Nothing Special   »   [go: up one dir, main page]

skip to main content
review-article

Literature review of Industry 4.0 and related technologies

Published: 01 January 2020 Publication History

Abstract

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

References

[1]
Aalaei A and Davoudpour H Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: A case study Engineering Applications of Artifical Intelligence 2016 47 3-15
[2]
Abdoa J and Demerjianb J Evaluation of mobile cloud architectures Pervasive and Mobile Computing 2017 39 284-303
[3]
Aburaia M, Markl E, and Stuje K New concept for design and control of 4 axis robot using the additive manufacturing technology Procedia Engineering 2015 100 1364-1369
[4]
Accenture. (2016). Industry 4.0 revolution report. https://www.accenture.com/us-en/insight-digital-industry-impact. Available on August 28, 2017.
[5]
Accorsi R, Bortolini M, Baruffaldi G, Pilati F, and Ferrari E Internet-of-things paradigm in food supply chains control and management Procedia Manufacturing 2017 11 889-895
[7]
Addo-Tenkorang R and Helo PT Big data applications in operations/supply-chain management: A literature review Computers & Industrial Engineering 2016 101 528-543
[8]
Adeyeri, S., Kanisuru, M., Khumbulani, M., & Olukorede T. (2015). Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0. In Proceedings of the 2015 international conference on industrial engineering and operations management Dubai, United Arab Emirates (UAE) (pp. 1625–1635).
[9]
Agency, M. (2008). The medical products agency’s working group on medical information systems. National Board of Health and Welfare in the regulations on quality management systems in health care. https://lakemedelsverket.se/upload/foretag/medicinteknik/en/Medical-Information-Systems-Report_2009-06-18.pdf. Available on August 22, 2017.
[10]
Ahmed E and Kohno R Error control coding and decoding with medical QoS constraints for Wban end to end connection via UMTS channel ICT Express 2017
[12]
Akoka J, Wattiau I, and Laoufi N Research on big data—A systematic mapping study Neurocomputing 2016 2 1023-1041
[13]
Al-Ali A and Aburukba R Role of internet of things in the smart grid technology Journal of Computer and Communications 2015 3 229-233
[14]
Alam K and Saddik A C2PS: A digital twin architecture reference model for the cloud-based cyber-physical system IEEE Access 2015 5 25-35
[15]
Alanso-Martin F, Castro A, Malfaz M, and Castillo J Identification and distance estimation of users and objects by means of electronic beacons in social robotics Expert Systems with Applications 2017 86 247-257
[16]
Alatoibi Y Business process modelling challenges and solutions: A literature review Journal of Intelligent Manufacturing 2016 27 701-723
[17]
Alayaa M, Banouara D, Monteila S, Chassota Z, and Drira T OM2M: Extensible ETSI-compliant M2M service platform with self-configuration capability Computer Science 2014 32 1079-1086
[18]
Albert A, Bartosz G, Tobias P, Viktoriia B, and Tobias S Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems Reconfigurable & Virtual Production 2016 52 262-267
[19]
Albodour R, James A, and Yaacob N QoS within business grid quality of service (BGQoS) Future Generation Computer Systems 2015 50 22-37
[20]
Aleina SC, Viola N, Fusara R, Saccoccia G, and Vercella V Using the ESA exploration technology roadmaps in support of new mission concepts and technology prioritization Acta Astronautica 2018
[21]
Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, and Ayyash M Internet of things: A survey on enabling technologies, protocols and applications IEEE Communications Surveys and Tutorials 2015 17 4 2347-2376
[22]
Alharthi A, Krotov V, and Bowman M Adressing bariers to big data Business Horizons 2017 60 285-292
[23]
Alkhamisi A and Monowar M Rise of augmented reality: Current and future application areas International Journal of Internet and Distributed Systems 2013 1 25-34
[24]
Alkoc E and Erbatur F Productivity improvement in concreting operations through simulation models Building Research and Information 1997 25 2 83-95
[25]
Amatoa F and Moscato F Exploiting cloud and workflow patterns for the analysis of composite cloud services Future Generation Computer Systems 2017 67 255-265
[26]
Anderl, R. (2014). Industries 4.0-advanced engineering of smart products and smart production. In 19th International seminar on high technology, technological innovations in the product development, Piracicaba, Brazil. https://www.researchgate.net/publication/270392830_Industrie_40_-_Advanced_Engineering_of_Smart_Products_and_Smart_Production_09_October_2014. Available on December 28, 2017.
[27]
Andrade A, Pereira A, Walter S, Almeida R, Loureiro R, Compagna D, and Kyberd J Bridging the gap between robotic technology and health care Biomedical Signal Processing and Control 2014 10 65-78
[28]
Angeles R RFID technologies: Supply-chain applications and implementation issues Information Systems Management 2005 22 51-65
[29]
Ângelo, A., Barata, J., da Cunha, P. R., & Almeida, V. (2017). Digital transformation in the pharmaceutical compounds supply chain: Design of a service ecosystem with e-labeling. In European, Mediterranean, and Middle Eastern conference on information systems (pp. 307–323).
[30]
Anitha R and Mukherjee S ‘MaaS’: Fast retrieval of E-file in cloud using metadata as a service Journal of Intelligent Manufacturing 2017 28 1871-1891
[31]
ARIZ. (2017). https://www.festo.com/group/en/cms/12690.htm. Available on August 30, 2017.
[32]
Armentia A, Gangoiti U, Orive D, and Marcos M Dynamic QoS management for flexible multimedia applications IFAC PapersOnLine 2017 50 5920-5925
[33]
Atanasov I, Nikolov A, Pencheva E, Dimova R, and Ivanov M An approach to data annotation for internet of things International Journal of Information Technology and Web Engineering (IJITWE) 2015 10 4 1-19
[34]
Atif Y, Dinga J, and Jeusfelda MA Internet of things approach to cloud-based smart car parking Computer Science 2016 98 193-198
[35]
Atzori L, Morabito G, and Lera A Understanding the internet of things: Definition Ad Hoc Networks 2017 56 122-140
[36]
AWS. (2017). https://aws.amazon.com/iot-platform/. Available on August 30, 2017.
[37]
Azevedo P, Azevedo C, and Romão M Application integration: Enterprise resource planning (ERP) systems in the hospitality industry Procedia Technology 2014 16 52-58
[38]
Backhaus J and Reinhart G Digital description of products, processes and resources for task-oriented programming of assembly systems Journal of Intelligent Manufacturing 2017 28 1787-1800
[39]
Badawi H, Dong H, and El Saddika Abdulmotaleb Mobile cloud-based physical activity advisory system using biofeedback sensors Future Generation Computer Systems 2017 66 59-70
[40]
Bagheri B, Yang S, Kao HA, and Lee J Cyber-physical systems architecture for selfaware machines in industry 4.0 environment IFAC-PapersOnLine 2015 48 3 1622-1627
[41]
Baheti R and Gill H Cyber-physical systems The İmpact of Control Technology 2011 12 161-166
[42]
Balina S, Baumgarte D, and Salna E Cloud based cross-system integration for small and medium-sized enterprises Computer Science 2017 104 127-132
[43]
Bartezzaghi E and Ronchi S Internet supporting the procurement process lessons from four case studies Integrated Manufacturing Systems 2003 14 632-641
[44]
Bauer, W., Schlund, S., Marrenbach, D., & Ganschar, O. (2014). Industry 4.0Volkswirtschaftliches Potenzial für Deutschland, BITKOM company. http://www.produktionsarbeit.de/content/dam/produktionsarbeit/de/documents/Studie-Industrie-4-0-Volkswirtschaftliches-Potential-fuer-Deutschland.pdf. Available on August 28, 2017 (in German).
[45]
Bauernhansl T Die vierte Industrylle Revolution Der Weg in ein wertschaffendes Produktionsparadigma 2014 4 3-35
[46]
Bauernhansl, T., ten Hompel, M., & Vogel-Heuser, B. (Eds.) (2014). Industry 4.0 in Produktion, Automatisierung und Logistik. Anwendung, Technologien und Migration 8, 30–40 (in German).
[47]
Baygin, M., Yetis, H., Karakose, M., & Akin, E. (2016). An effect analysis of industry 4.0 to higher education. In 2016 15th international conference on information technology based higher education and training (ITHET), July 10–12, 2017, Ohrid, Macedonia.
[48]
BCMCOM. (2017). Industry 4.0 technologies for new trends and developments for industry delivering quality. http://www.bcmcom.com/solutions_application_industry40.htm. Available on August 28, 2017.
[49]
Beckera T and Sterna H Future trends in human work area design for cyber-physical production systems Procedia CIRP 2016 57 404-409
[50]
Bellini, P., Bruno, I., Cenni, D., & Nesi, P. (2017). Managing cloud via smart cloud engine and knowledge base. In 2015 IEEE 8th international conference on cloud computing (CLOUD), 27 June–2 July 2015, New York, NY, USA.
[51]
Bello O, Zeadally S, and Badra M Network layer inter-operation of device-to-device communication technologies in internet of things (IoT) Ad Hoc Networks 2017 57 52-62
[52]
Bently, C. (2016). The manufacturer industry 4.0 UK readiness report. Oracle Company Report. https://www.oracle.com/webfolder/s/delivery_production/docs/FY16h1/doc19/Industry-Report.pdf. Available on August 28, 2017.
[53]
Bergera C, Heesa A, Braunreuthera S, and Reinharta G Characterization of cyber-physical sensor systems Manufacturing System 2016 41 638-643
[54]
Berryman D Augmented reality: A review Medical Reference Services Quarterly 2012 31 2 212-218
[55]
Bertacchini F, Bilotta E, and Pantano P Shopping with a robotic companion Computers in Human Behavior 2017 77 382-395
[56]
Biral A, Centenaro M, Zanella A, Vangelista L, and Zorzi M The challenges of M2M massive access in wireless cellular networks Digital Communications and Networks 2015 1 1 1-19
[57]
BMBF. (2014). Bundesministerium für Bildung und Forschung, 2014: Zukunftsbild Industry 4.0. http://www.bmbf.de/pubRD/Zukunftsbild_Industry_40.pdf. Available on August 28, 2017 (in German).
[58]
Boston Consulting Group. (2016). Industry 4.0: The future of productivity and growth in manufacturing industries. https://www.bcgperspectives.com/content/articles/engineered_products_project_business_industry_40_future_productivity_growth_manufacturing_industries/?chapter=4#chapter4_section2. Available on August 28, 2017.
[59]
Bourke R and Mentis M An assessment framework for inclusive education: Integrating assessment approaches Assesment in Education 2014 21 4 384-397
[60]
Bouwers E and Vis R Multidimensional software monitoring applied to ERP Electronic Notes in Theoretical Computer Science 2009 233 161-173
[61]
Boveta G and Hennebertb J Energy-efficient optimization layer for event-based communications on Wi-Fi thing Computer Science 2013 19 256-264
[62]
Bower M, Howe C, McCredie N, Robinson A, and Grover D Augmented reality in education—Cases, places and potentials Educational Media International 2014 51 1 1-15
[63]
Brandmeier M, Bognera E, Brossoga M, and Frankea J Product design improvement through knowledge feedback of cyber-physical systems Procedia CIRP 2016 50 186-191
[64]
Brennera A and Hummela V A seamless convergence of the digital and physical factory aiming in personalized Product Emergence Process (PPEP) for smart products within ESB Logistics Learning Factory at Reutlingen University Procedia CIRP 2016 54 227-232
[65]
Brettel M, Klein M, and Friederichsen N The relevance of manufacturing flexibility int he context of industries 4.0 Procedia CIRP 2016 41 105-110
[66]
Brioto, M., Hoque Z., Steinke R., & Willner A. (2016). Towards programmable fog nodes in smart factories. In 2016 IEEE 1st international workshops on foundations and applications of self systems, Augsburg, Germany, 12–16 September 2016.
[67]
Brunete A, Gambao E, Koskinen J, Heikkila T, Kaldestad K, Tyapin I, Hovland G, Surdilovic D, Hernando M, Bottero A, and Anton S Hard material small-batch industrial machining robot Robotics and Computer-Integrated Manufacturing 2017
[68]
Bryner M Smart manufacturing: The next revolution CEP Magazine 2012 7 1090-1098
[69]
Bui D, Yoon Y, Huh E, Jun S, and Lee S Energy efficiency for cloud computing system based on predictive optimization Journal of Parallel and Distributed Computing 2013 102 103-114
[70]
Bungart, S. (2014). Industrial internet versus industry 4.0. Produktion—Technik und Wirtschaft für die deutsche Industry. Retrieved from http://www.produktion.de/automatisierung/industrial-internet-versus-Industry-4-0/print. Available on August 28, 2017.
