Engineering management for high-end equipment intelligent manufacturing

Abstract

The high-end equipment intelligent manufacturing (HEIM) industry is of strategic importance to national and economic security. Engineering management (EM) for HEIM is a complex, innovative process that integrates natural science, technology, management science, social science, and the human spirit. New-generation information technology (IT), including the internet, cloud computing, big data, and artificial intelligence, have made a remarkable influence on HEIM and its engineering management activities, such as product system construction, product life cycle management, manufacturing resources organization, manufacturing model innovation, and reconstruction of the enterprise ecosystem. Engineering management for HEIM is a key topic at the frontier of international academic research. This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment. These issues include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories, quality and reliability management, information management, and intelligent decision making. The challenges presented by these issues and potential research opportunities are also summarized and discussed.

Keywords

high-end equipment / intelligent manufacturing / engineering management / information technology

References

[1] Abedjan Z, Golab L, Naumann F (2015). Profiling relational data: A survey. VLDB Journal, 24(4): 557–581

[2] Abu-Elkheir M, Hayajneh M, Ali N A (2013). Data management for the internet of things: Design primitives and solution. Sensors (Basel), 13(11): 15582–15612

[3] Adamson G, Wang L, Moore P (2017). Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems. Journal of Manufacturing Systems, 43: 305–315

[4] Ahmad R, Tichadou S, Hascoet J Y (2017). A knowledge-based intelligent decision system for production planning. International Journal of Advanced Manufacturing Technology, 89(5-8): 1717–1729

[5] Ameri F, McArthur C (2013). A multi-agent system for autonomous supply chain configuration. International Journal of Advanced Manufacturing Technology, 66(5-8): 1097–1112

[6] An D, Kim N H, Choi J H (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety, 133: 223–236

[7] Angles R, Gutierrez C (2008). Survey of graph database models. ACM Computing Surveys, 40(1): 1–39

[8] Aouchiche M, Hansen P (2013). A survey of Nordhaus–Gaddum type relations. Discrete Applied Mathematics, 161(4-5): 466–546

[9] Arshinder K, Kanda A, Deshmukh S G (2011). A review on supply chain coordination: Coordination mechanisms, managing uncertainty and research directions. In: Choi T-M, Cheng T C E, eds. Supply Chain Coordination under Uncertainty. Berlin: Springer

[10] Bao J M, Hu T T, Pan L, Xu H, Hu H F (2014). Heterogeneous data integration and fusion system based on metadata conflict algorithms in USPIOT. In: Proceedings of 2014 International Conference on Wireless Communication and Sensor Network. 95–100

[11] Baraldi P, Mangili F, Zio E (2012). A kalman filter-based ensemble approach with application to turbine creep prognostics. IEEE Transactions on Reliability, 61(4): 966–977

[12] Batini C, Cappiello C, Francalanci C, Maurino A (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3): 1–52

[13] Bergman M, Milo T, Novgorodov S, Tan W C (2015). Query-oriented data cleaning with oracles. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, New York, USA. 1199–1214

[14] Bertsimas D, Griffith J D, Gupta V, Kochenderfer M J, Mišić V V (2017). A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems. European Journal of Operational Research, 263(2): 664–678

[15] Bider I, Perjons E, Elias M, Johannesson P (2017). A fractal enterprise model and its application for business development. Software & Systems Modeling, 16(3): 663–689

[16] Bititci U S, Martinez V, Albores P, Parung J (2004). Creating and managing value in collaborative networks. International Journal of Physical Distribution & Logistics Management, 34(3/4): 251–268

[17] Brandes U, Borgatti S P, Freeman L C (2016). Maintaining the duality of closeness and betweenness centrality. Social Networks, 44: 153–159

[18] Browning T R (2016). Design structure matrix extensions and innovations: A survey and new opportunities. IEEE Transactions on Engineering Management, 63(1): 27–52

[19] Browning T R, Yassine A A (2016). Managing a portfolio of product development projects under resource constraints. Decision Sciences, 47(2): 333–372

[20] Buldyrev S V, Parshani R, Paul G, Stanley H E, Havlin S (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291): 1025–1028

[21] Byramjee F, Bhagat P, Klein A (2010). The Moderating Role of Relationship Quality in Determining Total Value Orientation.Rochester: Social Science Research Network

[22] Caesarendra W, Widodo A, Thom P H, Yang B S, Setiawan J D (2011). Combined probability approach and indirect data-driven method for bearing degradation prognostics. IEEE Transactions on Reliability, 60(1): 14–20

