Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks
Tài liệu tham khảo
Agami, 2014, An innovative fuzzy logic based approach for supply chain performance management, IEEE Systems Journal, 8, 336, 10.1109/JSYST.2012.2219913
Ahi, 2015, An analysis of metrics used to measure performance in green and sustainable supply chains, J. Clean. Prod., 86, 360, 10.1016/j.jclepro.2014.08.005
Akkoç, 2012, An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: the case of Turkish credit card data, Eur. J. Oper. Res., 222, 168, 10.1016/j.ejor.2012.04.009
Aksoy, 2011, Supplier selection and performance evaluation in just-in-time production environments, Expert Syst. Appl., 38, 6351, 10.1016/j.eswa.2010.11.104
Apichottanakul, 2012, The role of pig size prediction in supply chain planning, Biosyst. Eng., 113, 298, 10.1016/j.biosystemseng.2012.07.008
APICS, 2018
Bai, 2012, Evaluating ecological sustainable performance measures for supply chain management, Supply Chain Manag.: Int. J., 17, 78, 10.1108/13598541211212221
Balfaqih, 2016, Review of supply chain performance measurement systems: 1998-2015, Comput. Ind., 82, 135, 10.1016/j.compind.2016.07.002
Bastas, 2018, Sustainable supply chain quality management: a systematic review, J. Clean. Prod., 181, 726, 10.1016/j.jclepro.2018.01.110
Bilgehan, 2011, Comparison of ANFIS and NN models – with a study in critical buckling load estimation, Appl. Soft Comput., 11, 3779, 10.1016/j.asoc.2011.02.011
Bukhori, 2015, Evaluation of poultry supply chain performance in XYZ slaughtering house yogyakarta using SCOR and AHP method, Agriculture and Agricultural Science Procedia, 3, 221, 10.1016/j.aaspro.2015.01.043
Chan, 2003, An innovative performance measurement method for supply chain management, Supply Chain Manag.: Int. J., 8, 10.1108/13598540310484618
Chithambaranathan, 2015, Service supply chain environmental performance evaluation using grey based hybrid MCDM approach, Int. J. Prod. Econ., 166, 163, 10.1016/j.ijpe.2015.01.002
Choy, 2003, Design of an intelligent supplier relationship management system: a hybrid case based neural network approach, Expert Syst. Appl., 24, 225, 10.1016/S0957-4174(02)00151-3
Clivillé, 2012, Overall performance measurement in a supply chain: towards a supplier-prime manufacturer based model, J. Intell. Manuf., 23, 2459, 10.1007/s10845-011-0512-x
Cuthbertson, 2011, Performance measurement systems in supply chains, Int. J. Prod. Perform. Manag., 60, 583, 10.1108/17410401111150760
Didehkhani, 2009, Assessing flexibility in supply chain using adaptive neuro fuzzy inference system
Efendigil, 2012, An integration methodology based on fuzzy inference systems and neural approaches for multi-stage supply-chains, Comput. Ind. Eng., 62, 554, 10.1016/j.cie.2011.11.004
Efendigil, 2009, A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis, Expert Syst. Appl., 36, 6697, 10.1016/j.eswa.2008.08.058
Estampe, 2013, A framework for analysing supply chain performance evaluation models, Int. J. Prod. Econ., 142, 247, 10.1016/j.ijpe.2010.11.024
Fan, 2013, An evaluation model of supply chain performances using 5DBSC and LMBP neural network algorithm, JBE, 10, 383
Ganga, 2011, A fuzzy logic approach to supply chain performance management, Int. J. Prod. Econ., 134, 177, 10.1016/j.ijpe.2011.06.011
Golparvar, 2009, Application of SCOR model in an oil- producing company, J. Ind. Eng., 4, 59
Gunasekaran, 2007, Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications, Int. J. Prod. Res., 45, 2819, 10.1080/00207540600806513
Gunasekaran, 2004, A framework for supply chain performance measurement, Int. J. Prod. Econ., 87, 333, 10.1016/j.ijpe.2003.08.003
Haykin, 2009
Hecht-Nielsen, 1990
Hong, 2013, Supply chain dynamic performance measurement based on BSC and SVM, Int. J. Comput. Sci. Issues, 1, 271
Jalalvand, 2011, A method to compare supply chains of an industry, Supply Chain Manag.: Int. J., 16, 82, 10.1108/13598541111115347
Jassbi, 2010, An adaptive neuro fuzzy inference system for supply chain agility evaluation, Int. J. Ind. Eng. Prod. Res., 20, 187
