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In the increasingly competitive Industry 4.0 era, companies need to forecast product demand to meet consumer needs and improve operational efficiency. CV Mamifood Sukses Abadi, an MSME that produces milk and cheese-based foods, has faced sales fluctuations in the last two years, thus requiring accurate forecasting to plan production strategies and resource management. This research aims to forecast demand using the Fuzzy Mamdani method and the POM-QM application. Fuzzy Mamdani was chosen for its ability to handle decision-making with multiple criteria and balanced weights, while POM-QM was used to validate predictions through quantitative methods. Product sales data for the years 2022 and 2023 were analyzed to produce accurate forecasts. The methods used include Moving Average for forecasting and evaluation of the results using MAPE. The analysis results show that the Moving Average method with N = 2 produces a MAD value of 402.523 and a MAPE of 22.155%, while the results of Fuzzy Mamdani show that product demand in the next period tends to decrease. This research is expected to provide insight for CV Mamifood Sukses Abadi in planning a more efficient production strategy.

CV Wangun Mandiri is a manufacturing company that produces tapioca flour. In order to achieve maximum profit targets and smooth production activities, the company is faced with problems relating to the amount of tapioca flour products due to the uncertainty of its demand that tends to fluctuate and the imbalance of machine capacity. Therefore, it is required to plan the amount of production using the forecasting and fuzzy inference system approach as an effective method to determine the optimal production level. This research relies on the availability of datasets to determine the appropriate forecasting method and fuzzy method. The Fuzzy Mamdani method concludes that CV Wangun Mandiri can produce 82.9 tons to maximize existing demand and the capacity of its machines. 

Determining the provision of bonuses or rewards to employees plays a crucial role in maintaining the quality and motivation of employees. One effort that can be made to establish a bonus policy is by implementing the fuzzy Mamdani method as a systematic approach in determining employee bonuses, particularly at PT. ABC. By utilizing the fuzzy Mamdani method to process subjectivity in evaluations, it generates membership levels that allow for a more contextual employee assessment. Through the analysis of data involving various performance variables and bonus criteria, this research aims to present a fuzzy Mamdani system model that can support accurate and adaptive decision-making in determining employee bonuses. The results of this research and the evaluation of this model are expected to contribute significantly to the development of more effective bonus policies in the workplace of PT.