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| Content Provider | Springer Nature Link |
|---|---|
| Author | Singh, Lakhwinder Pal Challa, Ravi Teja |
| Copyright Year | 2015 |
| Abstract | To enhance the commercial competitive advantage of a firm in a constantly changing business environment, it is very important to enhance the supply chain performance by making it more flexible to adopt any type of changes in dynamic business environment. To improve the supply chain performance and make it more flexible it is essential to control the order amplification or bullwhip effect (BWE) through various stages of supply chain and control the inventory costs by controlling net stock amplification (NSA). These tasks should be done by using accurate demand forecasting. The current study demonstrates a forecasting methodology about nonlinear customer demand in a multilevel supply chain (SC) structure through; integrated techniques of discrete wavelet theory and artificial intelligence techniques including artificial neural networks and adaptive network-based fuzzy inference system. The effectiveness of forecasting models to deal with nonlinear data and how they improved the flexibility of SC is demonstrated by calculating BWE and NSA for real world data. |
| Starting Page | 157 |
| Ending Page | 169 |
| Page Count | 13 |
| File Format | |
| ISSN | 09722696 |
| Journal | Global Journal of Flexible Systems Management |
| Volume Number | 17 |
| Issue Number | 2 |
| e-ISSN | 09740198 |
| Language | English |
| Publisher | Springer India |
| Publisher Date | 2015-11-25 |
| Publisher Place | New Delhi |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Fuzzy inference system Business and Management Bullwhip effect Net stock amplification Artificial neural network Supplies chain flexibility Discrete wavelet theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Business and International Management Management Information Systems Strategy and Management |
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