[71]
Bunse B Industry: Based on “German Industry 4.0” report Journal of Applied Business and Economics 2016 18 40-50
[72]
Bürger, T., & Tragl, K. (2014). SPS-Automatisierung mit den Technologien der IT-Welt verbinden. Technologien und Migration (pp. 559–569) (in German).
[73]
Burke M, Quigley N, and Speed C The internet of things: Pink jumpers and Hungarian eggs in digital spaces Procedia Computer Engineering 2013 9 152-157
[74]
Calderona M, Delgadilloa S, and Antonio J A more human-centric Internet of Things with temporal and spatial context Computer Science 2016 83 2016 553-559
[75]
Candra S ERP implementation success and knowledge capability International Congress on Interdisciplinary Business and Social Science 2012 65 141-149
[76]
Canedoa A and Richterb J Architectural design space exploration of cyber-physical systems using the functional modeling compiler Engineering Services 2014 21 46-51
[77]
Carboneras, M., Insa, C., & Salort, E. (2003). ERP implementation in the stone industry: Special difficulties and solutions in the production area. In Emerging technologies and factory automation, 2003. Proceedings. ETFA’03. IEEE conference. Lisbon, Portugal.
[78]
Carniani E, Darenzo D, Lazouski A, Martinelli A, and Mori P Usage control on cloud systems Future Generation Computer Systems 2016 63 37-55
[79]
Carrera C and Asensio C Landscape interpretation with augmented reality and maps to improve spatial orientation skill Journal of Geography in Higher Education 2016 41 1 119-133
[80]
Carstensen J, Carstensen T, Pabs M, Schulz F, Friederichs J, Aden S, Kaczor D, Kotlarski J, and Ortmaier T Condition monitoring and cloud-based energy analysis for autonomous mobile manipulation—Smart factory concept with LUHbots Procedia Technology 2016 26 2016 560-569
[81]
Chang H, Kim J, and Park J IT convergence security Journal of Intelligent Manufacturing 2014 25 213-215
[82]
Chang H, Ma J, Loke S, Zimmermann H, and Li Z Intelligent ubiquitous IT policy and its industrial services Journal of Intelligent Manufacturing 2012 23 913-915
[83]
Chang V, Ramachandranb M, Wills G, Walters R, Li C, and Watters P Editorial for FGCS special issue: Big Data in the cloud Future Generation Computer Systems 2016 65 73-75
[84]
Chatterjee, S. (2015). ERP failure in developing countries: A case study in India. In India conference (INDICON), 2015 Annual IEEE, 17–20 December 2015, New Delhi, India.
[85]
Chelloug S Energy-efficient content-based routing in internet of things Journal of Computer and Communications 2015 3 9-20
[86]
Chen, G., & Liu, Y. (2012). Performance evaluation of ERP implementation based on uncertainty measurement theory. In 2012 International conference on information management, innovation management and industrial engineering, 20–21 October 2012, Sanya, China.
[87]
Chen, G., & Wang, J. (2010). Analysis on performance evaluation system of ERP implementation. In 2010 International conference of information science and management engineering, 7–8 August 2010, China.
[88]
Chen TC Cloud intelligence in manufacturing Journal of Intelligent Manufacturing 2018 28 1057-1059
[89]
Chen T and Chiu M Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation Robotics and Computer-Integrated Manufacturing 2017 45 133-143
[90]
Chen T and Wu C A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory Journal of Intelligent Manufacturing 2017 28 1095-1107
[91]
Chen X, Zhao Y, Zhang C, Wang X, and Chen L Robot needle-punching for manufacturing composite preforms Robotics and Computer-Integrated Manufacturing 2018 50 132-139
[92]
Chen X and Jin Z Research on key technology and applications for internet of things Physics Procedia 2012 33 2012 561-566
[93]
Cheng, G., Lıu, L., & Quıang, Z. (2016). Industry 4.0 development and application of intelligent manufacturing. In 2016 International conference on information system and artificial intelligence, 24–26 June 2016, Hong Kong, China.
[94]
Cheng-Yu W, Pi-Cheng T, and Chyun-Chau F Development of an automatic arc welding system using an adaptive sliding mode control Intelligent Manufacturing 2010 21 4 355-362
[95]
Chi X, Zhang J, and Ma L Queuing theory based service performance evaluation under H2H and M2M blending traffic arriving Procedia Environmental Science 2011 11 Part A 478-485
[96]
Chien C, Gen M, and Shi Y Manufacturing intelligence and innovation for digital manufacturing and operational excellence Journal of Intelligent Manufacturing 2014 25 845-847
[97]
Chlen C, Kim K, Liu B, and Gen M Advanced decision and intelligence technologies for manufacturing and logistics Journal of Intelligent Manufacturing 2012 22 2133-2135
[98]
Chu C, Weidong L, and Jiao R Design chain management: bridging the gap between engineering and management Journal of Intelligent Manufacturing 2013 24 541-544
[99]
Cooper, S. (2017). Designing a UK industrial strategy for the age of industry 4.0. Rethink Manufacturing (pp. 1–27).
[100]
Corcio, M. (2016). Manufacturing intelligence, group manager: Automation, MES & Electricity. http://www.iiconsortium.org/smart-factory-forum/MIGUEL-CORCIO-Keynote_IIC-MC-Smart_Manufacturing.pdf. Available on August 28, 2017.
[101]
Dagli C Engineering cyber physical systems: Applying theory to practice Procedia Computer Science 2016 95 7-8
[102]
Daim T, Yoon B, Linderberg J, Grizzi R, and Estep J Strategic roadmapping of robotics echnologies for the power industry: A multicriteria technology assessment Technological Forecasting and Social Change 2018 131 49-66
[103]
Damle A, Damle R, Flahive J, Schlussel AT, Davids J, Sturrock PR, Maykel J, and Alavi K Diffusion of technology: Trends in robotic-assisted colorectal surgery The American Journal of Surgery 2017 214 820-824
[104]
Dasgupta A, Nagaraj R, and Nagamani K An internet of things platform with Google Journal of Software Engineering and Applications 2016 9 291-295
[105]
Davali I, Belli L, Cilfone A, and Ferrari G Integration of Wifi mobile nodes in a web of things tested ICT Express 2016 2 3 96-99
[106]
Dechene D and Shami A Energy efficient QoS constrained scheduler for SC-FDMA uplink Physical Communication 2013 8 81-90
[107]
Decker M, Fischer M, and Ott I Service robotics and human labor: A first technology assessment of substitution and cooperation Robotics and Autonomous Systems 2017 87 348-354
[109]
Deja M and Siemiaatkowski M Feature-based generation of machining process plans for optimised parts manufacture Journal of Intelligent Manufacturing 2013 24 831-846
[110]
Dener M and Bostancıoğlu C Smart technologies with wireless sensor networks Social and Behavioral Sciences 2015 195 1915-1921
[111]
Deng G, Chen D, and Yao M Value structure analysis for cloud service ecosystem International Journal of Services, Technology and Management 2015 21 4/5/6 228-237
[112]
Ding L, Liu Y, Han B, and Zhang S HB-file: An efficient and effective high-dimensional big data storage structure based on US-ELM Proceedings of ELM 2017 1 489-500
[113]
Ding Y, Yaoa G, and Haoa K Fault-tolerant elastic scheduling algorithm for workflow in cloud systems Future Generation Computer Systems 2017 393 47-65
[114]
Do H, Minh P, Sheng W, Yang D, and Liu M RiSH: A robot-integrated smart home for elderly care Robotics and Autonomous Systems 2018 101 74-92
[115]
Dong H-S Anatomy of big data developmental process Telecommunication Policy 2016 40 9 837-854
[116]
Drath H and Horch A Industry 4.0: Hit or hype? Industry forum IEEE Industrial Electronics Magazine 2014 8 2 56-58
[117]
Du C, Tan L, and Dong Y Period selection for integrated controller tasks in cyber physical systems Aeronautics China 2015 28 3 894-902
[118]
Du Z, He L, Chen Y, Xiao Y, Gao P, and Wang T Robot cloud: Bridging the power of robotics and cloud computing Future Generation Computer Systems 2017 74 337-348
[119]
Duan Q Cloud service performance evaluation: status, challenges, and opportunities—A survey from the system modeling perspective Computer Science 2017 3 2 101-111
[120]
Dudek, J., Auersperg, J., Pantou, R., & Rzepka, S. (2015). Thermal and mechanical behavior of an RFID based smart system embedded in a transmission belt determined by FEM simulations for industry 4.0 applications. In 2015 16th international conference on Fraunhofer ENAS, 19–22 April 2015, Budapest, Hungary.
[121]
Dworschak B and Zaiser H Competencies for cyber-physical systems in manufacturing—First findings and scenarios Procedia CIRP 2014 25 345-350
[122]
EEF. (2017). The 4th industrial revolutionA primer for manufacturers. Technical report, EEF the manufacturers Organization, UK.
[123]
e-factory. (2017). https://tr3a.mitsubishielectric.com/fa/tr/solutions/efactory. Available on August 30, 2017.
[124]
Elmangousha A, Coricib A, Steinkeb R, Coricib M, and Magedanz T A framework for handling heterogeneous M2M traffic Procedia Computer Science 2015 63 112-119
[125]
Elmonem MA, Geith M, Nasr E, and Geith M Benefits and challenges of cloud ERP systems—A systematic literature review Future Computing and Informatics Journal 2017 1 1–2 1-9
[126]
Elmonem M, Nasr E, and Geith M Benefits and challenges of cloud ERP systems: A systematic literature view Future Computing and Informatics Journal 2016 1 1–2 1-9
[127]
Elragal A ERP and big data: The inept couple Procedia Technology 2014 16 242-249
[128]
Enget K A big data case Journal of Accounting Education 2016 39 1-84
[129]
ENTOC. (2017). https://www.festo.com/group/en/cms/12827.htm. Available on August 30, 2017.
[131]
Epstein B and Givoni M Analyzing the gap between the QOS demanded by PT users and QOS supplied by service operators Transportation Research Part A 2016 94 622-637
[132]
Ermilova E and Afsarmanesh E Modeling and management of profiles and competencies in VBEs Intelligent Manufacturing 2007 18 561-586
[133]
Erol S, Jäger A, Hold P, Ott K, and Sihn W Tangible industry 4.0: A scenario-based approach to learning for the future of production Procedia CIRP 2016 54 13-18
[134]
Esfahbodi A, Zhang Y, and Watson G Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance International Journal of Production Economics 2016 181 350-366
[135]
ESIMA. (2017). Industry 4.0 project. https://www.esima-projekt.de/. Available on August 28, 2017 (in Germany).
[136]
Eslava H, Rojas L, and Pereira R Implementation of machine-to-machine solutions using MQTT protocol in internet of things (IoT) environment to improve automation process for electrical distribution substations in Colombia Journal of Power and Energy Engineering 2014 3 92-96
[137]
Evans, A., & Annunziata, B. (2012). Industrial internet: Pushing the boundaries of minds and machines. https://www.ge.com/docs/chapters/Industrial_Internet.pdf. Available on May 28, 2017.
[138]
Fallera, C., & Feldmüllera, D. (2015). Industry 4.0 learning factory for regional SMEs. In The 5th conference on learning factories 2015 (Vol. 32, pp. 88–91).
[139]
Fanjiang Y, Syu Y, and Kuo J Search based approach to forecasting QoS attributes of web services using genetic programming Information and Software Technology 2016 80 158-174
[140]
Fariss M, Asaidi H, and Bellouki M Comparative study of skyline algorithms for selecting Web Services based on QoS The First International Conference On Intelligent Computing in Data Sciences 2018 127 408-415
[141]
Feldmann, A. (2011). A strategic perspective on plants in manufacturing networks. Division of Production Economics Department of Management and Engineering, Vol. 1, pp. 581–583. ISBN: 978-91-7393-134-2.
[142]
Filaretov VF and Pryyanichnikov VE Autonomous mobile university robots AMUR: Technology and applications to extreme robotics Procedia Engineering 2015 100 269-277
[143]
Filippi S and Barattin D Classification and selection of prototyping activities for interaction design Intelligent Information Management 2012 4 147-156
[144]
Finin, T., Labrou, Y., & Mayfied, J. (1995). KQML as an agent communication language. In J. M. Bradshaw (Ed.), Software agents. Cambridge: MIT Press. ISBN 9780262522342.
[145]
Flammini E and Sisinni E Wireless sensor networking in the internet of things and cloud computing era Procedia Engineering 2012 87 2014 672-679
[146]
Fleisch, E., Weinberger, M., & Wortmann, F. (2014). Business models and the internet of things. Bosch IoT Lab Whitepaper, University of St. Gallen. http://cocoa.ethz.ch/downloads/2014/10/2090_EN_Bosch%20Lab%20White%20Paper%20GM%20im%20IOT%201_2.pdf. Available on May 28, 2017.
