Forecasting is not an easy task; the area of time series analysis has always represented a challenge for those who intend to perform it. Forecasting and resource planning are of great importance in decision-making in virtually every area of the economy, manufacturing, labor, agriculture, tourism, and, of course, the supply chain sectors. There are many forecasting methods that often require countless statistical analyses. However, in most of them, the information is unreliable and there is a great deal of uncertainty. Therefore, the application of fuzzy logic in time series forecasting represents an option to overcome supply chain uncertainty. As a result of this research, we have found that by applying the Sugeno-Takagi-Kang fuzzy logic method, this model can obtain better prediction results, especially for small sample size data (<20 records). This research presents a methodology for incorporating limited or incomplete data into a modified Sugeno-Takagi-Kang model applied in the supply chain domain and proposes a fuzzy time series forecasting model. This model also incorporates the knowledge and experience of expert users, which allows for improved results with qualitative experience that would otherwise not be considered. The metric for calculating forecast error and evaluating its performance was: mean absolute percentage error (MAPE). The results obtained outperform other models and methodologies such as seasonal or temporal index or even data mining, which requires a much larger amount of information, obtaining barely better or marginal results (5.1%). The results show that the predictive capacity of the fuzzy time series grey forecasting model is better than the traditional approach, especially if we consider the amount of data available. In decision-making, especially in the field of forecasting and resource planning, it is essential to consider multiple factors that may influence the results. This process involves evaluating the available information, identifying patterns and trends, and considering the uncertainty inherent in the data. Fuzzy logic, in this context, provides a robust framework for handling uncertainty and imprecision in data, enabling better decision-making. In addition, the integration of experience and expert knowledge into forecasting models significantly improves the accuracy and relevance of predictions. By applying fuzzy logic, rules and heuristics based on expert knowledge can be incorporated, allowing for greater adaptability and flexibility in decision making. This approach not only improves forecast accuracy, but also facilitates strategic and operational planning, enabling organizations to respond more effectively to changes in the environment and variations in demand. In summary, fuzzy logic and the integration of experience and expert knowledge into forecasting models are essential tools for effective decision-making in complex and dynamic environments.