[23] Cai M, Zhang W Y, Zhang K (2011). ManuHub: A semantic web system for ontology-based service management in distributed manufacturing environments. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 41(3): 574–582

[24] Cao W, Jiang P (2013). Modelling on service capability maturity and resource configuration for public warehouse product service systems. International Journal of Production Research, 51(6): 1898–1921

[25] Castillo E D (2006). Statistical process adjustment: a brief retrospective, current status, and some opportunities for further work. Statistica Neerlandica, 60(3): 309–326

[26] Chang Y C, Hsieh Y L, Chen C C, Hsu W L (2017). A semantic frame-based intelligent agent for topic detection. Soft Computing, 21(2): 391–401

[27] Chen D (2015). A methodology for developing service in virtual manufacturing environment. Annual Reviews in Control, 39: 102–117

[28] Chen T, Xu R, He Y, Wang X (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72: 221–230

[29] Chen T Y, Chen Y M, Wang T S (2015). Developing an ontology-based knowledge combination mechanism to customise complementary knowledge content. International Journal of Computer Integrated Manufacturing, 28(5): 501–519

[30] Cheng C S, Cheng H P (2011). Using neural networks to detect the bivariate process variance shifts pattern. Computers & Industrial Engineering, 60(2): 269–278

[31] Cheng Y, Farooq S, Johansen J (2011). Manufacturing network evolution: a manufacturing plant perspective. International Journal of Operations & Production Management, 31(12): 1311–1331

[32] Ching W K, Choi S M, Huang X (2011). Inducing high service capacities in outsourcing via penalty and competition. International Journal of Production Research, 49(17): 5169–5182

[33] Chinnam R B (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40(17): 4449–4466

[34] Chu X, Ilyas I F, Papotti P (2013). Discovering denial constraints. The Proceeding of the VLDB Endowment, 6(13): 1498–1509

[35] Dai W, Maropoulos P G, Zhao Y (2015). Reliability modelling and verification of manufacturing processes based on process knowledge management. International Journal of Computer Integrated Manufacturing, 28(1): 98–111

[36] Dalal S, Jaglan D V, Sharma D K K (2014). Designing architecture of demand forecasting tool using multi-agent system. International Journal of Advanced Research in Engineering and Applied Sciences, 3(1): 11–20

[37] Ding J, Liu Y, Zhang L, Wang J (2014). MapReduce for large-scale monitor data analyses. In: Proceedings of IEEE international conference on trust. 747–754

[38] Ding K, Jiang P, Su S (2018). RFID-enabled social manufacturing system for inter-enterprise monitoring and dispatching of integrated production and transportation tasks. Robotics and Computer-integrated Manufacturing, 49: 120–133

[39] Doukas M, Psarommatis F, Mourtzis D (2014). Planning of manufacturing networks using an intelligent probabilistic approach for mass customised products. International Journal of Advanced Manufacturing Technology, 74(9-12): 1747–1758

[40] Dries A, Rückert U (2009). Adaptive concept drift detection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2: 311–327

[41] Elwell R, Polikar R (2011). Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks, 22(10): 1517–1531

[42] Eslami M H, Lakemond N (2016). Internal integration in complex collaborative product development projects. International Journal of Innovation Management, 20(01): 1650008

[43] Fan J, Ghurye S G, Levine R A (2000). Multicomponent lifetime distributions in the presence of ageing. Journal of Applied Probability, 37(02): 521–533

[44] Feldman K, Jazouli T, Sandborn P A (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Transactions on Reliability, 58(2): 305–316

[45] Figueroa F, Schmalzel J (2006). Rocket testing and integrated system health management. In: Wang L H, Gao R X, eds. Condition Monitoring and Control for Intelligent Manufacturing, 373–391. London: Springer

[46] Fink A, Kliewer N, Mattfeld D, Mönch L, Rothlauf F, Schryen G, Suhl L, Voß S (2014). Model-based decision support in manufacturing and service networks. Business & Information Systems Engineering, 6(1): 17–24

[47] Foreman J, Gallien J, Alspaugh J, Lopez F, Bhatnagar R, Teo C C, Dubois C (2010). Implementing supply-routing optimization in a make-to-order manufacturing network. M&SOM, 12(4): 547–568

[48] GóMez-Gasquet P, Lario F-C, Franco R-D, Anaya-Fons V (2011). A framework for improving planning-scheduling collaboration in industrial production environment. Studies in Informatics and Control, 20(20): 68

[49] Gröger C, Schlaudraff J, Niedermann F, Mitschang B (2012). Warehousing manufacturing data. In: Kambayashi Y, Winiwarier W, Arikawa M, eds. Data Warehousing and Knowledge Discovery. Berlin: Springer

