Operational Decision Support in Manufacturing and Technological Information Processing: A Systematic Literature Review
By: Patryk Machnik and Adam Deptuła
References
- Agritania, M.M. and Isnaini, M.M. (2025) „Development of an engineering drawing detection and extraction algorithm for quality inspection using deep neural networks”, Procedia CIRP, 132, pp. 135-140. doi: 10.1016/j.procir.2025.01.023.
- Balaha, F., Albinali, H., Alrabiah, H., Ali, M. and Bahroun, Z. (2025) „An analytical review of data integration for decision support in smart manufacturing”, Decision Analytics Journal, 17, Article 100647. doi: 10.1016/j.dajour.2025.100647.
- Botelho, J.G.S., Choueiri, A.C., Júnior, J.E.P. and Santos, E.A.P. (2026) „A process mining and machine learning based approach for remaining time prediction of production orders”, Journal of Intelligent Manufacturing. doi: 10.1007/s10845-026-02792-9.
- Carlin, H.M., Goodall, P.A., Young, R.I.M. and West, A.A. (2024) „An interactive framework to support decision-making for Digital Twin design”, Journal of Industrial Information Integration, 41, Article 100639. doi: 10.1016/j.jii.2024.100639.
- De Simone, V., Di Pasquale, V., Francalanza, E., Iannone, R. and Miranda, S. (2026) „A Masterplan-based system for lead time prediction in small and medium enterprises”, Production Engineering, 20(1), Article 33. doi: 10.1007/s11740-025-01385-4.
- Dhobale, N., Mulik, S., Jegdeeshwaran, R. and Ganer, K. (2021) „Multipoint milling tool supervision using artificial neural network approach”, Materials Today: Proceedings, 45, pp. 1898-1903. doi: 10.1016/j.matpr.2020.09.147.
- Eichenseer, P. and Winkler, H. (2024) „A data-oriented shopfloor management in the production context: a systematic literature review”, International Journal of Advanced Manufacturing Technology, 134(9-10), pp. 4071-4097. doi: 10.1007/s00170-024-14238-8.
- Fadda, E., Perboli, G., Rosano, M., Mascolo, J.E. and Masera, D. (2022) „A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry”, Sustainability (Switzerland), 14(4), Article 2408. doi: 10.3390/su14042408.
- Frey, A.M., May, M.C. and Lanza, G. (2023) „Creation and validation of systems for product and process configuration based on data analysis”, Production Engineering, 17(2), pp. 263-277. doi: 10.1007/s11740-022-01176-1.
- Guo, K., Zhang, D., Li, M., Sun, S., Miao, L. and Wang, J. (2025) „Intelligent Decision-Making Technology of Automobile Gearbox Parts Process Based on Knowledge Graph”, 2025 10th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2025, pp. 646-650. doi: 10.1109/ICCCBDA64898.2025.11030475.
- Hugo, P., Bezuidenhout, M., Damm, O. and Sacks, N. (2023) „Mapping of metal LPBF core technical capabilities for part value transformation”, Procedia CIRP, 120, pp. 1113-1118. doi: 10.1016/j.procir.2023.09.134.
- Imam, A.T. (2023) „The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English”, IEEE Access, 11, pp. 93381-93392. doi: 10.1109/ACCESS.2023.3310394.
- Japs, S., Anacker, H. and Dumitrescu, R. (2021) „SAVE: Security & safety by model-based systems engineering on the example of automotive industry”, Procedia CIRP, 100, pp. 187-192. doi: 10.1016/j.procir.2021.05.053.
- Kashevnik, A., Shilov, N., Teslya, N., Hasan, F., Kitenko, A., Dukareva, V., Abdurakhimov, M., Zingarevich, A. and Blokhin, D. (2023) „An Approach to Engineering Drawing Organization: Title Block Detection and Processing”, IEEE Access, pp. 1-1. doi: 10.1109/ACCESS.2023.3244603.
