| 1. | What is your organization ' s role in cpfr program
（请问贵公司在cpfr项目中的角色是什么？ ） |
| 2. | Demand - link management based on cpfr
策略的需求链管理 |
| 3. | To what extent do you think the cpfr importance in chinese enterprises )
（您认为cpfr对中国的企业来说有多重要？ ） |
| 4. | Which of the following software does your organization use in its cpfr initiative
（请问贵公司在cpfr项目中使用哪种软件？ ） |
| 5. | This paper concentrates on studying the collaborative forecasting step and collaborative replenishment step based on cpfr
本文着重研究cpfr流程中的协同预测和协同补货阶段。 |
| 6. | Collaborative planning , forecasting and replenishment ( cpfr ) is an important issue of supply chain management currently
协同规划、预测与补货( collaborativeplanningforecastingandreplenishment , cpfr )目前是供应链管理中一个热门的研究问题。 |
| 7. | The fifth chapter discusses and values some supply chain management modes at present , such as vmi ( vendor managed inventory ) , jmi ( jointly managed inventory ) and cpfr ( collaborative planning , forecasting and replenishment )
第五章，对现有的供应链管理模式： vmi (供应商管理库存) 、 jmi (联合库存管理) 、 cpfr (共同预测、计划与补充)进行了分析和评价。 |
| 8. | The simulated results verify forecasting accuracy of this model is superior to traditional time series method or linear regression model . as a result , this method presented in the paper can use in predicting the fact order data
实验结果显示,二阶段协同订单预测模型无论在4周、 8周或11周的预测绩效皆优于传统时间序列或一般线性回归模型,因此,该模型可作为cpfr流程下欲进行协同订单预测或一般订单预测的参考。 |
| 9. | The fourth chapter makes an analysis and evaluation on the existing supply chain management modes , such as vmi ( vendor managed inventory ) , jmi ( joint managed inventory ) , cpfr ( collaborative planning , forecasting and replenishment )
第四章，对现有的供应链库存管理模式： vmi (供应商管理库存) 、 jmi (联合库存管理) 、 cpfr (共同预测、计划与补给)进行了较深入的分析和评价，并对cpfr系统的绩效评价体系进行了研究。 |
| 10. | The main research contents include : 1 、 this paper constructs the mixed collaborative sale forecasting model based on cpfr via integrating time series forecasting , multivariate regression and ridge regression . in addition , the model takes sale information as explanation variable
具体研究内容包括: 1 、将时间序列预测、多元回归、岭回归相结合,并将销售信息作为销售量的解释变量,构建了cpfr流程下的混合协同预测模型。 |
