Supplier selection problem: A literature review of Multi-criteria approaches based on DEA
|
|
- Karin Lynch
- 7 years ago
- Views:
Transcription
1 Advances in Management & Applied Economics, vol.1, no.2, 2011, ISSN: (print), (online) International Scientific Press, 2011 Supplier selection problem: A literature review of Multi-criteria approaches based on DEA Alexios-Patapios Kontis 1 and Vassilios Vrysagotis 2 Abstract Supplier selection problem usually is very complicated, because variety of uncontrollable and unpredictable factors affect on evaluation and decision-making process. Various decision making approaches have been proposed to tackle the problem as part of a general tendering process, particularly those of multi-criteria analysis which use both quantitative and qualitative data. Specifically for this problem have been proposed methodologies and techniques included methods such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Case-Based Reasoning (CBR), Genetic Algorithms (GA), SMART theory (Simple Multi-Attribute Rating Technique) and Data Envelopment Analysis (DEA). This paper reviews the literature of the multi-criteria decision making approaches for evaluation and selection supplier based on Data Envelopment Analysis (DEA) and (its their) combinations. 1 Department of Logistics Management, Technological Educational Institute of Chalkida, Greece, alexis.kontis@gmail.com 2 Department of Logistics Management, Technological Educational Institute of Chalkida, Greece, brisxri@otenet.gr Article Info: Revised: October 24, Published online: October 31, 2011
2 208 Supplier selection problem: A literature review on DEA JEL Classification: C81 Keywords: Multi-criteria decision making, Data Envelopment Analysis (DEA), supplier evaluation and selection 1 Introduction In an era of global sourcing, business s success often hinges on the most appropriate selection of its partners and suppliers. Procurement is an increasingly important activity within most firms, and severe financial and operational consequences can result from the failure to optimize the procurement function. Specifically, appropriate suppliers selection is one of the fundamental strategies for enhancing the quality of output of any organization, which has a direct influence on the company s competitiveness and reputation. Suppliers evaluation and selection process became vital for the entire width of business sizes and activities [1]. The selection process involves the determination of quantitative and qualitative factors so as to select the best possible suppliers, which ensure business competitiveness, sustainability and success. Consequently, the supplier selection problem requires the consideration of multiple objectives, and hence can be viewed as a multi-criteria decision-making (MCDM) problem [2]. Over the last decade the research community has extensively studied the problem of evaluating and selecting suppliers, adopting approaches and implementations from a wide range of mathematical practices and methodologies. Consequently, numerous multi-criteria decision support tools have been developed for structuring and supporting such decisions [3] [4]. Major part of total body of research for supplier s evaluation and selection problem, consists from Data Envelopment Analysis (DEA) approaches. Other related approaches based on AHP (Analytic Hierarchy Process), Fuzzy Sets Theory, Goal Programming and combination of them have been proposed to solve the suppliers decision-making problem. Data
3 A-P. Kontis and V. Vrysagotis 209 envelopment analysis was developed by Rhodes [5] and initially detailed and publicized by Charnes, Cooper and Rhodes [6] for evaluating more than two decision outcomes and/or Decision Making Units (DMUs) with respect to their relative efficiencies, based upon multiple criteria. DEA is built on the theoretical foundations provided by Farrell [7] and continues to be popular for a wide variety of applications [8] [9]. Beyond identifying efficient outcomes, DEA has been used to identify the existence of technical and managerial efficiencies. 2 Implementations of Data Envelopment Analysis (DEA) in supplier evaluation and selection Much of the research for suppliers evaluation and selection lies in Data Envelopment Analysis (DEA) methods and its variants, because DEA is a non-parametric, linear programming based technique for measuring the relative efficiency of homogeneous units that consume incommensurable multiple inputs and produce multiple outputs. The inputs and outputs are assumed to be continuous positive variables and the weights are estimated in favor of each evaluating unit to maximize its efficiency. DEA achieves to classify the units into efficient units that achieve efficiency scores equal to the upper bound and inefficient units that are those that do not succeed to do so. DEA is a widely accepted evaluation method by researchers and practitioners because it has repeatedly proved its ability to effectively handle multiple conflicting properties associated with the modern requirements of administrative sciences inherent in supplier selection and beyond. In order to facilitate readers, presented DEA approaches divided in individual and integrated.
4 210 Supplier selection problem: A literature review on DEA 2.1 Evaluation and selection processes based on individual approaches of DEA Weber in 1996 [10] suggests that Data Envelopment Analysis (DEA) can be used as an objective method to evaluate suppliers based on multiple criteria and identified benchmarking values. Four years later, Liu, Ding an Lall [11] recognizing the usefulness of DEA as a multi-criteria evaluating methodology and the need for more functional and efficiently suppliers selection systems, proposed a simplified model of DEA to evaluate the overall performance of suppliers, considering three inputs and two outputs. The model aimed to highlight the supplier who offering the greatest supplies variety. Forker and Mendez [12] applied DEA in order to benchmarking the comparative efficiency of suppliers in order to help companies save time and resources by identify the best peer supplier(s). Best peer suppliers can be imitated by companies with similar organizational structures by paying the least amount of effort. Forker and Mendez method for each supplier calculated the maximum ratio of multiple outputs for each single input and use Cross-Efficiency to filter the total results. Narasimhan, Talluri and Mendez [13] recognizing that business performance based on total performance of suppliers network propose a DEA model for effective supplier performance evaluation, based on eleven critical factors, six inputs and five outputs. Combining the results of DEA with managerial performance rating, suppliers identifying into 4 clusters: High performance and Efficient (HE), High performance and Inefficient (HI), Low performance and Efficient (LE) and Low performance and Inefficient (LI). Based on this categorization firms on HI, LE and LI suppliers clusters could improve their operations across a variety of dimensions by benchmarking and analyzing High performance and Efficient (HE) suppliers.
