Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 7/2020

21-12-2019 | Original Article

A systematic review of the research trends of machine learning in supply chain management

Authors: Du Ni, Zhi Xiao, Ming K. Lim

Published in: International Journal of Machine Learning and Cybernetics | Issue 7/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Research interests in machine learning (ML) and supply chain management (SCM) have yielded an enormous amount of publications during the last two decades. However, in the literature, there was no systematic examination on the research development in the discipline of ML application, in particular in SCM. Therefore, this study was carried out to present the latest research trends in the discipline by analyzing the publications between 1998/01/01 and 2018/12/31 in five major databases. The quantitative analysis of 123 shortlisted articles showed that ML applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of ML applications in SCM and their publications were still at a low level; even though 10 ML algorithms were found to be frequently used in SCM, the use of these algorithms were unevenly distributed across the SCM activities most frequently reported in the articles of the literature. The aim of this study is to provide a comprehensive view of ML applications in SCM, working as a reference for future research directions for SCM researchers and application insight for SCM practitioners.
previous article Orientation space code and multi-feature two-phase sparse representation for palmprint recognition
next article An online PLA algorithm with maximum error bound for generating optimal mixed-segments

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
Aburto L, Weber R (2007) Improved supply chain management based on hybrid demand forecasts. Appl Soft Comput 7(1):136–144CrossRef
2.
Aksoy A, Öztürk N (2011) Supplier selection and performance evaluation in just-in-time production environments. Expert Syst Appl 38(5):6351–6359CrossRef
3.
Alfian G, Rhee J, Ahn H, Lee J, Farooq U, Ijaz M, Syaekhoni M (2017) Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. J Food Eng 212:65–75CrossRef
4.
Arunraj N, Ahrens D (2015) A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. Int J Prod Econ 170:321–335CrossRef
5.
Becker T, Illigen C, McKelvey B, Hülsmann M, Windt K (2016) Using an agent-based neural-network computational model to improve product routing in a logistics facility. Int J Prod Econ 174:156–167CrossRef
6.
Bhattacharya A, Kumar S, Tiwari M, Talluri S (2014) An intermodal freight transport system for optimal supply chain logistics. Trans Res Part C 38:73–84CrossRef
7.
Bousqaoui H, Achchab S (2017) Tikito K Machine learning applications in supply chains: an emphasis on neural network applications. In: 2017 3rd international conference of cloud computing technologies and applications (CloudTech). IEEE, pp 1–7
8.
Bowling M, Fürnkranz J, Graepel T, Musick R (2006) Machine learning and games. Mach Learn 63(3):211–215CrossRef
9.
Brandenburg M, Govindan K, Sarkis J, Seuring S (2014) Quantitative models for sustainable supply chain management: developments and directions. Eur J Oper Res 233(2):299–312MathSciNetMATHCrossRef
10.
Breaugh J (2008) Important considerations in using statistical procedures to control for nuisance variables in non-experimental studies. Hum Resource Manage Rev 18(4):282–293CrossRef
11.
Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res 184(3):1140–1154MATHCrossRef
12.
Carbonneau R, Vahidov R, Laframboise K (2007) Machine learning-based demand forecasting in supply chains. Int J Intell Inf Technol (IJIIT) 3(4):40–57MATHCrossRef
13.
Chae B (2015) Insights from hashtag supplychain and twitter analytics: considering twitter and twitter data for supply chain practice and research. Int J Prod Econ 16(5):247–259CrossRef
14.
Chatzidimitriou K, Symeonidis A, Kontogounis I, Mitkas P (2008) Agent Mertacor: a robust design for dealing with uncertainty and variation in SCM environments. Expert Syst Appl 35(3):591–603CrossRef
15.
Chen M, Tai T, Hung T (2012) Component selection system for green supply chain. Expert Syst Appl 39(5):5687–5701CrossRef
16.
Cheng J, Chen H, Lin Y (2010) A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C.45. Expert Syst Appl 37(3):1814–1820CrossRef
