Comparative Study of RCCP and System Dynamics in Productivity Capacity
DOI:
https://doi.org/10.22441/ijiem.v5i3.23806Keywords:
Manufacturing, Rough-cut capacity Planning, System dynamics, SimulationAbstract
One of the manufacturing companies in Indonesia which is engaged in foundry has a production line for various types of automotive components and has produced 2051 types of products. One of the products produced is the Trunion Bracket. In the molding process, cores are needed that can provide cavities in the product. The current production process has not been able to meet demand because the production cycle time does not match the predetermined time. This discrepancy will affect consumer satisfaction. Therefore, it is necessary to make a schedule for the period July to December 2022, to assist companies in facilitating the scheduling of the production process using Rough Cut Capacity Planning and System dynamics. The calculation results from Rough Cut Capacity Planning indicate that the 5th workstation experienced a shortage of production time from July to September 2022, so an additional number of working hours had to be made. The addition of the number of working hours before improvement was 367 hours and after improvement was 112 hours. Meanwhile, the results of system dynamics modeling for the period July to December 2022, require an additional working hour before improvement by 308 hours and 205 hours after improvement are carried out. The companies can implement strategies based on the results of system dynamics simulations to identify, forecast, and plan production capacity requirements for the next.Downloads
References
Ahmad, S., Mat Tahar, R., Muhammad-Sukki, F., Munir, A. B., & Abdul Rahim, R. (2016). Application of system dynamics approach in electricity sector modelling: A review. Renewable and Sustainable Energy Reviews, 56, 29–37. https://doi.org/10.1016/J.RSER.2015.11.034
Chandra Setiawan, I., Indarto, & Deendarlianto. (2021). Quantitative analysis of automobile sector in Indonesian automotive roadmap for achieving national oil and CO2 emission reduction targets by 2030. Energy Policy, 150, 112135. https://doi.org/10.1016/J.ENPOL.2021.112135
Cherkaoui, K., Pellerin, R., Baptiste, P., & Haït, A. (2015). A Time Driven RCCP Model with Two Levels of Planning and a Reactive Planning Approach for Tactical Project Planning. Procedia Computer Science, 64, 257–264. https://doi.org/10.1016/J.PROCS.2015.08.488
Cho, D. S. (2020). A dynamic approach to international competitiveness: The case of korea. The Competitive Advantages of Far Eastern Business, 17–36. https://doi.org/10.1201/9781315037462-2/DYNAMIC-APPROACH-INTERNATIONAL-COMPETITIVENESS-CASE-KOREA-DONG-SUNG-CHO
Dallasega, P., Rally, P., Rauch, E., & Matt, D. T. (2016). Customer-oriented Production System for Supplier Companies in CTO. Procedia CIRP, 57, 533–538. https://doi.org/10.1016/J.PROCIR.2016.11.092
Georgiadis, P. (2013). An integrated System Dynamics model for strategic capacity planning in closed-loop recycling networks: A dynamic analysis for the paper industry. Simulation Modelling Practice and Theory, 32, 116–137. https://doi.org/10.1016/J.SIMPAT.2012.11.009
Hidayat, T., Ulum, R. B., Widarman, A., Teknik, F., Jurusan, /, Industri, T., Tinggi, S., & Wastukancana, T. (2023). Rencanaan Kapasitas Produksi Pupuk Dengan Menggunakan Metode Rough Cut Capacity Planning (RCCP) Pada PT.Pupuk Kujang. Jurnal Ilmiah Multidisipilin (JUKIM), 2(4), 153–161. https://doi.org/10.56127/ju
Menezes, M. B. C., Ruiz-Hernández, D., & Verter, V. (2016). A rough-cut approach for evaluating location-routing decisions via approximation algorithms. Transportation Research Part B: Methodological, 87, 89–106. https://doi.org/10.1016/J.TRB.2016.03.003
Nasiatin, T., Adha, S., Julyanto, O., Puspita Rani, C., & Dimas, R. P. (2020). Analysis of Production Capacity Planning and Control in PT. Krakatau Wajatama With Rough Cut Capacity Planning (RCCP). Journal Industrial Engineering & Management Research (JIEMAR), 1(2), 2722–8878. https://doi.org/10.7777/jiemar.v1i2
Nurcahyo, R., & Wibowo, A. D. (2015). Manufacturing Capability, Manufacturing Strategy and Performance of Indonesia Automotive Component Manufacturer. Procedia CIRP, 26, 653–657. https://doi.org/10.1016/J.PROCIR.2014.07.046
Rebs, T., Brandenburg, M., & Seuring, S. (2019). System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach. Journal of Cleaner Production, 208, 1265–1280. https://doi.org/10.1016/J.JCLEPRO.2018.10.100
Seong, B. (2020). Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models. Economic Modelling, 91, 463–468. https://doi.org/10.1016/J.ECONMOD.2020.06.020
Silva, M. H. de P. e., Costa, L. B. M., Paredes, F. J. G., Barretti, J. W., & Silva, D. A. L. (2022). The effect of demand forecasting choices on the circularity of production systems: a framework and case study. Resources, Conservation & Recycling Advances, 15, 200088. https://doi.org/10.1016/J.RCRADV.2022.200088
Sudarto, S., Takahashi, K., & Morikawa, K. (2017). Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach. International Journal of Production Economics, 184, 179–192. https://doi.org/10.1016/J.IJPE.2016.12.013
Sugarindra, M., & Nurdiansyah, R. (2020). Production Capacity Optimization with Rough Cut Capacity Planning (RCCP). IOP Conference Series: Materials Science and Engineering, 722(1). https://doi.org/10.1088/1757-899X/722/1/012046
Suryani, E., Chou, S. Y., Hartono, R., & Chen, C. H. (2010). Demand scenario analysis and planned capacity expansion: A system dynamics framework. Simulation Modelling Practice and Theory, 18(6), 732–751. https://doi.org/10.1016/J.SIMPAT.2010.01.013
Wen, D., Liu, L., Wang, Y., & Zhang, Y. (2022). Forecasting crude oil market returns: Enhanced moving average technical indicators. Resources Policy, 76, 102570. https://doi.org/10.1016/J.RESOURPOL.2022.102570
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