Indonesian Scholars Scientific Summit Taiwan Proceeding 2021 e-ISSN: 2797-2437 10.52162/3.2021108 16 Application of Fuzzy Time Series (FTS) Algorithm in Production Planning of Indonesia’s Oil Refining Company Zakka Ugih Rizqi 1 , Tommy Aries Kurniawan 2 , Adinda Khairunisa 2 1 Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan 2,3 Department of Industrial Engineering, Universitas Islam Indonesia, Indonesia ABSTRACT CONTACT 1 ugihzakka@gmail.com KEYWORDS Aggregate Planning, Forecasting, Fuzzy Time Series, Production Planning. Forecasting and aggregate planning are crucial phases in production planning especially for oil refining company that takes expensive production cost. Accurate forecasting greatly influences the success of production planning since it is the starting point of production planning. Whereas aggregate planning becomes important because it functions to bridge between the demand or production target with the existing resource requirements. Seeing the importance of accurate forecasting and aggregate planning, this research emphasizes the use of Fuzzy Time Series (FTS) Algorithm to forecast Premium sales in Indonesia’s oil refining company. The comparison is also done between FTS with the other classical techniques in time series forecasting to test the reliability of algorithm and FTS outperforms the others based on the lowest MAPE value as much as 0.87%. FTS result is then used as an input in the aggregate planning by using heuristics method and comparing 3 strategies which are Level Strategy, Chase Strategy, and Hybrid Strategy. The result shows that Hybrid Strategy is the most efficient one because it produces the lowest production cost for three months production period as much as Rp 3,272,000,000. INTRODUCTION Z Company is one of the Republic of Indonesia State-Owned Enterprises (BUMN) companies that focuses on oil and energy processing. In order to meet the demand, Z Company has 6 refinery units spread across Indonesia. One of the processes carried out is refining crude oil and naptha into fuel oil (BBM) and one of its products is Premium. Premium is very popular in Indonesia and in high demand in 2019. It is relatively cheap price since it receives subsidies from the state budget. Even tough there is an issue that Premium production will be stopped due to bad impact for environment, but this research will still be useful by proposing an approach for accurate production planning applicable for other products. Seeing the large amount of Premium demand so that good planning is needed starting from accurate sales forecasting to effective aggregate planning strategies to allocate the capacity of the resources that are owned to meet the predicted demand so as to minimize production costs. With accurate sales forecasting, material requirements planning (MRP) and production scheduling will be effective which ultimately can satisfy the customer's desires [1]. At present, forecasting by Z Company uses qualitative methods which only rely on intuition from the expert so that the accuracy cannot be known and the subjectivity is very high. It will be very risky for the failure of planning. In this study, forecasting for the next 3 months namely March, April and May 2019 will be done using Fuzzy Time Series (FTS)-based Algorithm which is compared with other methods. Then, the most accurate forecasting method will be used as input in aggregate planning using the heuristics or graphical method so that low production costs can be obtained to meet Premium sales forecasting. A similar study was conducted by Gansterer (2015) [2] who used time series-based forecasting which is then continued with aggregate planning using the simulation model. Mantilla et al. (2017) [3] conducted aggregate planning by evaluating 2 strategies, namely Zero inventory and Vary Inventory with input demand obtained from MPS. Fajar and Lestari (2017) [4] conducted aggregate planning using Model 2 and Model 4 introduced by Heizer (2008) [5]. Whereas Gulsun et al. (2009) [6] conducted forecasting using the multiplicative decomposition model with trends and seasonal patterns which is then continued with aggregate planning using Linear Physical Programming method. Compared to previous research, it has not been studied about forecasting using the FTS- based Algorithm which is then continued with heuristic-based aggregate planning especially applied in Oil Refining Company.