- Khan, M.T., Chen, L., Ng, Y.H., Feng, W., Tan, N.Y.J. and Moon, S.K. (2024) „Fine-tuning vision-language model for automated engineering drawing information extraction”, arXiv preprint. doi: 10.48550/arXiv.2411.03707.
- Khan, M.T., Chen, L., Yong, Z., Tan, J.M., Feng, W. and Moon, S.K. (2025) „Automated parsing of engineering drawings for structured information extraction using a fine-tuned document understanding transformer”, arXiv preprint. doi: 10.48550/arXiv.2505.01530.
- Khan, M.T., Chen, L., Yong, Z., Tan, J.M., Feng, W. and Moon, S.K. (2025) „From drawings to decisions: a hybrid vision-language framework for parsing 2D engineering drawings into structured manufacturing knowledge”, preprint.
- Lang, S., Plenk, V. and Schmid, U. (2021) „A Case-Based Reasoning Approach for a Decision Support System in Manufacturing”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12799 LNAI, pp. 265-271. doi: 10.1007/978-3-030-79463-7_22.
- Lee, C.G. and Jun, S. (2025) „Feature Extraction With Genetic Programming for Root Cause Identification in Manufacturing With Interpretable Machine Learning”, IEEE Transactions on Evolutionary Computation, 29(4), pp. 1029-1040. doi: 10.1109/TEVC.2024.3388725.
- Lee, Y., Shin, J. and Lee, W. (2025) „Manufacturing process analysis framework for process mining: case study of fully automated factory applications”, International Journal of Advanced Manufacturing Technology, 136(11), pp. 5641-5664. doi: 10.1007/s00170-025-15029-5.
- Li, X., Liu, X., Yue, C., Liang, S.Y. and Wang, L. (2022) „Systematic review on tool breakage monitoring techniques in machining operations”, International Journal of Machine Tools and Manufacture, 176, Article 103882. doi: 10.1016/j.ijmachtools.2022.103882.
- Li, Y.L., Tsang, Y.P., Wu, C.H. and Lee, C.K.M. (2024) „A multi-agent digital twin–enabled decision support system for sustainable and resilient supplier management”, Computers and Industrial Engineering, 187, Article 109838. doi: 10.1016/j.cie.2023.109838.
- Lidberg, S. and Ng, A.H.C. (2023) „Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimisation data”, International Journal of Manufacturing Research, 18(4), pp. 454-480. doi: 10.1504/IJMR.2023.135645.
- Lidberg, S., Frantzén, M., Aslam, T. and Ng, A.H.C. (2022) „A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data”, Advances in Transdisciplinary Engineering, 21, pp. 725-736. doi: 10.3233/ATDE220191.
- Lin, Y.-H., Ting, Y.-H., Huang, Y.-C., Cheng, K.-L. and Jong, W.-R. (2023) „Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings”, Machines, 11(8), Article 802. doi: 10.3390/machines11080802.
- Lombardi, A., Duan, L., Elnagar, A., Zaalouk, A., Ismail, K. and Vakaj, E. (2025) „Title block detection and information extraction for enhanced building drawings search”, in 2025 European Conference on Computing in Construction/CIB W78 Conference on IT in Construction, Porto, Portugal, 14-17 July 2025.
- Luo, X., Li, S., Wang, Y., Zhan, T., Shi, X. and Liu, B. (2023) „MaMiNet: Memory-attended multi-inference network for surface-defect detection”, Computers in Industry, 145, Article 103834. doi: 10.1016/j.compind.2022.103834.
- Martín, A., Penalva, M., Veiga, F., Ruiz, C. and Martínez, V. (2025) „Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling”, Lecture Notes in Mechanical Engineering, pp. 171-179. doi: 10.1007/978-3-031-86489-6_18.
