Affiliation:
1. University of Nottingham, UK
Abstract
From 21st century, enterprises combine supply chain management with big data to improve their products and services level. In China healthcare industry, supply chain decisions are made based on experience, due to the environment complexities, such as changing policies and license delay. A flexible and dynamic big data driven analysis approach for supply chain decisions is urgently required. This report demonstrates a case study on CRT forecasting model of inventory data to predict the market demand based on pervious transaction data. First a basic statistic approach has been applied to represent the superficial patterns and suggest some decisions. After that a CRT model has been built based on the several independent variables. And there is also a comparison between CRT and CHAID models to choose a better one to further build an improved model. Finally some limitations and future work have been proposed.
Reference19 articles.
1. Improved supply chain management based on hybrid demand forecasts
2. Al-Anbuky, A., Bataineh, S., & Al-Aqtash, S. (1995). Power demand prediction using fuzzy logic (Vol. 3). Elsevier.
3. Aman, S., Frincu, M., Chelmis, C., Noor, M., Simmhan, Y., & Prasanna, V. K. (2015). Prediction Models for Dynamic Demand Response. Paper presented at the 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm): Data Management, Grid Analytics, and Dynamic Pricing.
4. Application of machine learning techniques for supply chain demand forecasting
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献