۱٫ Introduction

۲٫ The Relative Theoretical Research

۳٫ Self-Adaptive Newsvendor Model

۴٫ Self-Adaptive Newsvendor Model Support System

۵٫ Conclusions

References

The use of classical newsvendor model is limited by assumptions and prior probability statistics. Self-adaptive newsvendor model is proposed to divide the sales interval and automatically adjust the differential according to the sales information collected by retailer’s information system. Demand interval and its corresponding demand probability value could be adjusted timely to correct the next order prediction quantity. Based on management information system, model calculation tool is designed to improve the feasibility of the model application. Compared to traditional newsvendor model, self-adaptive newsvendor model can effectively meet the needs of short-period, small-quantity, multi-batch products purchasing.

Introduction

The classical newsvendor model helps retailers make demand decision for perishable products or innovative products. But, based on conditions assumptions and priori probability statistics, the classical newsvendor model do not adapt to the products procurement strategy of Multi-species, small-quantity, short- cycle and multi-batch, which is now widespread in the retail industry, not to mention those industries which adopt quick response strategy or VMI. This study firstly modifies the basic assumptions of the classical newsvendor model, then uses the sales management information system to automatically count sales frequency to correct expectations, and to construct the self-adaptive newsvendor replenishment model.