Sales forecasting of pet food at oyen petshop using the fuzzy time series–markov chain method - Pratama, Agung; Kurniawan, Rakhmat (Journal of Intelligent Decision Support System (IDSS), 2025)
Oyen Petshop faces stock management inaccuracies because sales records are still kept manually, while demand patterns are highly fluctuating and difficult to predict, often leading to overstock or stockouts that harm the business. The purpose of this study is to develop a Fuzzy Time Series–Markov Chain (FTS-MC) model to forecast pet food sales at Oyen Petshop and implement it in the form of a website. The research method applies FTS-MC to construct fuzzy intervals, generate FLR/FLRG, calculate transition probabilities, and produce forecasts based on dry-food sales data from April 2024 to March 2025. The results show that the FTS-MC model achieves a MAPE of 8.92%, with forecasted values that follow actual fluctuations and indicate a stable demand trend of 206–224 units for the next seven periods. Black Box Testing confirms that all web-based system functions operate correctly and all test scenarios pass, ensuring the system is ready for operational use. The findings indicate that the system enables more precise stock estimation, supports the establishment of safe reorder limits, and allows procurement decisions to be made faster and more consistently without manual calculations.