Vendor managed forecasting: A case study of small enterprise

Alon, I., Qi, M., & Sadowski, R.J. (2001). Forecasting aggregate retail sales: a
comparison of artificial neural networks and traditional methods. Journal of
Retailing and Consumer Services, 8, 147-156.
Biegel, J.E. (1987). Production Control - A Quantitative Approach, Prentice Hall
India Ltd., Delhi.
Box, G.E.P., Jenkins, G.M., & Reinsel, G.C. (2004). Time Series Analysis Forecasting
and Control. Pearson Education Asia: Delhi.
Chang, P.C., Wang, Y.W., & Tsai, C.Y. (2005). Evolving neural network for printed
circuit board sales forecasting. Expert System with Applications, 29, 83-92.
Chang, P.C., Lai, C.Y., & Lai K.R. (2006). A hybrid system by evolving case-based
reasoning with genetic algorithm in wholesaler's returning book. Decision Support
Systems, 42, 1715–1729.
Chang, P.C., & Wang, Y.W. (2006). Fuzzy Delphi and back- propagation model for
sales forecasting in PCB industry. Expert Systems with Applications, 30, 715–726.
Chang, K.K. (2005). Applying Fourier Transform and Linear Time Invariant Analysis
to Brain Imaging Data, Project for Machine Learning. Retrieved September 10th
from www.cs.cmu.edu/~kkchang/paper/Chang.2005.10-701.brainImaging.rtf
Chopra, S., & Meindl, P. (2001). Supply Chain management: Strategy, Planning and
Operation. Pearson Education: Asia, Delhi.
Chu, C.W., & Zhang, G.P. (2003). A comparative study of linear and nonlinear
models for aggregate retail sales forecasting. International Journal of Production
Economics, 86, 217–231.
Dekker, M., Donselaar, K., & Ouwehand, P. (2004). How to use aggregation and
combined forecasting to improve seasonal demand forecasts. International
Journal Production Economics, 90, 151–167.
Disney, S.M., & Towill, D.R. (2003). Vendor-managed inventory and bullwhip
reduction in a two-level supply chain. International Journal of Operations &
Production Management, 23 (6), 625-651
Frees, E.W., & Miller, T. W. (2004). Sales forecasting using longitudinal data
models. International Journal of Forecasting, 20, 99–114.
Garcia, S., & Herrera, F. (2008). An Extension on “Statistical Comparisons of
Classifiers over Multiple Data Sets” for all Pairwise Comparisons. Journal of
Machine Learning Research, 9, 2677-2694.
Harrison, P.J. (1965). Short term demand forecasting. Applied Statistics, 14, 102-
139.
Haq, A., & Kannan, G. (2006). Effect of forecasting on the multi echelon distribution
inventory supply chain cost using neural network, genetic algorithm and particle
swarm optimization. International Journal of Services Operations and Informatics,
1(1/2), 1-22.
Holt, C.C., Modigliani, F.J., Muth, F., & Simon, H.A. (1963). Planning Production,
Inventories and Work Force, Prentice Hall, Englewood Cliffs, N.J.
Hsu, P.H., Wang, C.H., Shyu, J.Z., & Yu, H.C.(2002). A litterman BVAR approach
for production forecasting of technology industries. Technological Foresating and
Social Change, 70, 67-82.
Jung, S., Chang, T.W, Sim, E., & Parl, J. (2004). Vendor Managed Inventory and Its
Effect in the Supply Chain , Asia Sim LNAI 3398, 545–552
Krajewski, L.J., & Ritzman,R.P. (2000). Operations Management Strategy and
Analysis. Pearson Education Asia: Delhi.
Kurawarwala, A.A., & Matsuo, H. (1998). Product growth models for medium-term
forecasting of short life cycle products. Technological Forecasting and Social
Change, 57, 169-196.
Lee, H.L., & Whang, S. (2000). Information sharing in a supply chain. International
Journal of Technology Management, 20, 373-387.
Raveh, A., & Tapiero, C.S. (1980). Finding common seasonal patterns among time
series. Journal of Econometrics, 12, 353–363
Rudberg, M., Klingenberg, M., & Kronhamn, K. (
Sharda, R., & Patil, R. (1992). Connectionist approach to time series prediction; an
empirical test. Journal of Intelligent Manufacturing, 3, 317-323.
Sharma, M.K., & Bhagwat, R. (2005). Practice of information systems Evidence
from select Indian SMEs. Journal of Manufacturing Technology Management,
17(2), 199-223.
Sinha, R.K., & Basu, S. (1998). Quality of customer service in supply chain system:
a diagnostic study. International Journal of Quality and Reliability Management,
15 (9/9), 844-859.
Snyder, R.D, Koehler, A.B., Hyndman, R.J., & Or, J.K. (2004). Exponential
smoothing models: Means and variances for lead-time demand .European Journal
of Operational Research, 158, 444-455.
Thomassey, S.T., & Fiordaliso, A. (2006). A hybrid sales forecasting system based
on clustering and decision trees. Decision Support Systems, 42, 408– 421.
Tseng , F.M., Yu, H.C., & Tzeng, G.H. (2001). Applied hybrid grey model to
forecast seasonal time series. Technological Forecasting and Social Change, 67,
291-302.
Tyan J., & Wee, H. (2003). Vendor managed inventory: a survey of the Taiwanese
grocery industry. Journal of Purchasing & Supply Management, 9(1), 11-18
Wang, T.Y., & Huang, C.Y. (2007). Improving forecasting performance by
employing the Taguchi method. European Journal of Operational Research, 176,
1052–1065.
Winklhofera, H., & Diamantopoulos, A. (2003). A model of export sales forecasting
behaviour and performance: development and testing. International Journal of
Forecasting, 19, 271–285.
Yelland, P.M. (2006). Stable seasonal pattern models for forecast revision: A
comparative study. International Journal of Forecasting, 22, 799–818.
Zhao, X., Xie, T., & Zhang, W.J. (2003). The impact of information sharing and
ordering co-ordination on supply chain performance. Supply Chain Management
An International Journal, 7(1), 24-40
Zhifang L., Guoqiao R., & Xiaoting S. (2006). Research of chaotic SVM with
incorporated intelligence algorithm forecasting model in SCM. International
Journal of Computer Science and Network Security, 6(10), 136 -141