Author : Vivek Jaglan 1
Date of Publication :28th December 2017
Abstract: Demand forecasting is difficult, and most demand forecasting conducted today produces disappointing results and significant forecast errors. It cannot easily identify trends in the demand data, and its limited ability to understand the underlying causes of demand variability makes that variability seem worse than it would if demand drivers were clearly understood. And because it is manually intensive, it suffers from persistent bias and poor planner productivity. Business volatility and the complexity of factors influencing demand are making it hard to reliably model the causes of demand variation. Machine learning can help companies overcome that challenge
Reference :
-
- Box, G.E., 1970. Time Series Analysis. HoldenDay, San Francisco.
- Chandra, C., Grabis, J., 2005. Application of multisteps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand. European Journal of Operational Research 166 (2), 337–350.
- Cox, A., Sanderson, J., Watson, G., 2001. Supply chains and power regimes: Toward an analytic framework for managing extended networks of buyer and supplier relationships. Journal of Supply Chain Management 37 (2), 28–35.
- Davis, E.W., Spekman, R., 2004. Extended Enterprise. PrenticeHall,Upper Saddle River, NJ.
- Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., 2003. Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research 147 (3), 567–590.
- R. Carbonneau et al. / European Journal of Operational Research 184 (2008) 1140–1154 1153 Demuth, H., Beale, M., 1998. In: Natick (Ed.), Neural Network Toolbox for Use with MATLAB, User’s Guide (version 3.0). The MathWorks, Inc., Massachusettes.
- Dorffner, G., 1996. Neural Networks for Time Series Processing. Neural Network World 96 (4), 447–468.
- Forrester, J., 1961. Industrial Dynamics. Productivity Press, Cambridge, MA.
- Frohlich, M., 2002. Demand chain management in manufacturing and services: Web-based integration, drivers and performance. Journal of Operations Management 20 (6), 729–745.
- Gunasekaran, A., 2004. Supply chain management: Theory and applications. European Journal of Operational Research 159 (2), 265–268.