Abstract
People in industries like manufacturing require, use, and produce knowledge on a daily basis. Tremendous quantity of data with difference in formats, structures and linkages need to be cautiously explored. However, the most valuable knowledge is not easy to identify or share because it is deep within the minds of experts. In manufacturing, it is very common to see dashboards on business performance, however, very few literatures available on technical knowledge management. Technical knowledge of an expert can be effectively managed and transferred by having an interface or dashboard that provides adequate information for the learners. Hence, this project aims to establish intelligent Decision Support System (iDSS) that can strategically manage, transfer, and share valuable knowledge of experts within the manufacturing organization based on machine learning and deep learning models. This study used English text data that is properly phrased to build a deep learning model in Natural Language Processing (NLP) for maintenance factory reports. As a result, interactive visualizations are presented to aid decision-makers in making knowledgeable decisions that includes the display of failure diagnostic and Named Entity Recognition (NER). These findings may provide troubleshooting insights as an assistance to new employees and deliver a precise management of decisions in looking back in history and preparing ahead. The investigation of this study will be further explored for complex numeric parameters from sensors data, integration of predictive maintenance in the dashboard, and utilizing a more sophisticated training model for better predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Joo, B.A., Shawl, S.: COVID-19 pandemic and the rising gig economy: an emerging perspective. Glob. Econ. Sci. 16–23 (2021)
Oz, E., Appolloni, A., Mavisu, M., Ozeren, E.: Knowledge management practices of manufacturing firms: a study from the Turkish Aegean free zone. Int. J. Intell. Enterp. 2, 169–195 (2014)
Tan, L.P., Wong, K.Y.: Linkage between knowledge management and manufacturing performance: a structural equation modeling approach. J. Knowl. Manag. 19, 814–835 (2015)
Ning, T.C., Ali, A.: Knowledge management strategies and organizational performance: a study on manufacturing, service and education sectors in the West Peninsular Malaysia. Eur. J. Manage. Mark. Stud. 6 (2015)
Alqhatani, A., et al.: 360° retail business analytics by adopting hybrid machine learning and a business intelligence approach. Sustainability 14(19), 11942 (2022). https://doi.org/10.3390/su141911942
Kongthanasuwan, T., Sriwiboon, N., Horbanluekit, B., Laesanklang, W., Krityakierne, T.: Market analysis with business intelligence system for marketing planning. Information 14(2), 116 (2023). https://doi.org/10.3390/info14020116
Wahyudi, I., Widyasari, Y.D.L.: Improving company performance by the correctness of management decision through implementation dashboard using power BI tools (case study at company Y). In: Proceedings – International Conference on Education and Technology, ICET 2022, pp. 32–37 (2022)
Jia, W., Wang, W., Zhang, Z.: From simple digital twin to complex digital twin part II: multi-scenario applications of digital twin shop floor. Adv. Eng. Inform. 56, 101915 (2023). https://doi.org/10.1016/j.aei.2023.101915
Gupta, V., Mitra, R., Koenig, F., Kumar, M., Tiwari, M.K.: Predictive maintenance of baggage handling conveyors using IoT. Comput. Ind. Eng. 177, 109033 (2023)
Moens, P., et al.: Event-driven dashboarding and feedback for improved event detection in predictive maintenance applications. Appl. Sci. 11(21), 10371 (2021). https://doi.org/10.3390/app112110371
Rosati, R., et al.: From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0. J. Intell. Manuf. 34, 107–121 (2023)
Mailliez, M., Hosseini, S., Battaiä, O., Roy, R.N.: Decision support system-like task to investigate operators’ performance in manufacturing environments. IFAC-PapersOnLine 53, 324–329 (2020)
Giberti, H., Abbattista, T., Carnevale, M., Giagu, L., Cristini, F.: A methodology for flexible implementation of collaborative robots in smart manufacturing systems. Robotics 11, 1–13 (2022)
Nacchia, M., Fruggiero, F., Lambiase, A., Bruton, K.: A systematic map** of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Appl. Sci. 11(6), 2546 (2021). https://doi.org/10.3390/app11062546
Rondinelli, A., Bongiovanni, L., Basile, V.: Zero-shot topic labeling for hazard classification. Information 13(10), 444 (2022). https://doi.org/10.3390/info13100444
Mansoor, H.H., Shaker, S.H.: Using classification techniques to SMS spam filter. Int. J. Innov. Technol. Explor. Eng. 8, 1734–1739 (2019)
Oh, Y.K., Kim, J.-M.: What improves customer satisfaction in mobile banking apps? An application of text mining analysis. Asia Mark. J. 23 (2022)
Auli, M., Galley, M., Quirk, C., Zweig, G.: Association for computational linguistics joint language and translation modeling with recurrent neural networks. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1044–1054 (2013)
Lu, S., Zhu, Y., Zhang, W., Wang, J., Yu, Y.: Neural Text Generation: Past, Present and Beyond. ar**v (2018)
Wadud, M.A.H., et al.: How can we manage offensive text in social media – a text classification approach using LSTM-BOOST. Int. J. Inf. Manage. Data Insights 2(2), 100095 (2022). https://doi.org/10.1016/j.jjimei.2022.100095
Vasantha Kumar, V., Sendhilkumar, S.: Develo** a conceptual framework for short text categorization using hybrid CNN-LSTM based Caledonian crow optimization. Expert Syst. Appl. 212, 118517 (2023). https://doi.org/10.1016/j.eswa.2022.118517
Liang, M., Niu, T.: Research on text classification techniques based on improved TF-IDF algorithm and LSTM inputs. Procedia Comput. Sci. 208, 460–470 (2022)
Pham, M.Q.N.: A feature-based model for nested named-entity recognition at VLSP-2018 NER evaluation campaign. J. Comput. Sci. Cybern. 34, 311–321 (2019)
Yin, D., Cheng, S., Pan, B., Qiao, Y., Zhao, W., Wang, D.: Chinese named entity recognition based on knowledge based question answering system. Appl. Sci. 12(11), 5373 (2022). https://doi.org/10.3390/app12115373
Li, F., Song, Y., Shan, Y.: Joint extraction of multiple relations and entities from building code clauses. Appl. Sci. 10, 1–18 (2020)
Dixit, S., Mulwad, V., Saxena, A.: Extracting semantics from maintenance records. In: International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Applied Semantics Extraction and Analytics (ASEA) (2021)
Chopra, D., Joshi, N., Mathur, I.: Named entity recognition in Hindi using hidden Markov model. In: 2nd International Conference on Computational Intelligence and Communication Technology, CICT, pp. 581–586 (2016)
Copyright 2020 The MathWorks, Inc. factoryReports data set. http://www.mathworks.com. Accessed 21 July 2023
Acknowledgements
This work was supported by the Ministry of Higher Education (MOHE), Malaysia under Fundamental Research Grant Scheme (FRGS) (FRGS/1/2020/TK0/UTM/02/36).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yusof, N.H.M., Subha, N.A.M., Hamzah, N., Hassan, F., Basri, M.A.M. (2024). Intelligent Decision Support System (iDSS) for Manufacturing Data Corpus. In: Hassan, F., Sunar, N., Mohd Basri, M.A., Mahmud, M.S.A., Ishak, M.H.I., Mohamed Ali, M.S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2023. Communications in Computer and Information Science, vol 1912. Springer, Singapore. https://doi.org/10.1007/978-981-99-7243-2_21
Download citation
DOI: https://doi.org/10.1007/978-981-99-7243-2_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7242-5
Online ISBN: 978-981-99-7243-2
eBook Packages: Computer ScienceComputer Science (R0)