Kaastra, 1996, Designing a neural network for forecasting financial and economics time series, Neurocomputing, 10, 215, 10.1016/0925-2312(95)00039-9
Kabir, 2013, Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and Artificial Neural Network, Int. J. Ind. Syst. Eng., 14, 74
Kocaoglu, 2013, A SCOR based approach for measuring a benchmarkable supply chain performance, J. Intell. Manuf., 24, 113, 10.1007/s10845-011-0547-z
Lemghari, 2018, Benefits and limitations of the SCOR ® model in automotive industries, vol. 200
Lenard, 1995, The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision, Decis. Sci. J., 26, 209, 10.1111/j.1540-5915.1995.tb01426.x
Li, 2018, On neural networks and learning systems for business computing, Neurocomputing, 275, 1150, 10.1016/j.neucom.2017.09.054
Lima-Junior, 2016, Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management, Int. J. Prod. Econ., 174, 128, 10.1016/j.ijpe.2016.01.023
Lima-Junior, 2017, Quantitative models for supply chain performance evaluation: a literature review, Comput. Ind. Eng., 113, 333, 10.1016/j.cie.2017.09.022
Lohman, 2004, Designing a performance measurement system: a case study, Eur. J. Oper. Res., 156, 267, 10.1016/S0377-2217(02)00918-9
Maestrini, 2017, Supply chain performance measurement systems: a systematic review and research agenda, Int. J. Prod. Econ., 183, 299, 10.1016/j.ijpe.2016.11.005
Mentzer, 2001, Defining supply chain management, J. Bus. Logist., 22, 1, 10.1002/j.2158-1592.2001.tb00001.x
Mishra, 2018, Supply chain performance measures and metrics: a bibliometric study, Benchmarking Int. J., 10.1108/BIJ-08-2017-0224
Moharamkhani, 2017, Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS, Int. J. Logist. Syst. Manag., 27
Montgomery, 2012
Moosmayer, 2013, A neural network approach to predicting price negotiation outcomes in business-to-business contexts, Expert Syst. Appl., 40, 3028, 10.1016/j.eswa.2012.12.018
Nudurupati, 2011, State of the art literature review on performance measurement, Comput. Ind. Eng., 60, 279, 10.1016/j.cie.2010.11.010
Parker, 2000, Performance measurement, Work. Stud., 49, 63, 10.1108/00438020010311197
Patuwo, 1993, Two-group classification using neural networks, Decis. Sci. J., 26, 749
Rezaee, 2018, Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange, Physica, 489, 78, 10.1016/j.physa.2017.07.017
SCC, 2012
Sellito, 2006, Comparative performance assessment in three supply-chains in manufacturing, Production, 16, 552
Sellitto, 2015, A SCOR-based model for supply chain performance measurement: application in the footwear industry, Int. J. Prod. Res., 53, 4917, 10.1080/00207543.2015.1005251
Shepherd, 2006, Measuring supply chain performance: current research and future directions, Int. J. Prod. Perform. Manag., 55, 242, 10.1108/17410400610653219
Silva, 2017
Slack, 2007
Smith, 2000, Neural networks in business: techniques and applications for the operations researcher, Comput. Oper. Res., 27, 1023, 10.1016/S0305-0548(99)00141-0
Sweeney, 2011, Towards a unified definition of supply chain management: the four fundamentals, Int. J. Appl. Logist., 2, 30, 10.4018/jal.2011070103
Tavana, 2016, A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection, Expert Syst. Appl., 61, 129, 10.1016/j.eswa.2016.05.027
Theeranuphattana, 2008, A conceptual model of performance measurement for supply chains: alternative considerations, J. Manuf. Technol. Manag., 19, 125, 10.1108/17410380810843480
Tkác, 2016, Artificial neural networks in business: two decades of research, Appl. Soft Comput., 38, 788, 10.1016/j.asoc.2015.09.040
Unahabhokha, 2007, Predictive performance measurement system: a fuzzy expert system approach, Benchmarking Int. J., 14, 77, 10.1108/14635770710730946
Wang, 2015, Measure for data partitioning in m × 2 cross-validation, Pattern Recogn. Lett., 65, 211, 10.1016/j.patrec.2015.08.002
Wong, 2007, Supply chain performance measurement system using DEA modeling, Ind. Manag. Data Syst., 107, 361, 10.1108/02635570710734271
Yang, 2012, Fuzzy evaluation on supply chains' overall performance based on AHM and M(1,2,3), J. Software, 12, 2779