5 A-P. Kontis and V. Vrysagotis 211 Talluri and Sarkis [14] using DEA to formulate a new model for performance monitoring of suppliers. The aims of this paper were to apply a new multi-criteria evaluation model for supplier performance evaluation by considering various performance criteria, such as to serve a monitoring and control mechanism for the performance of suppliers. The new model and its apply based on data of a previously published illustrative case study of Talluri and Baker [15]. Talluri and Narasimhan [16] aiming to propose an objective framework for effective supplier sourcing, which considers multiple strategic and operational factors in the evaluation process, applied a similar technique to Narasimhan, Talluri and Mendez (2001) based on statistical indicators and Cross-Efficiencies to classify suppliers compared to their effectiveness. Ross, Buffa, Dröge and Carrington [17] at their Action Research (AR) framework developed and suggested a DEA model which examines supplier s efficiency focused on buyer's perspective. Garfamy [18] focused on Total Cost of Ownership (TCO) applied a DEA model to evaluate the overall performance of suppliers, claiming that the supplier with the lowest cost per unit is the most efficient supplier. Sean s [19] objective is to propose an innovative method based on DEA for selecting suppliers in the presence of nondiscretionary factors from supplier s perspective. He introduce in evaluating process quality factors that measured by ordinal data. At this paper the factor "know-how transfer" was assessed on a qualitative scale of 5 points. In Seydel s [20] research, the suppliers evaluation model does not focus on inputs but on outputs which have qualitative characteristics rated on a seven level scale. Proposed method is compared with the SMART methodology, noting that requires less participation of decision maker and less data. Talluri, Narasimhan and Nair [21] addressing the complexities associated with stochastic approaches for effectiveness assessment, suggest a
6 212 Supplier selection problem: A literature review on DEA Chance-Constrained Data Envelopment Analysis (CCDEA) approach in the presence of multiple performance measures that are uncertain. In this model, the price used as an input while the quality and delivery is considered as outputs. Smirlis, Panta, Kaimakamis and Sfakianakis [22], in order to develop a systematic and reliability evaluation model for selecting Third Party Logistics (3PL) partners, suggest a DEA methodology based on the estimation of a superiority index which achieves to discriminate them into beneficiary and non beneficiary. Saen [23] facing with a main problem of supplier selection process, the use both quantitative and qualitative data, present an innovative variation of DEA (imprecise DEA) based on non-precise of Fuzzy data which include verity of cardinal and ordinal data. The proposed model can handle non-precise of fuzzy data as precise. Following the same path, Wu, Shunk, Blackhurst and Appalla [24] propose an improved version of supplier selection model based on non-precise data which had potential for further discrimination of efficiently selection. Wu and Blackhurst [25] in aim to eliminate the potential weaknesses of basic DEA model as well as the cross-efficiency and super-efficiency models, suggest an Augmented DEA approach with enhanced discriminatory power that can be used in supplier evaluation and selection. They have added two enhancements to the basic DEA model: the inclusion of virtual standards which are spanned across a range (rather than being a single point or benchmark); and the introduction of weight constraints. Panta, Smirlis and Sfakianakis [26] in order to propose a efficient decision making approach in relation with the existing fixed weighted framework for the procurement of products and services for Public Organizations, develop a DEA model that overcomes the above mentioned shortcoming. The main characteristics of this model are the ability to incorporate different price
7 A-P. Kontis and V. Vrysagotis 213 levels and the flexibility of setting the weights in favor of each evaluated bid, ensuring that the winner has uncontested superiority. 2.2 Evaluation and selection processes based on integrated approaches of DEA Except the approaches which use classical implementation of DEA to solve suppliers decision-making problem, research community suggest numerous of innovative combinations approaches whose at least a part based on DEA. Weber, Current and Desai [27] aiming to determine the optimal order quantity, suggests DEA in conjunction with Multi-Objective Programming (MOP). According to this approach, MOP is used to develop vendor-order quantity solutions, whom calls supervendors and then evaluating the efficiency of these supervendors on multiple criteria using DEA. Braglia and Petroni [28] applied Data Envelopment Analysis to measure the efficiencies of candidates suppliers. In order to evaluate and rank alternative suppliers they use nine key factors. For ensuring selection process they apply both Cross-Efficiency and Maverick index. Talluri and Baker [15] present a multi-phase mathematical programming approach for designing effectively supply chain networks. Specifically, they develop and apply a combination of multi-criteria efficiency models, based on Game Theory concepts, and Linear and Integer Programming methods. In first stage, potential suppliers and manufacturers were evaluated separately in two inputs and four outputs using DEA. Based on the results of this phase, at the second phase developed a supply and distribution model of goods in warehouses by identifying the optimal number of suppliers, manufactures and distributors. Finally, the third phase involves
8 214 Supplier selection problem: A literature review on DEA the initial deployment plans, which identify the optimal routing of material from selected suppliers to manufacturers by minimizing the total cost. Saydel [29] aiming to support sole-sourcing decision-making discussion, uses an example of ten supplier to present SMART (Simple Multi-Attribute Rating Technique) and DEA and demonstrate their efficiency. The results from these two approaches are compared to those based upon a pure aggregation and averaging procedure. Saen [30] aiming to evaluating and selecting slightly non-homogeneous suppliers proposed an innovative approach which combines AHP and DEA. Recognizing that suppliers do not consume common inputs to supply common outputs, used AHP to identify the relative weight of each supplier and DEA to compute the relative efficiency of each supplier. Ramanathan [31] study attempted to extend the analysis proposed by Bhutta and Huq [2], which evaluate suppliers performance using Total Cost of Ownership (TCO) considering the quantitative factor of cost, and Analytical Hierarchy Process (AHP) to consider a mix of qualitative factors. Through his research suggests a combination of objective and subjective information provided by the results of the Total Cost of Ownership (TCO) and Analytical Hierarchy Process (AHP) approaches via Data Envelopment Analysis (DEA). In order to select competitive suppliers in a supply chain, Ha and Krishnan [32] outlined a hybrid method, which incorporates multiple techniques (i.e., AHP, DEA, and ΑNN) into an integrated process. Proposed hybrid method uses an AHP to assign weight to the qualitative selection criteria, and combination of Artificial Neural Network (ANN) and DEA in order to rated the overall performance index of vendors. Final selection process is based in cluster analysis which returns a Supplier Map (SM) that imprints suppliers position within different segments, according to performance of qualitative and quantitative dimensions.
