BibTex RIS Cite

Fuzzy and Neuro-Fuzzy Forecasting Approaches to Whiplash Effect in Supply Chains

Year 2008, Volume: 4 Issue: 1, 27 - 42, 01.04.2008

Abstract

References

  • Christopher M., Logistic and Supply Chain Management, Pitman Publishing, London, UK, 1994, pp.30.
  • Tozan H., Vayvay O., Analyzing Demand Variability Through SC Using Fuzzy Regression and Grey GM(1,1) Forecasting Models, Information Sciences 2007, World Scientific, 2007, pp. 1088--1094.
  • Forrester J., Industrial Dynamics: A major breakthrough for decision makers, Harvard Business Review, 36, 1958, pp. 37--66.
  • Forrester J., Industrial Dynamics, MIT Press, Cambridge, MA, 1961.
  • Sterman J.D., Modeling Managerial Behavior: Misperception of Feedback in a Dynamic Decision Making Experiment, Management Science, 35, 1989, pp.321-399.
  • Sterman J.D., Deterministic Chaos in an Experimental Economic System, Journal of Economic Behavior and Organization, 12, 1989, pp.1-28.
  • http://beergame.uni-klu.ac.at/bg.htm, 2008.
  • Larsen E.R., Morecroft J.D., Thomsen J.S., Complex Behavior in a Production- Distribution model, European Journal of Operation Research, 119, 1999, pp. 61-14.
  • Lee H. L., Padmanabhan V., Whang S., The Bullwhip Effect in Supply Chains, MIT Sloan Management Rev., 38, 1997a, pp. 93-10 2.
  • Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 43, 1997b, pp. 546--558.
  • Lee H. L., Aligning Supply Chain Strategies with Product Uncertainties, California Management Review, 44, 2002, pp. 105-119.
  • Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 50, 2004, pp. 1875--1886.
  • Baganha M., Cohen M., The Stabilizing Effect of Inventory in Supply Chains, Operations Research, 46, 1998, pp.572-583.
  • Graves S. C., A Single Inventory Model foe a Nonstationary Demand Process, Manufacturing &Service Operations Management, 1, 1999, pp.50-61.
  • Drezner Z., Ryan J., Simchi-Levi D., Quantifiying the Bullwhip Effect: The impact of forecasting, lead time and information, Management Science, 46 (3), 2000, pp.436-443.
  • Chen F., Drezner Z., Ryan J., Simchi-Levi D.,The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect, Naval Research Logistics, 47, 2000, pp. 269-286.
  • Li G., Wang S., Yan H., Yu G., Information Transportation in Supply Chain, Computers and Operation Research, 32, 2005, pp. 707-725.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Tranfer function analysis of forecasting included bullwhip in supply chains, Int.J. Production Economics, 78, 2002, pp. 133-144.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Measuring and avoiding the bullwhip effect: A control theoretic approach, European Journal of Operational Research, 147 2003, pp.567-590.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective, European Journal of Operational Research, 153, 2004, pp. 727-750.
  • Disney S.M., Towill D.R., On the bullwhip and inventory variance produced by an ordering policiy, International Journal of Management Science, 31 2003a, pp. 157- 167.
  • Disney S.M., Towill D.R., The effect of vendor managed inventory (VMI) Dynamics on the bullwhip effect in supply chains, International Journal of Production Economics, 85 (2003b) 199-215.
  • Disney S.M., Towill D.R., Velde W., Variance amplification and the golden ratio in production and inventory control, International Journal of Production Economics, 85, 2004, pp. 295-309
  • Disney S.M., Farasyn I., Lambrecht M., Towill D.R., Van de Velde W. Taming the bullwhip effect whilst watching customer service in a singular supply chain echelon, European Journal of Operational Research, 173, 2006, pp.151-172.
  • Gaery S., Disney S.M., Towill D.R., On bullwhip in supply chains-historical review, present practice and expected future impact, International Journal of Production Echonomics, 101, 2006, pp. 2-18.
  • Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series, Fuzzy Sets and Systems, 54, 1993, pp. 1-9.
  • Song Q., Chissom B. S., Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 1993, pp. 269-277.
  • Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series Part II, Fuzzy Sets and Systems, 62, 1994, pp. 1-8.
  • Wang C.H., Predicting Tourism Demand Using Fuzzy Time Series and Hybrid Grey Theory, Tourism Management, 25, 2004, pp.367-374.
  • Li S., Cheng Y., A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series, WSEAS Transactions on System, 5, 2006, pp. 1919-1925
  • Kahraman C., Fuzzy Applications in Industrial Engineering,, 1th edt., Springer, 2006, pp.40–55.
  • Tanaka H., Uejima S., Asai K., Fuzzy Linear Regression Model, IEEE Trans. System, Man and Cybernet, 12 , 1982, pp. 903-907.,
  • Tanaka H., Watada J., Possibilistic Linear Systems and Their Application to the Linear Regression Model, Fuzzy Sets and Systems, 27, 1988, pp.275–289.
  • Wang, H.F., Tsaur R.H., Insight of a Fuzzy Regression Model, Fuzzy Sets and Systems, 2000, 112, pp. 355-369.
  • Escoda I., Ortega A., Sanz A., Herms A., Demand Forecasting by Neuro-Fuzzy Techniques, Proceedings of Sixth IEEE Int.Conf. on Fuzzy Systems, 1982, pp. 1381- 1386.
  • Kuo R.J., A Sales Forecasting System Based on Fuzzy Neural Network with Inıtial Weights Generated by Genetic Algorithm, European Journal of Operational Research, 129, 2001, pp.496-517.
  • George A., Ucenic C., Forecasting the Wind Energy Production Using a Neuro-fuzzy Model, WSEAS Transactions on Environment and Development, 2, 2006, pp. 823-829
  • Tozan H., Vayvay O., Effects of Fuzzy Forecasting Models on Supply Chain Performance, Advanced Topics on Fuzzy Systems, WSEAS, 2008, pp.107-113.
  • Ross T. J., Fuzzy Logic with Engineering Applications, 2th edt., John Wiley and Sons, 2005, pp.555-567.
  • Hwang J., Chen S.M., Lee C.H., Handling Forecasting Problems Using Fuzzy Time Series, Fuzzy Sts and Systems, 100, 1998, pp.217-228.
  • Hecht-Nielsen R., Neurocomputing, Addision-Wesley, 1990.
  • Maduko A., Developing and Testing a Neuro-Fuzzy Classification System for IOS Data in Asthmatic Children, Texas University Press, 2007.
  • McCullough B., Pitts W., A Logical calculus of the Ideas Immanent in Nervous Activity, Bulletin on Mathematical Biophysics, 5, 1973, pp.115-133.
  • Rosenblatt F., The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, 65, 1958, pp.386-408.
  • Parker D.B., Learning Logic, Invention Report S81-64, Stanford University Press, 1982.
  • Rymelhart D., McClelland J., Paralel Distribution Processing: Explorations in the Microstructure of Cognition, MIT Press, 1, 1986
  • Jang J.S., ANFIS: Adaptive Network Based Fuzzy Interference System, IEEE Transactions on Systems, Man and Cybernetics, 23, 1993, pp.665-684.
  • Paik S.K., Analysis of the Causes of Bullwhip Effect in Supply Chain: A Simulation Approach, George Washington University Press, 2003