[50] Gu J, Barker D, Pecht M (2007). Prognostics implementation of electronics under vibration loading. Microelectronics and Reliability, 47(12): 1849–1856

[51] Guide V D R Jr, Van Wassenhove L N (2009). OR FORUM—The evolution of closed-loop supply chain research. Operations Research, 57(1): 10–18

[52] Guo C Q (2015). “Internet+”: Disruptive innovation- an industrial, economic and social revolution. In: Proceedings of forum on collaborative innovation and development of intelligent manufacturing (in Chinese)

[53] Guo H, Tao F, Zhang L, Laili Y J, Liu D K (2012). Research on measurement method of resource service composition flexibility in service-oriented manufacturing system. International Journal of Computer Integrated Manufacturing, 25(2): 113–135

[54] Guo X, Sun S X, Vogel D (2014). A dataflow perspective for business process integration. ACM Transactions on Management Information Systems, 5(4): 1–33

[55] Guo Z X, Ngai E W T, Yang C, Liang X (2015). An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. International Journal of Production Economics, 159: 16–28

[56] Han L, Jiang P, Yu Y, Guo B (2014). Bayesian reliability evaluation for customized products with zero-failure data under small sample size. In: Proceedings of IEEE International Conference on Reliability. 904–907

[57] Heckmann I, Comes T, Nickel S (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52: 119–132

[58] Hegge H M H, Wortmann J C (1991). Generic bill-of-material: A new product model. International Journal of Production Economics, 23(1-3): 117–128

[59] Helo P, Suorsa M, Hao Y, Anussornnitisarn P (2014). Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65(4): 646–656

[60] Hirshorn S R V (2017). NASA Systems Engineering Handbook.New York: Diane Publishing

[61] Hong S, Lv C, Zhao T, Wang B, Wang J, Zhu J (2016). Cascading failure analysis and restoration strategy in an interdependent network. Journal of Physics. A, Mathematical and Theoretical, 49(19): 195101

[62] Hornik K, Stinchcombe M, White H (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359–366

[63] Houshmand M, Valilai O F (2013). A layered and modular platform to enable distributed CAx collaboration and support product data integration based on STEP standard. International Journal of Computer Integrated Manufacturing, 26(8): 731–750

[64] Jaw L C (2005). Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step.Reno: ASME

[65] Jayaram J, Kannan V R, Tan K C (2004). Influence of initiators on supply chain value creation. International Journal of Production Research, 42(20): 4377–4399

[66] Jia X, Nadarajah S, Guo B (2017). Bayes estimation of P(Y<X) for the Weibull distribution with arbitrary parameters. Applied Mathematical Modelling, 47: 249–259

[67] Jiao H, Zhang J, Li J H, Shi J (2017). Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S. International Journal of Computer Integrated Manufacturing, 30: 433–441

[68] Jin Y, Ryan J K (2012). Price and service competition in an outsourced supply chain. Production and Operations Management, 21(2): 331–344

[69] Judd J (1987). Learning in networks is hard. In: Proceedings of IEEE International Conference on Neural Networks. 2: 685–692

[70] Kacprzynski G J, Roemer M J, Hess A J (2002). Health management system design: Development, simulation and cost/benefit optimization. In: Proceedings of IEEE Aerospace Conference. 6: 3065–3072

[71] Kadry S (2013). Diagnostics and Prognostics of Engineering Systems: Methods and Techniques.Pennsylvania: IGI Global

[72] Kayış E, Erhun F, Plambeck E L (2012). Delegation vs. control of component procurement under asymmetric cost information and simple contracts. M&SOM, 15(1): 45–56

[73] Khalfallah M, Figay N, Silva C F D, Ghodous P (2016). A cloud-based platform to ensure interoperability in aerospace industry. Journal of Intelligent Manufacturing, 27(1): 119–129

[74] Kim S H, Netessine S (2013). Collaborative cost reduction and component procurement under information asymmetry. Management Science, 59(1): 189–206

[75] Kolahi S, Lakshmanan L V S (2009). On approximating optimum repairs for functional dependency violations. In: Proceedings of the 12th International Conference on Database Theory, New York, USA. 53–62

[76] La Rosa M, Dumas M, Uba R, Dijkman R (2013). Business process model merging: An approach to business process consolidation. ACM Transactions on Software Engineering and Methodology, 22(2): 1–42

[77] Laalaoui Y, Bouguila N (2014). Pre-run-time scheduling in real-time systems: Current researches and Artificial Intelligence perspectives. Expert Systems with Applications, 41(5): 2196–2210