17.
Chi H, Ersoy O, Moskowitz H, Ward J (2007) Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms. Eur J Oper Res 180(1):174–193MATHCrossRef
18.
Chiu M, Lin G (2004) Collaborative supply chain planning using the artificial neural network approach. J Manuf Technol Manage 15(8):787–796CrossRef
19.
Choy K, Lee W, Lo V (2003) Design of an intelligent supplier relationship management system: a hybrid case based neural network approach. Expert Syst Appl 24(2):225–237CrossRef
20.
Choy K, Lee W, Lo V (2002) An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing. Expert Syst Appl 22(3):213–224CrossRef
21.
22.
Chung W, Ho C, Wong K, Soon P (2007) An ANN-based DSS system for quality assurance in production network. J Manuf Technol Manage 18(7):836–857CrossRef
23.
Ćirović G, Pamučar D, Božanić D (2014) Green logistic vehicle routing problem: routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Syst Appl 41(9):4245–4258CrossRef
24.
Cruz J, Wishart D (2007) Applications of machine learning in cancer prediction and prognosis. Cancer Inf 2:59–77
25.
De’Ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88(1):243–251CrossRef
26.
Ciccio C, Aa H, Cabanillas C, Mendling J, Prescher J (2016) Detecting flight trajectory anomalies and predicting diversions in freight transportation. Decis Support Syst 88:1–17CrossRef
27.
Dubey R, Gunasekaran A, Papadopoulos T, Childe S, Shibin K, Wamba S (2017) Sustainable supply chain management: framework and further research directions. J Clean Prod 142:1119–1130CrossRef
28.
Efendigil T, Önüt S (2012) An integration methodology based on fuzzy inference systems and neural approaches for multi-stage supply-chains. Comput Ind Eng 62(2):554–569CrossRef
29.
Efendigil T, Önüt S, Kongar E (2008) A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Comput Ind Eng 54(2):269–287CrossRef
30.
Estelles-Lopez L et al (2017) An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling. Food Res Int 99:206–215CrossRef
31.
Fallahpour A, Wong K, Olugu E, Musa S (2017) A predictive integrated genetic-based model for supplier evaluation and selection. Int J Fuzzy Syst 19(4):1041–1057MathSciNetCrossRef
32.
Fan X, Zhang S, Wang L, Yang Y, Hapeshi K (2013) An evaluation model of supply chain performances using 5DBSC and LMBP neural network algorithm. J Bionic Eng 10(3):383–395CrossRef
33.
Feng X, Xiao Z, Zhong B, Qiu J, Dong Y (2018) Dynamic ensemble classification for credit scoring using soft probability. Appl Soft Comput 65:139–151CrossRef
34.
Freeman D (2000) Alternative panel estimates of alcohol demand, taxation, and the business cycle. Southern Econ J 67(2):325–344CrossRef
35.
Gao L, Shen G, Wang S (2010) Intelligent scheduling model and algorithm for manufacturing. Prod Plan Control 11(3):234–243
36.
García S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959CrossRef
37.
Ghasri M, Maghrebi M, Rashidi T, Waller S (2016) Hazard-based model for concrete pouring duration using construction site and supply chain parameters. Autom Constr 71:283–293CrossRef
38.
Giannakis M, Papadopoulos T (2016) Supply chain sustainability: a risk management approach. Int J Prod Econ 171:455–470CrossRef
39.
Golmohammadi D, Creese R, Valian H, Kolassa J (2009) Supplier selection based on a neural network model using genetic algorithm. IEEE Trans Neural Netw 20(9):1504–1519CrossRef
40.
Gumus A, Guneri A, Ulengin F (2010) A new methodology for multi-echelon inventory management in stochastic and neuro-fuzzy environments. Int J Prod Econ 128(1):248–260CrossRef
41.
Guo X, Yuan Z, Tian B (2009) Supplier selection based on hierarchical potential support vector machine. Expert Syst Appl 36(3):6978–6985CrossRef
42.
Gupta S, Keen M, Shah A, Verdier G (2017) International monetary fund (2017) Digital revolutions in public finance. International Monetary Fund, Washington
43.
Ha S, Krishnan R (2008) A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Syst Appl 34(2):1303–1311CrossRef
44.
He X, Ai X, Jing Y, Liu Y (2016) Partner selection of agricultural products supply chain based on data mining. Concurrency Comput 28(4):1246–1256CrossRef
45.
Hilbert M (2016) Big data for development: a review of promises and challenges. Dev Policy Rev 34(1):135–174CrossRef
46.