Fuzzy and Neuro-Fuzzy Forecasting Approaches to Whiplash Effect in Supply Chains

Year 2008, Volume: 4 Issue: 1, 27 - 42, 01.04.2008

Abstract

Supply chain, Whiplash Effect, Forecasting, Fuzzy regression, Fuzzy time series, Neuro-fuzzy, ANFIS, Exponential Smoothing

References

  • Christopher M., Logistic and Supply Chain Management, Pitman Publishing, London, UK, 1994, pp.30.
  • Tozan H., Vayvay O., Analyzing Demand Variability Through SC Using Fuzzy Regression and Grey GM(1,1) Forecasting Models, Information Sciences 2007, World Scientific, 2007, pp. 1088--1094.
  • Forrester J., Industrial Dynamics: A major breakthrough for decision makers, Harvard Business Review, 36, 1958, pp. 37--66.
  • Forrester J., Industrial Dynamics, MIT Press, Cambridge, MA, 1961.
  • Sterman J.D., Modeling Managerial Behavior: Misperception of Feedback in a Dynamic Decision Making Experiment, Management Science, 35, 1989, pp.321-399.
  • Sterman J.D., Deterministic Chaos in an Experimental Economic System, Journal of Economic Behavior and Organization, 12, 1989, pp.1-28.
  • http://beergame.uni-klu.ac.at/bg.htm, 2008.
  • Larsen E.R., Morecroft J.D., Thomsen J.S., Complex Behavior in a Production- Distribution model, European Journal of Operation Research, 119, 1999, pp. 61-14.
  • Lee H. L., Padmanabhan V., Whang S., The Bullwhip Effect in Supply Chains, MIT Sloan Management Rev., 38, 1997a, pp. 93-10 2.
  • Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 43, 1997b, pp. 546--558.
  • Lee H. L., Aligning Supply Chain Strategies with Product Uncertainties, California Management Review, 44, 2002, pp. 105-119.
  • Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 50, 2004, pp. 1875--1886.
  • Baganha M., Cohen M., The Stabilizing Effect of Inventory in Supply Chains, Operations Research, 46, 1998, pp.572-583.
  • Graves S. C., A Single Inventory Model foe a Nonstationary Demand Process, Manufacturing &Service Operations Management, 1, 1999, pp.50-61.
  • Drezner Z., Ryan J., Simchi-Levi D., Quantifiying the Bullwhip Effect: The impact of forecasting, lead time and information, Management Science, 46 (3), 2000, pp.436-443.
  • Chen F., Drezner Z., Ryan J., Simchi-Levi D.,The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect, Naval Research Logistics, 47, 2000, pp. 269-286.
  • Li G., Wang S., Yan H., Yu G., Information Transportation in Supply Chain, Computers and Operation Research, 32, 2005, pp. 707-725.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Tranfer function analysis of forecasting included bullwhip in supply chains, Int.J. Production Economics, 78, 2002, pp. 133-144.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Measuring and avoiding the bullwhip effect: A control theoretic approach, European Journal of Operational Research, 147 2003, pp.567-590.
  • Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective, European Journal of Operational Research, 153, 2004, pp. 727-750.
  • Disney S.M., Towill D.R., On the bullwhip and inventory variance produced by an ordering policiy, International Journal of Management Science, 31 2003a, pp. 157- 167.
  • Disney S.M., Towill D.R., The effect of vendor managed inventory (VMI) Dynamics on the bullwhip effect in supply chains, International Journal of Production Economics, 85 (2003b) 199-215.
  • Disney S.M., Towill D.R., Velde W., Variance amplification and the golden ratio in production and inventory control, International Journal of Production Economics, 85, 2004, pp. 295-309
  • Disney S.M., Farasyn I., Lambrecht M., Towill D.R., Van de Velde W. Taming the bullwhip effect whilst watching customer service in a singular supply chain echelon, European Journal of Operational Research, 173, 2006, pp.151-172.