[78] Lee J, Kao H A, Yang S (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16: 3–8

[79] Lee S D, Park P (2015). The development of carrier aviation support system architecture using DoDAF. Journal of the Korea Society of Systems Engineering, 11(1): 33–39

[80] Li J Q, Yu F R, Deng G, Luo C, Ming Z, Yan Q (2017). Industrial internet: a survey on the enabling technologies, applications, and challenges. IEEE Communications Surveys and Tutorials, 19(3): 1504–1526

[81] Li L, Qiao F (2012). A modular simulation system for semiconductor manufacturing scheduling. Przeglad Elektrotechniczny, 88: 12–18

[82] Li P, Wang H, Zhu K Q, Wang Z, Hu X, Wu X (2015). A large probabilistic semantic network based approach to compute term similarity. IEEE Transactions on Knowledge and Data Engineering, 27(10): 2604–2617

[83] Li Q, Luo H, Xie P X, Feng X Q, Du R Y (2015). Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment. Computers in Industry, 70: 23–45

[84] Li W, Pham H (2005). Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE Transactions on Reliability, 54(2): 297–303

[85] Liang L, Atkins D (2013). Designing service level agreements for inventory management. Production and Operations Management, 22: 1103–1117

[86] Liang Z, Wang Y (2013). A web content recommendation method based on data provenance tracing and forecasting. In: Wong W E, Ma T, eds. Emerging Technologies for Information Systems, Computing, and Management. New York: Springer

[87] Lim S C J, Liu Y, Lee W B (2011). A methodology for building a semantically annotated multi-faceted ontology for product family modelling. Advanced Engineering Informatics, 25(2): 147–161

[88] Lin J T, Chiu C C (2018). A hybrid particle swarm optimization with local search for stochastic resource allocation problem. Journal of Intelligent Manufacturing, 29(3): 481–495

[89] Lin T Y, Yang C, Zhuang C, Xiao Y, Tao F, Shi G, Geng C (2017). Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system. Journal of Engineering Manufacture, 231(12): 2159–2172

[90] Lin W, Qian Y, Li X (2000). Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Computers & Chemical Engineering, 24(2-7): 423–429

[91] Lin Y H, Li Y F, Zio E (2015). Integrating random shocks into multi-state physics models of degradation processes for component reliability assessment. IEEE Transactions on Reliability, 64(1): 154–166

[92] Liu J, Chen M, Wang L, Wu Q (2014). A task-oriented modular and agent-based collaborative design mechanism for distributed product development. Chinese Journal of Mechanical Engineering, 27(3): 641–654

[93] Liu S, Chen H, Guo B, Jia X, Qi J (2017). Residual life estimation by fusing few failure lifetime and degradation data from real-time updating. In: Proceedings of 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). 177–184

[94] Liu W, Xie D, Xu X (2013). Quality supervision and coordination of logistic service supply chain under multi-period conditions. International Journal of Production Economics, 142(2): 353–361

[95] Liu W H, Xie D (2013). Quality decision of the logistics service supply chain with service quality guarantee. International Journal of Production Research, 51(5): 1618–1634

[96] Lu C J, Meeker W Q, Escobar L A (1996). A comparison of degradation and failure-time analysis methods for estimating a time-to-failure distribution. Statistica Sinica, 6: 531–546

[97] Luca D (2015). Neural networks for parameters prediction of an electromagnetic forming process of FeP04 steel sheets. International Journal of Advanced Manufacturing Technology, 80(1-4): 689–697

[98] Lusch R F, Vargo S L, Tanniru M (2010). Service, value networks and learning. Journal of the Academy of Marketing Science, 38(1): 19–31

[99] Madni A M, Sievers M (2014). Systems integration: Key perspectives, experiences, and challenges. Systems Engineering, 17(1): 37–51

[100] Maier A, Schriegel S, Niggemann O (2017). Industrial Internet of Things.Cham: Springer

[101] Maltzahn S, Anderl R (2011). Early BOM Derivation from Requirement Specifications by Reusing Product Knowledge. In: Proceedings of ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers. 1189–1195

[102] Manupati V K, Kanigalpula P K C, Varela M L R, Putnik G D, Araújo A F, Vieira G G (2018). Developments and Advances in Intelligent Systems and Applications.Cham: Springer

[103] Marsillac E, Roh J J (2014). Connecting product design, process and supply chain decisions to strengthen global supply chain capabilities. International Journal of Production Economics, 147: 317–329

[104] Martinez-Hernandez V (2003). Understanding value creation : The value matrix and the value cube. Dissertation for the Doctoral Degree.Scotland: University of Strathclyde