[147]
Flores-Abad A, Ma Q, Pham K, and Ulrich S A review of space robotics technologies for on-orbit servicing Progress in Aerospace Sciences 2014 68 1-26
[148]
Foehr Matthias, Vollmar Jan, Calà Ambra, Leitão Paulo, Karnouskos Stamatis, and Colombo Armando Walter Engineering of Next Generation Cyber-Physical Automation System Architectures Multi-Disciplinary Engineering for Cyber-Physical Production Systems 2017 Cham Springer International Publishing 185-206
[149]
Foerstl K, Azadegan A, Leppelt T, and Hartmann E Drivers of supplier sustainability: Moving beyond compliance to commitment Journal of Supply Chain Management 2015 51 1 67-92
[150]
Forti T and Munteanub V Topics in cloud incident management Future Generation Computer Systems 2017 72 163-164
[151]
Foster K, Smith G, Ariyachandra T, and Frolick M Business intelligence competency center: Improving data and decisions Information Systems Management 2015 32 3 229-233
[152]
Framinan J and Pierreval H Special issue on pull strategies in manufacturing systems and supply chains: Recent advances Journal of Intelligent Manufacturing 2012 23 1-3
[153]
Francis H and Kusiak A Prediction of engine demand with a data-driven approach Procedia Computer Science 2017 103 28-35
[154]
Friedberg I, McLaughlin K, Smith P, Laverty D, and Seze S STPA-SafeSec: Safety and security analysis for cyber-physical systems Journal of Information Security and Applications 2016 2 2 123-133
[155]
FUSION. (2016). http://fusion-edu.eu/FUSION/. Available on August 30, 2017.
[156]
Gabrel V, Manouvrier M, Moreau K, and Murat C QoS-aware automatic syntactic service composition problem: Complexity and resolution Future Generation Computer Systems 2018 80 311-321
[157]
Gaikwad, P. P., Gabhane, J. P., & Golait, S. S. (2015). A survey based on Smart Homes system using Internet-of-Things. In Computation of power, energy information and communication (ICCPEIC) (pp. 0330–0335).
[158]
Gajos K, Weisman L, and Shrobe H Design principles for resource management systems for intelligent spaces International Workshop on Self-Adaptive Software 2001 36 198-215
[159]
Galaske N and Anderl R Disruption management for resilient processes in cyber-physical production systems Procedia CIRP 2016 50 442-447
[160]
Gao, Y., Yang, T., & Bo, H. (2014). Improving the transmission reliability in smart factory through spatial diversity with ARQ. In IEEE/CIC international conference on communication in China, 27–29 July 2016, Chengdu, China.
[161]
Gash D, Ariyachandra T, and Frolick M Looking to the clouds for business intelligence Journal of Internet Commerce 2011 10 4 261-269
[162]
Gaurav, D. (2017). What is the difference between digital manufacturing and virtual manufacturing, Quora. https://www.quora.com/What-is-the-difference-between-Digital-Manufacturing-and-Virtual-Manufacturing. Available on August 28, 2017.
[163]
Gawanda H and Roya K Online monitoring of a cyber physical system against control aware cyber attacks Engineering Services 2015 70 238-244
[164]
Gay S and Nieuwoudt L Results of a trade simulation model for the South African fresh orange industry Agrekon 2010 38 4 707-715
[165]
Ge M, Hong J, Guttman W, and Kim D A framework for automating security analysis of the internet of things Procedia Technology 2014 83 12-27
[166]
Geeta RB, Totad G, Reddy P, and Shobha RB Big data structure and usage mining coalition International Journal of Services, Technology and Management 2015 21 6 252-271
[167]
Gelbmann U and Hammerl B Integrative re-use systems as innovative business models for devising sustainable product–service-systems Journal of Cleaner Production 2015 97 50-60
[168]
Gen M and Hwang H Advanced models and optimization in manufacturing and logistics systems Journal of Intelligent Manufacturing 2011 22 343-344
[169]
German Ministry of Education. (2016). Industry 4.0 platform, recommendations of industry 4.0 applications. http://www.din.de/blob/65354/f5252239daa596d8c4d1f24b40e4486d/roadmap-i4-0-e-data.pdf. Available on August 28, 2017.
[170]
Gharbic G, Guermoucheb N, and Monteil T Timed verification of machine-to-machine communications Procedia Computer Science 2014 32 1071-1078
[171]
Giasiranis S and Sofos L Production and evaluation of educational material using augmented reality for teaching the module of “representation of the information on computers” in junior high school Creative Education 2016 7 1270-1291
[172]
Giusto, D., Lera, A., Morabito, G., & Atzori, L. (Eds.) (2010). The internet of things: 20th Tyrrhenian workshop on digital communications. Springer. ISBN-10: 1441916733.
[173]
Gjeldum N, Mladineoa M, and Vezaa I Transfer of model of innovative smart factory to croatian economy using lean learning factory Procedia CIRP 2016 54 158-163
[174]
Gökalp, M., Kayabay, K., Akyol, M., Eren, E., & Kocyigit. A. (2016). Big data for industry 4.0: A conceptual framework. In 2016 International conference on computational science and computational intelligence, 15–17 December 2016, Las Vegas, NV, USA.
[175]
Golova N and Rönnbäck L Big data normalization for massively parallel processing database Computer Standard 2016 54 Part 2 86-93
[176]
Golparvar-Fard M, Peña-Mora F, and Savarese S D4AR—A 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication Journal of Information Technology in Construction 2009 14 81-97
[177]
Gonzales-Coma JP, Joham M, Castro P, and Castedo L QoS constrained power minimization in the multiple stream MIMO broadcast channel Signal Processing 2018 143 48-55
[178]
Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D., (2014). Human–machine-interaction in the industry 4.0 era. In 12th IEEE international conference on industrial informatics (INDIN) (pp. 289–294).
[179]
Granell C, Havlik D, Schade S, Sabeur Z, Delaney C, Pielorz J, Usllander T, Mazzetti P, Schleidt K, Kobernus M, Havlik F, Bodsberg N, Berre A, and Mon JM Future internet technologies for environmental applications Enviromental Modelling and Software 2016 78 1-15
[180]
Greenyera J, Gritznera D, Katzb G, Marronb A, Gladea N, Gutjahra T, and Konig F Distributed execution of scenario-based specifications of structurally dynamic cyber-physical systems Engineering Services 2016 26 552-559
[181]
Grzenda M, Bustillo A, and Zawistowski P A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling Intelligent Manufacturing Systems 2012 23 5 1733-1743
[183]
Gubbi J, Buyya R, Marusic S, and Palaniswami M Internet of Things (IoT): A vision, architectural elements, and future directions Future Generation Computer Systems 2013 29 1645-1660
[184]
Gudfinnsson K, Strand M, and Berntsson M Analyzing business intelligence maturity Journal of Decision Systems 2015 24 1 37-54
[185]
Guide VDR Jr and Van Wassenhove LN OR FORUM—The evolution of closedloop supply chain research Operations Research 2009 57 1 10-18
[186]
Gunasekaran A and Kobu B Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications International Journal of Production Research 2007 45 12 2819-2840
[187]
Guo K, Liang Z, Tang Y, and Chi T SOR: An optimized semantic ontology retrieval algorithm for heterogeneous multimedia big data International Journal of Information 2016 4 25-35
[188]
Guoa Z, Zhanga Z, and Li W Establishment of intelligent identification management platform in railway logistics system by means of the internet of things Procedia Engineering 2012 29 726-730
[189]
Gupta M and George J Toward the development of a big data analytics capability Information Management 2016 53 8 1049-1064
[190]
Gursoy MC, Qiao D, and Velipasalar S Analysis of energy efficiency in fading channels under QoS constraints IEEE Transactions on Wireless Communications 2008 8 1276-1536
[191]
Haddara M and Elragal A The readiness of ERP systems for the factory of the future Procedia Computer Science 2015 64 721-728
[192]
Haquea S and Aziz S False alarm detection in cyber-physical systems for healthcare applications Engineering Services 2013 5 54-61
[193]
Hardy K and Maurushat A Opening up government data for Big Data analysis and public benefit Journal of Business Research 2016 33 1 30-37
[194]
Hartunga R, Hakanssonb A, and Moradianc E A prescription for cyber physical systems Manufacturing System 2015 5 4-9
[195]
Hashem I, Chang V, Anuar N, Adewole K, Yaquub I, Gani A, Ahmed E, and Chiroma H The role of big data in smart city International Journal of information 2016 36 5 748-758
[196]
Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., et al. (2015). Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. In IEEE international conference on services computing (SCC) (pp. 285–292).
[197]
Hayyolalam V and Kazem A A systematic literature review on QoS-aware service composition and selection in cloud environment Journal of Network and Computer Applications 2018 110 52-74
[198]
Hazen B, Boone C, Farmer LA, and Ezell J Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications Internal Journal of Production 2014 154 72-80
[199]
Hazen BT, Skipper JB, Ezell JD, and Boone CA Big Data and predictive analytics for supply chain sustainability: A theory-driven research agenda Computers & Industrial Engineering 2016 101 592-598
[200]
He, J., Chen, H., & Hu, F. (2015). ERP: An enhanced read policy for HDFS to improve read performance for files under construction. In 2015 IEEE international conference on progress in informatics and computing (PIC), 18–20 December 2015, Nanjing, China.
[201]
He K and Li X A quantitative estimation technique for welding quality using local mean decomposition and support vector machine Journal of Intelligent Manufacturing 2016 27 525-533
[202]
Hea Y, Chena L, and Wang L An improved direct anonymous attestation scheme for M2M networks Computer Science 2016 15 1481-1486
[203]
Hecklau, F., Galeitzkea, M., Flachsa, S., & Kohl, H. (2015). Holistic approach for human resource management in industry 4.0. In Conference on learning factories, 10–11 October 2009, Changsha, Hunan, China.
[204]
Heng, S., Slomka, L., Ag, D. B., & Hoffmann, R. (2014). Industry 4.0. Upgrading of Germany’s industrial capabilities on the horizon. Frankfurt am Main: Deutsche Bank Research. SSRN: https://ssrn.com/abstract=2656608.
[205]
Henriques CI, Sobreiro VA, and Kimura H Science and technology park: Future challenges Technology in Society 2018 53 144-160
[206]
Heragu S and Kusiak A Analysis of expert systems in manufacturing design IEEE Transactions on Systems, Man, and Cybernetics 1987 17 6 898-912
[207]
Hermann, M., Tobias, P., & Otto, B. (2016). Design principles for industry 4.0 scenarios. http://www.thiagobranquinho.com/wp-content/uploads/2016/11/Design-Principles-for-Industrie-4_0-Scenarios.pdf. Available on August 28, 2017.
[208]
Herron J Augmented reality in medical education and training Journal of Electronic Resources in Medical Libraries 2016 13 2 51-55
[209]
Herterich M, Uebernickel F, and Brenner W The impact of cyber-physical systems on industrial services in manufacturing Procedia CIRP 2015 30 323-328
[210]
Higashinoa W, Capretz M, and Bittencourt L CEPSim: Modelling and simulation of complex event processing systems in cloud environments Future Generation Computer Systems 2017 65 122-139
[211]
Hofmann E and Rüsch M Industry 4.0 and the current status as well as future prospects on logistics Computers in Industry 2017 89 23-34
[212]
Holm, A., Wang, L., & Brewster, R. (2016). Localizing operators in the smart factory: A review of existing techniques and systems. In 2016 International symposium on flexible automation, 1–3 August 2016, Cleveland, Ohio, USA.
[213]
Hong E-K, Baek J, Jang Y, Na J, and Kim K QoS-guaranteed scheduling for small cell networks ICT Express 2017
[214]
Hossain MS and Muhammad G Cloud-assisted industrial internet of things (iiot)—Enabled framework for health monitoring Computer Networks 2016 101 192-202
[215]
Houda K and Lakel R Synchronized communication in a set of autonomous mobile robots using bluetooth technology Procedia Computer Science 2015 73 154-161
[216]
Hsiao M A conceptual framework for technology-enabled and technology dependent user behavior toward device mesh and mesh app Future Business Journal 2018 4 130-138
[217]
Hu T, Xiao M, Hu C, Gao G, and Wang B A QoS-sensitive task assignment algorithm for mobile crowdsensing Pervasive and Mobile Computing 2017 41 333-342
