10.1016/j.ifacol.2021.08.077
Makazhe, E. C., & Maramura, T. C. (2023, November). The Role of Fourth Industrial Revolution (4IR) Technologies in Achieving Sustainable Development Goals. In The Global Conference on Entrepreneurship and the Economy in an Era of Uncertainty (pp. 985-998). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0996-0_58
Mashaly, M. (2021). Connecting the twins: A review on digital twin technology & its networking requirements. Procedia Computer Science, 184, 299-305. https://doi.org/10.1016/j.procs.2021.03.039
Mohamed Shaffril, H. A., Samsuddin, S. F., & Abu Samah, A. (2021). The ABC of systematic literature review: the basic methodological guidance for beginners. Quality & quantity, 55, 1319-1346. https://doi.org/10.1007/s11135-020-01059-6
Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949. https://doi.org/10.1080/00207543.2019.1636321
Muehlbauer, K., Rissmann, L., & Meissner, S. (2022, October). Machine Learning Decision Support for Production Planning and Control Based on Simulation-Generated Data. In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management (pp. 257-279). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43471-6_12
Müller, R., Hörauf, L., Speicher, C., Koch, J., & Drieß, M. (2019). Simulation based online production planning. Procedia Manufacturing, 38, 1473-1480. https://doi.org/10.1016/j.promfg.2020.01.140
Nakao, J., & Nishi, T. (2022). A bilevel production planning using machine learning-based customer modeling. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 16(4),  https://doi.org/10.1299/jamdsm.2022jamdsm0037
Negri, E., Cattaneo, L., Pandhare, V., Macchi, M., & Lee, J. (2022). Integrating PHM into production scheduling through a Digital Twin-based framework. IFAC-PapersOnLine, 55(19), 31-36. https://doi.org/10.1016/j.ifacol.2022.09.180
Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2021). Field-synchronized digital twin framework for production scheduling with uncertainty. Journal of Intelligent Manufacturing, 32(4), 1207-1228. https://doi.org/10.1007/s10845-020-01685-9
Nekrasov, I., & Pravdivets, N. (2022). Simulation Approach for Day-Ahead Production Scheduling of a Power Plant. IFAC-PapersOnLine, 55(10), 2887-2892. https://doi.org/10.1016/j.ifacol.2022.10.169
Novák, P., Vyskočil, J., & Wally, B. (2020). The digital twin as a core component for industry 4.0 smart production planning. IFAC-PapersOnLine, 53(2), 10803-10809.  https://doi.org/10.1016/j.ifacol.2020.12.2865
Okubo, Y., & Mitsuyuki, T. (2022). Ship production planning using shipbuilding system modeling and discrete time process simulation. Journal of Marine Science and Engineering, 10(2), 176.  https://doi.org/10.3390/jmse10020176
Olanrele, O. O., Ismaila, S. O., Adeaga, O. A., Adeyemi, O. A., & Akintaro, A. S. (2023, April). Managing Uncertainty in Production Planning for Fast-Moving Consumer Goods: A Linear Programming and Monte Carlo Simulation Framework. In 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG) (Vol. 1, pp. 01-08). IEEE. https://doi.org/10.1109/SEB-SDG57117.2023.10124567
Panasri, J., Samattapapong, N., & Sangthong, S. (2022, December). Production scheduling for parallel machines using simulation techniques: case study of plastic packaging factory. In 2022 Winter Simulation Conference (WSC) (pp. 1876-1887). IEEE. https://doi.org/10.1109/WSC57314.2022.10015428
Pandhare, V., Negri, E., Ragazzini, L., Cattaneo, L., Macchi, M., & Lee, J. (2024). Digital twin-enabled robust production scheduling for equipment in degraded state. Journal of Manufacturing Systems, 74, 841-857. https://doi.org/10.1016/j.jmsy.2024.04.027
Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR‐4‐SLR). International Journal of Consumer Studies, 45(4), 1-16.  https://doi.org/10.1111/ijcs.12695
Pereira, D. F., Oliveira, J. F., & Carravilla, M. A. (2020). Tactical sales and operations planning: A holistic framework and a literature review of decision-making models. International Journal of Production Economics, 228, 107695.https://doi.org/10.1016/j.ijpe.2020.107695
Pereira, M. S., Aquino, P. T., De Mattos, C. A., & Lima, F. (2024). Energy Estimation and Production Scheduling in Job shop Using Machine Learning. IEEE Access, 12, 104477-1041189. https://doi.org/10.1109/ACCESS.2024.3430218
Phoong, S. W., Phoong, S. Y., & Khek, S. L. (2022). Systematic literature review with bibliometric analysis on Markov switching model: Methods and applications. Sage Open, 12(2).  https://doi.org/10.1177/21582440221093
Pirola, F., Zambetti, M., & Cimini, C. (2021). Applying simulation for sustainable production scheduling: a case study in the textile industry. IFAC-PapersOnLine, 54(1), 373-378. https://doi.org/10.1016/j.ifacol.2021.08.041
Rad, F. F., Oghazi, P., Palmié, M., Chirumalla, K., Pashkevich, N., Patel, P. C., & Sattari, S. (2022). Industry 4.0 and supply chain performance: A systematic literature review of the benefits, challenges, and critical success factors of 11 core technologies. Industrial Marketing Management, 105, 268-293. https://doi.org/10.1016/j.indmarman.2022.06.009
Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778.  https://doi.org/10.1080/00207543.2021.1956675
Ramirez Lozano, J. P., Peñaflor Guerra, R., & Sanagustin-Fons, M. V. (2024). Responsible consumption trend in Generation Z and millennials its impact on SDG 12. A Peruvian case study. Academia Revista Latinoamericana de Administración, 37(3), 483-508.  https://doi.org/10.1108/ARLA-07-2022-0142
Rashidifar, R., Chen, F. F., & Tran, T. (2022). Simulation and analysis of production scheduling in eyeglasses industry for productivity improvement. IISE Annual Conference and Expo 2022. https://doi.org/10.31224/2561.
Rodrigues, R. P., de Pinho, A. F., & Sena, D. C. (2020). Application of Hybrid Simulation in production scheduling in job shop systems. Simulation, 96(3), 253-268. https://doi.org/10.1177/0037549719861724
Russkikh, P. A., & Kapulin, D. V. (2020, November). Simulation modeling for optimal production planning using Tecnomatix software. In Journal of Physics: Conference Series (Vol. 1661, No. 1, p. 012188). IOP Publishing. https://doi.org/10.1088/1742-6596/1661/1/012188
Ryback, T., Lukas, L., Gaal, A., Gallina, V., & Gyulai, D. (2019). Improving the planning quality in production planning and control with machine learning. 16th IMEKO TC10 Conference 2019. 131 -136.
Sabah, B., Nikolay, T., & Sylverin, K. T. (2019, September). Production planning under demand uncertainty using Monte Carlo simulation approach: a case study in fertilizer industry. In 2019 International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1-6). IEEE. https://doi.org/10.1109/IESM45758.2019.8948112
Santander, O., Kuppuraj, V., Harrison, C. A., & Baldea, M. (2023). A machine learning framework for improving refinery production planning. AIChE Journal, 69(8), e18109. https://doi.org/10.1002/aic.18109
Saturno, M., Pertel, V. M., Deschamps, F., &  de FR, L. E. (2018). Proposal of an automation solutions architecture for industry 4.0. LogForum, 14 (2), 185-195. http://dx.doi.org/10.17270/J.LOG.2018.266  
Sobottka, T., Kamhuber, F., Faezirad, M., & Sihn, W. (2019). Potential for machine learning in optimized production planning with hybrid simulation. Procedia Manufacturing, 39, 1844-1853.  https://doi.org/10.1016/j.promfg.2020.01.254