9 A-P. Kontis and V. Vrysagotis 215 Ozdemir and Temur [33] developed an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). Proposed model uses a total of variables such as Material Quality (MQ), Discount Of Amount (DOA), Discount Of Cash (DOC), Payment Term (PT), Delivery Time (DT) and Annual Revenue (AR). Desheng Wu [34] presents a hybrid model which combining Data Envelopment Analysis (DEA), Decision Trees (DT) and Neural Networks (NNs) to evaluate supplier performance aiming to a favorable classification and prediction accuracy rate. Proposed hybrid model is consisted of two modules, can function as both a classification model and a regression model. Module 1 applies two-stage DEA and classifies suppliers into efficient and inefficient clusters based on the computed efficiency scores. Module 2 is a classification or regression module based on the Decision Tree or the Neural Network. 3 Observations and recommendations The primary advantages of Data Envelopment Analysis are that it considers multiple factors and does not require parametric assumptions of traditional multivariate methods. However, in the application of DEA models there are some critical factors which have to be considered. Efficiency scores could be very sensitive to changes in the data and depend heavily on the number and type of input and output factors considered. In general, inputs can include any resources utilized by a DMU, and the outputs can range from actual products to a range of performance and activity measures. The size of the data set is also an important factor when using some of the traditional DEA models, however, some of these sample size problems can be overcome by using cross-efficiency models.
10 216 Supplier selection problem: A literature review on DEA 4 Conclusions In this paper we review recently literature about solving supplier's evaluation and selection problem, based on DEA and some methodological extensions that could be utilized to improve its discriminatory power in performance evaluation, therefore by no means exhausts all the developments occurring on DEA technique. In general literature, in the last period there is significant work in the field of sensitivity analysis in DEA, target setting in DEA, stochastic DEA, profiling in DEA, among other developments. In the field of supplier evaluation, it s clear over the time, that DEA methodology develops, enriches and improves both the discretionary ability and effectiveness in managing multi-criteria, decisions -making problems. Moreover, research community adds new, innovative and integrated methodological approaches utilizing the multiple capabilities of DEA creatively, making the method more user-friendly, handling and functional. References [1] C.A. Weber, J.R. Current and W.C. Benton, Vendor selection criteria and methods, European Journal of Operational Research, 50(1), (1991), [2] K.S. Bhutta and F. Huq, Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches, Supply Chain Management: An International Journal, 7(3), (2002), [3] D. Olson, Decision Aids for Selection Problems, Springer-Verlag, Berlin, [4] O. Larichev and D. Olson, Multiple Criteria Analysis in Strategic Siting Problems, Kluwer, New York, [5] E. Rhodes, Data Envelopment Analysis and Related Approaches for Measuring the Efficiency of Decision Making Units with an Application to Program Follow Through in U.S. Education (Ph.D. dissertation), Pittsburgh,
11 A-P. Kontis and V. Vrysagotis 217 PA: Carnegie-Mellon University School of Urban and Public Affairs, [6] A. Charnes, W. Cooper and E. Rhodes, Measuring the efficiency of Decision Making Units, European Journal of Operational Research, 2, (1978), [7] M. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society, 3, (1957), [8] R. Norton, Which offices or stores really perform best? A new tool tells, Fortune, 130(31), (1994), 38. [9] H. Dyckhoff and K. Allen, Measuring ecological efficiency with data envelopment analysis (DEA), European Journal of Operational Research, 132(2), (2001), [10] C.A. Weber, A data envelopment analysis approach to measuring vendor performance, Supply Chain Management, 1(1), (1996), [11] J. Liu, F.Y. Ding and V. Lall, Using data envelopment analysis to compare suppliers for supplier selection and performance improvement, Supply Chain Management: An International Journal, 5(3), (2000), [12] L.B. Forker, D. Mendez, An analytical method for benchmarking best peer suppliers. International Journal of Operations and Production Management, 21(1 2), (2001), [13] R. Narasimhan, S. Talluri and D. Mendez, Supplier evaluation and rationalization via data envelopment analysis: An empirical examination, Journal of Supply Chain Management, 37 (3), (2001), [14] S. Talluri and J. Sarkis, A model for performance monitoring of suppliers, International Journal of Production Research, 40(16), (2002), [15] S. Talluri and R.C. Baker, A multi-phase mathematical programming approach for effective supply chain design, European Journal of Operational Research, 141(3), (2002), [16] S. Talluri and R. Narasimhan, A methodology for strategic sourcing, European Journal of Operational Research, 154(1), (2004),
12 218 Supplier selection problem: A literature review on DEA [17] A. Ross, F.P. Buffa, C. Dröge and D. Carrington, Supplier evaluation in a dyadic relationship: An action research approach, Journal of Business Logistics, 27(2), (2006), [18] R.M. Garfamy, A data envelopment analysis approach based on total cost of ownership for supplier selection, Journal of Enterprise Information Management, 19(6), (2006), [19] R.F. Saen, A decision model for selecting technology suppliers in the presence of nondiscretionary factors, Applied Mathematics and Computation, 181(2), (2006), [20] J. Seydel, Data envelopment analysis for decision support, Industrial Management and Data Systems, 106(1), (2006), [21] S. Talluri, R. Narasimhan and A. Nair, Vendor performance with supply risk: A chance-constrained DEA approach, International Journal of Production Economics, 100(2), (2006), [22] Y. Smirlis, M. Panta, G. Kaimakamis and M. Sfakianakis, Evaluating bids for logistics services, Spoudai, 57(3), (2007), [23] R.F. Saen, Suppliers selection in the presence of both cardinal and ordinal data, European Journal of Operational Research, 183(2), (2007), [24] T. Wu, D. Shunk, J. Blackhurst and R. Appalla, AIDEA: A methodology for supplier evaluation and selection in a supplier-based manufacturing environment, International Journal of Manufacturing Technology and Management, 11(2), (2007), [25] T. Wu and J. Blackhurst, Supplier evaluation and selection: An augmented DEA approach, International Journal of Production Research, 47(16), (2009), [26] M. Panta, Y. Smirlis, and M. Sfakianakis, Assessing bids of Greek public organizations service providers using data envelopment analysis, Operational Research: The International Journal, DOI /s , (2011), 9-19.
