Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach

186 Pages·2013·6.89 MB·English

MASTER'S THESIS
Sales Prediction for Pharmaceutical
Distribution Companies
A Data Mining Based Approach
Neda Khalilzadeh
Master (120 credits)
Master of Science in Electronic Commerce
Luleå University of Technology
Department of Business Administration, Technology and Social Sciences
MASTER'S THESIS
Sales Prediction for Pharmaceutical Distribution
Companies- A Data Mining Based Approach
Supervisors:
Prof. Mohammad Mehdi Sepehri
Prof. Peter Naude
Referees:
Dr. Amir Albadvi
Dr. Lars Backstrom
Prepared by:
Neda Khalilzadeh
Tarbiat Modares UniversityFaculty of Engineering
Department of Industrial Engineering
Lulea University of Technology
Department of Business Administration and Social Sciences
Division of Industrial Marketing and E-Commerce
JOINTMSc PROGRAM IN MARKETING AND ELECTRONIC COMMERCE
2008
ABSTRACT
Due to the tough competitions that exist today, most pharmaceutical distribution
companies are in acontinuous effort to increasetheir profits and reduce their costs. Actually,
both shortage and surplus of goods can lead to loss of income for these companies. One of the
problems in pharmaceutical distribution organizations which deal with public health and
pharmaceutical products ishow tocontrol inventory levels by means of accurate sales prediction
in order to prevent costs of excessive inventory also prevent losing their customers because of
drug shortage.Accurate sales prediction is certainly a valuable management tool to meet the
mentioned goals since this leads to improved customer service, also, reduced lost sales and costs.
However, most pharmaceutical distribution companies in Iran are still using heuristic or
traditional statistical techniques to make sales prediction for their products. Thus, the purpose of
this research is to apply an innovative and reliablesales prediction method for pharmaceutical
distribution companies.
To make sales prediction for a pharmaceutical distribution company, we needed to have
past sales records of each drug. Accordingly, we gathered sales data of three years from Pakhsh
Hejrat Co. which is one of the leading pharmaceutical distributors in Iran. We choseneural
networks as our basic tools for sales prediction since most traditional methods like ARIMA are
incapable of modeling nonlinearities that exist in most real data; also, they need forecaster’s
supervision fortheparameter estimation phase. In fact, neuralnetworks are versatile tools for
sale prediction sinceestimation with neural networks can be automatized,and theyhave proved
very effective in order to make prediction by handling non-linearinput and output variables. Due
to the fact that we did not have enough past sales records of drug items, we came up to a new
idea of grouping drugs to find group members and make use of co-members’ sales data for each
other. Thus, we did a comprehensive network based analysis in order to find clique-setsand
group members. Afterwards, we built sales forecasting models with three different approaches:
a) ARIMA methodology for time series forecasting, b) Hybrid neural network approach for time
series forecasting by means of each drug’s past recodes,and c) Hybrid neural network approach
for time series forecasting by means of each drug’s past records and its group members’past
records. Ourevaluations and results indicated that our new methodology (number 3 above)was
the best methodology, and the weakest one was ARIMA model.
Keywords: Pharmaceutical Distribution Companies, Data Mining, Time Series Sales
Forecasting,Network Based Analysis,Neural Networks
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ACKNOWLEDGMENTS
Actually, this researchwas carried out from spring of 2007 till spring of 2008 atTarbiat
Modares University (TMU) in fulfillment of the MSc Joint program in marketing and e-
commerce with Lulea University of Technology (LTU). While working on this thesis, I have
learned manyunknown issues, which are really of great value to me.
Here, I wouldlike to express my gratitude to all who helped me with their worthwhile
supports during all stepsof this thesis.
First of all, I would like to express my appreciation to my supervisors Prof. Mohammad
Mehdi Sepehriand Prof. Peter Naudefor all their patience, kind supports, encouragements, and
helpful and valuable advicewhile performing this research. Without their encouragements and
valuable comments,I was not able to fulfill this project research.
I also would like to thank Prof. Amir Albadvi, the head of Industrial Engineering
Department of TMU,and Prof. Esmail Salehi-Sangari,the chairman of Industrial Marketing and
E-commerce Division of LTU,for theirstrong and continuous supports in arranging this joint
master program.
Furthermore, I wantto express my thanks and regards to Dr. Hamid Farvaresh, the PHD
candidatein Industrial Engineering Department in TMU, for his valuable and great helps and
supports, in all steps of this research.
In addition, I would like to extend my special thanks to the experts and sales managers of
pharmaceutical distribution companies especially to Dr. Mostafavi and Prof. Sarkandi who
guided me with their valuable comments and ideas. I also want to thank Mr. Haj Sadeghi for
providing me with Pakhsh Hejrat’s past sales records.
And last but not least, I want to thank my families and friends, specially my parentsand
my dear brother, for their encouragements, patience, and kind supports while accomplishing this
research. I would like to dedicate my thesis tomy lovely parents in order to express my deep
appreciation towardsthem for their unending and notable supports in all aspects of my life.