[105] McElheran K (2015). Do market leaders lead in business process innovation? The case(s) of E-business adoption. Management Science, 61(6): 1197–1216

[106] Meeker W Q, Escobar L A, Lu C J (1998). Accelerated degradation tests: Modeling and analysis. Technometrics, 40(2): 89–99

[107] Mendes J M, Leitão P, Colombo A W, Restivo F (2012). High-level Petri nets for the process description and control in service-oriented manufacturing systems. International Journal of Production Research, 50(6): 1650–1665

[108] Millar R C (2007). A systems engineering approach to phm for military aircraft propulsion systems. In: Proceedings of 2007 IEEE Aerospace Conference. 1–9

[109] Modekurthy V P, Liu X F, Fletcher K K, Leu M C (2015). Design and implementation of a broker for cloud additive manufacturing services. Journal of Manufacturing Science and Engineering, 137(4): 040904

[110] Morris H, Lee S, Shan E, Zeng S (2004). Information integration framework for product life-cycle management of diverse data. Journal of Computing and Information Science in Engineering, 4(4): 352–358

[111] Moura M C, Zio E, Lins I D, Droguett E, and the Moura M das C (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 96(11): 1527–1534

[112] Müller M (2007). Dynamic Time Warping, Information Retrieval for Music and Motion.Berlin: Springer

[113] Nasser S, Turcic D (2017). Temporary Price Discount to a Retailer with a Private Demand Forecast.Beijing: Social Science Electronic Publishing

[114] Neches R, Madni A M (2013). Towards affordably adaptable and effective systems. Systems Engineering, 16(2): 224–234

[115] Nguyen H, Dumas M, Hofstede A H M, Rosa M L, Maggi F M (2016). Advanced Information Systems Engineering.Cham: Springer

[116] Nugraheni E, Akbar S, Saptawati G A P (2016). Framework of semantic data warehouse for heterogeneous and incomplete data. In: Proceedings of 2016 IEEE Region 10 Symposium (TENSYMP). 161–166

[117] Oh S, Özer Ö (2012). Mechanism design for capacity planning under dynamic evolutions of asymmetric demand forecasts. Management Science, 59(4): 987–1007

[118] Orchard M, Wu B, Vachtsevanos G (2005). A particle filtering framework for failure prognosis. Intelligent Control Systems Laboratory, 883–884

[119] Papakostas N, Pintzos G, Giannoulis C, Chryssolouris G (2016). An agent-based collaborative platform for the design of assembly lines. International Journal of Computer Integrated Manufacturing, 29(4): 374–385

[120] Parraguez P, Eppinger S, Maier A (2016). Characterizing design process interfaces as organization networks: Insights for engineering systems management. Systems Engineering, 19(2): 158–173

[121] Paulheim H (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3): 489–508

[122] Pecht M (2009). Prognostics and health management of electronics. Encyclopedia of Structural Health Monitoring, American Cancer Society

[123] Pirani M, Bonci A, Longhi S (2016). A scalable production efficiency tool for the robotic cloud in the fractal factory. In: Proceedings of IECON 2016- 42nd Annual Conference of the IEEE Industrial Electronics Society. 6847–6852

[124] Priore P, Gómez A, Pino R, Rosillo R (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 28(01): 83–97

[125] Qian Y, Yan R (2015). Remaining useful life prediction of rolling bearings using an enhanced particle filter. IEEE Transactions on Instrumentation and Measurement, 64(10): 2696–2707

[126] Qiu T, Luo D, Xia F, Deonauth N, Si W, Tolba A (2016). A greedy model with small world for improving the robustness of heterogeneous internet of things. Computer Networks, 101: 127–143

[127] Qiu X, Lau H Y K (2014). An AIS-based hybrid algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing, 25(3): 489–503

[128] Quintanilla G F, Cardin O, L’Anton A, Castagna P (2016). A modeling framework for manufacturing services in Service-oriented Holonic Manufacturing Systems. Engineering Applications of Artificial Intelligence, 55: 26–36

[129] Rahmani D, Heydari M (2014). Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times. Journal of Manufacturing Systems, 33(1): 84–92

[130] Reiner G (2005). Customer-oriented improvement and evaluation of supply chain processes supported by simulation models. International Journal of Production Economics, 96(3): 381–395

[131] Roels G, Karmarkar U S, Carr S (2010). Contracting for collaborative services. Management Science, 56(5): 849–863

[132] Romanowski C J, Nagi R (2005). On comparing bills of materials: a similarity/distance measure for unordered trees. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 35(2): 249–260

