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Engineering >> 2020, Volume 6, Issue 7 doi: 10.1016/j.eng.2019.11.014

Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

a Department of Medicine and Surgery, University of Parma, Parma 43126, Italy
b Sidel S.p.a., Parma 43126, Italy
c Department of Engineering and Architecture, University of Parma, Parma 43124, Italy
d Center for Research in Toxicology (CERT), University of Parma, Parma 43126, Italy

Received: 2019-09-19 Revised: 2019-10-25 Accepted: 2019-11-01 Available online: 2020-01-29

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Abstract

The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education.

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References

[ 1 ] Poli R, Healy M, Kameas A. Theory and applications of ontology: computer applications. New York: Springer; 2010. link1

[ 2 ] Cao G, Duan Y, Cadden T. The link between information processing capability and competitive advantage mediated through decision-making effectiveness. Int J Inf Manage 2019;44:121–31. link1

[ 3 ] Allam Z, Dhunny ZA. On big data, artificial intelligence and smart cities. Cities 2019;89:80–91. link1

[ 4 ] Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: from big data to big impact. Quarterly 2018;36(4):1165–88. link1

[ 5 ] Mehmood R, Meriton R, Graham G, Hennelly P, Kumar M. Exploring the influence of big data on city transport operations: a Markovian approach. Int J Oper Prod Manage 2017;37(1):75–104. link1

[ 6 ] Mehmood R, Graham G. Big data logistics: a health-care transport capacity sharing model. Procedia Comput Sci 2015;64:1107–14. link1

[ 7 ] Shirazi F, Mohammadi M. A big data analytics model for customer churn prediction in the retiree segment. Int J Inf Manage 2019;48:238–53. link1

[ 8 ] Kovácˇová L, Vacková M. Applying innovative trends in the process of higher education security personnel in order to increase efficiency. Procedia Soc Behav Sci 2015;186:120–5. link1

[ 9 ] Lee J, Kao HA, Yang S. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 2014;16:3–8. link1

[10] Kaw JA, Loan NA, Parah SA, Muhammad K, Sheikh JA, Bhat GM. A reversible and secure patient information hiding system for IoT driven e-health. Int J Inf Manage 2018;1. link1

[11] Karjaluoto H, Shaikh AA, Saarijärvi H, Saraniemi S. How perceived value drives the use of mobile financial services apps. Int J Inf Manage 2019;47:252–61. link1

[12] Arai T, Aiyama Y, Maeda Y, Sugi M, Ota J. Agile assembly system by ‘‘plug and produce”. Cirp Ann Manuf Technol 2000;49(1):1–4. link1

[13] Avventuroso G, Foresti R, Silvestri M, Frazzon EM. Production paradigms for additive manufacturing systems: a simulation-based analysis. In: Proceedings of the 2017 International Conference on Engineering, Technology, and Innovation (ICE/ITMC); 2017 Jun 27–29; Funchal, Portugal. New York: IEEE; 2017. link1

[14] Mehmood R, See S, Katib I, Chlamtac I. Smart infrastructure and applications: foundations for smarter cities and societies. New York: Springer; 2019. link1

[15] Ahmad N, Mehmood R. Enterprise systems: are we ready for future sustainable cities. Supply Chain Manage 2015;20(3):264–83. link1

[16] Amin A, Shah B, Khattak AM, Lopes Moreira FJ, Ali G, Rocha A, et al. Crosscompany customer churn prediction in telecommunication: a comparison of data transformation methods. Int J Inf Manage 2019;46:304–19. link1

[17] Hussain ZI, Sivarajah U, Hussain N. The role of a digital engineering platform in appropriating the creation of new work-related mind-set and organisational discourse in a large multi-national company. Int J Inf Manage 2019;48:218–25. link1

[18] Mehmood R, Alam F, Albogami NN, Katib I, Albeshri A, Altowaijri SM. UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access 2017;5:2615–35. link1

[19] Cooke R, Paulsen J. Concepts for measuring maintenance performance and methods for analysing competing failure modes. Reliab Eng Syst Saf 1997;55 (2):135–41. link1

[20] Zhou J, Zhou Y, Wang B, Zang J. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent. Manuf Eng 2019;5(4):624–36. link1

[21] Bokrantz J, Skoogh A, Ylipää T. The use of engineering tools and methods in maintenance organisations: mapping the current state in the manufacturing industry. Procedia CIRP 2016;57:556–61. link1