13 A-P. Kontis and V. Vrysagotis 219 [27] C.A. Weber, J.R. Current and A. Desai, An optimization approach to determining the number of vendors to employ, Supply Chain Management: An International Journal, 5(2), (2000), [28] M. Braglia and A. Petroni, A quality assurance-oriented methodology for handling trade-offs in supplier selection, International Journal of Physical Distribution and Logistics Management, 30 (2), (2000), [29] J. Seydel, Supporting the paradigm shift in vendor selection: Multi-criteria methods for sole-sourcing, Managerial Finance, 31(3), (2005), [30] R.F. Saen, A new mathematical approach for supplier selection: Accounting for non-homogeneity is important, Applied Mathematics and Computation, 185(1), (2007-B), [31] R. Ramanathan, Supplier selection problem: Integrating DEA with the approaches of total cost of ownership and AHP, Supply Chain Management: An International Journal, 12 (4), (2007), [32] S.H. Ha and R. Krishnan, A hybrid approach to supplier selection for the maintenance of a competitive supply chain, Expert Systems with Applications, 34 (2), (2008), [33] D. Ozdemir and G. T. Temur, DEA ANN approach in supplier evaluation system, World Academy of Science, Engineering and Technology, 54, (2009), [34] D. Wu, Supplier selection: A hybrid model using DEA, decision tree and neural network, Expert Systems with Applications, 36(5), (2009),
Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking
1 st International Conference of Recent Trends in Information and Communication Technologies Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking Mohammadreza
More informationOverview on Supplier Selection of Goods versus 3PL Selection
Journal of Logistics Management 20, 1(3): 18-23 DOI: 10.5923/j.logistics.200103.02 Overview on Supplier Selection of Goods versus 3PL Selection Aicha Aguezzoul 1, 2 1 LGIPM, ENIM-Lorraine University, route
More informationSupplier Performance Criteria
Supplier Performance Criteria The Case of SME s in Former Yugoslavian Republic of Macedonia (FYROM) Fotis Missopoulos, Shpend Imeri, Ioanna Chacha International Conference for Entrepreneurship, Innovation
More informationVendor Evaluation and Rating Using Analytical Hierarchy Process
Vendor Evaluation and Rating Using Analytical Hierarchy Process Kurian John, Vinod Yeldho Baby, Georgekutty S.Mangalathu Abstract -Vendor evaluation is a system for recording and ranking the performance
More informationKEY PERFORMANCE CRITERIA FOR VENDOR SELECTION A LITERATURE REVIEW
KEY PERFORMANCE CRITERIA FOR VENDOR SELECTION A LITERATURE REVIEW Shpend IMERI EVN Macedonia, AD Skopje, Ilindenska bb, 120 Tetovo, Macedonia shpend_85@hotmail.com Abstract The selection process of vendors
More informationIMPROVING SUPPLY CHAIN ACTIVITIES BY ADVANCING AND TEACHING AHP APPLICATIONS
ISAHP Article: Joo/Session Proposal To Be Submitted to the the Analytic Hierarchy 2014, Washington D.C., U.S.A. IMPROVING SUPPLY CHAIN ACTIVITIES BY ADVANCING AND TEACHING AHP APPLICATIONS Seong-Jong Joo
More informationSupplier Evaluation and Rationalization via Data Envelopment Analysis: An Empirical Examination
Supplier Evaluation and Rationalization via Data Envelopment Analysis: An Empirical Examination AUTHORS Ram Narasimhan is distinguished professor of operations management in the Eli Broad College of Business
More informationContractor selection using the analytic network process
Construction Management and Economics (December 2004) 22, 1021 1032 Contractor selection using the analytic network process EDDIE W. L. CHENG and HENG LI* Department of Building and Real Estate, The Hong
More informationThe current issue and full text archive of this journal is available at www.emeraldinsight.com/1741-0398.htm
The current issue and full text archive of this journal is available at www.emeraldinsight.com/1741-0398.htm JEIM 662 A data envelopment analysis approach based on total cost of ownership for supplier
More informationDeveloping a Supplier Performance Analysis Model
Developing a Supplier Performance Analysis Model Case study: Aker MH supplier performance Adomas Zagarnauskas Supervisor Gøril Hannås This Master s Thesis is carried out as a part of the education at the
More informationANALYTIC HIERARCHY PROCESS AS A RANKING TOOL FOR DECISION MAKING UNITS
ISAHP Article: Jablonsy/Analytic Hierarchy as a Raning Tool for Decision Maing Units. 204, Washington D.C., U.S.A. ANALYTIC HIERARCHY PROCESS AS A RANKING TOOL FOR DECISION MAKING UNITS Josef Jablonsy
More informationEfficiency in Software Development Projects
Efficiency in Software Development Projects Aneesh Chinubhai Dharmsinh Desai University aneeshchinubhai@gmail.com Abstract A number of different factors are thought to influence the efficiency of the software
More informationPrediction of Stock Performance Using Analytical Techniques
136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University
More informationPerformance Evaluation of a Drilling Project in Oil and Gas Service Company in Indonesia by
Home Search Collections Journals About Contact us My IOPscience Performance Evaluation of a Drilling Project in Oil and Gas Service Company in Indonesia by MACBETH Method This content has been downloaded
More informationUsing Analytic Hierarchy Process (AHP) Method to Prioritise Human Resources in Substitution Problem
Using Analytic Hierarchy Process (AHP) Method to Raymond Ho-Leung TSOI Software Quality Institute Griffith University *Email:hltsoi@hotmail.com Abstract In general, software project development is often
More informationPerformance Appraisal System using Multifactorial Evaluation Model