[133] Sankararaman S, Mahadevan S (2015). Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems. Reliability Engineering & System Safety, 138: 194–209

[134] Saranga H, Moser R (2010). Performance evaluation of purchasing and supply management using value chain DEA approach. European Journal of Operational Research, 207(1): 197–205

[135] Schouten K, Frasincar F (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3): 813–830

[136] Senderovich A, Weidlich M, Yedidsion L, Gal A, Mandelbaum A, Kadish S, Bunnell C A (2016). Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Information Systems, 62: 185–206

[137] Shen W, Han J, Wang J, Yuan X, Yang Z (2018). SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks. IEEE Transactions on Knowledge and Data Engineering, 30(2): 353–366

[138] Shin H J, Cho K W, Oh C H (2018). SVM-based dynamic reconfiguration cps for manufacturing system in industry 4.0. Wireless Communications and Mobile Computing, 2018: 1–13

[139] Sicotte H, Langley A (2000). Integration mechanisms and R&D project performance. Journal of Engineering and Technology Management, 17(1): 1–37

[140] Sohail A, Dominic P D D (2015). Business process improvement: A process warehouse based resource management method. In: Proceedings of International Symposium on Technology Management. 291–296

[141] Song S, Chen L (2011). Differential dependencies: Reasoning and discovery. ACM Transactions on Database Systems, 36(3): 1–41

[142] Stavrulaki E, Davis M M (2014). A typology for service supply chains and its implications for strategic decisions. Service Science, 6(1): 34–46

[143] Stuckenbruck L C (1997). Integration: The essential function of project management. In: Cleland D I, King W R, eds. Project Management Handbook. Hoboken: John Wiley & Sons

[144] Sun J, Debo L (2014). Sustaining long-term supply chain partnerships using price-only contracts. European Journal of Operational Research, 233(3): 557–565

[145] Sun W, Shao S, Yan R (2016). Induction motor fault diagnosis based on deep neural network of sparse auto-encoder. Journal of Mechanical Engineering, 52(9): 65–71

[146] Sun Y, Du Y, Li M (2017). A repair of workflow models based on mirroring matrices. International Journal of Parallel Programming, 45(4): 1001–1020

[147] Tang X, Yun H (2008). Data model for quality in product lifecycle. Computers in Industry, 59(2-3): 167–179

[148] Tanriverdi H, Konana P, Ge L (2007). The choice of sourcing mechanisms for business processes. Information Systems Research, 18(3): 280–299

[149] Taratukhin V V, Yadgarova Y V (2016). Emerging Trends in Information Systems.Cham: Springer

[150] Teran H, Hernandez J C, Vizán A, Ríos J (2014). Performance measurement integrated information framework in e-Manufacturing. Enterprise Information Systems, 8(6): 607–629

[151] Thiraviam A, Mudge W, Malone L (2009). Six challenges in implementation of effective Accelerated Life Tests. In: Proceedings of 2009 Annual Reliability and Maintainability Symposium. 47–52

[152] Tobon-Mejia D A, Medjaher K, Zerhouni N, Tripot G (2012). A data-driven failure prognostics method based on mixture of gaussians hidden markov models. IEEE Transactions on Reliability, 61(2): 491–503

[153] Tsai C J, Huang H P (2007). A real-time scheduling and rescheduling system based on rfid for semiconductor foundry fabs. Journal of the Chinese Institute of Industrial Engineers, 24(6): 437–445

[154] Tummala R, Schoenherr T (2011). Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management, 16(6): 474–483

[155] Venkatesan D, Kannan K, Saravanan R (2009). A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing & Applications, 18(2): 135–140

[156] Wang J M, Ren G Q, Zhang L, Liu Y B, Mo X N (2010). Maintenance repair and overhaul/operations support technology. Jisuanji Jicheng Zhizao Xitong, 16: 2017–2025(in Chinese)

[157] Wang S, Du Y, Deng Y (2017). A new measure of identifying influential nodes: Efficiency centrality. Communications in Nonlinear Science and Numerical Simulation, 47: 151–163

[158] Wang T Y, Chen L H (2002). Mean shifts detection and classification in multivariate process: A neural-fuzzy approach. Journal of Intelligent Manufacturing, 13(3): 211–221

[159] Wang X, Balakrishnan N, Guo B (2014). Residual life estimation based on a generalized Wiener degradation process. Reliability Engineering & System Safety, 124: 13–23

[160] Wang X, Balakrishnan N, Guo B (2015). Residual life estimation based on nonlinear-multivariate Wiener processes. Journal of Statistical Computation and Simulation, 85(9): 1742–1764

