Demand forecasting is an essential task in retail and manufacturing industries and has been the subject of numerous studies. Conventional popular time-series forecasting methods, such as the ARIMA model, require us to develop a forecasting model for each product. However, when products are frequently replaced and have short sales periods, we do not have enough data to build models individually. This study focuses on zero-shot time-series forecasting methods for demand forecasting with limited data. Zero-shot time-series forecasting is a framework for time-series prediction that does not require fine-tuning with specific time-series data to be predicted. To address the data shortage in practical situations, we propose a zero-shot demand forecasting model that considers exogenous variables. Our experiments with real data demonstrate that our proposed method achieved higher prediction accuracy than existing time-series forecasting methods, especially for products with short sales periods.