  • Gaery S., Disney S.M., Towill D.R., On bullwhip in supply chains-historical review, present practice and expected future impact, International Journal of Production Echonomics, 101, 2006, pp. 2-18.
  • Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series, Fuzzy Sets and Systems, 54, 1993, pp. 1-9.
  • Song Q., Chissom B. S., Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 1993, pp. 269-277.
  • Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series Part II, Fuzzy Sets and Systems, 62, 1994, pp. 1-8.
  • Wang C.H., Predicting Tourism Demand Using Fuzzy Time Series and Hybrid Grey Theory, Tourism Management, 25, 2004, pp.367-374.
  • Li S., Cheng Y., A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series, WSEAS Transactions on System, 5, 2006, pp. 1919-1925
  • Kahraman C., Fuzzy Applications in Industrial Engineering,, 1th edt., Springer, 2006, pp.40–55.
  • Tanaka H., Uejima S., Asai K., Fuzzy Linear Regression Model, IEEE Trans. System, Man and Cybernet, 12 , 1982, pp. 903-907.,
  • Tanaka H., Watada J., Possibilistic Linear Systems and Their Application to the Linear Regression Model, Fuzzy Sets and Systems, 27, 1988, pp.275–289.
  • Wang, H.F., Tsaur R.H., Insight of a Fuzzy Regression Model, Fuzzy Sets and Systems, 2000, 112, pp. 355-369.
  • Escoda I., Ortega A., Sanz A., Herms A., Demand Forecasting by Neuro-Fuzzy Techniques, Proceedings of Sixth IEEE Int.Conf. on Fuzzy Systems, 1982, pp. 1381- 1386.
  • Kuo R.J., A Sales Forecasting System Based on Fuzzy Neural Network with Inıtial Weights Generated by Genetic Algorithm, European Journal of Operational Research, 129, 2001, pp.496-517.
  • George A., Ucenic C., Forecasting the Wind Energy Production Using a Neuro-fuzzy Model, WSEAS Transactions on Environment and Development, 2, 2006, pp. 823-829
  • Tozan H., Vayvay O., Effects of Fuzzy Forecasting Models on Supply Chain Performance, Advanced Topics on Fuzzy Systems, WSEAS, 2008, pp.107-113.
  • Ross T. J., Fuzzy Logic with Engineering Applications, 2th edt., John Wiley and Sons, 2005, pp.555-567.
  • Hwang J., Chen S.M., Lee C.H., Handling Forecasting Problems Using Fuzzy Time Series, Fuzzy Sts and Systems, 100, 1998, pp.217-228.
  • Hecht-Nielsen R., Neurocomputing, Addision-Wesley, 1990.
  • Maduko A., Developing and Testing a Neuro-Fuzzy Classification System for IOS Data in Asthmatic Children, Texas University Press, 2007.
  • McCullough B., Pitts W., A Logical calculus of the Ideas Immanent in Nervous Activity, Bulletin on Mathematical Biophysics, 5, 1973, pp.115-133.
  • Rosenblatt F., The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, 65, 1958, pp.386-408.
  • Parker D.B., Learning Logic, Invention Report S81-64, Stanford University Press, 1982.
  • Rymelhart D., McClelland J., Paralel Distribution Processing: Explorations in the Microstructure of Cognition, MIT Press, 1, 1986
  • Jang J.S., ANFIS: Adaptive Network Based Fuzzy Interference System, IEEE Transactions on Systems, Man and Cybernetics, 23, 1993, pp.665-684.
  • Paik S.K., Analysis of the Causes of Bullwhip Effect in Supply Chain: A Simulation Approach, George Washington University Press, 2003
There are 48 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Hakan Tozan This is me

Özalp Vayvay This is me

Publication Date April 1, 2008
Published in Issue Year 2008 Volume: 4 Issue: 1

Cite

APA Tozan, H. ., & Vayvay, Ö. . (2008). Fuzzy and Neuro-Fuzzy Forecasting Approaches to Whiplash Effect in Supply Chains. Journal of Naval Sciences and Engineering, 4(1), 27-42.