Document Type : Research Paper
1 Department of Industrial Engineering, Sampoerna University, Jakarta 12780, Indonesia.
2 Department of Industrial Engineering, University Bunda Mulia, Jakarta 14430, Indonesia.
This paper discusses the inefficient and inaccurate raw material supply at a food company which results in a backlog. This means the overstock occurs so that the improvement of inventory control needs to be done. ABC classification is firstly utilized as input for Material Requirements Planning (MRP). This paper focuses on four products which are classified into A class. Then, this paper discusses the Triple Exponential Smoothing (TES) as the forecasting method. Aggregate planning is also conducted for better production planning. The results of aggregate planning provide solutions to increase the workforce to balance production capacity by the number of demands. Squared Coefficient of Variation (SCV) calculations indicates the demand follows a static pattern. Therefore, the appropriate lot sizing method is the Economic Order Quantity (EOQ) to carry out the production needs. Finally, this paper uses capacity planning using Rough-Cut Capacity Planning (RCCP) and Capacity Requirement Planning (CRP) methods. As a result, the capacity meets the Master Production Schedule (MPS) as well as MRP and they are feasible to be implemented.
- Tannady, H., Gunawan, E., Nurprihatin, F., & Wilujeng, F. R. (2019). Process improvement to reduce waste in the biggest instant noodle manufacturing company in South East Asia. Journal of applied engineering science, 17(2), 203-212. https://doi.org/10.5937/jaes17-18951
- Tannady, H., Nurprihatin, F., & Hartono, H. (2018). Service quality analysis of two of the largest retail chains with minimart concept in Indonesia. Business: theory and practice, 19, 177-185. https://doi.org/10.3846/BTP.2018.18
- Kauppila, O., Valikangas, K., & Majava, J. (2020). Improving supply chain transparency between a manufacturer and suppliers: a triadic case study. Management and production engineering review, 11(3), 84–91. https://doi.org/10.24425/mper.2020.134935
- Tannady, H., & Maimury, Y. (2018). Increasing the efficiency and productivity in the production of low voltage switchboard using resource constrained project scheduling. Journal of industrial engineering and management (JIEM), 11(1), 1-33. https://doi.org/https://doi.org/10.3926/jiem.2228
- Jacobs, F. R., & Chase, R. B. (2018). Operations and supply chain management (15th ed.). McGraw-Hill Education. https://www.amazon.com/Operations-Supply-Management-Robert-Jacobs/dp/1259666107
- Garcia-Herreros, P., Agarwal, A., Wassick, J. M., & Grossmann, I. E. (2016). Optimizing inventory policies in process networks under uncertainty. Computers & chemical engineering, 92, 256-272. https://doi.org/10.1016/j.compchemeng.2016.05.014
- Ejlali, B., Bagheri, S. F., & Ghaziyani, K. (2019). Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by PSO algorithm. International journal of research in industrial engineering, 8(4), 294-311. https://doi.org/10.22105/riej.2020.219060.1120
- Ke, J., Zheng, H., Yang, H., & Chen, X. M. (2017). Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transportation research part c: emerging technologies, 85, 591-608. https://doi.org/10.1016/j.trc.2017.10.016
- Nielsen, P., & Michna, Z. (2018). The impact of stochastic lead times on the bullwhip effect–an empirical insight. Management and production engineering review, 9(1), 65-70.
- Brunaud, B., Laínez‐Aguirre, J. M., Pinto, J. M., & Grossmann, I. E. (2019). Inventory policies and safety stock optimization for supply chain planning. AIChE journal, 65(1), 99-112. https://doi.org/10.1002/aic.16421
- Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of cleaner production, 133, 1074-1085. https://doi.org/10.1016/j.jclepro.2016.05.133
- Mahmoud, A. A., Aly, M. F., Mohib, A. M., & Afefy, I. H. (2020). A two-stage stochastic programming approach for production planning system with seasonal demand. Management and production engineering review, 11, 31-42. https://doi.org/10.24425/mper.2020.132941
- Ke, G., Chen, R. S., Chen, Y. C., Wang, S., & Zhang, X. (2020). Using ant colony optimisation for improving the execution of material requirements planning for smart manufacturing. Enterprise information systems, 1-23. https://doi.org/10.1080/17517575.2019.1700552
- Barzegar, M., Ehtesham Rasi, R., & Niknamfar, A. H. (2018). Analyzing the drivers of green manufacturing using an analytic network process method: a case study. International journal of research in industrial engineering, 7(1), 61-83. https://doi.org/10.22105/riej.2018.108563.1031
- Zheng, S., Fu, Y., Lai, K. K., & Liang, L. (2017). An improvement to multiple criteria ABC inventory classification using Shannon entropy. Journal of systems science and complexity, 30(4), 857-865. https://doi.org/10.1007/s11424-017-5061-8