[22] Hu YM, Du RS, Yang SZ. Intelligent data acquisition technology based on agents. Int J Adv Manuf Technol 2003;21(10–11):866–73. link1

[23] Roblek V, Meško M, Krapezˇ A. A complex view of Industry 4.0. SAGE Open 2016;6(2):23. link1

[24] Muhammed T, Mehmood R, Albeshri A, Katib I. UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 2018;6:32258–85. link1

[25] Spinuzzi C, Bodrozˇic´ Z, Scaratti G, Ivaldi S. ‘‘Coworking is about community”: but what is ‘‘community” in coworking? J Bus Tech Commun 2019;33 (2):112–40. link1

[26] Government of Japan. The 5th science and technology basic plan. Tokyo: Government of Japan; 2016. link1

[27] Sachsenmeier P. Industry 5.0—the relevance and implications of bionics and synthetic biology. Engineering 2016;2(2):225–9. link1

[28] Schlingensiepen J, Nemtanu F, Mehmood R, McCluskey L. Autonomic transport management systems—enabler for smart cities, personalized medicine, participation and industry grid/Industry 4.0. Intelligent transportation systems–problems and perspectives. Cham: Springer; 2016. link1

[29] Angelidou M. Smart cities: a conjuncture of four forces. Cities 2015;47: 95–106. link1

[30] Yigitcanlar T, Kamruzzaman M, Foth M, Sabatini-Marques J, Da Costa E, Ioppolo G. Can cities become smart without being sustainable? A systematic review of the literature. Sustain Cities Soc 2019;45:348–65. link1

[31] Mehmood R, Bhaduri B, Katib I, Chlamtac I, editors. Smart societies, infrastructure, technologies and applications. New York: Springer; 2018. link1

[32] Hollands RG. Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City 2008;12(3):303–20. link1

[33] Komninos N. Intelligent cities: variable geometries of spatial intelligence. Intell Build Int 2011;3(3):172–88. link1

[34] Nam T, Pardo TA. Petri nets for systems and synthetic biology. Lect Notes Comput Sci 2011;5016:215–64. link1

[35] Wolfram M. Deconstructing smart cities: an intertextual reading of concepts and practices for integrated urban and ICT development. In: Proceedings of the 2012 International Conference on Urban Planning and Regional Development in the Information Society; 2012 May 14–16; Schwechat, Austria; 2012. link1

[36] Zuehlke D. SmartFactory—towards a factory-of-things. Annu Rev Contr 2010;34(1):129–38. link1

[37] Foresti R. Plug and produce 2013 [Internet]. [cited 2019 Oct 2]. Available from: https://it.wikipedia.org/wiki/Plug_and_Produce. link1

[38] De Faria H Jr, Costa JGS, Olivas JLM. A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renew Sustain Energy Rev 2015;46:201–9.

[39] Ariyaluran Habeeb RA, Nasaruddin F, Gani A, Targio Hashem IA, Ahmed E, Imran M. Real-time big data processing for anomaly detection: a survey. Int J Inf Manage 2019;45:289–307. link1

[40] Tong X, Lai KH, Zhu Q, Zhao S, Chen J, Cheng TCE. Multinational enterprise buyers’ choices for extending corporate social responsibility practices to suppliers in emerging countries: a multi-method study. J Oper Manage 2018;63:25–43. link1

[41] Reychav I, Weisberg J. Going beyond technology: knowledge sharing as a tool for enhancing customer-oriented attitudes. Int J Inf Manage 2009;29 (5):353–61. link1

[42] Lim C, Kim KJ, Maglio PP. Smart cities with big data: reference models, challenges, and considerations. Cities 2018;82:86–99. link1

[43] Belletti B, Berardengo M, Collini L, Foresti R, Garziera R. Design of an instrumentation for the automated damage detection in ceilings. NDT Int 2018;94:31–7. link1

[44] Rouvinen P, Ketokivi M, Turkulainen V. Why locate manufacturing in a highcost country? A case study of 35 production location decisions. J Oper Manage 2017;49–51:20–30. link1

[45] Gellert A, Florea A, Fiore U, Palmieri F, Zanetti P. A study on forecasting electricity production and consumption in smart cities and factories. Int J Inf Manage 2019;49:546–56. link1

[46] Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 2017;3(5):616–30. link1

[47] Al-Qahtani ND, Alshehri SS, Abdaziz A. The impact of total quality management on organizational performance. Eur J Bus Manag 2015;7:119–27. link1

