An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification

A multi-criteria inventory classification method was developed.Machine learning algorithms are integrated with multi-criteria decision making.A case study at an automotive company validates the model with its high accuracy.The proposed method yields significantly better results than others in literature.It is flexibly applicable to other multi-criteria inventory classification cases. The purpose of this study is to develop a hybrid methodology that integrates machine learning algorithms with multi-criteria decision making (MCDM) techniques to effectively conduct multi-attribute inventory analysis. In the proposed methodology, first, ABC analyses using three different MCDM methods (i.e. simple-additive weighting, analytical hierarchy process, and VIKOR) are employed to determine the appropriate class for each of the inventory items. Following this, naive Bayes, Bayesian network, artificial neural network (ANN), and support vector machine (SVM) algorithms are implemented to predict classes of initially determined stock items. Finally, the detailed prediction performance metrics of algorithms for each method are determined. The comprehensive case study executed at a large-scale automotive company revealed that the best classification accuracy is achieved by SVMs. The results also revealed that Bayesian networks, SVMs and ANNs are all capable of successfully dealing with the unbalanced data problems associated with Pareto distribution, and each of these algorithms performed well against all examined measures, thus validating the fact that machine learning algorithms are highly applicable to inventory classification problems. Therefore, this study presents uniqueness in that it is the first and foremost of its kind to effectively combine MCDM methods with machine learning algorithms in multi-attribute inventory classification and is practically applicable in various inventory settings. Furthermore, this study also provides a comprehensive chronological overview of the existing literature of machine learning methods within inventory classification problems.

[1]  M. Jaber,et al.  Vendor managed inventory (VMI) with consignment considering learning and forgetting effects , 2012 .

[2]  Mohamad Y. Jaber,et al.  An integrated supply chain model with errors in quality inspection and learning in production , 2014 .

[3]  Murugan Anandarajan,et al.  Classifying inventory using an artificial neural network approach , 2002 .

[4]  Fatemeh Zahedi.  Intelligent Systems for Business: Expert Systems with Neural Networks , 1993 .

[5]  Ramakrishnan Ramanathan,et al.  ABC inventory classification with multiple-criteria using weighted linear optimization , 2006, Comput. Oper. Res..

[6]  Chi-Jie Lu,et al.  Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting , 2010 .

[7]  Vipin Kumar,et al.  Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[8]  Kjetil Fagerholt,et al.  Vendor managed inventory in tramp shipping , 2014 .

[9]  Ming-Liang Li.  Goods classification based on distribution center environmental factors , 2009 .

[10]  Golam Kabir,et al.  Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification , 2013 .

[11]  Remco R. Bouckaert,et al.  Bayesian Network Classifiers in Weka for Version 3-5-7 , 2007 .

[12]  Ching-Wu Chu,et al.  Controlling inventory by combining ABC analysis and fuzzy classification , 2008, Comput. Ind. Eng..

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[15]  H. Altay Güvenir,et al.  Multicriteria inventory classification using a genetic algorithm , 1998, Eur. J. Oper. Res..

[16]  Peng Zhou,et al.  A note on multi-criteria ABC inventory classification using weighted linear optimization , 2007, Eur. J. Oper. Res..

[17]  Ralescu Anca,et al.  ISSUES IN MINING IMBALANCED DATA SETS - A REVIEW PAPER , 2005 .

[18]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[19]  S. M. Hatefi,et al.  Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria , 2014 .

[20]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[21]  S. G. Deshmukh,et al.  Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems , 1992 .

[22]  Russell L. Ackoff,et al.  An Approximate Measure of Value , 1954, Oper. Res..

[23]  L. Duckstein,et al.  Multiobjective optimization in river basin development , 1980 .

[24]  Ozden Ustun,et al.  An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection , 2008 .

[25]  David L. Olson,et al.  Management of multicriteria inventory classification , 1992 .

[26]  D. Clay Whybark,et al.  Multiple Criteria ABC Analysis , 1986 .

[27]  Jonathan M. Garibaldi,et al.  A 'non-parametric' version of the naive Bayes classifier , 2011, Knowl. Based Syst..

[28]  D. Clay Whybark,et al.  Implementing multiple criteria ABC analysis , 1987 .

[29]  T. Comes,et al.  A critical review on supply chain risk – Definition, measure and modeling ☆ , 2015 .

[30]  Liliane Pintelon,et al.  Criticality classification of spare parts: A case study , 2012 .

[31]  Manoj Kumar Tiwari,et al.  Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM , 2012, Expert Syst. Appl..

[32]  Gary M. Weiss.  Mining with rarity: a unifying framework , 2004, SKDD.

[33]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[34]  Sohyung Cho,et al.  Tool breakage detection using support vector machine learning in a milling process , 2005 .

[35]  Chi-Yang Tsai,et al.  A multiple objective particle swarm optimization approach for inventory classification , 2008 .

[36]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[37]  Nigel J. Smith,et al.  Application of a fuzzy based decision making methodology to construction project risk assessment , 2007 .

[38]  Jin-Xiao Chen.  Peer-estimation for multiple criteria ABC inventory classification , 2011, Comput. Oper. Res..

[39]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[40]  S. Greco,et al.  Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE , 2013 .

