Demand Forecasting and Capacity Planning for Eyewear Cleaner Products at PT RAS
DOI:
https://doi.org/10.61194/ijjm.v6i3.1469Keywords:
Demand Forecasting, MPS, RCCP, Workforce ManagementAbstract
This study examines the production capacity challenges faced by PT RAS following the launch of RAS Gleam in November 2023. The significant increase in demand has put pressure on the production, filling, and packing workstations, leading to potential bottlenecks in fulfilling orders. To address this issue, time-series forecasting was applied to project demand for the next year for three products: RAS Self-Cleaning, RAS Instant Antifog, and RAS Gleam. These forecasts guided the development of a Master Production Schedule (MPS) to align production with projected demand and informed Rough-Cut Capacity Planning (RCCP) to identify capacity constraints. The analysis revealed gaps between available and required work hours, particularly during peak periods. To bridge these gaps, the study proposed workforce management solutions, including a controlled overtime system and the strategic use of freelance workers for filling and packing workstations. These measures enabled PT RAS to meet demand while complying with Indonesian labour regulations. The findings demonstrate how accurate forecasting, workforce optimization, and flexible labour management enhance production efficiency and operational flexibility at PT RAS. By forecasting demand, PT RAS can prepare for future conditions, ensuring it has the capacity to meet demand.
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Alalmai, A., Arun, A., Alalmai, A. A., & Gunaseelan, D. (2020). Operational Need and Importance of Capacity Management into Hotel Industry-A Review. International Journal of Advanced Science and Technology, 29(7), 122–130. https://www.researchgate.net/publication/350616399
Allred, S., Stedman, R., Heady, L., & Strong, K. (2021). Incorporating biodiversity in municipal land-use planning: An assessment of technical assistance, policy capacity, and conservation outcomes in New York’s Hudson Valley. Land Use Policy, 104. https://doi.org/10.1016/j.landusepol.2021.105344
Alturki, R. (2021). Research Onion for Smart IoT-Enabled Mobile Applications. Scientific Programming, 2021. https://doi.org/10.1155/2021/4270998
Atherton, A. (2013). Promotion private sector development in China: the challenge of building capacity at the local level. Environtment and Planning C : Government and Policy, 31, 5–23.
Boussalis, C., Nelson, H. T., & Swaminathan, S. (2012). Towards comprehensive malaria planning: The effect of government capacity, health policy, and land use variables on malaria incidence in India. Social Science and Medicine, 75(7), 1213–1221. https://doi.org/10.1016/j.socscimed.2012.05.023
Briseño-Oliveros, H., Guzmán-García, L. A., Cano-Olivos, P., & Sánchez-Partida, D. (2019). Forecasting demand improvement for replenishment in a retail painting company. Acta Logistica, 6(4), 155–164. https://doi.org/10.22306/al.v6i4.143
Dang, T. K., Hamakers, I. J., & Arts, B. (2015). A Framework for assessing governance capacity: An illustration from Vietnam’s forestory reform. Environment and Planning C: Government, 0(0), 1–21.
Debellut, F., Hendrix, N., Ortiz, J. R., Lambach, P., Neuzil, K. M., Bhat, N., & Pecenka, C. (2018). Forecasting demand for maternal influenza immunization in low- and lower-middle-income countries. PLoS ONE, 13(6). https://doi.org/10.1371/journal.pone.0199470
F. Robert Jacobs, & Richard B. Chase. (2021). Operations and Supply Chain Management. McGrawHill.
F. Robert Jacobs, William Lee Berry, D. Clay Whybark, & Thomas E. Vollmann. (2024). Manufacturing Planning and Control for Supply Chain Management: The CPIM Reference, Third Edition (3rd ed.). McGrawHill.