Song, J., Xie, H., & Rou, J. (2019). Simulation of batch production scheduling in discrete manufacturing workshop based on improved genetic algorithm. Academic Journal of Manufacturing Engineering, 17(2), 153-159.
Togo, H., Asanuma, K., Nishi, T., & Liu, Z. (2022). Machine learning and inverse optimization for estimation of weighting factors in multi-objective production scheduling problems. Applied sciences, 12(19), 9472. https://doi.org/10.3390/app12199472
United Nations (2024). The 17 Sustainable Development Goals. [online] United Nations. Available at: https://sdgs.un.org/goals
Vaz, L. V., Gonçalves, M. C., Dias, I. C. P., & Nara, E. O. B. (2024). Application of a production planning model based on linear programming and machine learning techniques. ITEGAM-JETIA, 10(45), 17-29. https://doi.org/10.5935/jetia.v10i45.920 
Vithitsoontorn, C., & Chongstitvatana, P. (2022, March). Demand forecasting in production planning for dairy products using machine learning and statistical method. In 2022 international electrical engineering congress (iEECON) (pp. 1-4). IEEE. https://doi.org/10.1109/iEECON53204.2022.9741683
Wang, D., Liu, X., Hu, L., & Hu, F. (2022, March). Optimization Simulation of Hybrid Assembly Line Production Scheduling in Garment Enterprises. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 1375-1379). IEEE. https://doi.org/10.1109/ITOEC53115.2022.9734580
Wang, Y., & Wu, Z. (2022). Digital twin-based production scheduling system for heavy truck frame shop. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 236(4), 1931-1942. https://doi.org/10.1177/0954406220913306.
Woschank, M., Dallasega, P., König, A., & Hoffelner, M. (2024). Potentials of using real-time data to increase the update frequency of production planning and control strategies in MTO: a discrete event simulation study. Flexible Services and Manufacturing Journal, 1-20. https://doi.org/10.1007/s10696-024-09550-0
Xu, L. Z., & Xie, Q. S. (2021). Dynamic Production Scheduling of Digital Twin Job-Shop Based on Edge Computing. Journal of Information Science & Engineering, 37(1). http://doi:10.6688/JISE.202101_37(1).0007
Yu, Y., Guan, Z., Zhang, Z., & Yue, L. (2023, January). Application of Multi-Dimensional Clearing Functions Estimated by Machine Learning to Production Planning Problems: Clearing Functions Estimated by Machine Learning. In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (pp. 289-294). https://doi.org/10.1145/3587889.3588213.
Yu, Y., Li, Y., Zhang, Z., Gu, Z., Zhong, H., Zha, Q., ... & Chen, E. (2020). A bibliometric analysis using VOSviewer of publications on COVID-19. Annals of translational medicine, 8(13), 816. http://dx.doi.org/10.21037/atm-20-4235.
Zhang, F., Song, J., Dai, Y., & Xu, J. (2020). Semiconductor wafer fabrication production planning using multi-fidelity simulation optimization. International Journal of Production Research, 58(21), 6585-6600. https://doi.org/10.1080/00207543.2019.1683252
Zhang, S., Tang, F., Li, X., Liu, J., & Zhang, B. (2021). A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context. Computers & Operations Research, 132, 105267. https://doi.org/10.1016/j.cor.2021.105267
Ziarnetzky, T., Mönch, L., & Uzsoy, R. (2019). Simulation-based performance assessment of production planning models with safety stock and forecast evolution in semiconductor wafer fabrication. IEEE Transactions on Semiconductor Manufacturing, 33(1), 1-12. https://doi.org/10.1109/TSM.2019.2958526
International Journal of Supply and Operations Management

Articles in Press, Accepted Manuscript
Available Online from 22 May 2025

Files

Share

How to cite

Statistics