Model of e-commerce cloud warehouse grading based on mass simulation data
WANG Fan, WANG Yan-Li, LIU Zhi-Jie, CUI Xiao, ZHANG Peng
Journal of Digital Information Management, Vol. 14, No. 5, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i5/jdimv14i5_4.pdf
Abstract:
E-commerce cloud warehouse is a logistic
service model developed to meet e-commerce demands.
The model is characterized by multi-site warehousing,
big data prediction, accurate stock, and nearby
distribution. A set of unified management standards must
be established to effectively address the difficulty in ecommerce
cloud warehouse management. For the
convenience of quantitative grading management of
branch warehouses, the method for confirming ecommerce
cloud warehouse grading based on mass
simulation data analysis was proposed in this paper. The
discrete event simulation model was built, the simulation
experiment was designed, and the mass experimental data
were analyzed and integrated to finally obtain the model
of e-commerce cloud warehouse grading. The
effectiveness of the grading model was also verified.
Analysis results suggest that historical data cannot
guarantee adequate big data size and sufficiently high
data accuracy for the issues of e-commerce cloud
warehouse grading. Thus, experimental data must be
obtained by building a discrete event simulation model.
The validity and universality of the simulation model are
necessary to ensure the rationality of experimental data.
Through simulation modeling, simulation experiment
design, and experimental data mining, the quantitative
standards for e-commerce cloud warehouse grading can
be obtained. Verification results show that the quantitative
grading results are consistent with the experience-based
grading results. This research proves the feasibility of
obtaining the quantitative standards for e-commerce cloud
warehouse grading based on mass simulation
experimental data. The proposed grading model is suitable
for e-commerce cloud warehouses of different industries.