Performance Appraisal System using Multifactorial Evaluation Model C. C. Yee, and Y.Y.Chen Abstract Performance appraisal of employee is important in managing the human resource of an organization. With
More informationDEA implementation and clustering analysis using the K-Means algorithm
Data Mining VI 321 DEA implementation and clustering analysis using the K-Means algorithm C. A. A. Lemos, M. P. E. Lins & N. F. F. Ebecken COPPE/Universidade Federal do Rio de Janeiro, Brazil Abstract
More informationAnalytic hierarchy process (AHP)
Table of Contents The Analytic Hierarchy Process (AHP)...1/6 1 Introduction...1/6 2 Methodology...1/6 3 Process...1/6 4 Review...2/6 4.1 Evaluation of results...2/6 4.2 Experiences...3/6 4.3 Combinations...3/6
More informationDegree of Uncontrollable External Factors Impacting to NPD
Degree of Uncontrollable External Factors Impacting to NPD Seonmuk Park, 1 Jongseong Kim, 1 Se Won Lee, 2 Hoo-Gon Choi 1, * 1 Department of Industrial Engineering Sungkyunkwan University, Suwon 440-746,
More informationINVOLVING STAKEHOLDERS IN THE SELECTION OF A PROJECT AND PORTFOLIO MANAGEMENT TOOL
INVOLVING STAKEHOLDERS IN THE SELECTION OF A PROJECT AND PORTFOLIO MANAGEMENT TOOL Vassilis C. Gerogiannis Department of Project Management, Technological Research Center of Thessaly, Technological Education
More information6 Analytic Hierarchy Process (AHP)
6 Analytic Hierarchy Process (AHP) 6.1 Introduction to Analytic Hierarchy Process The AHP (Analytic Hierarchy Process) was developed by Thomas L. Saaty (1980) and is the well-known and useful method to
More informationThe Application of ANP Models in the Web-Based Course Development Quality Evaluation of Landscape Design Course
, pp.291-298 http://dx.doi.org/10.14257/ijmue.2015.10.9.30 The Application of ANP Models in the Web-Based Course Development Quality Evaluation of Landscape Design Course Xiaoyu Chen 1 and Lifang Qiao
More informationEditorial: some measurement methods are applied to business performance management
Int. J. Business Performance Management, Vol. 9, No. 1, 2007 1 Editorial: some measurement methods are applied to business performance management Kuen-Horng Lu* Department of Asia-Pacific Industrial and
More informationDEA for Establishing Performance Evaluation Models: a Case Study of a Ford Car Dealer in Taiwan
DEA for Establishing Performance Evaluation Models: a Case Study of a Ford Car Dealer in Taiwan JUI-MIN HSIAO Department of Applied Economics and management, I-Lan University, TAIWAN¹, jmhsiao@ems.niu.edu.tw
More informationA Big Data Analytical Framework For Portfolio Optimization Abstract. Keywords. 1. Introduction
A Big Data Analytical Framework For Portfolio Optimization Dhanya Jothimani, Ravi Shankar and Surendra S. Yadav Department of Management Studies, Indian Institute of Technology Delhi {dhanya.jothimani,
More informationOperations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras
Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras Lecture - 41 Value of Information In this lecture, we look at the Value
More informationSupplier Selection through Analytical Hierarchy Process: A Case Study In Small Scale Manufacturing Organization
Supplier Selection through Analytical Hierarchy Process: A Case Study In Small Scale Manufacturing Organization Dr. Devendra Singh Verma 1, Ajitabh pateriya 2 1 Department of Mechanical Engineering, Institute
More informationNeural Network Applications in Stock Market Predictions - A Methodology Analysis
Neural Network Applications in Stock Market Predictions - A Methodology Analysis Marijana Zekic, MS University of Josip Juraj Strossmayer in Osijek Faculty of Economics Osijek Gajev trg 7, 31000 Osijek
More informationComparative Analysis of FAHP and FTOPSIS Method for Evaluation of Different Domains
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) August 2015, PP 58-62 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Comparative Analysis of
More informationCombining ANP and TOPSIS Concepts for Evaluation the Performance of Property-Liability Insurance Companies
Journal of Social Sciences 4 (1): 56-61, 2008 ISSN 1549-3652 2008 Science Publications Combining ANP and TOPSIS Concepts for Evaluation the Performance of Property-Liability Insurance Companies 1 Hui-Yin
More informationDEA IN MUTUAL FUND EVALUATION
DEA IN MUTUAL FUND EVALUATION E-mail: funari@unive.it Dipartimento di Matematica Applicata Università Ca Foscari di Venezia ABSTRACT - In this contribution we illustrate the recent use of Data Envelopment
More informationI01-S01 Page 1. Jeffrey A. Joines (NC State, Textiles); Shu-Cherng Fang, Russell E. King, Henry L.W. Nuttle (NC State, Engineering)
I01-S01 Page 1 Business-to-Business Collaboration in a Softgoods E-Supply Chain I01-S01 Jeffrey A. Joines (NC State, Textiles); Shu-Cherng Fang, Russell E. King, Henry L.W. Nuttle (NC State, Engineering)
More informationWestminsterResearch http://www.westminster.ac.uk/research/westminsterresearch
WestminsterResearch http://www.westminster.ac.uk/research/westminsterresearch Partner selection in agile supply chains: a fuzzy intelligent approach Chong Wu 1 David Barnes 2 1 School of Management, Xiamen
More informationProposing an approach for evaluating e-learning by integrating critical success factor and fuzzy AHP
2011 International Conference on Innovation, Management and Service IPEDR vol.14(2011) (2011) IACSIT Press, Singapore Proposing an approach for evaluating e-learning by integrating critical success factor
More informationForecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network
Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)
More informationE-PROCUREMENT SYSTEM WITH EMBEDDED SUPPLIER SELECTION DSS FOR AN AUTOMOBILE MANUFACTURING INDUSTRY
E-PROCUREMENT SYSTEM WITH EMBEDDED SUPPLIER SELECTION DSS FOR AN AUTOMOBILE MANUFACTURING INDUSTRY P.Priya 1, Dr.K.Iyakutti 2, Dr.S.Prasanna Devi 3 Research Scholar, Bharathiar University, Coimbatore,
More informationIMPORTANCE OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS
IMPORTANCE OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS Abstract The term Quantitative techniques refers to the methods used to quantify the variables in any discipline. It means the application
More informationList of Ph.D. Courses
Research Methods Courses (5 courses/15 hours) List of Ph.D. Courses The research methods set consists of five courses (15 hours) that discuss the process of research and key methodological issues encountered
More informationMSCA 31000 Introduction to Statistical Concepts
MSCA 31000 Introduction to Statistical Concepts This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced
More informationSuccessful joint venture strategies based on data mining
Successful joint venture strategies based on data mining JIN HYUNG KIM, SO YOUNG SOHN* Department of Information & Industrial Systems Engineering Yonsei University 134 Shinchon-dong, Seoul 120-749 KOREA
More informationERP SYSTEM SELECTION BY AHP METHOD: CASE STUDY FROM TURKEY
ERP SYSTEM SELECTION BY AHP METHOD: CASE STUDY FROM TURKEY Babak Daneshvar Rouyendegh (Babek Erdebilli) Atılım University Department of Industrial Engineering P.O.Box 06836, İncek, Ankara, Turkey E-mail:
More informationThe efficiency of fleets in Serbian distribution centres
The efficiency of fleets in Serbian distribution centres Milan Andrejic, Milorad Kilibarda 2 Faculty of Transport and Traffic Engineering, Logistics Department, University of Belgrade, Belgrade, Serbia
More informationA REVIEW AND CRITIQUE OF HYBRID MADM METHODS APPLICATION IN REAL BUSINESS
Application in Real Business, 2014, Washington D.C., U.S.A. A REVIEW AND CRITIQUE OF HYBRID MADM METHODS APPLICATION IN REAL BUSINESS Jiri Franek Faculty of Economics VSB-Technical University of Ostrava
More informationHow To Choose An Optimal Supplier
Factors Affecting the Selection of Optimal Suppliers in Procurement Management Ruth Mwikali The Mombasa Polytechnic University College Po Box 1135-80100, Mombasa, Kenya. Stanley Kavale, PhD Student Jomo
More informationINTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY. Ameet.D.Shah 1, Dr.S.A.Ladhake 2. ameetshah1981@gmail.com
IJFEAT INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY Multi User feedback System based on performance and Appraisal using Fuzzy logic decision support system Ameet.D.Shah 1, Dr.S.A.Ladhake
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationManaging effective sourcing teams
Viewpoint Managing effective sourcing teams Boudewijn Driedonks & Prof. Dr. Arjan van Weele Richard Olofsson Bart van Overbeeke Today, international cross-functional sourcing teams are the standard in
More informationConceptual Modeling of Performance Indicators of Higher Education Institutions
Conceptual Modeling of Performance Indicators of Higher Education Institutions Tuba Canvar Kahveci, Harun Taşkın, Merve Cengiz Toklu Department of Industrial Engineering,Sakarya University, Serdivan,Turkey
More informationNEW VERSION OF DECISION SUPPORT SYSTEM FOR EVALUATING TAKEOVER BIDS IN PRIVATIZATION OF THE PUBLIC ENTERPRISES AND SERVICES
NEW VERSION OF DECISION SUPPORT SYSTEM FOR EVALUATING TAKEOVER BIDS IN PRIVATIZATION OF THE PUBLIC ENTERPRISES AND SERVICES Silvija Vlah Kristina Soric Visnja Vojvodic Rosenzweig Department of Mathematics
More informationCombining AHP and DEA Methods for Selecting a Project Manager UDC: 005.22:005.8 DOI: 10.7595/management.fon.2014.0016
Management 2014/71 Baruch Keren, Yossi Hadad, Zohar Laslo Industrial Engineering and Management Department, SCE Shamoon College of Engineering, Beer Sheva, Israel Combining AHP and DEA Methods for Selecting
More information2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering
2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering Compulsory Courses IENG540 Optimization Models and Algorithms In the course important deterministic optimization
More informationPOST-HOC SEGMENTATION USING MARKETING RESEARCH
Annals of the University of Petroşani, Economics, 12(3), 2012, 39-48 39 POST-HOC SEGMENTATION USING MARKETING RESEARCH CRISTINEL CONSTANTIN * ABSTRACT: This paper is about an instrumental research conducted
More informationThe ABC Wind Power Station Construction Project Management Performance Study. 15356-Project Performance Improvement
The ABC Wind Power Station Construction Management Performance Study 15356- Performance Improvement Yi Gao 11672096 16-06-2014 Abstract At the present, there are too many academic researches concentrated
More informationTheoretical Perspective
Preface Motivation Manufacturer of digital products become a driver of the world s economy. This claim is confirmed by the data of the European and the American stock markets. Digital products are distributed
More informationIdentifying, Ranking and Sensitivity Analysis for financing. Methods of deteriorated areas renovation projects
Identifying, Ranking and Sensitivity Analysis for financing Methods of deteriorated areas renovation projects VahidrezaYousefi a,*1, Aida Rahimi Golkhandan b, Sarmad Kiani c a.phd candidate of Construction
More informationOptimal Health Care Inventory Management Using Analytics