[218]
Huang C, Liang W, and Yi S Cloud-based design for disassembly to create environmentally friendly products Journal of Intelligent Manufacturing 2017 28 1203-1218
[219]
Hubert C and Chan Y Internet of things business models Journal of Service Science and Management 2015 50 1020-1030
[220]
Huckle S, Bhattacharya R, White M, and Beloff N Internet of things blockchain, shared economy applications Procedia Computer Science 2016 98 2016 461-466
[221]
Hufnagel, J., & Vogel-Heuser, B. (2015). Data integration in manufacturing industry model-based integration of data distributed from ERP to PLC. In 13th International conference on industrial informatics (INDIN), 22–24 July 2015, Cambridge, UK.
[222]
Hwang G, Lee J, Park J, and Chang T Developing performance measurement system for Internet of Things and smart factory environment International Journal of Production Research 2016 55 9 2590-2602
[223]
I4MTS. (2016). http://www.the-mtc.org/pdf/Industry-4-Report-2016-e.pdf. Available on August 30, 2017.
[224]
Iavazzo C and Gkegkes I Cost–benefit analysis of robotic surgery in gynaecological oncology Best Practice & Research Clinical Obstetrics and Gynaecology 2017 45 7-18
[225]
ICV. (2016). International controller association report. http://integratedreporting.org/wp-content/uploads/2013/08/137_International-Controller-Association-Discussion-Paper.pdf. Available on August 28, 2017.
[226]
Iera A, Floerkemeier C, Mitsugi J, and Morabito G The internet of things IEEE Wireless Communications 2010 17 8-9
[227]
Ignaccolo M A simulation model for airport capacity and delay analysis Transportation Planning and Technology 2003 26 2 135-170
[229]
Ince H, Imamoglu SZ, Keskin H, Akgun A, and Efe MA The impact of ERP systems and supply chain management practices on firm performance: Case of Turkish companies International Strategic Management Conference 2013 99 1124-1133
[230]
Inderfurth K, de Kok AG, and Flapper SDP Product recovery in stochastic remanufacturing systems with multiple reuse options European Journal of Operational Research 2001 133 130-152
[231]
INESA. (2016). http://journal.jp.fujitsu.com/en/2016/10/31/01/. Available on August 30, 2017.
[233]
Intel IOT Report. (2016). Developing solutions for the internet of things. http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/developing-solutions-for-iot.pdf. Available on August 28, 2017.
[234]
Iqbal A, Zhang H, Kong L, and Hussain G A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process Journal of Intelligent Manufacturing 2015 26 1217-1232
[235]
Issa H, Regenbrecht H, and Hale R Augmented reality applications in rehabilitation to improve physical outcomes Physical Therapy Reviews 2012 17 1 16-28
[236]
Ivanov D, Dolgui A, Sokolov B, and Ivanova M A dynamic model and an algorithm for short term supply chain scheduling in the smart factory industry 4.0 International Journal of Production Research 2015 54 2 386-402
[237]
Jäckel M, Falk T, and Landgrebe D Concept for further development of self-pierce riveting by using cyber physical systems Procedia CIRP 2016 44 293-297
[238]
Jaehne, J., & KalalChelvan, S. (2017). Towards a connected world of supply chainIndustry 4.0 presentation. https://www.slideshare.net/sarathygurushankar1/shaping-towards-a-connected-world-of-supply-chain-industrie-40. Available on August 22, 2017.
[239]
Jannsenn M, Voort H, and Wahyudi A Factors influencing big data decision making quality Journal of Business Research 2017 70 338-345
[240]
Jararweha Y, Al-Ayyoub M, Darabseh A, Benkhelifa E, Vouk M, and Rindos A Software defined cloud: Survey, system and evaluation Future Generation Computer Systems 2017 58 56-74
[241]
Jatzkowskia J and Kleinjohanna B Towards self-reconfiguration of real-time communication within cyber-physical systems Manufacturing Systems 2016 15 54-61
[242]
Jayanthi S, Roth V, Kristal M, and Venu L Strategic resource dynamics of manufacturing firms Management Science 2009 55 6 1060-1076
[243]
Jeang A Robust product design and process planning in using process capability analysis Intelligent Manufacturing Systems 2015 26 3 459-470
[244]
Jeang A Robust product design and process planning in using process capability analysis Journal of Intelligent Manufacturing 2015 26 459-470
[245]
Jeng T, Tzeng S, Tseng C, and Liu Y The design and fabrication of a temperature diagnosis system for the intelligent rotating spindle of industry 4.0 Smart Science 2016 4 38-43
[246]
Jernigan D, Fernandez S, Pensyl R, and Shangping L Digitally augmented reality characters in live theatre performances International Journal of Performance Arts and Digital Media 2009 5 1 35-49
[247]
Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., & Eschert, T. (2017). Industrial internet of things and cyber manufacturing systems. In Industrial internet of things, international publishing (pp. 3–19).
[248]
Ji Z, Ganchev I, O’Droma M, Zhao L, and Zhang X A cloud-based car parking middleware for IoT-based smart cities: Design and implementation Sensors 2014 14 22372-22393
[249]
Jianjuna S, Xub W, Jizhenc G, and Yangzhou C The analysis of traffic control cyber-physical systems Social and Behavioral Science 2016 96 2487-2496
[250]
Jiao B, Zhou Y, Du J, Huang C, Wang J, and Li B A heuristic nonlinear operator for the aggregation of incomplete judgment matrices in group decision making Journal of Intelligent Manufacturing 2015 26 1253-1266
[251]
Jing Q, Vasilakos AV, Wan J, Lu J, and Qiu D Security of the internet of things: Perspectives and challenges Wireless Networks 2014 20 2481-2501
[252]
Johansson, B., Alajbegovic, A., & Alexopoulos, V. (2015). Cloud ERP adoption opportunities and concerns: The role of organizational size, system sciences (HICSS). In 2015 48th Hawaii international conference on system sciences (pp 1530–1605), 5–8 January 2015, Kauai, HI, USA.
[253]
Jones, A., Vidalis, S., & Abouzakhar, N. (2016). Information security and digital forensics in the world of cyber physical systems. In 2016 Eleventh international conference on digital information management (ICDIM), 19–21 September, Porto, Portugal.
[254]
Jourdan Z, Rainer K, and Marshall T Business intelligence: An analysis of the literature Information Systems Management 2008 25 2 121-131
[255]
Junghanns P, Fabian B, and Ermakova T Engineering of secure multi-cloud storage Computers in Industry 2016 83 108-120
[256]
Kagermann, H. (2014). Chancen von Industry 4.0 nutzen. In Bauernhansl, T., M. ten Hompel and B. Vogel-Heuser, Vol. 4, pp. 603–614 (in German).
[257]
Kagermann, H., Lukas, W., & Wahlster, W. (2011). Industry 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. Industryllen Revolution. VDI nachrichten, Vol. 13, pp. 1090–1100.
[258]
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative Industry 4.0. Final report of the industry 4.0 working group, http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf. Available on August 22, 2017.
[259]
Kaidanren. (2016). Toward realization of the new economy and society. Japan Business Federation (p. 8). http://www.keidanren.or.jp/en/policy/2016/029_outline.pdf. Available on August 22, 2017.
[260]
Karakus M and Durresi A Quality of service (QoS) in software defined networking (SDN): A survey Journal of Network and Computer Applications 2017 80 200-218
[261]
Kba S Cloud based health system Computer Science 2015 18 989-1000
[262]
Ke, Y., Wang, P., Chen, Y., Gu, B., Qi, H., Zhou, P., et al. (2015). Concurrent mental activities affect ERPs and impair performance of ERP-spellers. In 2015 7th International IEEE/EMBS conference on neural engineering (NER), 22–24 April 2015, Montpellier, France.
[263]
Kermorgant O A magnetic climbing robot to perform autonomous welding in the shipbuilding industry Robotics and Computer Integrated Manufacturing 2018 53 178-186
[264]
Khan, R., Khan, S. U., Zaheer, R., Khan, S. (2012). Future internet: The internet of things architecture, possible applications and key challenges. In 10th International conference on frontiers of information technology (FIT) (pp. 257–260).
[265]
Kiel D, Arnold C, and Voigt KI The influence of the Industrial Internet of Things on business models of established manufacturing companies—A business level perspective Technovation 2017 68 4-19
[266]
Kim H, Lee S, Park H, and Lee G A model for a simulation-based shipbuilding system in a shipyard manufacturing process International Journal of Computer Integrated Manufacturing 2005 18 6 427-441
[267]
Kim, J., Kim, H., Lakshmanan, K., & Rajkumar, R. R. (2013). Parallel scheduling for cyber-physical systems: Analysis and case study on a self-driving car. In Proceedings of the ACM/IEEE 4th international conference on cyber-physical systems (pp. 31–40).
[268]
Kim J, Lee S, Seo J, and Kamat V Modular data communication methods for a robotic excavator Automation in Construction 2018 90 166-177
[269]
Kim W and Jo O Cost-optimized configuration of computing instances for large sized cloud systems Computer Science 2015 5 20-30
[270]
Kim Y and Suzuki K Social context representation in product-service systems with internet of things Open Journal of Social Sciences 2015 3 187-193
[271]
Kirthica S and Sridhar R CIT: A cloud inter-operation toolkit to enhance elasticity and tolerate shut down of external clouds Journal of Network and Computer Applications 2016 85 32-46
[272]
Klaus, H. (2016). Siemens industry 4.0 report for german industry and applications. On the way industry 4.0. https://www.siemens.com/press/pool/de/events/2015/digitalfactory/2015-04-hannovermesse/presentation-e.pdf. Available on August 22, 2017.
[273]
Klimeš J Using formal concept analysis for control in cyber-physical systems Engineering Services 2014 69 1518-1522
[274]
Kokuryo, D., Kaihara, T., Suginouchi, S., & Kuik, S. (2016). A study on value co-creative design and manufacturing system for tailor-made rubber shoes production. In 2016 International symposium on flexible automation, 1–3 August 2016, Ohio, USA.
[275]
Kolberg D, Berger C, Pirvu B, Franke M, and Michniewicz J Insights from a framework for designing cyber-physical systems in production environments Procedia CIRP 2016 57 32-37
[276]
Koo D, Piratla K, and Matthews J Towards sustainable water supply: Schematic development of big data collection using internet of things Procedia Computer Engineering 2015 4 45-55
[277]
Koseleva N and Ropaite G Big data in building energy efficiency: Understanding of big data and main challenges Procedia Engineering 2017 172 2017 544-549
[278]
Kothandaraman D and Chellappan C Direction detecting system of indoor Smartphone users using BLE in IoT Circuits and Systems 2016 7 1492-1503
[279]
Kowalska M, Pazdzior M, and Maziopa A Erratum to: Implementation of QFD method in quality analysis of confectionery products Journal of Intelligent Manufacturing 2018 29 449-450
[280]
Kozhirbayev Z and Sinnott R A performance comparison of container-based technologies for the cloud Future Generation Computer Systems 2017 68 175-182
[281]
Krawatzeck R and Dinter B Agile business intelligence: Collection and classification of agile business intelligence actions by means of a catalog and a selection guide Information Systems Management 2015 32 3 177-191
[282]
Kumar J and Zaveri M Hierarchical clustering for dynamic and heterogeneous internet of things Computer Science 2016 93 276-282
[283]
Kurth, M., & Syleyer, C. (2016). Smart factory and education. In 2016 IEEE 11th conference on industrial electronics and applications (ICIEA) (pp. 110–119), 5–7 June 2016, Hefei, China.
[284]
Kusiak A Short-term prediction of wind farm power: A data mining approach Wind Energy Journal 2009 12 3 275-293
[285]
Kusiak A A data-mining approach to monitoring wind turbines Transactions on Sustainable Energy 2012 3 1 150-165
[286]
Kusiak A Innovation: The living laboratory perspective Computer-Aided Design and Applications 2013 4 6 196-206
[287]
Kusiak A Smart manufacturing International Journal of Production Research 2017
[288]
Kusiak A Smart manufacturing must embrace big data Nature 2017 544 7648 23-25
[289]
Kusiak A, Zheng H, and Song Z Power optimization of wind turbines with data mining and evolutionary computation Renewable Energy 2010 35 3 695-702
[290]
Kyriazisa D and Varvarigoua T Smart, autonomous and reliable Internet of Things Computer Science 2013 21 2013 442-448
[291]