[41]  Qing Zhou,et al.  Multiple Criteria Inventory Classification Based on Principal Components Analysis and Neural Network , 2005, ISNN.

[42]  Ronald M. Summers,et al.  Machine learning and radiology , 2012, Medical Image Anal..

[43]  Yeou-Ren Shiue,et al.  Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach , 2009 .

[44]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[45]  Angappa Gunasekaran,et al.  Service supply chain environmental performance evaluation using grey based hybrid MCDM approach , 2015 .

[46]  Abdollah Hadi-Vencheh,et al.  An improvement to multiple criteria ABC inventory classification , 2010, Eur. J. Oper. Res..

[47]  Jingzhu Wei,et al.  The Multiple Attribute Decision-Making VIKOR Method and its Application , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[48]  Keith W. Hipel,et al.  A case-based distance model for multiple criteria ABC analysis , 2008, Comput. Oper. Res..

[49]  Najla Aissaoui,et al.  Supplier selection and order lot sizing modeling: A review , 2007, Comput. Oper. Res..

[50]  Alireza Afshari,et al.  Simple Additive Weighting approach to Personnel Selection problem , 2010 .

[51]  Golam Kabir,et al.  Multiple criteria inventory classification using fuzzy analytic hierarchy process , 2012 .

[52]  Cengiz Kahraman,et al.  A multiattribute ABC classification model using fuzzy AHP , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[53]  Nachiappan Subramanian,et al.  A review of applications of Analytic Hierarchy Process in operations management , 2012 .

[54]  Diyar Akay,et al.  Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting , 2009, Expert Syst. Appl..

[55]  Timothy J. Lowe,et al.  Consignment or wholesale: Retailer and supplier preferences and incentives for compromise☆ , 2014 .

[56]  Cengiz Kahraman,et al.  Single and Multiple Attribute Fuzzy Pareto Models , 2012, J. Multiple Valued Log. Soft Comput..

[57]  Ricardo Ernst,et al.  MULTI-ITEM CLASSIFICATION AND GENERIC INVENTORY STOCK CONTROL POLICIES , 1988 .

[58]  Angappa Gunasekaran,et al.  Special Issue On Logistics: New Perspectives and Challenges , 2008 .

[59]  Wan Lung Ng,et al.  Production , Manufacturing and Logistics A simple classifier for multiple criteria ABC analysis , 2006 .

[60]  Gwo-Hshiung Tzeng,et al.  Extended VIKOR method in comparison with outranking methods , 2007, Eur. J. Oper. Res..

[61]  Jonathan Burton,et al.  Using the Analytic Hierarchy Process for ABC Analysis , 1993 .

[62]  Thomas L. Saaty,et al.  Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation , 1990 .

[63]  K. Lai,et al.  Responsive supply chain : A competitive strategy in a networked economy , 2008 .

[64]  David de la Fuente,et al.  Abc classification with uncertain data. a fuzzy model vs. a probabilistic model , 2002, Appl. Artif. Intell..

[65]  Ting-Yu Chen,et al.  Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints , 2012, Expert Syst. Appl..

[66]  Hasan Kartal,et al.  Support Vector Machines for Multi-Attribute ABC Analysis , 2013 .

[67]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU)--an outline , 2005, Inf. Sci..

[68]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[69]  Gilbert Laporte,et al.  Rich routing problems arising in supply chain management , 2013, Eur. J. Oper. Res..

[70]  Ozan Çakir,et al.  A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology , 2008, Expert Syst. Appl..

[71]  Angappa Gunasekaran,et al.  Expert systems and artificial intelligence in the 21st century logistics and supply chain management , 2014, Expert Syst. Appl..

[72]  Min-Chun Yu,et al.  Multi-criteria ABC analysis using artificial-intelligence-based classification techniques , 2011, Expert Syst. Appl..

[73]  Cengiz Kahraman,et al.  Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul , 2010 .

[74]  Andrea Bacchetti,et al.  Empirically-driven hierarchical classification of stock keeping units , 2013 .

[75]  Manoj Kumar Tiwari,et al.  Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method , 2012, Expert Syst. Appl..

[76]  Hüseyin Basligil,et al.  A distribution network optimization problem for third party logistics service providers , 2011, Expert Syst. Appl..

[77]  Stanisław Bylka,et al.  Non-cooperative consignment stock strategies for management in supply chain , 2013 .

[78]  Fariborz Y. Partovi,et al.  THE ANALYTIC HIERARCHY PROCESS AS APPLIED TO TWO TYPES OF INVENTORY PROBLEMS , 1993 .

[79]  Jun-Yeon Lee,et al.  Vendor-managed inventory in a global environment with exchange rate uncertainty , 2011 .

[80]  Davood Mohammaditabar,et al.  Inventory control system design by integrating inventory classification and policy selection , 2012 .

[81]  P. Yu.  A Class of Solutions for Group Decision Problems , 1973 .

[82]  Kevin McCormack,et al.  Analysing risks in supply networks to facilitate outsourcing decisions , 2010 .

[83]  Ashutosh Tiwari,et al.  A review of soft computing applications in supply chain management , 2010, Appl. Soft Comput..

[84]  Abdollah Hadi-Vencheh,et al.  A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification , 2011, Expert Syst. Appl..