- Maupou, C., Yang, Y., Fodop, G., Qie, Y., Migliorini, C., Mehdi-Souzani, C. and Anwer, N. (2024) „Automatic raster engineering drawing digitisation for legacy parts towards advanced manufacturing”, Procedia CIRP, 129, pp. 234-239. doi: 10.1016/j.procir.2024.10.041.
- Meyers, B., Vangheluwe, H., Lietaert, P., Vanderhulst, G., Van Noten, J., Schaffers, M., Maes, D. and Gadeyne, K. (2024) „Towards a knowledge graph framework for ad hoc analysis in manufacturing”, Journal of Intelligent Manufacturing, 35(8), pp. 3731-3752. doi: 10.1007/s10845-023-02319-6.
- Moravej, Z. and Ghahremani, M. (2023) „High Impedance Fault Detection and Classification Based on Pattern Recognition”, Modernization of Electric Power Systems: Energy Efficiency and Power Quality, pp. 487-512. doi: 10.1007/978-3-031-18996-8_16.
- Moreno-García, C.F., Elyan, E. and Jayne, C. (2019) „New trends on digitisation of complex engineering drawings”, Neural Computing and Applications, 31(6), pp. 1695-1712. doi: 10.1007/s00521-018-3583-1.
- Oliveri, L.M., Lo Iacono, N., Chiacchio, F., Facchini, F. and Mossa, G. (2024) „A Decision Support System tailored to the Maintenance Activities of Industry 5.0 Operators”, IFAC-PapersOnLine, 58(8), pp. 186-191. doi: 10.1016/j.ifacol.2024.08.118.
- Pehrsson, L. and Karlsson, I. (2022) „Optimisation with multi-objective rule extraction for manufacturing management”, International Journal of Manufacturing Research, 17(4), pp. 452-475. doi: 10.1504/ijmr.2022.127107.
- Peng, M., Qian, H., Marx, S. and Kang, C. (2026) „Optimizing 2D bridge engineering drawing digitization: A comparative study of text recognition tools and development of lightweight post-recognition structured information extraction methods”, Results in Engineering, 30, Article 110186. doi: 10.1016/j.rineng.2026.110186.
- Picard, C., Edwards, K.M., Doris, A.C., Man, B., Giannone, G., Alam, M.F. and Ahmed, F. (2025) „From concept to manufacturing: evaluating vision-language models for engineering design”, Artificial Intelligence Review, 58(9), Article 288. doi: 10.1007/s10462-025-11290-y.
- Raheem, A., De Marchi, M. and Dallasega, P. (2025) „Digital twin driven factory and production planning (FPP)”, Production and Manufacturing Research, 13(1), Article 2507954. doi: 10.1080/21693277.2025.2507954.
- Ramanath, N., Bhaskar, R.R., Girish, S.M., Lüder, A. and Hoffmann, D. (2025) „Bridging Manual & Automated Workflows: CAD Drawing Data Extraction in I5.0”, IFAC-PapersOnLine, 59(10), pp. 398-403. doi: 10.1016/j.ifacol.2025.09.069.
- Rammo, J.-P., Bouhadjer, Y., Rouvelle, C.R., Bernhard, O., Wegmann, M., Reuter, C. and Zaeh, M.F. (2026) „Manufacturing change management – an AI- and data-enhanced Delphi study and algorithm to support change process tailoring and the identification of suitable methods and digital tools”, Production Engineering, 20(2), Article 43. doi: 10.1007/s11740-026-01422-w.
- Rokoss, A., Syberg, M., Tomidei, L., Hülsing, C., Deuse, J. and Schmidt, M. (2024) „Case study on delivery time determination using a machine learning approach in small batch production companies”, Journal of Intelligent Manufacturing, 35(8), pp. 3937-3958. doi: 10.1007/s10845-023-02290-2.
- Sardinha, L., Baleiras, J.V., Sousa, S., Lima, T.M. and Gaspar, P.D. (2024) „Decision Support System (DSS) for Improving Production Ergonomics in the Construction Sector”, Processes, 12(11), Article 2503. doi: 10.3390/pr12112503.