Lakhmi, C. J., & Nguyen, N. T. (2009). Knowledge processing and decision making in agent-based systems. Berlin: Springer. ISBN 978-3-540-88048-6.
[292]
Lakshimi R, Babu S, and Bhalaji N Analysis of clustered QoS routing protocol for distributed wireless sensor network Computers & Electrical Engineering 2017 64 173-181
[293]
Lasi H, Fettke P, Kemper G, Feld T, and Hoffmann M Industry 4.0: Bedarfssog und Technologiedruck als Treiber der vierten Industrillen Revolution The İnternational Journal of Wirtschaftsinformatik 2014 56 261-264
[294]
Layuan, L., & Chunlin, L. (2002). A multicast routing protocol supporting multiple QoS constraints. In 10th IEEE international conference on networks (Vol. 2). 10.1109/icon.2002.1033285.
[295]
Lee, A. (2008). Cyber physical systems: Design challenges. In 11th IEEE symposium on object oriented real-time distributed computing (ISORC), 5–7 May 2008, Orlando, FL, USA.
[296]
Lee D Robots in the shipbuilding industry Robotics and Computer-Integrated Manufacturing 2014 30 442-450
[297]
Lee, H., Leu, J., & Huang, Y. (2015c). Implementation of enterprise resource planning using the value engineering and system dynamics methods. In 2015 2nd International conference on information science and control engineering (ICISCE), 24–26 April 2015, Shanghai, China.
[298]
Lee J and Shin K Development and use of a new task model for cyber-physical systems: A real-time scheduling perspective Journal of System 2017 126 45-56
[299]
Lee J, Ardakani H, Yang S, and Bagheri B Industrial big data analytics and cyber-physical systems for future maintenance & service innovation Procedia CIRP 2015 38 3-7
[300]
Lee J, Bagheri B, and Kao H A cyber systems architecture for industry 4.0 based manufacturing systems Manufacturing Letters 2015 3 18-23
[301]
Lee J, Kao HA, and Yang S Service innovation and smart analytics for industry 4.0 and big data environment Procedia Cirp 2014 16 3-8
[302]
Lee J and Lapira E Industry 4.0 environment Asset Condition Management 2014 15 54-61
[303]
Lee J, Lapira E, Bagheri B, and Kao HA Recent advances and trends in predictive manufacturing systems in big data environment Manufacturing Letters 2013 1 1 38-41
[304]
Lee, H., Yoo, S., & Kim, Y. (2016). An energy management framework for smart factory on context awareness. In 18th International conference on advanced communication technology (ICACT), 31 January–3 February 2016, Pyeongchang, South Korea.
[305]
Lei C, Wan K, and Man K Developing a smart learning environment in universities via cyber-physical systems Information Technology and Quantitative Management 2013 17 583-585
[306]
Leloglu E A review of security concerns in internet of things Journal of Computer and Communications 2017 5 121-136
[307]
Leppelt T, Foerstl K, Reuter C, and Hartmann E Sustainability management beyond organizational boundaries–sustainable supplier relationship management in the chemical industry Journal of Cleaner Production 2013 56 94-102
[308]
Li B, Song AM, and Song J A distributed QoS-constraint task scheduling scheme in cloud computing environment: Model and algorithm Advances in information Sciences and Service Sciences (AISS) 2012 4 283-291
[309]
Li G, Zhang D, Zheng K, Ming X, Pan H, and Jiang K A kind of new multicast routing algorithm for application of internet of things Journal of Applied Research and Technology 2013 11 4 578-585
[310]
Li J, Xie T, and Du S Requirements analysis on flexibility of ERP system of medium and small publishers Procedia Engineering 2011 15 5493-5497
[311]
Li Z, Shen H, Li H, Xia G, Gamba P, and Zhang L Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery Remote Sensing of Environment 2017 191 342-358
[312]
Lia B and Yub B Research and application on the smart home based on component technologies and Internet of Things Procedia Engineering 2011 15 2087-2092
[313]
Liang H and Du Y Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution Future Generation Computer Systems 2017 76 119-135
[314]
Lian-yue, W. (2012). Think of construction lean SCM based on IOT. In IEEE Symposium on Robotics and Applications (ISRA) (pp. 436–438).
[315]
Liao T Augmented or admented reality? The influence of marketing on augmented reality technologies Information, Communication and Society 2015 18 3 310-326
[316]
Liao Y, Deschamps F, Loures E, and Ramos L Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal International Journal of Production Research 2017 55 12 3609-3629
[317]
Lichtblau, K., Stıch, V., Bertenrath, R., Blum, R., Bleider, M., Millack, A., et al. (2016). IMPULS, Industry 4.0 readiness, VDMA. http://industrie40.vdma.org/documents/4214230/5356229/Industrie%204.0%20Readiness%20Study%20English.pdf/f6de92c1-74ed-4790-b6a4-74b30b1e83f0. Available on August 28, 2017.
[318]
Lilis G, Conus G, Asadi N, and Kayal M Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design Sustainable Cities and Society 2017 28 473-481
[319]
Lim MK, Tseng ML, Tan KH, and Bui TD Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach Journal of Cleaner Production 2017 162 806-816
[320]
Lin, T., Chen, M., Yang, D., & Chen, Y. (2016). New method for industry 4.0 machine status prediction—A case study with the machine of a spring factory. In 2016 International computer symposium, 15–17 December 2016, Chiayi, Taiwan.
[321]
Lin YC, Hung MH, Huang HC, Chen CC, Yang HC, Hsieh YS, and Cheng FT Development of advanced manufacturing cloud of things (AMCoT)—A smart manufacturing platform IEEE Robotics and Automation Letters 2017 2 1809-1816
[322]
Lin D, Lee C, Lau H, and Yang Y Strategic response to Industry 4.0: An empirical investigation on the Chinese automotive industry Industrial Management & Data Systems 2017 118 3 589-605
[323]
Lin B, Lin F, and Tung L The roles of 5G mobile broadband in the development of IoT, big data, cloud and SDN Communications and Network 2016 8 9-21
[324]
Lin C, Wnag K, and Deng G A QoS-aware routing in SDN hybrid networks Procedia Computer Science 2017 110 242-249
[325]
Linton JD, Klassen R, and Jayaraman V Sustainable supply chains: An introduction Journal of operations management 2007 25 1075-1082
[326]
Liu D and Hu X Firm real-time system scheduling based on a novel QoS constraint IEEE Transactıons on Computers 2006 55 1-14
[327]
Liu J and Tonga W Device service networks maintenance based on components migration in the internet of things Procedia Engineering 2012 29 3418-3423
[328]
Liu M, Ma J, Lin L, Ge M, Wang Q, and Liu C Intelligent assembly system for mechanical products and key technology based on internet of things Journal of Intelligent Manufacturing 2017 28 2 271-299
[329]
Liu X, Guo X, Chen L, Zhou Y, and Xin C The use of three-dimensional integrated design system in smart substation design Journal of Power and Energy Engineering 2014 2 632-638
[330]
Liu Z, Choo KKR, and Zhao M Practical-oriented protocols for privacy-preserving outsourced big data analysis: Challenges and future research directions Computers and Security 2017 69 97-113
[331]
Lokers R, Knapen K, Sander J, Randen Y, and Jansen J Analysis of big data technologies for use in agro-environmental science Modelling Software 2016 4 1090-1105
[332]
Lom, M., Pribyl, O., & Svitek, M. (2016). Industry 4.0 as a part of smart cities. Smart Cities Symposium, 26–27 May 2016, Prague, Czech Republic.
[333]
Longo F, Nicoletti L, and Padovano A Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context Computers & Industrial Engineering 2017 113 144-159
[334]
Lorenc, A., & Szkoda, M. (2015). Customer logistic service in the automotive industry with the use of the SAP ERP system. In 2015 4th International conference on advanced logistics and transport (ICALT), 20–22 May 2015, Valenciennes, France.
[335]
Loseto G, Ieva S, Gramegna F, Ruta M, Scioscia F, and Sciascio E Linking the web of things: LDP-CoAP mapping Computer Science 2016 83 1182-1187
[336]
Lucke, A. (2008). Manufacturing systems and technologies for the new frontier. In The 41st CIRP conference on manufacturing systems, Tokyo, Japan (Vol 2, pp. 115–118).
[337]
Maansman, J., Böcker, S., Rettberg, F., Wietfeld, C., & Rehtanz, C. (2014). Renewable energies in smart factories with electric vehicle fleets. In 49th International universities power engineering conference (UPEC), Cluj-Napoca, Romania.
[338]
Macabee S, Landis R, and Burke M Inductive reasoning: The promise of big data Human Resource Management 2017 27 2 277-290
[339]
Machowiak W Risk management—Unappreciated instrument of supply chain strategy LogForum 2012 8 277-285
[340]
Madani SR and Rasti-Barzoki M Sustainable supply chain management with pricing, greening and governmental tariffs determining strategies: A game-theoretic approach Computers & Industrial Engineering 2017 105 287-298
[341]
Magdić, J., & Car, Z. (2013). A company model supporting ERP and CRM software development and implementation processes. In 12th International conference on telecommunications (ConTEL), 26–28 June 2013, Zagreb, Croatia.
[342]
Majeed AA and Rupasinghe TD Internet of things (IoT) embedded future supply chains for industry 4.0: An assessment from an ERP-based fashion apparel and footwear industry International Journal of Supply Chain Management 2017 6 25-40
[343]
Marron JS Big data in context and robustness against heterogeneity Computer Science 2014 2 73-80
[344]
Martin P and Dantan J Virtual manufacturing: Prediction of work piece International Journal of Computer Integrated Manufacturing 2011 24 620-626
[345]
Martinez G and Munizaga M Workshop 5 report: Harnessing big data Research in Transportation economics 2016 59 236-241
[346]
Matena, V., Bures, T., Gerostathopoulos, I., & Hnetynka, P. (2016). Model problem and testbed for experiments with adaptation in smart cyber-physical systems. In Software engineering for IEEE/ACM, 11th international symposium on adaptive and self-managing systems (SEAMS), 16–17 May 2016, Austin, TX, USA.
[347]
Matutinovic I, Salthe S, and Ulanowicz R The mature stage of capitalist development: Models, signs, policy, implications Structural Change and Economic Dynamics 2016 39 17-30
[348]
Mawlawi, B., Dore, J., Lebedev, N., & Gorce, J. (2014). Performance evaluation of multiband CSMA/CA with RTS/CTS or M2M. In International conference on selected topics in mobile and wireless networking, Rome, Italy (Vol. 40, pp. 108–115).
[349]
Mayer S, Verborgh R, Kovatsch M, and Mattern F Smart configuration on smart environments IEEE Transactions on Automation Science and Engineering 2016 13 3 1247-1255
[350]
McCullough A, Gempesaw C, Daniels W, and Bacon R Simulating the economic viability of crawfish production: A two stage modeling approach Aquaculture Economics and Management 2008 5 2 69-79
[351]
Mckinsey. (2016). Industry 4.0: How to navigate digitization of the manufacturing sector. https://www.mckinsey.de/files/mck_industry_40_report.pdf. Available on August 22, 2017.
[352]
McKinsey. (2017). China develops from ‘sponge’ into innovation leader. https://www.your-bizbook.com/en/Club-China-News/mckinsey-china-develops-from-sponge-into-innovation-leader. Available on November 19, 2017.
[353]
Meddeb M, Alaya S, Monteil T, Dhraief A, and Drira K M2M platform with autonomic device management service Computer Science 2014 32 1063-1070
[354]
MESA. (2009). Smart manufacturing in industry 4.0 systems, mesa international report for industry 4.0 systems. http://www.mesa.org/en/resources/MESAWhitePaper52-SmartManufacturing-LandscapeExplainedShortVersion.pdf. Available on August 22, 2017.
[355]
MetamoFAB. (2017). https://www.festo.com/group/en/cms/10275.htm. Available on August 30, 2017.
[356]
Meziane F, Vadera S, Kobbacy K, and Proudlove N Intelligent systems in manufacturing: Current developments and future prospects Integrated Manufacturing Systems 2014 11 4 218-238
[357]
Michniewicza J and Reinharta G Cyber-physical robotics—Automated analysis, programming and configuration of robot cells based on cyber-physical-systems Engineering Services 2016 15 566-575
[358]
Michona E, Gossa J, Genaud S, Unbekandt L, and Kherbache V Schlouder: A broker for IaaS clouds Future Generation Computer Systems 2017 69 11-23
[359]
Mikusz M Towards an understanding of cyber-physical systems as industrial software-product-service systems Procedia CIRP 2014 16 385-389
[360]
Miloslavskaya N and Tolstoy A Big data, fast data and data lake concepts Procedia Engineering 2017 88 2016 300-305
[361]
