Newsvendor problems with demand forecast updating and supply constraints

M Zheng, K Wu, Y Shu - Computers & Operations Research, 2016 - Elsevier
M Zheng, K Wu, Y Shu
Computers & Operations Research, 2016Elsevier
This study investigates an extension of the newsvendor model with demand forecast
updating under supply constraints. A retailer can postpone order placement to obtain a
better demand forecast with a shorter supply lead time. However, the manufacturer would
charge the retailer a higher cost for a shorter lead time and set restrictions on the ordering
times and quantities. This prevents retailers from taking full advantage of demand forecast
updating to improve profits. In studying the manufacturer-related effects, two supply modes …
Abstract
This study investigates an extension of the newsvendor model with demand forecast updating under supply constraints. A retailer can postpone order placement to obtain a better demand forecast with a shorter supply lead time. However, the manufacturer would charge the retailer a higher cost for a shorter lead time and set restrictions on the ordering times and quantities. This prevents retailers from taking full advantage of demand forecast updating to improve profits. In studying the manufacturer-related effects, two supply modes are investigated: supply mode A, which has a limited ordering time scale, and supply mode B, which has a decreasing maximum ordering quantity. For supply mode A, it is proven under justifiable assumptions that a retailer should order either as early or as late as possible. For supply mode B, an algorithm is proposed to simplify the ordering policy by appropriately relaxing the ordering quantity restrictions. Numerical analysis is conducted to investigate the influence of product and demand parameters on the value of demand forecast updating in the two supply modes. A comparison of the different supply scenarios demonstrates the negative effects of increased purchasing cost and ordering time and quantity restrictions when demand forecast updating is implemented.
Elsevier
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