Hong G, Ha S (2008) Evaluating supply partner’s capability for seasonal products using machine learning techniques. Comput Ind Eng 54(4):721–736CrossRef
47.
Hosseini S, Khaled A (2016) A hybrid ensemble and AHP approach for resilient supplier selection. J Intell Manuf 30(1):207–228CrossRef
48.
Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRef
49.
Jaipuria S, Mahapatra S (2014) An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Syst Appl 41(5):2395–2408CrossRef
50.
Kaelbling L, Littman M, Moore A (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285CrossRef
51.
Keller T, Thiesse F, Fleisch E (2014) Classification models for RFID-based real-time detection of process events in the supply chain. ACM Trans Manage Inf Syst 5(4):1–30CrossRef
52.
Kiekintveld C, Jain M, Tsai J, Pita J, Ordóñez F, Tambe M (2009) Computing optimal randomized resource allocations for massive security games. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 689–696
53.
Kim M (2012) Ensemble learning with support vector machines for bond rating. J Intell Inf Syst 18(2):29–45
54.
Ko T, Lee J, Cho H, Cho S, Lee W, Lee M (2017) Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data. Ind Manage Data Syst 117(5):927–945CrossRef
55.
Kuo R, Chen J (2004) A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm. Expert Syst Appl 26(2):141–154CrossRef
56.
Kuo RJ, Wang YC, Tien FC (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18(12):1161–1170CrossRef
57.
Kuo R, Xue K (1998) A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights. Decis Support Syst 24(1):105–126CrossRef
58.
Kwon I, Kim C, Jun J, Lee J (2008) Case-based myopic reinforcement learning for satisfying target service level in supply chain. Expert Syst Appl 35(1–2):389–397CrossRef
59.
Lau H, Hui I, Chan F, Wong C (2002) Monitoring the supply of products in a supply chain environment: a fuzzy neural approach. Expert Syst 19(4):235–243CrossRef
60.
Lau H, Lee W, Lau P (2001) Development of an intelligent decision support system for benchmarking assessment of business partners. Benchmarking 8(5):376–395CrossRef
61.
Lau H, Tsui E, Ning A, Pun K, Chin K, Ip W (2005) A knowledge-based system to support procurement decision. J Knowl Manage 9(1):87–100CrossRef
62.
Lau R, Zhang W, Xu W (2018) Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Prod Oper Manage 27(10):1775–1794CrossRef
63.
64.
Lee C, Ho W, Ho G, Lau H (2011) Design and development of logistics workflow systems for demand management with RFID. Expert Syst Appl 38(5):5428–5437CrossRef
65.
Lee J, Park S (2005) Intelligent profitable customers segmentation system based on business intelligence tools. Expert Syst Appl 29(1):145–152CrossRef
66.
Lessmann S, Baesens B, Seow H, Thomas L (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur J Oper Res 247(1):124–136MATHCrossRef
67.
Li H, Sun J, Wu J, Wu X (2012) Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): the case of general competence trust diagnosis. Appl Soft Comput 12(8):2312–2321CrossRef
68.
Libbrecht M, Noble W (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16(6):321CrossRef
69.
Liu C, Shu T, Chen S, Wang S, Lai K, Gan L (2016) An improved grey neural network model for predicting transportation disruptions. Expert Syst Appl 45:331–340CrossRef
70.
71.
Lo Y, Rensi S, Torng W, Altman R (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23(8):1538–1546CrossRef
72.
Louzada F, Ara A (2012) Bagging k-dependence probabilistic networks: an alternative powerful fraud detection tool. Expert Syst Appl 39(14):11583–11592CrossRef
73.
Lu C, Kao L (2016) A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. Eng Appl Artif Intell 55:231–238CrossRef
74.
Ma H, Wang Y, Wang K (2018) Automatic detection of false positive RFID readings using machine learning algorithms. Expert Syst Appl 91:442–451CrossRef
75.
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proc fifth Berkeley Symp Math Stat Probab 14:281–297MathSciNetMATH
76.
Maghrebi M, Monty S, Profes A, Sammut C, Waller S (2015) Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques. Eng Constr Archit Manage 22(5):573–590CrossRef
77.
Maleki M, Cruz-Machado V (2013) Supply chain performance monitoring using Bayesian network. Int J Bus Perform Supply Chain Model 5(2):177–196CrossRef
78.
79.
Mao D, Wang F, Hao Z, Li H (2018) Credit evaluation system based on blockchain for multiple stakeholders in the food supply chain. Int J Environ Res Public Health 15(8):1027CrossRef