- Li, J., Moghaddam, M., & Nof, S. Y. (2016). Dynamic storage assignment with product affinity and ABC classification—a case study. The international journal of advanced manufacturing technology, 84(9), 2179-2194. https://doi.org/10.1007/s00170-015-7806-7
- Nallusamy, S., Balaji, R., & Sundar, S. (2017). Proposed model for inventory review policy through ABC analysis in an automotive manufacturing industry. International journal of engineering research in Africa, 29, 165-174. https://doi.org/10.4028/www.scientific.net/JERA.29.165
- Srisuk, K., & Tippayawong, K. Y. (2020). Improvement of raw material picking process in sewing machine factory using lean techniques. Management and production engineering review, 11(1), 79–85. https://doi.org/10.24425/mper.2020.132946
- Boroojeni, K. G., Amini, M. H., Bahrami, S., Iyengar, S. S., Sarwat, A. I., & Karabasoglu, O. (2017). A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electric power systems research, 142, 58-73. https://doi.org/10.1016/j.epsr.2016.08.031
- Englberger, J., Herrmann, F., & Manitz, M. (2016). Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment. International journal of production research, 54(20), 6192-6215. https://doi.org/10.1080/00207543.2016.1162917
- Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: demand forecasting and price optimization. Manufacturing & service operations management, 18(1), 69-88. https://doi.org/10.1287/msom.2015.0561
- Arias, M. B., & Bae, S. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied energy, 183, 327-339. https://doi.org/10.1016/j.apenergy.2016.08.080
- Türkay, M., Saraçoğlu, Ö., & Arslan, M. C. (2016). Sustainability in supply chain management: aggregate planning from sustainability perspective. PloS one, 11(1), e0147502. https://doi.org/10.1371/journal.pone.0147502
- Ahmed, S. M., Biswas, T. K., & Nundy, C. K. (2019). An optimization model for aggregate production planning and control: a Genetic algorithm approach. International journal of research in industrial engineering, 8(3), 203-224. https://doi.org/10.22105/riej.2019.192936.1090
- Garside, A. K. (2019). An optimization model for cold chain food distribution. International journal of research in industrial engineering, 8(3), 243-253. https://doi.org/10.22105/riej.2019.202178.1097
- Ghahremani Nahr, J., & Zahedi, M. (2021). Modeling of the supply chain of cooperative game between two tiers of retailer and manufacturer under conditions of uncertainty. International journal of research in industrial engineering, 10(2), 95–116. (In Persian). https://doi.org/10.22105/RIEJ.2021.276520.1190
- Nour, A., Galal, N. M., & El-Kilany, K. S. (2017, April). Energy-based aggregate production planning for porcelain tableware manufacturer in Egypt. Proceedings of the international conference on industrial engineering and operations management (pp. 2351-2358), Rabat, Morocco.
- Díaz-Madroñero, M., Mula, J., Jiménez, M., & Peidro, D. (2017). A rolling horizon approach for material requirement planning under fuzzy lead times. International journal of production research, 55(8), 2197-2211. https://doi.org/10.1080/00207543.2016.1223382
- Vogel, T., Almada-Lobo, B., & Almeder, C. (2017). Integrated versus hierarchical approach to aggregate production planning and master production scheduling. OR spectrum, 39(1), 193-229. https://doi.org/10.1007/s00291-016-0450-2
- Herrera, C., Belmokhtar-Berraf, S., Thomas, A., & Parada, V. (2016). A reactive decision-making approach to reduce instability in a master production schedule. International journal of production research, 54(8), 2394-2404. https://doi.org/10.1080/00207543.2015.1078516
- Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016). Inventory and production management in supply chains. CRC Press.
- Chopra, S., & Meindl, P. (2016). Supply chain management: strategy, planning, and operation (6th Ed.). Pearson Education.
- Ravinder, H., & Misra, R. B. (2014). ABC analysis for inventory management: bridging the gap between research and classroom. American journal of business education, 7(3), 257–264. https://doi.org/10.19030/ajbe.v7i3.8635
- Tirkeş, G., Güray, C., & Çelebi, N. (2017). Demand forecasting: a comparison between the holt-winters, trend analysis and decomposition models. Technical gazette, 24(2), 503–509. https://doi.org/10.17559/TV-20160615204011
- Baydoun, G., Haït, A., Pellerin, R., Clément, B., & Bouvignies, G. (2016). A rough-cut capacity planning model with overlapping. OR spectrum, 38, 335–364. https://doi.org/10.1007/s00291-016-0436-0
- Nurprihatin, F., Jayadi, E. L., & Tannady, H. (2020). Comparing heuristic methods’ performance for pure flow shop scheduling under certainand uncertain demand. Management and production engineering review, 11(2), 50-61. https://doi.org/10.24425/mper.2020.133728
- 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