Ming B, Shuo T, Mingsan M, Jiaojiao J, and Weiyun X Big data applications in traditional Chinese medicine research International Journal of Services, Technology and Management 2015 21 4 294-300
[362]
Mirsanei HS, Zandieh M, Moayed MJ, and Khabbazi MR A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times Journal of Intelligent Manufacturing 2011 22 965-978
[363]
Miškuf, M., & Zolotová, I. (2016). Comparison between multi-class classifiers and deep learning with focus on industry 4.0. Cybernetics & Informatics (pp. 1–5), 2–5 February 2016.
[364]
Mohammed A and Wang L Brainwaves driven human–robot collaborative assembly CIRP Annals Manufacturing Technology 2018 1781 1-4
[365]
Mokhtar B and Eltoweissy M Big data and semantics management system Ad Hoc Networks 2017 57 32-51
[366]
Monostori L Cyber-physical production systems: Roots, expectations and R&D challenges Procedia CIRP 2014 17 9-13
[367]
Monteiroa V, Ferreirab J, and Afonso J Smart platform towards batteries analysis based on internet-of-things Procedia Computer Egineering 2014 17 2014 520-527
[368]
Moon, S., Kang, S., Jeon, J., & Chun, I. (2016). Simulation modeling of sewing process, for evaluation, of production schedule in smart factory. In 2016 International conference on industrial engineering, management science and application (ICIMSA), 23–26 May 2016, Jeju, South Korea.
[369]
Moregård A, Haubenwallera A, and Vandikasb K Computations on the edge in the internet of things Computer Science 2015 52 29-34
[370]
Mourtzis D, Zogopoulos V, and Vlachou E Augmented reality application to support remote maintenance as a service in the robotics industry Procedia CIRP 2017 63 46-51
[371]
Mucci, H., Sharaf, M., & Weyns, D. (2016). Self-adaptation for cyber-physical systems: A systematic literature review. In 2016 IEEE/ACM 11th international symposium on software engineering for adaptive and self-managing systems (SEAMS), 16–17 May 2016, Austin, TX, USA.
[372]
Müller R Planning and developing cyber-physical assembly systems by connecting virtual and real worlds Procedia CIRP 2016 52 35-40
[373]
Munera, E., Luis, L., Lujan, P., Luis, J., Yagüe, P., Simo, J., et al. (2015). Control kernel in smart factory, environments, smart resources integration. In The 5th annual IEEE international conference on cyber technology in automation, 8–12 June 2015, Shenyang, China.
[374]
Murray T Authoring intelligent tutoring systems: An analysis of the state of the art International Journal of Artificial Intelligence in Education 1999 10 98-129
[375]
Nawrocki P and Reszelewski W Resource usage optimization in mobile cloud computing Journal Computer Communications 2017 99 C 1-12
[376]
Nazarko L Future-oriented technology assessment Procedia Engineering 2017 182 504-509
[377]
Negash B, Rahmani A, Westelund T, Liljeberg P, and Tenhunen H LISA: Lightweight internet of things service bus architecture Computer Science 2015 52 2015 436-443
[378]
Neisse, R., Steri, G., & Favino, I. (2014). A model based security toolkit for the IOT. In 9th International conference on availability, reliability and security (ARES), 8–12 September 2014, Fribourg, Switzerland (pp.78–87).
[379]
Netland, T. (2016). Augmented reality: Ready for manufacturing industries. Better Operations, The Routledge Companion to Lean Management. http://better-operations.com/2016/10/07/augmented-reality-manufacturing/. Available on August 28, 2017.
[380]
Nguyen P, Shaukat A, and Tao Y Model-based security engineering for cyber-physical systems: A systematic mapping study Information Software 2017 83 116-135
[381]
Ning H and Liu H Cyber-physical-social based security architecture for future internet of things Advances in Internet of Things 2012 2 1-7
[382]
Nishioka, Y. (2016). https://iv-i.org/en/docs/doc_160428_hannover.pdf. Available on August 30, 2017.
[383]
Nofal M and Yusof Z Integration of business intelligence and enterprice resource planning within organizations Procedia Technology 2013 11 658-665
[384]
Nordahla M and Magnussona B A lightweight data interchange format for Internet of Things in the PalCom middleware framework Computer Science 2015 56 2015 284-291
[385]
[386]
Nuñez D, Fernández G, and Luna J Cloud system Procedia Computer Engineering 2017 62 149-164
[387]
Oesterreich DT and Teuteberg F Understanding the implications of digitalization and automation in the context of Industry 4.0 Computers in Industry 2016 83 121-139
[388]
Ojha T, Misra S, and Raghuwanshi N Sensing-cloud: Leveraging the benefits for agricultural applications Computers and Electronics in Agriculture 2017 135 96-107
[389]
Olszak C Toward better understanding and use of business intelligence in organizations Information Systems Management 2016 32 2 105-123
[390]
Ong SK, Yuan ML, and Nee AYC Augmented reality applications in manufacturing: A survey International Journal of Production Research 2008 46 2707-2742
[391]
Onime C and Abiona O 3D mobile augmented reality interface for laboratory experiments International Journal of Communications, Network and System Sciences 2016 9 67-76
[392]
OPAK. (2017). A industry 4.0 project “open engineering platform for autonomous mechatronic automation components in a function-oriented architecture”. https://www.automation.com/automation-news/industry/festo-to-demonstrate-opak-industry-40-research. Available on August 28, 2017.
[393]
Orasız, S., & Yörök, G. (2012). Key performance indicators used in ERP performance measurement applications. In IEEE 10th jubilee international symposium on intelligent systems and informatics (SISY) (pp.43–48), 20–22 September 2012, Subotica, Serbia.
[394]
Ospennikova A, Ershov M, and Iljin I Educational robotics as an inovative educational technology Social and Behavioral Sciences 2015 214 18-26
[395]
Ou CS, Liu FC, Hung YC, and Yen DC A structural model of supply chain management on firm performance International Journal of Operations & Production Management 2010 30 526-545
[396]
Oztemel E Benyoucef L and Grabot B Intelligent manufacturing systems Artificial intelligence techniques for networked manufacturing enterprises management, chapter 1 2010 Berlin Springer
[397]
Oztemel E Special issue on “Current progress of intelligent technologies, for manufacturing society” Journal of Intelligent Manufacturing 2015 26 959-960
[398]
Oztemel E and Tekez K A general framework of a reference model for intelligent integrated manufacturing systems (REMIMS) Engineering Applications of Artificial Intelligence 2009 22 6 855-864
[399]
Oztemel E and Tekez E Integrating manufacturing systems through knowledge exchange protocols within an agent based knowledge network Robotics and Computer-Integrated Manufacturing 2009 25 1 235-245
[400]
Oztemel, E., & Tekez, E. (2009c). Knowledge protocols. In M. M. Cunha, E. F. Olivera, A. J.Tavares, & L. G.Ferreira (Eds.), Handbook of research on social dimensions of semantic technologies and web services (pp. 304–324). ISBN: 978-1-60566-650-1, Chapter 15, IGI Global, USA, PA.
[401]
Paelke, V. (2014). Augmented reality in the smart factory: Supporting workers in an industry 4.0. Environment, emerging technology and factory automation (ETFA) (pp. 1–4).
[402]
Pagell M and Shevchenko A Why research in sustainable supply chain management should have no future Journal of Supply Chain Management 2014 50 1 44-55
[403]
Palanisamy R Organizational culture and knowledge management in ERP implementation: An empirical study Journal of Computer Information Systems 2008 48 2 100-120
[404]
Pan M and Kraft M Applying industry 4.0 to the Jurong Island eco-park Energy Procedia 2015 75 1536-1541
[405]
Pandey RK and Panda SS Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach Journal of Intelligent Manufacturing 2015 26 1121-1129
[406]
Pandya A, Siadat M, and Auner G Design, implementation and accuracy of a prototype for medical augmented reality Computer Aided Surgery 2005 10 1 23-35
[407]
Pang, Z. (2013). Technologies and architectures of the ınternet-of-things (IoT) for health and well-being. Doctoral dissertation, KTH Royal Institute of Technology. https://pdfs.semanticscholar.org/222d/206e8fc758c19ac06680db61a555fd6b71ed.pdf.
[408]
Pang Z, Chen Q, and Zheng L Value creation, sensor portfolio and information fusion of internet-of-things solutions for food supply chains Information Systems Frontiers, Information Systems Fronties 2012 17 289-319
[409]
Papadakis L, Schober A, and Zaeh M Considering manufacturing effects in automotive structural crashworthiness: A simulation chaining approach International Journal of Crashworthiness 2013 18 3 276-287
[410]
Park H, Kim H, Joo H, and Song J Recent advancement in the IOT related standards a one M2M perspective ICT Express 2016 2 3 126-129
[411]
Park, J. (2010). A smart factory operation method for a smart grid, information systems engineering. In 2010 40th international conference on computers and industrial engineering (CIE), 25–28 July 2010, Awaji, Japan.
[412]
Park S Development of innovative strategies for the Korean manufacturing industry by use of the connected smart factory Computer Science 2016 91 2016 744-750
[413]
Parkhi S, Joshi S, Gupta S, and Sharma M a study of evolution and future of supply chain management Supply Chain Management 2015 9 95-106
[414]
ParsiFAI. (2017). https://www.festo.com/group/en/cms/12002.htm. Available on August 30, 2017.
[415]
Pence H Smartphones, smart objects, and augmented reality The Reference Librarian 2010 52 1 136-145
[416]
Peng Q, Chung C, Yu C, and Luan T A networked virtual manufacturing system for SMEs International Journal of Computer Integrated Manufacturing 2007 20 71-79
[417]
Peng Y, Xie D, and Shemshadi A A network storage framework for internet of things Computer Science 2013 19 1136-1141
[418]
Peres, R., Parreira-Rocha, M., Rocha, A., Barbosa, J., Leitão, P., & Barata, J.(2016). Selection of a data exchange format for industry 4.0 manufacturing systems, industrial electronics society. In IECON 201642nd annual conference of the IEEE (pp. 5723–5728), 23–26 October 2016, Florence, Italy.
[419]
Perkinsa C and Mullera G Using discrete event simulation to model attacker interactions with cyber and physical security systems Procedia Computer Science 2015 61 221-226
[420]
Persson M and Håkansson A A communication protocol for different communication technologies in cyber-physical system Engineering Services 2015 60 1697-1706
[421]
Petnga L and Austin M Ontologies of time and time-based reasoning for MBSE of cyber-physical systems Procedia Computer Science 2013 16 403-412
[422]
Pfohl, H., & Yahsi, B. (2016). The impact of industry supply chain. Published in: Innovations and strategies for logistics an Wolfgang Kersten, Thorsten Blecker and Christian M. Ri, Vol. 2, pp. 120–131, Proceedings of the Hamburg International Conference of Logistics (HICL) ISBN (online): 978-3-7375-4059-9, 4430.
[423]
Piccialli F, Benedusi P, and Amato F S-InTime: A social cloud analytical service oriented system Future Generation Computer Systems 2017 45 699-705
[424]
Pimenov DY, Bustillo A, and Mikolajczyk T Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth Journal of Intelligent Manufacturing 2018 29 1045-1061
[425]
Pisching, M. A., Junqueira, F., Santos Filho, D. J., & Miyagi, P. E., (2015). An architecture for organizing and locating services to the industry 4.0. In Proceedings of 23rd ABCM international congress of mechanical engineering (pp. 1–4).
[427]
Plattform Industry 4.0. (2014). Plattform industry 4.0. http://ec.europa.eu/information_society/newsroom/image/document/2016-27/10__pi40_diemer_16494.pdf. Available on August 28, 2017.
[428]
PNC. (2016). PNC industry 4.0 report. https://www.pnc.com/content/dam/pnc-ideas/articles/insurance-industry-article.pdf. Available on August 28, 2017.
[429]
Poghosyana G, Pefkianakisb I, Guyadecc P, and Christophidesd V Mining usage patterns in residential intranet of things Computer Science 2016 83 2016 988-993
[430]
Pokharel S and Mutha A Perspectives in reverse logistics: A review Resources, Conservation and Recycling 2009 53 175-182
[431]
Pollock N and Cornford J Customizing manufacturing system for universities International Journal of Mass Customization 1999 4 3 171-194
[432]
Potts J and Cunningham S Four models of creative industries International Journal of Cultural Policy 2008 14 3 233-247
[433]
