Production Line Forecasting with Data Analytics
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Abstract
In an industrial factory, the production line has different machine arrangements based on the objectives of producing specific parts. This causes the problem where employees have to adjust the machines to suit the parts being produced. Addressing this issue, the research focuses on program development to help increase employee efficiency and reduce working time of planner staff. The program includes Python Flask framework as the backend, utilizing data science and artificial intelligence, and web application as the user interface for input and display. The experimental results indicated that the program can process within 5 seconds per command, leading to increased efficiency and speed in the work of the production planning staff.
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References
N. Liyanaarachchi, J. Weng and S. Akasaka. “A review of literature on engineer-to-order production systems,” Asian Journal of Management Science and Applications, Vol. 8 (1), pp. 53-82, 2023. doi: 10.1504/AJMSA.2023.134445
S. Jia, C. Wang, X. Qing, and Z. Ma., "Organization design of production line for an enterprise". International Conference on Industrial Engineering and Applications. 23-26 April. Chengdu, China : pp. 252-256, 2021.
M. Mallampati, K. Srivinivas and T. Krishna. M. “Design process to reduce production cycle time in product development,” IAES International Journal of Artificial Intelligence, Vol. 7 (3), pp. 125-129, 2018. doi: 10.11591/ijai.v7.i3.pp125-129
M. R. Prajapati† and V. A. Deshpande. “Cycle time reduction using lean principles and techniques: a review,” International Journal of Advance Industrial Engineering, Vol. 3 (4), pp. 208-213, 2015.
N. B. Kacar, L. Mönch and R. Uzsoy., "Problem reduction approaches for production planning using clearing functions". International Conference on Automation Science and Engineering. 20-24 August. Munich, Germany : pp. 931-938, 2018.
N. C. Nwasuka and U. Nwaiwu. “Computer-based production planning, scheduling and control: a review,” Journal of Engineering Research, Vol. 12 (1), pp. 275-280, 2024. doi: 10.1016/j.jer.2023.09.027
R. Bandinelli and V. Fani., “Combined use of AI techniques and simulation to support production scheduling: evidence from empirical research”. ECMS International Conference on Modelling and Simulation. 4-7 June. Cracow, Poland : pp 319-325, 2024.
A. Köcher and et al., "A research agenda for AI planning in the field of flexible production systems". International Conference on Industrial Cyber-Physical Systems. 24-26 May. Coventry, United Kingdom : pp. 1-8, 2022.
Pallets. (22 December 2024). Flask. [Online] Available : https://flask.palletsprojects.com/
Pallets. (22 December 2024). Jinja. [Online] Available : https://jinja.palletsprojects.com/
Pallets. (22 December 2024). Werkzeug. [Online] Available : https://werkzeug.palletsprojects.com/
Mozilla Corporation. (22 December 2024). HTML: HyperText Markup Language. [Online] Available : https://developer.mozilla.org/en-US/docs/Web/HTML
