An intelligent algorithm for final product demand forecasting in pharmaceutical units

Accurate demand forecasting in pharmaceutical industries has always been one of the main concerns of planning managers because a lot of downstream supply chain activities depend on the amount of final product demand. In the current study, a five-step intelligent algorithm is presented based on data mining and neural network techniques to forecast demand in pharmaceutical industries. The main idea of the proposed approach is clustering samples and developing separate neural network models for each cluster. Using the obtained data, the performance of the proposed approach was assessed in a pharmaceutical factory. The optimal number of clusters for this case was four. Mean arctangent absolute percentage error, average relative variance, and correlation coefficient (R) were used to evaluate the performance of different neural network structures. The results of performing the models once for all data and once for the data of each single cluster showed that the forecasting error significantly decreased thanks to using this approach. Furthermore, the results indicated that clustering products not only raises the prediction accuracy but also enables a more reliable assessment of forecasted values for each single cluster. Such analyses are very important and useful for managers of marketing and planning departments in pharmaceutical units.

[1]  S. Saeedeh Sadegh,et al.  Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm , 2016 .

[2]  Jianjun Wang,et al.  An annual load forecasting model based on support vector regression with differential evolution algorithm , 2012 .

[3]  Rustam M. Vahidov,et al.  Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..

[4]  Mohammad Mahdi Nasiri,et al.  Improvement of operating room performance using a multi-objective mathematical model and data envelopment analysis: A case study , 2018 .

[5]  Mahdi Hamid,et al.  A multi-objective mathematical model for nurse scheduling problem with hybrid DEA and augmented ε-constraint method: A case study , 2018 .

[6]  Jie Wu,et al.  Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model , 2013 .

[7]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[8]  Erik Hofmann,et al.  Big data analytics and demand forecasting in supply chains: a conceptual analysis , 2018 .

[9]  Cengiz Kahraman,et al.  A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis , 2009, Expert Syst. Appl..

[10]  Krzysztof Koszela,et al.  Short-term forecast of generation of electric energy in photovoltaic systems , 2018 .

[11]  Bruno Agard,et al.  Forecasting Supply Chain Demand by Clustering Customers , 2015 .

[12]  Heeyoung Kim,et al.  A new metric of absolute percentage error for intermittent demand forecasts , 2016 .

[13]  Qingli Dong,et al.  A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China , 2017 .

[14]  Numan Çelebi,et al.  Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods , 2013 .

[15]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[16]  Amit Jain,et al.  Short term load forecasting using fuzzy adaptive inference and similarity , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[17]  Harun Resit Yazgan,et al.  Demand Forecasting In Pharmaceutical Industry Using Neuro-Fuzzy Approach , 2014 .

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  Purnima Bholowalia,et al.  EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN , 2014 .

[20]  Mahdi Hamid,et al.  An Intelligent Algorithm for Optimization of Resource Allocation Problem by Considering Human Error in an Emergency Department , 2018 .

[21]  Saeid Nahavandi,et al.  Short term load forecasting using Interval Type-2 Fuzzy Logic Systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[22]  Yi Zeng,et al.  Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network , 2017 .

[23]  Tiago Alessandro Espínola Ferreira,et al.  A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks , 2008, Neural Processing Letters.

[24]  Juan Antonio Rodríguez Díaz,et al.  Optimisation of water demand forecasting by artificial intelligence with short data sets , 2019, Biosystems Engineering.

[25]  Gabriele Comodi,et al.  The role of data sample size and dimensionality in neural network based forecasting of building heating related variables , 2016 .

[26]  Guillaume Foggia,et al.  Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems , 2016, IEEE Transactions on Power Systems.

[27]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[28]  R. Fildes,et al.  Measuring forecasting accuracy : the case of judgmental adjustments to SKU-level demand forecasts , 2013 .

[29]  H ElHassani.,et al.  Causal Method and Time Series Forecasting model based on Artificial Neural Network , 2013 .

[30]  John T. Mentzer,et al.  Sales Forecasting Management: A Demand Management Approach , 2004 .

[31]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[32]  Sarat Kumar Patra,et al.  Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[33]  Jolanta Szoplik,et al.  Forecasting of natural gas consumption with artificial neural networks , 2015 .

[34]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[35]  Annastiina Kerkkänen.  Improving demand forecasting practices in the industrial context , 2010 .

[36]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[37]  Desheng Dash Wu,et al.  Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..

[38]  Chia-Yen Lee,et al.  Aggregate demand forecast with small data and robust capacity decision in TFT-LCD manufacturing , 2016, Comput. Ind. Eng..

[39]  Muralitharan Krishnan,et al.  Neural network based optimization approach for energy demand prediction in smart grid , 2018, Neurocomputing.

[40]  Ali Azadeh,et al.  A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area. , 2016, Accident; analysis and prevention.