Neda Khalilzadeh
24thMarch 2008
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TABLE OF CONTENTS
CHAPTER 1...................................................................................................................................9
INTRODUCTION..........................................................................................................................9
1. Introduction.............................................................................................................................9
1.1.Background ofthe Study....................................................................................................10
1.2. Motivation of the Study......................................................................................................15
1.3. Pharmaceutical Distribution Companies in Iran................................................................16
1.4. Problem Discussion............................................................................................................19
1.4.1. Problem Definition......................................................................................................20
1.5. Research Objectives and Question.....................................................................................23
1.6. Research Framework..........................................................................................................24
1.7. Contributions of the Research............................................................................................25
1.8. Outline of the Thesis..........................................................................................................26
CHAPTER 2.................................................................................................................................28
DEFINITIONS AND LITERATURE REVIEW..........................................................................28
2. Definitions and Literature Review........................................................................................28
2.1. Data Mining and Knowledge Discovery............................................................................29
2.1.1. An Overview of Data Mining......................................................................................29
2.1.2. Difference between knowledge Discovery and DataMining.........................................30
2.1.3. Descriptions of the Knowledge Discovery and Data Mining in Literatures...............31
2.1.4. The Process of Data Mining and Knowledge Discovery............................................32
2.1.5. Data Mining Machine Learning and Statistics............................................................34
2.1.6. Data Mining Tasks and Techniques............................................................................35
2.1.7. Choose Appropriate Data Mining Techniques............................................................38
2.1.8. Data Mining Applications...........................................................................................38
2.2. Neural Networks................................................................................................................41
2.2.1. Neural Networks Architectures...................................................................................43
2.2.2. Learning Process..........................................................................................................47
2.2.3. Back Propagation Algorithm.......................................................................................49
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2.2.4. Single-layer Perceptron...............................................................................................51
2.2.5. Multilayer Perceptron (MLP)......................................................................................52
2.2.6. Radial basis function network (RBF)..........................................................................54
2.2.7. Time Delay Neural Networks (TDNN).......................................................................55
2.2.8. Recurrent neural networks...........................................................................................56
2.2.9. Benefits and Limitations of Neural Networks.............................................................56
2.2.10. Applications of Neural Networks..............................................................................58
2.3. Supply Chain......................................................................................................................63
2.3.1. Inventory and Inventory Management........................................................................65
2.3.2. Participants in the Supply Chain.................................................................................66
2.3.3. Logistics.......................................................................................................................67
2.3.4. Why Demand Forecasting is Essential in a Supply Chain?.........................................68
2.4. Forecasting.........................................................................................................................69
2.4.1. Sales Forecasting.........................................................................................................71
2.4.2. Time Series Forecasting..............................................................................................71
2.4.3. Time Series Forecasting Models.................................................................................79
2.4.4. Comparisons of Different Time Series Forecasting Models.......................................81
CHAPTER 3.................................................................................................................................85
RESEARCH METHODOLOGY..................................................................................................85
3. Research Methodology..........................................................................................................85
3.1. Research Design.................................................................................................................86
3.1.1. Research Purpose.........................................................................................................87
3.1.2. Research Approach......................................................................................................88
3.1.3. Research Strategy........................................................................................................89
3.2. Research Process................................................................................................................89
3.2.1. Data Collection and Description..................................................................................92
3.2.2. Data Preparing.............................................................................................................93
3.3. Exploratory Analysis..........................................................................................................98
3.4. NetworkBased Analysis....................................................................................................99
3.5. Methods Used for Modeling and Time Series Forecasting...............................................99
CHAPTER 4...............................................................................................................................104
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RESULTS AND ANALYSIS.....................................................................................................104
4. Results and Analysis............................................................................................................104
4.1. Data preprocessing...........................................................................................................105
4.2. Exploratory Analysis........................................................................................................107
4.2.1. Data Visualization.....................................................................................................107
4.2.2.Descriptive Statistics.................................................................................................111
4.3. Statistical Analysis of Drugs’ Networks..........................................................................113
4.3.1. Network Identification...............................................................................................114
4.4. Building Sales Forecasting Model...................................................................................125
4.4.1. ARIMA Methodology for Time Series Forecasting..................................................128
4.4.2. A Hybrid Neural Network Approach for Time Series Forecasting...........................138
4.4.3. Model Evaluation......................................................................................................144
4.5. Summary..........................................................................................................................147
CHAPTER 5...............................................................................................................................148
CONCLUSION...........................................................................................................................148
5. Conclusion...........................................................................................................................148
5.1. Conclusion........................................................................................................................148
5.2. Contributions....................................................................................................................152