Prajogo D, Chowdhury M, Yeung AC, and Cheng TCE The relationship between supplier management and firm’s operational performance: A multi-dimensional perspective International Journal of Production Economics 2012 136 123-130
[434]
Prinz C, Morlock F, Freith S, Kreggenfeld N, Kreimeier D, and Kuhlenkötter B Learning factory modules Procedia CIRP 2016 54 113-118
[435]
Puttonen J, Lobov A, Soto M, and Lastra ML Cloud computing as a facilitator for web service composition in factory automation Journal of Intelligent Manufacturing 2016 27 689-700
[436]
Qiao D The impact of QoS constraints on the energy efficiency of fixed-rate wireless transmissions IEEE Transactions on Wireless Communications 2009 8 5957-5969
[437]
Qin J, Liu Y, and Grosvenor R A categorical framework of manufacturing for industry 4.0 and beyond Procedia CIRP 2016 52 173-178
[438]
Qiu X, Luo H, Xu G, Zhong R, and Huang GQ Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP) International Journal of Production Economics 2015 159 4-15
[439]
Qiuping W, Shunbinga Z, and Chunquan D Study on key technologies of internet of things perceiving mine Procedia Engineering 2011 2011 2326-2333
[440]
Radziwon A, Bilberg A, Bogers M, and Madsen ES The smart factory: Exploring adaptive and flexible manufacturing solutions Procedia Engineering 2014 69 1184-1190
[441]
Rago F A smart adaptable architecture based on contexts for cyber physical systems Engineering Services 2015 61 301-306
[442]
Ramezani, J., & Jassbi, J. (2017). A hybrid expert decision support system based on artificial neural networks in process control of plaster production—An industry 4.0 perspective, technological innovation for smart systems. IFIP advances in information and communication technology (Vol 499, pp. 55–71).
[443]
Ranjan A and Hussain M Terminal authentication in M2M communications in the context of internet of things Computer Science 2016 89 2016 34-42
[444]
Rashid, M., Riaz, Z., Turan, E., Haskilic, V., Sunje, A., & Khan, N. (2012). Smart factory: E-business perspective of enhanced ERP in aircraft manufacturing industry. In 2012 Proceedings of technology management for emerging technologies (PICMET’12) (pp. 3262–3275), 29 July–2 August 2012, Vancouver, BC, Canada.
[445]
Raza S, Misra P, He Z, and Voigt T Building the internet of things with bluetooth smart AdHoc Networks 2017 57 19-31
[446]
Remon, D. (2017). Smart factory: Reducing maintenance costs and ensuring quality in the manufacturing process. http://www.libelium.com/smart-factory-reducing-maintenance-costs-ensuring-quality-manufacturing-process/. Available on August 22, 2017.
[447]
Rennunga F, Luminosua C, and Draghicia A Service provision in the framework of industry 4.0 Behavioral Science 2016 221 372-377
[448]
Reuter, T. (2016). Kuka industry 4.0 research, KUKA Aktiengesellschaft Zugspitzstraße 140, Augsburg, Vol. 1, pp. 1–50 (in German).
[449]
Richert, A., Shehadeh, M., Plumanns, M, Groß, K., Schuster, K., & Jeschke, S. (2016). Educating engineers for industry 4.0: Virtual worlds and human–robot-teams empirical studies towards a new educational age. In Global engineering education conference (EDUCON), 2016 IEEE, 10–13 April 2016, Abu Dhabi, United Arab Emirates.
[450]
Riedl M, Zipper H, Meier M, and Diedric C Cyber-physical systems alter automation architectures Annual Reviews in Control 2014 38 123-133
[451]
Riel, A., & Flatscher, M. (2017). A design process approach to strategic production planning for industry 4.0. In European conference on software process improvement (pp. 323–333).
[452]
Rihab C, Ellouze F, Koubaa A, Qureshi B, Preira N, Youssef H, and Tovar E Cyber-physical systems clouds: A survey Computer Networks 2016 108 260-278
[453]
Risso NA, Neyem A, Benedetto J, Carillo M, Farias A, Gajordo M, and Loyola A A cloud-based mobile system to improve respiratory therapy services at home Journal of Biomedical Informatics 2016 94 467-479
[454]
Rosas, J. C., Aguilar, J. A., Tripp-Barba, C., Espinosa, R., & Aguilar P. (2017). A mobile sensor fire prevention system based on the internet of things. In International conference on computational science and its applications (pp. 274–283).
[455]
Rosendahl, R., Schmidt, N., Lüder, A., & Ryashentseva, D. (2016). Industry 4.0 value networks in legacy systems. In IEEE 20th conference on emerging technologies & factory automation (ETFA) (pp. 1–4), 8–11 September 2015, Luxembourg.
[457]
Ruivo P, Johansson B, Oliveira T, and Netoa M Commercial ERP systems and user productivity: A study across European SMEs Procedia Technology 2013 9 2013 84-93
[458]
Ruivo, P., Mestrea, A., Johanssonb, B., & Oliveira, T. (2014). Defining the ERP and CRM integrative value. In Conference on enterprise information systems (CENTERIS) (Vol 16, pp. 704–709).
[459]
Ruivo, P., Oliveira, T., & Neto, M. (2012). ERP post-adoption: Value impact on firm performance. In 7th Iberian conference on information systems and technologies (CISTI) (pp. 1–6), 20–23 June 2012, Madrid, Spain.
[460]
Ruiz A, Canovas O, and Lopez-de-Teruel P A vision-enhanced multi-sensor LBS suitable for augmented reality applications Journal of Location Based Services 2013 7 3 145-164
[461]
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., et al. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group. https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries.aspx. Available on December 28, 2017.
[462]
Sacala I and Moisescu M Cyber physical systems oriented robot development platform Engineering Services 2015 65 203-209
[463]
Sachsenmeier P Industry 5.0—The relevance and implications of bionics and synthetic biology Engineering 2016 2 225-229
[464]
Sadrzadehrafieia S, Chofrehb S, Hosseinia N, and Sulaimana R The benefits of enterprise resource planning (ERP) system implementation in dry food packaging industry International Conference on Electronics Engineering and Informatics 2013 11 220-226
[465]
Safari H, Faraji Z, and Majidian S Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR Journal of Intelligent Manufacturing 2016 27 475-486
[466]
Sah P Saving environment using internet of things: Challenges and the possibilities Advances in Internet of Things 2016 6 55-64
[467]
Saikrishna P and Pasumarthy R Multi-objective switching controller for cloud computing systems Control Engineering Practice 2016 57 72-83
[468]
Samani A, Ghenniva H, and Wahaishi A Privacy in internet of things: A model and protection framework Computer Science, Lecture Notes in Computer Science 2015 52 606-613
[469]
Samaniego M and Deters R Management and internet of things Computer Science 2016 94 137-143
[470]
Sampaio AZ and Rosário D Virtual reality technology applied on maintenance of painted walls of buildings Journal of Software Engineering and Applications 2012 5 297-303
[471]
Sangmahachai, K. (2015). Kasetsart energy and technology management center.http://www.wise.co.th/wise/Knowledge_Bank/References/Everything_4/Revolution_to_Industry_4.pdf. Available on August 30, 2017.
[472]
Sangregorio, P., Cologni, A. L., Owen, F. C., & Previdi, F. (2015). Remote maintenance system for semi-automated manufacturing machines. In 2015 IEEE 1st international forum on research and technologies for society and industry leveraging a better tomorrow (RTSI) (pp. 457–461), 16–18 September 2015, Turin, Italy.
[473]
Santosa A, Macedoa J, Costaa A, and Nicolau M Internet of things and smart objects for M-health monitoring and control Procedia Technology 2014 16 1351-1360
[474]
Sasikala B, Rajanarajana M, and Geethavani B Internet of things: A survey on security issues analysis and countermeasures International Journal of Engineering and Computer Science 2017 6 5 21435-21442
[475]
Scheer, S. (2013). Industry 4.0: Wie sehen Produktionsprozesse im Jahr 2020, e-book, published by AWS-Institute for Digitized Products and Processes, ISBN: 978-398-1583-328 (in Germany).
[476]
Scheuermann, C., Verclas, S., & Bruegge, B. (2015). Agile factory—An example of an industry 4.0 manufacturing process, cyber-physical systems. In IEEE 3rd international conference on networks, and applications (CPSNA) (pp. 43–47), 19–21 August 2015, Hong Kong, China.
[477]
Schlick, J. (2014). Industry 4.0 in der praktischen Anwendung. In T. Bauernhansl, M. ten Hompel, & B. Vogel-Heuser (Eds.), Industry 4. 0 in Produktion, Automatisierung und Logistik (Vol. 4, pp. 57–84). Anwendung, Technologien und Migration (in German).
[478]
Schouh G, Gartzen T, and Marks A Promoting work-based learning through industry 4.0 CIRP Conference on Learning Factorie 2015 32 82-87
[479]
Schuh G, Pitscha M, Rudolfa S, Karmanna W, and Sommera M Modular sensor platform for service-oriented cyber-physical systems in the European tool making industry Engineering Services 2014 17 374-379
[480]
Schuh G, Potente T, Wesch-Potente C, Weber AR, and Prote JP Collaboration mechanisms to increase productivity in the context of industrie 4.0 Procedia CIRP 2014 19 51-56
[481]
Schuhmacher J and Hummel V Decentralized control of logistic processes in cyber-physical production systems at the example of ESB logistics learning factory Procedia CIRP 2016 54 19-24
[482]
Schumacher A, Erol S, and Sihna W A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises Reconfigurable and Virtual Production 2016 52 161-166
[483]
Schumann A Integrated production control for batch plants European Control Conference 1999
[484]
Schweer, D., & Sahl, J. C. (2017). The digital transformation of industry—The benefit for Germany. In The drivers of digital transformation (Vol. 10, pp. 23–31). Springer.
[485]
Sedera D and Gable GG Knowledge management competence for enterprise system success The Journal of Strategic Information Systems 2010 19 4 296-306
[486]
Seethamraju R and Sundar D Influence of ERP systems on business process agility Management Review 2013 25 3 137-149
[487]
Seitza, K., & Nyhuis, P. (2015). Cyber-physical production systems combined with logistic models—A learning factory concept for an improved production planning and control. In The 5th conference on learning factories (Vol. 32, pp. 92–97).
[488]
Sena D, Ozturk M, and Vayvay O An overview of big data for growth in SMEs Social and Behavioral Sciences 2016 235 159-167
[489]
Seok H and Nof S Intelligent information sharing among manufacturers in supply networks: Supplier selection case Journal of Intelligent Manufacturing 2018 29 1097-1113
[490]
Shafiq SI, Sanin C, Toro C, and Szczerbicki E Virtual engineering object (VEO): Toward experience-based design and manufacturing for industry 4.0 Cybernetics and Systems 2015 46 35-50
[491]
Shah, M. (2016). Big data and the internet of things. In Big data analysis: New algorithms for a new society (pp. 207–237). Springer.
[492]
Shah LA, Etienne A, Siadat A, and Vernadat F Decision-making in the manufacturing environment using a value-risk graph Journal of Intelligent Manufacturing 2016 27 617-630
[493]
Shahabi C, Kashani F, Khoshgozaran A, Nocera L, and Xing S GeoDec: A framework to effectively visualize and query geospatial data for decision-making IEEE Multi Media 2010 10 99 1-11
[494]
Shaikh FK, Zeadally S, and Exposito E Enabling technologies for green internet of things IEEE Systems Journal 2017 11 2 983-994
[495]
Shallock B, Rybski C, Jochem R, and Kohl H Learning factory for industry 4.0 to provide future skills beyond technical training Procedia Manufacturing 2018 23 27-32
[496]
Shamsuzzoha A, Ferreira F, Azevado A, and Helo P Collaborative smart process monitoring within virtual factory environment: An implementation issue International Journal of Computer Integrated Manufacturing 2016 30 1 167-181
[497]
Shaoshuai F, Wenxiao S, Nan W, and Yan W MODM-based evaluation model of service quality in the internet of things Procedia Environmental Sciences 2011 11 Part A 63-69
[498]
Shariatzadeh N, Lundholma T, Lindberga L, and Sivarda G Integration of digital factory with smart factory based on Internet of Things CIRP 2016 50 2016 512-517
[499]
Sharma, A., & Gupta, S. (2014). Identifying the role of ERP in enhancing operational efficiency and supply chain mobility in aircraft manufacturing industry. In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 330–333), 7–8 February 2014, Ghaziabad, India.
[500]
Sharma Y, Javadi B, Si W, and Sun D Reliability and energy efficiency in cloud computing systems: Survey and taxonomy Journal of Network and Computer Applications 2016 74 66-85
References 501 through 620 have been omitted.

