The theory of constraint suggests the application of a demand-pull replenishment strategy combined with buffer management (DPBM) in order to effectively manage inventory. A demand-pull strategy caters to the customers' demand to drive inventory replenishment, while buffer management is designed to adjust target inventory levels (buffer size). However, there is very limited literature looking into the parameters of buffer management in depth, such as the timing and the amount of buffer adjusted. Therefore, the objective of this study was to explore the product demand characteristics that affect the parameters of choice in buffer management when applying the DPBM strategy. This study first used a DPBM strategy (implemented via simulation, based on historical demand data) under different buffer management parameters to simulate the inventory replenishment for multiple products. The products were then grouped according to the simulated results by statistical analysis. A decision tree was applied to find the critical demand pattern factors that determined the product groups. An appropriate DPBM strategy is suggested for each product group. This study uses real demand data for 21 products, provided by a wafer foundry company located in Taiwan, to demonstrate the feasibility and effectiveness of the proposed method. The results show that it is possible to determine whether a product is suitable for DPBM strategy application after analyzing only its historical demand data.