80.
Marr B (2016) Why everyone must get ready for the 4th industrial revolution https://​www.​forbes.​com/​sites/​bernardmarr/​2016/​04/​05/​why-everyone-must-get-readyfor-4th-dustrial-revolution/​#26be9e2f3f90
81.
Martínez A, Schmuck C, Pereverzyev S Jr, Pirker C, Haltmeier M (2018) A machine learning framework for customer purchase prediction in the non-contractual setting. Eur J Oper Res 28(23):1–13
82.
Mercier S, Uysal I (2018) Neural network models for predicting perishable food temperatures along the supply chain. Biosys Eng 171:91–100CrossRef
83.
Min H (2009) Artificial intelligence in supply chain management: theory and applications. Int J Logistics Res Appl 13(1):13–39CrossRef
84.
Mirkouei A, Haapala K, Sessions J, Murthy G (2017) A mixed biomass-based energy supply chain for enhancing economic and environmental sustainability benefits: a multi-criteria decision making framework. Appl Energy 206:1088–1101CrossRef
85.
Mishra N, Singh A (2018) Use of twitter data for waste minimisation in beef supply chain. Ann Oper Res 270(1–2):337–359CrossRef
86.
87.
Mori J, Kajikawa Y, Kashima H, Sakata I (2012) Machine learning approach for finding business partners and building reciprocal relationships. Expert Syst Appl 39(12):10402–10407CrossRef
88.
Mortazavi A, Arshadi Khamseh A, Azimi P (2015) Designing of an intelligent self-adaptive model for supply chain ordering management system. Eng Appl Artif Intell 37:207–220CrossRef
89.
Ngai E, Peng S, Alexander P, Moon K (2014) Decision support and intelligent systems in the textile and apparel supply chain: an academic review of research articles. Expert Syst Appl 41(1):81–91CrossRef
90.
Ni D, Xiao Z, Zhong B, Feng X (2018) Multiple human-behaviour indicators for predicting lung cancer mortality with support vector machine. Sci Rep 8(1):16596CrossRef
91.
Ning A, Lau H, Zhao Y, Wong T (2009) Fulfillment of retailer demand by using the MDL-optimal neural network prediction and decision policy. IEEE Trans Industr Inf 5(4):495–506CrossRef
92.
Noroozi A, Mokhtari H, Kamal A (2013) Research on computational intelligence algorithms with adaptive learning approach for scheduling problems with batch processing machines. Neurocomputing 101:190–203CrossRef
93.
Pan Y, Pavur R, Pohlen T (2016) Revisiting the effects of forecasting method selection and information sharing under volatile demand in SCM applications. IEEE Trans Eng Manage 63(4):377–389CrossRef
94.
Piendl R, Matteis T, Liedtke G (2019) A machine learning approach for the operationalization of latent classes in a discrete shipment size choice model. Trans Res Part E 121:149–161CrossRef
95.
Piramuthu S (2008) Adaptive framework for collisions in RFID tag identification. J Inf Knowl Manage 7(1):9–14CrossRef
96.
Ratner B (2000) A comparison of two popular machine learning methods: Common Pitfalls. DM STAT-1: Online Newsletter about Quantitative Methods in Direct Marketing, 4
97.
Raut R, Priyadarshinee P, Gardas BB, Narkhede BE, Nehete R (2018) The incident effects of supply chain and cloud computing integration on the business performance. Benchmarking 25(8):2688–2722CrossRef
98.
Rodger J (2014) Application of a fuzzy feasibility bayesian probabilistic estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets. Expert Syst Appl 41(16):7005–7022CrossRef
99.
Rohde J (2004) Hierarchical supply chain planning using artificial neural networks to anticipate base-level outcomes. OR Spectrum 26(4):471–492MathSciNetMATHCrossRef
100.
Shervais S, Shannon TT, Lendaris GG (2003) Intelligent supply chain management using adaptive critic learning. IEEE Trans Syst Man Cybern Part A 33(2):235–244CrossRef
101.
Singh A, Shukla N, Mishra N (2018) Social media data analytics to improve supply chain management in food industries. Trans Res Part E 114:398–415CrossRef
102.
Stockheim T, Schwind M, Koenig W (2003) A reinforcement learning approach for supply chain management. Electron News August 5(4):1–3
103.
Sun Z, Choi T, Au K, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419CrossRef
104.
Swain A, Cao R (2017) Using sentiment analysis to improve supply chain intelligence. Inf Syst Front 21(2):469–484CrossRef
105.