Cited By

View all
  • (2024)Learning by doingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2023.10267386:COnline publication date: 1-Apr-2024
  • (2024)Strategic Scenario Study of Industry 4.0 ProspectsProcedia Computer Science10.1016/j.procs.2024.05.117237:C(371-379)Online publication date: 24-Jul-2024
  • (2024)Establishing the fuzzy integrated hybrid MCDM framework to identify the key barriers to implementing artificial intelligence-enabled sustainable cloud system in an IT industryExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121732238:PAOnline publication date: 15-Mar-2024
  • Show More Cited By

Index Terms

  1. Literature review of Industry 4.0 and related technologies
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Please enable JavaScript to view thecomments powered by Disqus.

        Information & Contributors

        Information

        Published In

        cover image Journal of Intelligent Manufacturing
        Journal of Intelligent Manufacturing  Volume 31, Issue 1
        Jan 2020
        262 pages

        Publisher

        Springer-Verlag

        Berlin, Heidelberg

        Publication History

        Published: 01 January 2020
        Accepted: 28 June 2018
        Received: 30 January 2018

        Author Tags

        1. Industry 4.0
        2. Smart factory
        3. Internet of things (IoT)
        4. Cyber-physical systems
        5. Cloud systems
        6. Big data

        Qualifiers

        • Review-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 16 Nov 2024

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)Learning by doingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2023.10267386:COnline publication date: 1-Apr-2024
        • (2024)Strategic Scenario Study of Industry 4.0 ProspectsProcedia Computer Science10.1016/j.procs.2024.05.117237:C(371-379)Online publication date: 24-Jul-2024
        • (2024)Establishing the fuzzy integrated hybrid MCDM framework to identify the key barriers to implementing artificial intelligence-enabled sustainable cloud system in an IT industryExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121732238:PAOnline publication date: 15-Mar-2024
        • (2024)A preliminary step toward intelligent forming of fabric compositesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108262133:PDOnline publication date: 1-Jul-2024
        • (2024)An automated voice command classification model based on an attention-deep convolutional neural network for industrial automation systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107120126:PDOnline publication date: 27-Feb-2024
        • (2024)Smart vibratory peeningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107118126:PDOnline publication date: 27-Feb-2024
        • (2024)PrioMQTTComputer Communications10.1016/j.comcom.2024.03.018220:C(43-51)Online publication date: 15-Apr-2024
        • (2024)A personalized bidirectional feedback mechanism by combining cooperation and trust to improve group consensus in social networkComputers and Industrial Engineering10.1016/j.cie.2024.109888188:COnline publication date: 17-Apr-2024
        • (2024)A probabilistic reliable linguistic model for blockchain-based student information management system assessmentApplied Soft Computing10.1016/j.asoc.2024.111645159:COnline publication date: 1-Jul-2024
        • (2024)Evaluating the latest trends of Industry 4.0 based on LDA topic modelThe Journal of Supercomputing10.1007/s11227-024-06247-x80:13(19003-19030)Online publication date: 1-Sep-2024
        • Show More Cited By

        View Options

        View options

        Login options

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media