Applying Grey Forecasting and Time-series Methods in Demand Forecasting System for Pharmacy Chain Stores
Date Issued
2015
Date
2015
Author(s)
Chiang, Yu-Min
Abstract
Forecasting enhances the efficiency and effectiveness in decision-making. Through demand forecasting, retailers not only handle demand uncertainties but also improve inventory management. However, demand forecasting for drugs is more complicated due to the various types and features. In this paper, we propose a demand forecasting system for regional pharmacy chain stores. Drugs are categorized into four types based on sufficiency, randomness and seasonality, namely, new products, random long-term products, non-seasonal long-term products, and seasonal long-term products. Grey forecasting method is applied to forecast new products and random long-term products. We also apply time-series methods, which include moving average methods, exponential smoothing methods, and ARIMA, to forecast non-seasonal long-term products and seasonal long-term products. Then, the seasonality of historical data is analyzed through decomposition method. Moreover, we discuss whether the model and parameters should be reconstructed with new data. The suitability of data is simultaneously discussed in the system. Finally, we verify our forecasting system with real data. Results indicate that the proposed forecasting system can determine a suitable model for predicting demand accurately.
Subjects
demand forecasting
grey forecasting method
time-series methods
autoregressive integrated moving average (ARIMA)
Type
thesis