Syam N, Sharma A (2018) Waiting for a sales renaissance in the fourth industrial revolution: machine learning and artificial intelligence in sales research and practice. Ind Mark Manage 69:135–146CrossRef
106.
Tavana M, Fallahpour A, Caprio D, Santos-Arteaga F (2016) A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Syst Appl 61:129–144CrossRef
107.
Thomassey S (2010) Sales forecasts in clothing industry: the key success factor of the supply chain management. Int J Prod Econ 128(2):470–483CrossRef
108.
Timme S, Williams-Timme C (2003) The real cost of holding inventory. Supply Chain Manage Rev 7(4):30–37
109.
Trapero J, Kourentzes N, Fildes R (2012) Impact of information exchange on supplier forecasting performance. Omega 40(6):738–747CrossRef
110.
Tseng T, Huang C, Jiang F, Ho J (2006) Applying a hybrid data-mining approach to prediction problems: a case of preferred suppliers prediction. Int J Prod Res 44(14):2935–2954MATHCrossRef
111.
Vahdani B, Iranmanesh S, Mousavi S, Abdollahzade M (2012) A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Appl Math Model 36(10):4714–4727MathSciNetMATHCrossRef
112.
Vaat T, Donk D (2004) Buyer focus: evaluation of a new concept for supply chain integration. Int J Prod Econ 92(1):21–30CrossRef
113.
Vahdani B, Razavi F, Mousavi S (2015) A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain. Neural Comput Appl 27(8):2441–2451CrossRef
114.
Vapnik V (2013) The nature of statistical learning theory. Springer Science & business media, BerlinMATH
115.
Wan X, Pekny J, Reklaitis G (2005) Simulation-based optimization with surrogate models—application to supply chain management. Comput Chem Eng 29(6):1317–1328CrossRef
116.
Werbos P (1975) Experimental implications of the reinterpretation of quantum mechanics. II Nuovo Cimento B 29(1):169–177CrossRef
117.
Wong W, Guo Z (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int J Prod Econ 128(2):614–624CrossRef
118.
Wu D (2009) Supplier selection: a hybrid model using DEA, decision tree and neural network. Expert Syst Appl 36(5):9105–9112CrossRef
119.
Wu H, Evans G, Bae K (2015) Production control in a complex production system using approximate dynamic programming. Int J Prod Res 54(8):2419–2432CrossRef
120.
Xia M, Zhang Y, Weng L, Ye X (2012) Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowledge-Based Systems 36253-259
121.
Xiao R, Li J, Chen T (2019) Modeling and intelligent optimization of social collective behavior with online public opinion synchronization. Int J Mach Learn Cybern 10(8):1979–1996CrossRef
122.
Xie G, Zhao Y, Jiang M, Zhang N (2013) A novel ensemble learning approach for corporate financial distress forecasting in fashion and textiles supply chains. Math Prob Eng 1:1–9
123.
Zhao Y, Chen Q (2014) Online order priority evaluation based on hybrid harmony search algorithm of optimized support vector machines. J Netw 9(4):972–978
124.
Zhang L, Hu H, Zhang D (2015) A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation 1(1):14–25MathSciNetCrossRef
125.
Zhu Y, Xie C, Wang G, Yan X (2016) Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Comput Appl 28(S1):41–50CrossRef
126.
Zuo Y, Kajikawa Y, Mori J (2016) Extraction of business relationships in supply networks using statistical learning theory. Heliyon 2(6):e00123CrossRef
Metadata
Title
A systematic review of the research trends of machine learning in supply chain management
Authors
Du Ni
Zhi Xiao
Ming K. Lim
Publication date
21-12-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 7/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01050-0

Other articles of this Issue 7/2020

International Journal of Machine Learning and Cybernetics 7/2020 Go to the issue

Original Article

Supervised feature selection by constituting a basis for the original space of features and matrix factorization

Original Article

An ORESTE approach for multi-criteria decision-making with probabilistic hesitant fuzzy information

Original Article

Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

Original Article

Parameter self-tuning schemes for the two phase test sample sparse representation classifier

Original Article

Unsupervised feature learning with sparse Bayesian auto-encoding based extreme learning machine

Original Article

A survey of robust optimization based machine learning with special reference to support vector machines