5.2.1. Methodological Contribution....................................................................................152
5.2.2. Empirical Contribution..............................................................................................152
5.3. Managerial Implications...................................................................................................153
5.4. Research Limitations........................................................................................................156
5.5. Further Research..............................................................................................................157
REFERENCES...........................................................................................................................159
Ho, T.B., (2002), “Introductionto Knowledge Discovery and Data Mining”, Available at:
ttp://www.netnam.vn/unescocourse/knowlegde/know_frm.htm................................................164
APPENDIX 1..............................................................................................................................172
Tables of Prediction Results with Neural Networks...................................................................172
APPENDIX 2..............................................................................................................................184
Definitions of Codesin Result Tables of Forecasting................................................................184
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LIST OF TABLES
Table 1: Applications of data mining in marketing and Business................................................41
Table 2: Applications of Neuronal Networks in Business Forecasting........................................62
Table 3: Related Works on Applications of Neural Networks in Sales and Demand Forecasting77
Table 4: Sales Data Information Collected from Pakhsh Hejrat Company..................................93
Table 5: Amount of Indices for Different өs................................................................................97
Table 6: Descriptive Measures of 21 Drugs...............................................................................112
Table 7: Cliques Sets..................................................................................................................116
Table 8: Descriptive Statistics of Degrees..................................................................................120
Table 9:Frequency and Cumulative Frequency of Degrees.......................................................121
Table 10: Group Members..........................................................................................................128
Table 11: Parameter Estimation..................................................................................................135
Table 12: Four Months’ Forecasting Results for the Original Data...........................................135
Table 13: Sensitivity Analysis of Drug 71 for the best 5 models of 20 tried models.................144
Table 14: Forecasting Results and Accuracy Measures of Test Data for Drug 24 (with Its Own
Past Records)..............................................................................................................................146
Table 15: Forecasting Results and Accuracy Measures of Test Data for Drug 24 (with Its
Partner’s Past Records)...............................................................................................................146
Table 16: Forecasting Results and Accuracy Measures of Test Data for Drug 71 (with Its Own
Past Records)..............................................................................................................................146
Table 17: Forecasting Results and Accuracy Measures of Test Data for Drug 71 (with Its
Partner’s Past Records)...............................................................................................................146
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LIST OF FIGURES
Figure 1: Pharmaceutical Distribution Channel............................................................................18
Figure 3: A Schematic Representation of the Research Framework............................................25
Figure 4: Outline of the Thesis....................................................................................................27
Figure 5: A Typical knowledge Discovery Process......................................................................30
Figure 6: Combining cognitive psychology with artificial intelligence, databases, and statistics31
Figure 7: Application of Data Mining for Marketing...................................................................40
Figure 8: A Simple Neural Network.............................................................................................44
Figure 9: A Neural Network with one Hidden Layer...................................................................44
Figure 10: A Neural Network with a Wide Hidden Layer............................................................45
Figure 11: A Neural Network with Multiple Outputs...................................................................45
Figure 12: The Unit of an Artificial Neural Network...................................................................46
Figure 13: Three Common Transfer Functions............................................................................47
Figure 14: A Back-Propagation Network Model..........................................................................51
Figure 15: Structure of the MLP...................................................................................................53
Figure 16: The RBF Neural Network Topology...........................................................................55
Figure 17: TDNN Architecture.....................................................................................................55
Figure 18: Recurrent neural network............................................................................................56
Figure 19: A time-delay neural network.......................................................................................60
Figure 20: An Example of a Non-Stationary Time Series............................................................73
Figure 21: An Example of a Stationary Time Series....................................................................74
Figure 22: Research Design of this Study.....................................................................................86
Figure 23: Research Process.........................................................................................................91
Figure 24: Amount of Indices for Different өs.............................................................................97
Figure 25: Time Series Plot of Sales Data for Drug 211............................................................108
Figure 26: Time Series Plot of Sales Data for Drug 174............................................................108
Figure 27: Time Series Plot of Sales Data for Drug 24..............................................................109
Figure 28: Multiple Time Series Plot of 3 Drugs.......................................................................109
Figure 29: 3D Surface Plot for Drugs 24, 78, 84........................................................................110
Figure 30: 3D Surface Plot for Drugs 24, 31, 37........................................................................110
Figure 31: 3D Surface Plot for Drugs 24, 174, 86......................................................................111
Figure 32: A Part of Output of UCINET for Clique-sets ...........................................................118
Figure 33: Cluster (Tree) Diagram.............................................................................................119
Figure 34: A Part of Output of Degree Centrality Measures......................................................120
Figure 35: Degree Distribution of the market graph...................................................................121
Figure 36: Networks of Drugs....................................................................................................123
Figure 37: Network of Drugs after Deleting Isolated Nodes and Nodes with Degree 1............123
Figure 38: Network of Clique Indicators....................................................................................125
Figure 39: Time Series Plot of Drug 24 Sales Data....................................................................130
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Figure 40: Autocorrelation Diagram...........................................................................................130
Figure 41: Partial Autocorrelation Diagram...............................................................................131
Figure 42: Time series plot of drug 24 sales data after differentiation and log transforming....131
Figure 43: Auto-correlation Function of Drug 24 Sales Data.....................................................133
Figure 44: Partial Auto-correlation Function of Drug 24 Sales Data.........................................134
Figure 45: Plot of Forecasting.....................................................................................................136
Figure 46: Normal Probability Plot of Residuals for ARMA (1, 1, 0).......................................137
Figure 47: Autocorrelation Function of Residuals......................................................................137
Figure 48: Partial Autocorrelation Function of Residuals.........................................................138
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