Penjadwalan Produksi Optimal Air Mineral Kemasan Gelas Menggunakan Pendekatan jadwal Induk Produksi

  • Sesar Husen Santosa Institut Pertanian Bogor
  • Purana Indrawan Industrial Management Study Program, Vocational School, IPB University
  • Fany Apriliani Industrial Management Study Program, Vocational School, IPB University
  • Ridwan Siskandar Computer Engineering Technology Study Program, Vocational School, IPB University
  • Aulia Nabil Bayyinah Industrial Management Study Program, Vocational School, IPB University
  • Lutfi Septiyaningsih Industrial Management Study Program, Vocational School, IPB University
Keywords: Forecasting; Regression; Master Production Schedule; Stock


The Stock buildup problem has led to an increase in product rejects at PT Marina. This condition is caused because the company cannot identify fluctuating demand conditions and only focuses on Customer Orders. Based on this problem, an analysis was carried out regarding demand using the Multiplicative decomposition forecasting technique. The company has three seasons in the demand for glass bottled water products. Based on the demand forecasting analysis results, the demand regression model is demand (y) = 153052 + 139.88 period (x). Based on the regression results, the demand forecast results are 159,819 boxes. The results of production schedules are based on the results of demand forecasting, so the Stock condition in the company is obtained, namely 3430 - 4760 boxes/week, and this condition is the optimal condition because it is below the maximum product inventory capacity in the company.


Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151–163.
Dardanella, D., Hidayat, A. P., Santosa, S. H., & Siskandar, R. (2022). Peramalan Harga Jual Cabai Merah Di Pasar Rakyat Kemang Perusahaan Umum Daerah Pasar Tohaga Kabupaten Bogor. Indonesian Journal of Science Learning, 3(1), 16–23.
Fachid, S., & Triayudi, A. (2022). Perbandingan Algoritma Regresi Linier dan Regresi Random Forest Dalam Memprediksi Kasus Positif Covid-19. Jurnal Media Informatika Budidarma, 6(1), 68.
He, F., Zhou, J., Feng, Z. kai, Liu, G., & Yang, Y. (2019). A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Applied Energy, 237(January), 103–116.
Hu, Y. C. (2020). Constructing grey prediction models using grey relational analysis and neural networks for magnesium material demand forecasting. Applied Soft Computing Journal, 93, 106398.
Iksan, N., Putra, Y. P., & Udayanti, E. D. (2018). Regresi Linier Untuk Prediksi Permintaan Sparepart Sepeda Motor. ITEJ (Information Technology Engineering Journals, 03(02), 2548–2157.
Jeon, S. M., & Kim, G. (2016). A survey of simulation modeling techniques in production planning and control (PPC). Production Planning and Control, 27(5), 360–377.
Jiang, P., Liu, Z., Niu, X., & Zhang, L. (2021). A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy, 217, 119361.
Kourentzes, N., Trapero, J. R., & Barrow, D. K. (2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225(November 2019), 107597.
Kück, M., & Freitag, M. (2021). Forecasting of customer demands for production planning by local k-nearest neighbor models. International Journal of Production Economics, 231, 107837.
Mansouri, S. A., Ph, D., Golmohammadi, D., Associate, P. D., Miller, J., & Assistant, P. D. (2019). International Journal of Production Economics The moderating role of master production scheduling method on throughput in job shop systems. Intern. Journal of Production Economics, 216(April), 67–80.
Mbuli, N., Mathonsi, M., Seitshiro, M., & Pretorius, J. H. C. (2020). Decomposition forecasting methods: A review of applications in power systems. Energy Reports, 6(2020), 298–306.
Oey, E., Wijaya, W. A., & Hansopaheluwakan, S. (2020). Forecasting and aggregate planning application - A case study of a small enterprise in Indonesia. International Journal of Process Management and Benchmarking, 10(1), 1–21.
Qurnia Sari, A., Sukestiyarno, Y., & Agoestanto, A. (2017). Batasan Prasyarat Uji Normalitas dan Uji Homogenitas pada Model Regresi Linear. Unnes Journal of Mathematics, 6(2), 168–177.
Santosa, S. H., Hidayat, A. P., & Siskandar, R. (2022). Raw material planning for tapioca flour production based on fuzzy logic approach: a case study. Jurnal Sistem Dan Manajemen Industri, 6(1), 67–76.
Santosa, S. H., Hidayat, A. P., Siskandar, R., & Rizkiriani, A. (2021). Effect of Selling Price on Demand for Chicken Eggs Using a Regression Approach. Jurnal Sains Indonesia, 2(3), 106–112.
Shih, S. Y., Sun, F. K., & Lee, H. yi. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8–9), 1421–1441.
Wang, C., & Yang, B. (2020). Multi-Objective Master Production Schedule. 19, 678–688.