[161] Wang X, Balakrishnan N, Guo B, Jiang P (2015). Residual life estimation based on bivariate non-stationary gamma degradation process. Journal of Statistical Computation and Simulation, 85(2): 405–421

[162] Wang Y G, Li Y J, Zheng B, Wang C (2008). Industrial value chain modeling based on industrial value matrix. In: Proceedings of 2008 Chinese Control and Decision Conference. 1100–1105

[163] Wasmer A, Staub G, Vroom R W (2011). An industry approach to shared, cross-organisational engineering change handling- The road towards standards for product data processing. Computer Aided Design, 43(5): 533–545

[164] Wei Y, Hu Q, Xu C (2013). Ordering, pricing and allocation in a service supply chain. International Journal of Production Economics, 144(2): 590–598

[165] Whitaker J, Mithas S, Krishnan M S (2010). Organizational learning and capabilities for onshore and offshore business process outsourcing. Journal of Management Information Systems, 27(3): 11–42

[166] Whyte J, Stasis A, Lindkvist C (2016). Managing change in the delivery of complex projects: Configuration management, asset information and ‘big data’. International Journal of Project Management, 34(2): 339–351

[167] Williams B D, Waller M A (2010). Creating order forecasts: point-of-sale or order history? Journal of Business Logistics, 31(2): 231–251

[168] Winnig L W (2016). GE’s big bet on data and analytics. MIT Sloan Management Review, 57

[169] Wu S D, Wu P H, Wu C W, Ding J J, Wang C C (2012). Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy (Basel, Switzerland), 14(8): 1343–1356

[170] Wu X, Ryan S M (2011). Optimal replacement in the proportional hazards model with semi-markovian covariate process and continuous monitoring. IEEE Transactions on Reliability, 60(3): 580–589

[171] Xie C, Cai H, Xu L, Jiang L, Bu F (2017). Linked semantic model for information resource service toward cloud manufacturing. IEEE Transactions on Industrial Informatics, 13(6): 3338–3349

[172] Xiong J, Xing L, Chen Y (2013). Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns. International Journal of Production Economics, 141: 112–126

[173] Xu D F, Li Q, Jun H-B, Browne J, Chen Y L, Kiritsis D (2009). Modelling for product information tracking and feedback via wireless technology in closed-loop supply chains. International Journal of Computer Integrated Manufacturing, 22: 648–670

[174] Xu R, Zhai X (2010). Analysis of supply chain coordination under fuzzy demand in a two-stage supply chain. Applied Mathematical Modelling, 34(1): 129–139

[175] Xu W, Yu J, Zhou Z, Xie Y, Pham D T, Ji C (2015). Dynamic modeling of manufacturing equipment capability using condition information in cloud manufacturing. Journal of Manufacturing Science and Engineering, 137(4): 040907

[176] Xu X, Hua Q (2017). Industrial big data analysis in smart factory: Current status and research strategies. IEEE Access : Practical Innovations, Open Solutions, 5: 17543–17551

[177] Yang X, Shi G, Zhang Z (2014). Collaboration of large equipment complete service under cloud manufacturing mode. International Journal of Production Research, 52(2): 326–336

[178] Yang Y, Chen Y, Chen X, Liu X (2012). Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis. Chemometrics and Intelligent Laboratory Systems, 116: 94–101

[179] Yang Y, Ren G (2017). Design of real time data acquisition system framework for production workshop based on OPC technology. MATEC Web of Conferences, 128: 02014

[180] Yang Z, Zhang J, Wang S, Wang J, Huang X (2016). Building ontology-based bill of material design and knowledge management in power gird. 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 1664–1669

[181] Yazdi A S H, Kahani M (2014). A novel model for mining association rules from semantic web data. 2014 Iranian Conference on Intelligent Systems (ICIS), 1–4

[182] Yeung W K, Choi T M, Cheng T C E (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132(2): 223–229

[183] Yousefi-Azar M, Hamey L (2017). Text summarization using unsupervised deep learning. Expert Systems with Applications, 68: 93–105

[184] Yu Z, Wang H, Lin X, Wang M (2016). Understanding short texts through semantic enrichment and hashing. IEEE Transactions on Knowledge and Data Engineering, 28(2): 566–579

[185] Yuan X, Hu Y, Stanley H E, Havlin S (2017). Eradicating catastrophic collapse in interdependent networks via reinforced nodes. Proceedings of the National Academy of Sciences of the United States of America, 114(13): 3311

