Demand Forecasting In Pharmaceutical Industry Using Neuro-Fuzzy Approach

Yıl 2014, Cilt: 2 Sayı: 2, 41 - 49, 21.03.2014

Gökçe Candan M.fatih Taskin Harun Yazgan

https://doi.org/10.17858/jmisci.06816

Cited By: 12

Öz

Because of human healthcare, the pharmaceutical industry can be considered as one of the most significant industrial sector. For this 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 main objective of this study is to predict future periods’ demand from previous sales quantity with considering effects of the external factors by employing a neuro-fuzzy approach. Because of the biases of external effects in Artificial Neural Network (ANN) topology, an ANFIS as neuro fuzzy approach is applied. An example is given to illustrate effectiveness of the approach. 

Anahtar Kelimeler

Demand forecasting, Neuro-Fuzzy, Fuzzy Logic, Pharmaceutical Industry

Kaynakça

• 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 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, 1651 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 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/s10845-0090356-9 Watson, G., http://glennwatson.net/, last accessed: 002010
• Wei, S., Zhang, J., & Li, Z. (1997), A supplier-selecting system using a neural network, IEEE International Conference on Intelligent Processing Systems, 468–471