[48] Chandrasekaran A, Linderman K, Sting FJ. Avoiding epistemological silos and empirical elephants in OM: how to combine empirical and simulation methods?. J Oper Manage 2018;63(1):1–5. link1

[49] Jimenez-Marquez JL, Gonzalez-Carrasco I, Lopez-Cuadrado JL, Ruiz-Mezcua B. Towards a big data framework for analyzing social media content. Int J Inf Manage 2019;44:1–12. link1

[50] Villena VH, Gioia DA. On the riskiness of lower-tier suppliers: managing sustainability in supply networks. J Oper Manage 2018;64(1):65–87. link1

[51] EL Idrissi T, Idri A, Bakkoury Z. Systematic map and review of predictive techniques in diabetes self-management. Int J Inf Manage 2019;46:263–77. link1

[52] Eastman C, Teicholz P, Sacks R, Liston K. BIM handbook: a guide to building information modeling for owners, managers, designers, engineers and contractors. Hoboken: John Wiley & Sons, Inc.; 2008. link1

[53] Dekker R, Wildeman RE, Van der Duyn Schouten FA. A review of multicomponent maintenance models with economic dependence. Math Methods Oper Res 1997;45(3):411–35. link1

[54] Nicolai RP, Dekker R. Optimal maintenance of multi-component systems: a review. In: Kobbacy KAH, Murthy DNP, editors. Complex system maintenance handbook. London: Springer; 2008. p. 263–86. link1

[55] Wang S, Xie J. Integrating Building Management System and facilities management on the Internet. Autom Construct 2002;11(6):707–15. link1

[56] Wang WC. Simulation-facilitated model for assessing cost correlations. Comput Civ Infrastruct Eng 2002;17(5):368–80. link1

[57] Gupta M, George JF, Xia W. Relationships between IT department culture and agile software development practices: an empirical investigation. Int J Inf Manage 2019;44:13–24. link1

[58] Karaca Y, Moonis M, Zhang YD, Gezgez C. Mobile cloud computing based stroke healthcare system. Int J Inf Manage 2018;45(45):250–61. link1

[59] Curry E. The big data value chain: definitions, concepts, and theoretical approaches. In: Cavanillas J, Curry E, Wahlster W, editors. New horizons a data-driven economy. Cham: Springer; 2016. p. 29–37. link1

[60] Hossain A, Quaresma R, Rahman H. Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: an empirical study. Int J Inf Manage 2019;44:76–87. link1

[61] Huovila A, Bosch P, Airaksinen M. Comparative analysis of standardized indicators for smart sustainable cities: what indicators and standards to use and when? Cities 2019;89:141–53. link1

[62] Chang YC, Ku CH, Chen CH. Social media analytics: extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. Int J Inf Manage 2019:263–79. link1

[63] Shao Z. Interaction effect of strategic leadership behaviors and organizational culture on IS-Business strategic alignment and Enterprise Systems assimilation. Int J Inf Manage 2019;44:96–108. link1

[64] Karambakhsh A, Kamel A, Sheng B, Li P, Yang P, Feng DD. Deep gesture interaction for augmented anatomy learning. Int J Inf Manage 2018;45:328–36. link1

[65] Chen HM, Chang KC, Lin TH. A cloud-based system framework for performing online viewing, storage, and analysis on big data of massive BIMs. Autom Construct 2016;71:34–48. link1

[66] Li J, Tao F, Cheng Y, Zhao L. Big data in product lifecycle management. Int J Adv Manuf Technol 2015;81(1–4):667–84. link1

[67] Carvalho JV, Rocha Á, Vasconcelos J, Abreu A. A health data analytics maturity model for hospitals information systems. Int J Inf Manage 2019;46:278–85. link1

[68] Wang X, Li L, Yuan Y, Ye P, Wang FY. ACP-based social computing and parallel intelligence: Societies 5.0 and beyond. CAAI Trans Intell Technol 2016;1 (4):377–93. link1

[69] Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent. Manuf Eng 2018;4:11–20. link1

[70] Kakegawa M. Smart & safe energy society. Energy Procedia 2017;143:880–3. link1

[71] Savic´ D, Vamvakeridou-Lyroudia L, Kapelan Z. Smart meters, smart water, smart societies: the iWIDGET project. Procedia Eng 2014;89:1105–12. link1

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