Smart Production Planning Model for T-Shirt Products at Raensa Convection

Sesar Husen Santosa(1*), Agung Prayudha Hidayat(2), Ridwan Siskandar(3), Khoirul Aziz Husyairi(4),

(1) IPB University
(2) IPB University
(3) IPB University
(4) IPB University
(*) Corresponding Author


The increasing stock condition of cotton combed 30s t-shirts causes Convection to require optimal production planning, influenced by demand, safety stock, and selling price. The analysis of forecasting demand for t-shirts obtained a need for 217 t-shirts using the Multiplicative Decomposition forecasting method with MAD = 22.47 and MAPE = 0.17. Based on demand data for 1 year, the Safety Stock that must be maintained is 126 shirts/month or 3 shirts/week. The optimal production defuzzification results are 369 t-shirts with 81 fuzzy rules used. The Master Production Planning (MPS) directs the production schedule to be carried out in January, scheduled for the 4th week of December the previous year, as many as 123 shirts and ended on the third Sunday in January as many as 92 shirts to maintain stock conditions and meet the demand of consumer.


Forecasting; Safety Stock; Defuzzification; Master Production Schedule

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Bahroun, Z., & Belgacem, N. (2019). Determination of dynamic safety stocks for cyclic production schedules. 2, 62–93.

Bendul, J. C., & Blunck, H. (2019). Computers in Industry The design space of production planning and control for industry 4 . 0. Computers in Industry, 105, 260–272.

Cheng, M. L., Chu, C. W., & Hsu, H. L. (2021). A study of univariate forecasting methods for crude oil price. Maritime Business Review.

Dev, N. K., Shankar, R., & Swami, S. (2020). Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system. International Journal of Production Economics, 223.

Dias, L. S., & Ierapetritou, M. G. (2019). Data ‑ driven feasibility analysis for the integration of planning and scheduling problems. In Optimization and Engineering (Issue 0123456789). Springer US.

Díaz-madroñero, A. G. M. M., & Mula, J. (2019). Master production schedule using robust optimization approaches in an automobile second-tier supplier. Central European Journal of Operations Research.

Englberger, J., Herrmann, F., & Manitz, M. (2016). Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment. 7543(April).

Han, J., Liu, Y., Luo, L., & Mao, M. (2020). Jou. Knowledge-Based Systems, 106056.

Hu, G., Bakhtavar, E., Hewage, K., Mohseni, M., & Sadiq, R. (2019). Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach. Journal of Environmental Management, 250(September), 109514.

Huang, C. H., & Hsieh, S. H. (2020). Predicting BIM labor cost with random forest and simple linear regression. Automation in Construction, 118(December 2019), 103280.

Jayetileke, H. R., & Mel, W. R. De. (2022). applied sciences Real-Time Metaheuristic Algorithm for Dynamic Fuzzification , De-Fuzzification and Fuzzy Reasoning Processes.

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.

Khatua, D., & Kar, K. M. S. (2019). A Fuzzy Optimal Control Inventory Model of Product – Process Innovation and Fuzzy Learning Effect in Finite Time Horizon. International Journal of Fuzzy Systems, 21(5), 1560–1570.

Kim, S., Kim, H., & Lu, J. (2019). A practical approach to measuring the impacts of stockouts on demand. 4(February), 891–901.

Kück, M., & Freitag, M. (2020). Jou rna. International Journal of Production Economics, 107837.

Lee, C. (2019). A Mathematical Safety Stock Model for DDMRP Inventory Replenishment. 2019.

Lohmer, J., & Lasch, R. (2020). Production planning and scheduling in multi- factory production networks : a systematic literature review systematic literature review. International Journal of Production Research, 0(0), 1–27.

Mönch, L., Uzsoy, R., & Fowler, J. W. (2018). A survey of semiconductor supply chain models part III : master planning , production planning , and demand fulfilment. International Journal of Production Research, 7543, 1–20.

Naimoli, A., & Storti, G. (2019). Heterogeneous component multiplicative error models for forecasting trading volumes. International Journal of Forecasting, 35(4), 1332–1355.

Pourjavad, E., & Shahin, A. (2018). The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance. International Journal of Fuzzy Systems, 20(3), 901–912.

Santosa, S. H., & Hidayat, A. P. (2019). Model Penentuan Jumlah Pesanan Pada Aktifitas Supply Chain Telur Ayam Menggunakan Fuzzy Logic. Jurnal Ilmiah Teknik Industri, 18(2), 224–235.

Santosa, S. H., Hidayat, A. P., & Siskandar, R. (2021). Safea application design on determining the optimal order quantity of chicken eggs based on fuzzy logic. IAES International Journal of Artificial Intelligence, 10(4), 858–871.

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., Sulaeman, S., Hidayat, A. P., & Ardani, I. (2020). Fuzzy Logic Approach to Determine the Optimum Nugget Production Capacity. Jurnal Ilmiah Teknik Industri, 19(1), 70–83.

Tang, X., He, Y., & Salling, M. (2021). International Journal of Production Economics Optimal pricing and production strategies for two manufacturers with industrial symbiosis. International Journal of Production Economics, 235(June 2020), 108084.

Thürer, M., Fernandes, N. O., & Stevenson, M. (2020). Production planning and control in multi-stage assembly systems : an assessment of Kanban , MRP , OPT ( DBR ) and DDMRP by simulation. International Journal of Production Research, 0(0), 1–15.

Vargas, V., & Metters, R. (2011). Int . J . Production Economics A master production scheduling procedure for stochastic demand and rolling planning horizons. Intern. Journal of Production Economics, 132(2), 296–302.

Vieira, M., Moniz, S., Gonçalves, B. S., Pinto-varela, T., Barbosa-póvoa, P., Neto, P., Vieira, M., Moniz, S., Gonçalves, B. S., & Pinto-varela, T. (2021). A two-level optimisation-simulation method for production planning and scheduling : the industrial case of a human – robot collaborative assembly line.

Yao, K., & Qin, Z. (2020). Barrier option pricing formulas of an uncertain stock model. Fuzzy Optimization and Decision Making.

Yildirim, A. N., Bas, E., & Egrioglu, E. (2021). Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting ABSTRACT.

Yu, Y., Zhou, D., Zha, D., Wang, Q., & Zhu, Q. (2021). Optimal production and pricing strategies in auto supply chain when dual credit policy is substituted for subsidy policy. 226.

Zohoori, B., Verbraeck, A., Bagherpour, M., & Khakdaman, M. (2018). Monitoring production time and cost performance by combining earned value analysis and adaptive fuzzy control. Computers & Industrial Engineering.

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