[186] Zampou E, Plitsos S, Karagiannaki A, Mourtos I (2014). Towards a framework for energy-aware information systems in manufacturing. Computers in Industry, 65(3): 419–433

[187] Zhang C, Yao X, Zhang J (2015). Abnormal condition monitoring of workpieces based on rfid for wisdom manufacturing workshops. Sensors (Basel), 15(12): 30165–30186

[188] Zhang D (2006). A network economic model for supply chain versus supply chain competition. Omega, 34(3): 283–295

[189] Zhang F (2010). Procurement mechanism design in a two-echelon inventory system with price-sensitive demand. M&SOM, 12(4): 608–626

[190] Zhang F, Xue F, Xu F (2016). Collaborative modeling method of performance prototype for aerospace products based on ontology. Jisuanji Jicheng Zhizao Xitong, 22: 1887–1899

[191] Zhang H, Diao Y, Immerman N (2010). Recognizing patterns in streams with imprecise timestamps. Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, 3(1-2): 244–255

[192] Zhang J, Jiang P, Guo B, Cheng Z (2017). The mixture of multi-kernel relevance vector machine with dynamic weights for real-time capacity estimation of lithium-ion batteries. In: Proceedings of 7th Asian-Pacific International Symposium on Advanced Reliability and Maintenance Modeling

[193] Zhang J, Martin E B, Morris A J (1997). Process monitoring using non-linear statistical techniques. Chemical Engineering Journal, 67(3): 181–189

[194] Zhang O Q, Ko R K L, Kirchberg M, Suen C H, Jagadpramana P, Lee B S (2012). How to track your data: Rule-based data provenance tracing algorithms. In: Proceedings of IEEE International Conference on Trust. 1429–1437

[195] Zhang T, Gu X, He E (2014). Heterogeneous problems and elimination methods for modular ontology of product knowledge engineering. Lecture Notes in Electrical Engineering, 271: 43–50

[196] Zhang W, Zhang S, Zhang S, Yu D (2016). A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory. Journal of Algorithms & Computational Technology, 10(1): 40–51

[197] Zhang W, Zhou D, Liu L (2013). Contracts for changing times: Sourcing with raw material price volatility and information asymmetry. M&SOM, 16(1): 133–148

[198] Zhang W N, Ming Z Y, Zhang Y, Liu T, Chua T S (2016). Capturing the semantics of key phrases using multiple languages for question retrieval. IEEE Transactions on Knowledge and Data Engineering, 28(4): 888–900

[199] Zhang W Y, Zhang S, Chen Y G, Pan X W (2013). Combining social network and collaborative filtering for personalised manufacturing service recommendation. International Journal of Production Research, 51(22): 6702–6719

[200] Zhang Y, Guo B (2015). Online capacity estimation of lithium-ion batteries based on novel feature extraction and adaptive multi-kernel relevance vector machine. Energies, 8(11): 12439–12457

[201] Zhang Y, Ren Z J (2011). Service outsourcing. In: Cochran J J, eds. Wiley Encyclopedia of Operations Research and Management Science. New York: Wiley

[202] Zhao F, Tian Z, Bechhoefer E, Zeng Y (2015). An integrated prognostics method under time-varying operating conditions. IEEE Transactions on Reliability, 64(2): 673–686

[203] Zhao F, Tian Z, Zeng Y (2013). Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method. IEEE Transactions on Reliability, 62(1): 146–159

[204] Zheng W, Hsu H, Zhong M, Yun M (2015). Requirements analysis for future satellite gravity mission improved-GRACE. Surveys in Geophysics, 36(1): 87–109

[205] Zhou S, Wang P (2009). The integration of multi-source heterogeneous data based on middleware. In: Proceedings of First International Conference on Information Science and Engineering. 2213–2216

[206] Zhou Y, Chen J, Dong G M, Xiao W B, Wang Z Y (2012). Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis. Mechanical Systems and Signal Processing, 26: 229–243

[207] Zhu S P, Huang H Z, Peng W, Wang H K, Mahadevan S (2016). Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering & System Safety, 146: 1–12

[208] Zhu X, Song S, Lian X, Wang J, Zou L (2014). Matching heterogeneous event data. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, New York, USA. 1211–1222

[209] Zio E, Di Maio F (2012). Fatigue crack growth estimation by relevance vector machine. Expert Systems with Applications, 39(12): 10681–10692

[210] Žliobaitė I, Bifet A, Pfahringer B, Holmes G (2011). Active learning with evolving streaming data. In: Machine Learning and Knowledge Discovery in Databases. Berlin: Springer

RIGHTS & PERMISSIONS

The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)