Optimal Health Care Inventory Management Using Analytics Neeraj Agrawal Prakarsh Paritosh Ashish Paralikar Dibyajyoti Pati General Electric JFWTC, 122 EPIP Bangalore, India 560066 neeraj.agrawal@ge.com
More informationUSE OF AN ALTERNATIVE DECISION SUPPORT SYSTEM IN VENDOR SELECTION DECISIONS
Revista Empresarial Inter Metro / Inter Metro Business Journal Fall 2007 / Vol. 3 No. 2 / p. 1 USE OF AN ALTERNATIVE DECISION SUPPORT SYSTEM IN VENDOR SELECTION DECISIONS By José Gerardo Martínez-Martínez
More informationPERFORMANCE MANAGEMENT AND COST-EFFECTIVENESS OF PUBLIC SERVICES:
PERFORMANCE MANAGEMENT AND COST-EFFECTIVENESS OF PUBLIC SERVICES: EMPIRICAL EVIDENCE FROM DUTCH MUNICIPALITIES Hans de Groot (Innovation and Governance Studies, University of Twente, The Netherlands, h.degroot@utwente.nl)
More informationMERGING BUSINESS KPIs WITH PREDICTIVE MODEL KPIs FOR BINARY CLASSIFICATION MODEL SELECTION
MERGING BUSINESS KPIs WITH PREDICTIVE MODEL KPIs FOR BINARY CLASSIFICATION MODEL SELECTION Matthew A. Lanham & Ralph D. Badinelli Virginia Polytechnic Institute and State University Department of Business
More informationFUZZY APPROACH ON OPTIMAL ORDERING STRATEGY IN INVENTORY AND PRICING MODEL WITH DETERIORATING ITEMS
International Journal of Pure and Applied Mathematics Volume 87 No. 03, 65-80 ISSN: 3-8080 (printed version; ISSN: 34-3395 (on-line version url: http://www.ijpam.eu doi: http://dx.doi.org/0.73/ijpam.v87i.0
More informationThe Applications of Business Intelligence to the Improvement of Supply Chain Management A Case of an Electronic Company
JOURNAL OF SOFTWARE, VOL. 6, NO. 11, NOVEMBER 2011 2173 The Applications of Business Intelligence to the Improvement of Supply Chain Management A Case of an Electronic Company Chwei-Jen Fan Dept. of Information
More informationMiracle Integrating Knowledge Management and Business Intelligence
ALLGEMEINE FORST UND JAGDZEITUNG (ISSN: 0002-5852) Available online www.sauerlander-verlag.com/ Miracle Integrating Knowledge Management and Business Intelligence Nursel van der Haas Technical University
More informationA Novel Feature Selection Method Based on an Integrated Data Envelopment Analysis and Entropy Mode
A Novel Feature Selection Method Based on an Integrated Data Envelopment Analysis and Entropy Mode Seyed Mojtaba Hosseini Bamakan, Peyman Gholami RESEARCH CENTRE OF FICTITIOUS ECONOMY & DATA SCIENCE UNIVERSITY
More informationANALYTICS CENTER LEARNING PROGRAM
Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals
More informationAppendix: Evaluating Cloud Services Using Methods of Supplier Selection
Appendix: Evaluating Cloud Services Using Methods of Supplier Selection Stefan Harnisch 1 and Peter Buxmann 1, 1 Technische Universität Darmstadt, Chair of Information Systems, Hochschulstraße 1, 64289
More informationON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION
ISSN 9 X INFORMATION TECHNOLOGY AND CONTROL, 00, Vol., No.A ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION Danuta Zakrzewska Institute of Computer Science, Technical
More informationGreen Supplier Assessment in Environmentally Responsive Supply Chains through Analytical Network Process
Green Supplier Assessment in Environmentally Responsive Supply Chains through Analytical Network Process Gopal Agarwal and Lokesh Vijayvargy ; Abstract Suppliers assessment is a critical function within
More informationAnalysis of Appropriate Methods for Assessment of Safety in Aviation
Analysis of Appropriate Methods for Assessment of Safety in Aviation Jakub Kraus ATM Systems Laboratory, Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University Horská
More informationDecision Making and Evaluation System for Employee Recruitment Using Fuzzy Analytic Hierarchy Process
International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 2, Issue 7 (July 2013), PP.24-31 Decision Making and Evaluation System for Employee Recruitment
More informationMATERIAL PURCHASING MANAGEMENT IN DISTRIBUTION NETWORK BUSINESS
MATERIAL PURCHASING MANAGEMENT IN DISTRIBUTION NETWORK BUSINESS Turkka Kalliorinne Finland turkka.kalliorinne@elenia.fi ABSTRACT This paper is based on the Master of Science Thesis made in first half of
More informationFuzzy Numbers in the Credit Rating of Enterprise Financial Condition
C Review of Quantitative Finance and Accounting, 17: 351 360, 2001 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Fuzzy Numbers in the Credit Rating of Enterprise Financial Condition
More informationAppraisal of Trust Degree among Innovation Team Members Based on Analytic Hierarchy Process
1094 Appraisal of Trust Degree among Innovation Team Members Based on Analytic Hierarchy Process Gao Xia Department of Business Administration, ZhengZhou Institute of Aeronautical Industry Management,
More informationSoftware Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model
Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model Iman Attarzadeh and Siew Hock Ow Department of Software Engineering Faculty of Computer Science &
More informationAn Evaluation Model for Determining Insurance Policy Using AHP and Fuzzy Logic: Case Studies of Life and Annuity Insurances
Proceedings of the 8th WSEAS International Conference on Fuzzy Systems, Vancouver, British Columbia, Canada, June 19-21, 2007 126 An Evaluation Model for Determining Insurance Policy Using AHP and Fuzzy
More informationEvaluation of educational open-source software using multicriteria decision analysis methods
1 Evaluation of educational open-source software using multicriteria decision analysis methods Georgia Paschalidou 1, Nikolaos Vesyropoulos 1, Vassilis Kostoglou 2, Emmanouil Stiakakis 1 and Christos K.