Farida, I., & Setiawan, D. (2022). Business Strategies and Competitive Advantage: The Role of Performance and Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(3). https://doi.org/10.3390/joitmc8030163
Fauzan, F. M. (n.d.). DEMAND FORECASTING AND INVENTORY MODEL ANALYSIS CASE STUDY: PT ABADINUSA USAHASEMESTA FINAL PROJECT Undergraduate Program School of Business and Management.
Fiori, A. M., & Foroni, I. (2019). Reservation forecasting models for hospitality SMEs with a view to enhance their economic sustainability. Sustainability (Switzerland), 11(5). https://doi.org/10.3390/su11051274
Healey, P. (1998). Building institutional capacity trough collaborative aproaches to urban planning. Environment and Planning A, 30, 1531–1546.
Husen Santosa, S., Prayudha Hidayat, A., Siskandar, R., & Rizkiriani, A. (2023). Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products. E3S Web of Conferences, 454. https://doi.org/10.1051/e3sconf/202345403003
Lapping, K., Frongillo, E. A., Nguyen, P. H., Coates, J., Webb, P., & Menon, P. (2014). Organizational factors, planning capacity, and integration challenges constrain provincial planning processes for nutrition in decentralizing Vietnam. Food and Nutrition Bulletin, 35(3), 382–391. https://doi.org/10.1177/156482651403500310
Li, J., Cui, T., Yang, K., Yuan, R., He, L., & Li, M. (2021). Demand forecasting of e-commerce enterprises based on horizontal federated learning from the perspective of sustainable development. Sustainability (Switzerland), 13(23). https://doi.org/10.3390/su132313050
Liu, H., Kan, X. D., Shladover, S. E., Lu, X.-Y., & Ferlis, R. E. (2018). Impact of cooperative adaptive cruise control on multilane freeway merge capacity. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 22(3), 263–275. https://doi.org/10.1080/15472450.2018.1438275
Loh, C. G. (2015). Conceptualizing and Operationalizing Planning Capacity. State and Local Government Review, 47(2), 134–145.
Martín, A. G., Díaz-Madroñero, M., & Mula, J. (2020). Master production schedule using robust optimization approaches in an automobile second-tier supplier. Central European Journal of Operations Research, 28(1), 143–166. https://doi.org/10.1007/s10100-019-00607-2
Melnikovas, A. (2018). Towards an explicit research methodology: Adapting research onion model for futures studies. Journal of Futures Studies, 23(2), 29–44. https://doi.org/10.6531/JFS.201812_23(2).0003
Nirmala, W., Harjadi, D., & Awaluddin, R. (n.d.). Sales Forecasting by Using Exponential Smoothing Method and Trend Method to Optimize Product Sales in PT. Zamrud Bumi Indonesia During the Covid-19 Pandemic. https://doi.org/10.52088/ijesty.v1i1.169
Setiabudi, Y., Methalina Afma, V., & Irwan, H. (n.d.). PERENCANAAN KAPASITAS PRODUKSI ATV12 DENGAN MENGGUNAKAN METODE ROUGH CUT CAPACITY PLANNING (RCCP) UNTUK MENGETAHUI TITIK OPTIMASI PRODUKSI (Studi kasus di PT Schneider Electric Manufacturing Batam).
Van Belle, J., Guns, T., & Verbeke, W. (2021). Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains. European Journal of Operational Research, 288(2), 466–479. https://doi.org/10.1016/j.ejor.2020.05.059
Wang, Y., Li, J., & Zhang, M. (2020). Evaluation of tourism environmental carrying capacity in Diaoshuihu national forest park. International Journal of Sustainable Development and Planning, 15(5), 761–766. https://doi.org/10.18280/ijsdp.150518
Więcek, P., & Kubek, D. (2024). The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains. Energies, 17(16). https://doi.org/10.3390/en17164163
Zopounidis, C., & Lemonakis, C. (2024). The company of the future: Integrating sustainability, growth, and profitability in contemporary business models. Development and Sustainability in Economics and Finance, 1, 100003. https://doi.org/10.1016/j.dsef.2024.100003
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