Buradasınız

Demand Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Neuro-Fuzzy Approach

Journal Name:

Publication Year:

Abstract (2. Language): 
Because of human healthcare, the pharmaceutical industry is considered as one of the most significant industrial sectors. For that reason, demand forecasting in pharmaceutical industry has more complex structure than other sectors. Human factors, seasonal and epidemic diseases, market shares of the competitive products and marketing conditions are considered as main external factors for forecasting pharmaceutical product. Additionally, active ingredients rate is also important factor for forecasting process. The objective of this study is to predict future demands from previous sales quantity with considering effects of the external factors by employing a neuro-fuzzy approach. Because of the biases of the external effects in Artificial Neural Network (ANN) topology, an ANFIS is applied as a neuro fuzzy approach. Given application illustrates the effectiveness of the approach
41
49

REFERENCES

References: 

Abdollahzade, M., Miranian, A. & Faraji, S. (2012), Application of emotional learning fuzzy inference systems and locally linear neuro-fuzzy models for prediction and simulation in dynamic systems , FUZZ IEEE , WCCI, 2012 IEEE World Congress On Computational Intelligence Abraham, A. & Nath, B. (2001), A neuro-fuzzy approach for modelling electricity demand in Victoria, Applied Soft Computing, 1, 2, 127–138
Alizadeh, M., Jolai, F., Aminnayer, M. & Rada, R. (2012), Comparison of different input selection algorithms in neuro-fuzzy modeling, Expert Systems with Applications, 39, 1536–154
Babuška, R., & Verbruggen, H. (2003), Neuro-fuzzy methods for nonlinear system identification, Annual Reviews in Control, 27, 73–85
Caner, M. & Akarslan, E. (2009), Estimation of specific energy factor in marble cutting process using ANFIS and ANN, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 15, 2, 221-226.
Craig, A. & Malek, M. (1995), Market structure and conduct in the pharmaceutical industry, Phormac. Ther. 301 337, 0163-7258/95
Confessore, G., Fabiano, M. & Liotta, G. (2011), A network flow based heuristic approach for optimising AGV movements, Journal of Intelligent Manufacturing , DOI 10.1007/s10845-011-0612-7
Erkollar, A., Goztepe, K., & Sahin, N. (2013). A Study on Innovation Performance Forecasting in Advanced Military Education Using Neuro-Fuzzy Networks, International Journal of Science and Advanced Technology, 3(4), 5-12.
Erginel, N. (2010), Modeling and analysis of packing properties through a fuzzy inference system, Journal of Intelligent Manufacturing, 21:869-874, DOI 10.1007/ s10845 -009- 0262-1
Fisher, J. A., & Ronald, L. M. (2010), Sex, gender, and pharmaceutical politics: from drug development to marketing, Gender Medicine, 7, 4.
Fruggiero, F., Iannone, R. & Martino, G. (2012), a forecast model for pharmaceutical requirements based on an artificial neural network service operations and logistics, and informatics, IEEE International Conference on July 2012
Giuffrida, A. (2001), learning from the experience: the inter-American development bank and pharmaceuticals, Inter-American Development Bank, Washington. Haykin, S., Neural networks; a comprehensive foundation, MacMillan College Publishing, 1, New York. (1994) http://www.learnartificialneuralnetworks.com/, last accessed:01.12.2012 Jang, J. S. & Gulley N., Fuzzy Logic Toolbox User’s Guide, The Mathworks Inc., (1995)
Jang, J.S. (1993), ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. On System, Man and Cybernetics. 23, 3, 665-685.
Kosko, B., Neural networks and fuzzy systems, a dynamical systems approach, Englewood Ciffs., NJ: Prentice Hall, (1991)
Markopoulos, A. P., Manolakos, D. E. & Vaxevanidis, N. M. (2008), Artificial neural network models for the prediction of surface roughness in electrical discharge machining, Journal of Intelligent Manufacturing, 19:283–292 DOI 10.1007/s10845-008-0081-9 Mok, S. L. & Kwong, C. K. (2002), Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding, Journal of Intelligent Manufacturing, 13, 3, 165-176.
Ogbru, O., Why drugs cost so much, http://www.medicinenet.com, last accessed: 01.12.2012
Papageorgiou, L.G., Rotstein, G.E. & Shah, N. (2001), Strategic supply chain optimization for the pharmaceutical industries, Ind. Eng. Chem. Res., 40, 275-286
Prest, R., Real Demand Forecasting, http:// www. pharmamanufacturing.com/ articles/ 2007/178.html, last accessed: 02.12.2012
Rotstein, G.E., Papageorgiou, L.G., Shah, N., Murphy, D.C. & Mustafa, R. (1999), A product portfolio approach in the pharmaceutical industry, Computers and Chemical Engineering Supplement, 5883-5886
Saritas, I., Ozkan, I. A., Allahverdi, N. & Argindogan, M. (2009), Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation, Journal of Intelligent Manufacturing 20:169–176 DOI 10.1007/s10845-008-0226-x
Sekhri, N. (2006), Forecasting for global health: new money, new products & new markets, Center for Global Development
Tian, Z. (2012), An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring, Journal of Intelligent Manufacturing, 23:227–237, DOI 10.1007/s10845-009-0356-9
Watson, G., http://glennwatson.net/, last accessed: 01.06.2010
Wei, S., Zhang, J., & Li, Z. (1997), A supplier-selecting system using a neural network, IEEE International Conference on Intelligent Processing Systems, 468–471

Thank you for copying data from http://www.arastirmax.com