More informationNine Common Types of Data Mining Techniques Used in Predictive Analytics
1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better
More informationA Multi-Criteria Decision-making Model for an IaaS Provider Selection
A Multi-Criteria Decision-making Model for an IaaS Provider Selection Problem 1 Sangwon Lee, 2 Kwang-Kyu Seo 1, First Author Department of Industrial & Management Engineering, Hanyang University ERICA,
More informationAddress for Correspondence
IJAET/Vol.III/ Issue I/January-March, 2012/275-279 Research Paper STANDARDISATION OF VENDOR PERFORMANCE INDEX USING ANALYTICAL HIERARCHY PROCESS Saravanan,B.A a., Jayabalan,V b., Moshe.J.Aaron c, Jesu
More informationApplication of the Multi Criteria Decision Making Methods for Project Selection
Universal Journal of Management 3(1): 15-20, 2015 DOI: 10.13189/ujm.2015.030103 http://www.hrpub.org Application of the Multi Criteria Decision Making Methods for Project Selection Prapawan Pangsri Faculty
More informationDATA MINING TECHNIQUES AND APPLICATIONS
DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,
More informationFuzzy Probability Distributions in Bayesian Analysis
Fuzzy Probability Distributions in Bayesian Analysis Reinhard Viertl and Owat Sunanta Department of Statistics and Probability Theory Vienna University of Technology, Vienna, Austria Corresponding author:
More informationA Fuzzy AHP based Multi-criteria Decision-making Model to Select a Cloud Service
Vol.8, No.3 (2014), pp.175-180 http://dx.doi.org/10.14257/ijsh.2014.8.3.16 A Fuzzy AHP based Multi-criteria Decision-making Model to Select a Cloud Service Hong-Kyu Kwon 1 and Kwang-Kyu Seo 2* 1 Department
More informationEvaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring
714 Evaluation of Feature election Methods for Predictive Modeling Using Neural Networks in Credits coring Raghavendra B. K. Dr. M.G.R. Educational and Research Institute, Chennai-95 Email: raghavendra_bk@rediffmail.com
More informationERP SYSTEM SELECTION MODEL FOR LOW COST NGN PHONE COMPANY
International Journal of Electronic Business Management, Vol. 6, No. 3, pp. 153-160 (2008) 153 ERP SYSTEM SELECTION MODEL FOR LOW COST NGN PHONE COMPANY Joko Siswanto 1* and Anggoro Prasetyo Utomo 2 1
More informationThe Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
, pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and
More informationModule 1: Supply Chain Design
Module 1: Supply Chain Design Module 1 Introduction Section A: Develop the Supply Chain Strategy Chapter 1: Inputs to Supply Chain Strategy o Topic 1: Business Model o Topic 2: External Inputs to Supply
More informationBestChoice SRM: A Simple and Practical Supplier Relationship Management System for e-procurement *
BestChoice SRM: A Simple and Practical Supplier Relationship Management System for e-procurement * Dongjoo Lee, Seungseok Kang, San-keun Lee, Young-gon Kim, and Sang-goo Lee School of Computer Science
More informationD A T A M I N I N G C L A S S I F I C A T I O N
D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.
More informationManaging Consumer Credit Risk *
Managing Consumer Credit Risk * Peter Burns Anne Stanley September 2001 Summary: On July 31, 2001, the Payment Cards Center of the Federal Reserve Bank of Philadelphia hosted a workshop that examined current
More informationUSING LOGIT MODEL TO PREDICT CREDIT SCORE
USING LOGIT MODEL TO PREDICT CREDIT SCORE Taiwo Amoo, Associate Professor of Business Statistics and Operation Management, Brooklyn College, City University of New York, (718) 951-5219, Tamoo@brooklyn.cuny.edu
More informationImproving proposal evaluation process with the help of vendor performance feedback and stochastic optimal control
Improving proposal evaluation process with the help of vendor performance feedback and stochastic optimal control Sam Adhikari ABSTRACT Proposal evaluation process involves determining the best value in
More informationSupplier Performance Evaluation and Selection in the Herbal Industry
Supplier Performance Evaluation and Selection in the Herbal Industry Rashmi Kulshrestha Research Scholar Ranbaxy Research Laboratories Ltd. Gurgaon (Haryana), India E-mail : rashmi.kulshreshtha@ranbaxy.com
More informationIdentifying & Prioritizing of Electronic Commerce Factors in B2B Relationships using Fuzzy ANP (Case study: Nanotechnology High tech Organizations)
Identifying & Prioritizing of Electronic Commerce Factors in B2B Relationships using Fuzzy ANP (Case study: Nanotechnology High tech Organizations) Zahra Javidian Department Of Engineering, Darab Branch,
More informationWORKFLOW ENGINE FOR CLOUDS
WORKFLOW ENGINE FOR CLOUDS By SURAJ PANDEY, DILEBAN KARUNAMOORTHY, and RAJKUMAR BUYYA Prepared by: Dr. Faramarz Safi Islamic Azad University, Najafabad Branch, Esfahan, Iran. Workflow Engine for clouds
More informationHealthcare Measurement Analysis Using Data mining Techniques
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik
More informationStudying Achievement
Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us
More informationBusiness Intelligence. Data Mining and Optimization for Decision Making
Brochure More information from http://www.researchandmarkets.com/reports/2325743/ Business Intelligence. Data Mining and Optimization for Decision Making Description: Business intelligence is a broad category
More informationBOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL
The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University
More informationJournal of Engineering Research and Studies
Research rticle VENOR SELETION USING NLYTIL HIERRHY PROESS IN SUPPLY HIN MNGEMENT. Elanchezhian 1,. Vijaya Ramnath 2, r. R. Kesavan 3 ddress for orrespondence 1 Research Scholar, epartment of Production
More informationBachelor's Degree in Business Administration and Master's Degree course description
Bachelor's Degree in Business Administration and Master's Degree course description Bachelor's Degree in Business Administration Department s Compulsory Requirements Course Description (402102) Principles
More information