- Scheibel, B., Mangler, J. and Rinderle-Ma, S. (2021) „Extraction of dimension requirements from engineering drawings for supporting quality control in production processes”, Computers in Industry, 129, Article 103442. doi: 10.1016/j.compind.2021.103442.
- Scheibel, O., Radnejad, A.B. and Osiyevskyy, O. (2025) „Redline innovations: Strategic responses to stake-holder opposition in innovation management”, Business Horizons. doi: 10.1016/j.bushor.2025.05.002.
- Schlagenhauf, T., Netzer, M. and Hillinger, J. (2023) „Text Detection on Technical Drawings for the Digitization of Brown-field Processes”, Procedia CIRP, 118, pp. 372-377. doi: 10.1016/j.procir.2023.06.064.
- Sherif, Z. and Salonitis, K. (2025) „A systematic review of decision tools for process selection and performance improvement in manufacturing”, International Journal of Advanced Manufacturing Technology, 141(3-4), pp. 1113-1141. doi: 10.1007/s00170-025-16806-y.
- Skėrė, S., Žvironienė, A., Juzėnas, K. and Petraitienė, S. (2022) „Decision Support Method for Dynamic Production Planning”, Machines, 10(11), Article 994. doi: 10.3390/machines10110994.
- Stavropoulos, P., Tzimanis, K., Souflas, T. and Bikas, H. (2022) „Knowledge-based manufacturability assessment for optimization of additive manufacturing processes based on automated feature recognition from CAD models”, International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-022-09948-w.
- Toro, J.V. and Tarkian, M. (2025) „Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach”, Machines, 13(3), Article 254. doi: 10.3390/machines13030254.
- Toro, J.V., Wiberg, A. and Tarkian, M. (2023) „Optical character recognition on engineering drawings to achieve automation in production quality control”, Frontiers in Manufacturing Technology, 3, Article 1154132. doi: 10.3389/fmtec.2023.1154132.
- Wagner, S., Gonnermann, C., Wegmann, M., Listl, F., Reinhart, G. and Weyrich, M. (2024) „From framework to industrial implementation: the digital twin in process planning”, Journal of Intelligent Manufacturing, 35(8), pp. 3793-3813. doi: 10.1007/s10845-023-02268-0.
- Wang, N., Zhang, S., Wang, Z., Xu, J. and Liu, D. (2023) „Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(10), pp. 1596-1607. doi: 10.1177/09544054221136310.
- Wegmann, M., Steinmassl, L., Wagner, S.B. and Zaeh, M.F. (2025) „Optimizing production planning and control: A potential analysis of reinforcement learning”, Production Engineering, 19(5), pp. 993-1004. doi: 10.1007/s11740-025-01354-x.
- Xie, L., Lu, Y., Furuhata, T., Yamakawa, S., Zhang, W., Regmi, A., Kara, L. and Shimada, K. (2022) „Graph neural network-enabled manufacturing method classification from engineering drawings”, Computers in Industry, 142, Article 103697. doi: 10.1016/j.compind.2022.103697.
- Yun, H., Kim, E., Kim, D.M., Park, H.W. and Jun, M.B.-G. (2023) „Machine Learning for Object Recognition in Manufacturing Applications”, International Journal of Precision Engineering and Manufacturing, 24(4), pp. 683-712. doi: 10.1007/s12541-022-00764-6.
- Zong, Y., Xu, Y., Cai, M., Tong, X., Ning, F. and Zhang, Y. (2025) „A deep learning based method for identifying product manufacturing information in engineering drawings”, Journal of Intelligent Manufacturing. doi: 10.1007/s10845-025-02731-0.
Language: English
Page range: 453 - 480
Submitted on: May 1, 2026
Accepted on: Jul 1, 2026
Published on: Aug 6, 2026
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Patryk Machnik, Adam Deptuła, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.