Production Capacity Planning Strategy to Deal with Seasonal Demand in the Food and Beverage Industry

Nia aulya

doi:10.37899/mjdm.v1i3.99

Authors

Nia aulya Manajemen dan Biusnis, Universitas Cokrominoto, Indonesia

DOI:

https://doi.org/10.37899/mjdm.v1i3.99

Keywords:

Production Capacity , Seasonal Demand , Flexible Manufacturing Systems , Forecasting Techniques

Abstract

This study aims to analyze the effectiveness of production capacity planning strategies in managing seasonal demand fluctuations in the food and beverage industry. Seasonal variability often presents challenges such as stockouts and operational inefficiencies, requiring companies to adopt flexible strategies to optimize production. The research employs a descriptive-quantitative design, surveying industry professionals and analyzing operational data from 10 food and beverage companies. Key strategies evaluated include Flexible Manufacturing Systems (FMS), advanced forecasting techniques, and supplier collaboration, focusing on their impact on production volume, lead time, and inventory turnover. Results show that companies implementing FMS and advanced forecasting achieved significant improvements in production volume (50% increase) and lead time reduction (up to 50%). Multivariate analysis of variance (MANOVA) confirmed that advanced forecasting and FMS had the most substantial effects on operational efficiency during peak demand periods. These findings underscore the importance of integrating flexible systems and predictive analytics in capacity planning to enhance responsiveness and mitigate seasonal demand challenges.

References

Bacchetti, A., & Saccani, N. (2012). The role of forecasting in production capacity planning. International Journal of Production Economics, 140(2), 546-553.

Choi, T. M., Gu, F., & Huang, Y. (2020). Predictive analytics for managing supply chain risk: A case study of the food industry. International Journal of Production Research, 58(23), 7084-7098.

Cousins, J., Foskett, D., Graham, D., & Hollier, A. (2019). Food and beverage management: for the hospitality, tourism and event industries. Goodfellow Publishers Ltd.

Ghelani, H. (2023). Six Sigma and Continuous Improvement Strategies: A Comparative Analysis in Global Manufacturing Industries. Valley International Journal Digital Library, 954-972. http://dx.doi.org/10.18535/ijsrm/v11i08.ec05

Gomes, L. D. C. (2022). Mitigation of supply chain vulnerability through collaborative planning, forecasting, and replenishment (CPFR). In Supply Chain Risk Mitigation: Strategies, Methods and Applications (pp. 95-119). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-031-09183-4_5

Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). Improving supply chain performance through management capabilities. Production planning & control, 28(6-8), 473-477. https://doi.org/10.1080/09537287.2017.1309680

Hong, P., & Leffakis, Z. M. (2017). Managing demand variability and operational effectiveness: case of lean improvement programmes and MRP planning integration. Production Planning & Control, 28(13), 1066-1080. https://doi.org/10.1080/09537287.2017.1329956

Ivanov, D. (2020). Viable Supply Chain Model: Integrating Sustainability and Resilience in the Context of COVID-19. Sustainability, 12(20), 8397. https://doi.org/10.1007/s10479-020-03640-6

Jack, E. P., & Raturi, A. (2002). Sources of volume flexibility and their impact on performance. Journal of operations management, 20(5), 519-548. https://doi.org/10.1016/S0272-6963(01)00079-1

Jawad, Z. N., & Balázs, V. (2024). Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), 4. https://doi.org/10.1186/s43088-023-00460-y

Jirashayanon, P. (2021, September). Fully Connected Autonomous Subassemblies to Enable FMS and Apply Lean’s Kanban for Workflow Management. In 2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics (RI2C) (pp. 327-335). IEEE. http://dx.doi.org/10.1109/ICECCS.2011.37

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.

Khan, M. M., Singh, K. P., & Khan, W. U. (2023). A critical study on the implementation of operation, control and maintenance techniques for flexible manufacturing systems in small scale industries. Materials Today: Proceedings. http://dx.doi.org/10.1016/j.matpr.2023.05.225

Kharfan, M., Chan, V. W. K., & Firdolas Efendigil, T. (2021). A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches. Annals of Operations Research, 303(1), 159-174. https://doi.org/10.1007/s10479-020-03666-w

King, P. L. (2019). Lean for the process industries: dealing with complexity. Productivity Press.

Mao, J., Hu, W., & Wen, X. (2024). Forecasting emerging product trends in smart supply chains. Computer and Decision Making: An International Journal, 1, 196-210. http://dx.doi.org/10.59543/comdem.v1i.10699

Obayi, R., Koh, S. C., Oglethorpe, D., & Ebrahimi, S. M. (2017). Improving retail supply flexibility using buyer-supplier relational capabilities. International Journal of Operations & Production Management, 37(3), 343-362. https://doi.org/10.1108/IJOPM-12-2015-0775

Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Productivity Press.

Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024). Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management. Open Access Research Journal of Multidisciplinary Studies, 8(1), 139-149. http://dx.doi.org/10.53022/oarjms.2024.8.1.0055

Qrunfleh, S., & Tarafdar, M. (2013). Lean and agile supply chain strategies and supply chain responsiveness: the role of strategic supplier partnership and postponement. Supply Chain Management: An International Journal, 18(6), 571-582. https://doi.org/10.1108/SCM-01-2013-0015

Raj, T., Attri, R., & Jain, V. (2012). Modelling the factors affecting flexibility in FMS. International Journal of Industrial and Systems Engineering, 11(4), 350-374. https://doi.org/10.1504/IJISE.2012.047542

Rajani, R. L., Heggde, G. S., Kumar, R., & Bangwal, D. (2023). Demand management approaches in services sector and influence on company performance. International Journal of Productivity and Performance Management, 72(10), 2808-2837. http://dx.doi.org/10.1108/IJPPM-02-2022-0080

Saha, P., Gudheniya, N., Mitra, R., Das, D., Narayana, S., & Tiwari, M. K. (2022). Demand forecasting of a multinational retail company using deep learning frameworks. IFAC-PapersOnLine, 55(10), 395-399. https://doi.org/10.1016/j.ifacol.2022.09.425

Seifert, D. (2003). Collaborative Planning. Forecasting, and Replenishment: How to Create a Supply Chain Advantage, AMACOM Div American Mgmt Assn.

Üstündağ, A., & Ungan, M. C. (2020). Supplier flexibility and performance: an empirical research. Business Process Management Journal, 26(7), 1851-1870. https://doi.org/10.1108/BPMJ-01-2019-0027

Yu, Z., Yan, H., & Edwin Cheng, T. C. (2001). Benefits of information sharing with supply chain partnerships. Industrial management & Data systems, 101(3), 114-121. https://doi.org/10.1108/02635570110386625

Yusof, Z. B. (2024). Analyzing the role of predictive analytics and machine learning techniques in optimizing inventory management and demand forecasting for e-commerce. International Journal of Applied Machine Learning, 4(11), 16-31. http://dx.doi.org/10.17613/wyzcw-vm321