References
- Chauhan A, Jakhar SK, Chauhan C. The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal. J Clean Prod. 2021;279:123854.
- Junior WTS, Montevechi JAB, Miranda AC, et al. Discrete simulation-based optimization methods for industrial engineering problems: a systematic literature review. Comput Ind Eng. 2019;128:526–540.
- Enyoghasi C, Badurdeen F. Industry 4.0 for sustainable manufacturing: opportunities at the product, process, and system levels. ResouConserv Recycl. 2021;166:105362.
- Benitez GB, Ayala NF, Frank AG. Industry 4.0 innovation ecosystems: an evolutionary perspective on value cocreation. Int J Prod Econ. 2020;228:107735. ( Article in Press). .
- Onu P, Mbohwa C. Industry 4.0 opportunities in manufacturing SMEs: sustainability outlook. Mater Today Proc. 2021;44(1):1925–1930.
- Alcácer V, Cruz-Machado V. Scanning the Industry 4.0: a literature review on technologies for manufacturing systems. Eng Sci Technol Int J. 2019;22(3):899–919.
- Sharma R, Jabbour CJC, De Sousa Jabbour ABL. Sustainable manufacturing and Industry 4.0: what we know and what we don’t. J Enterp Inf Manage. 2020;34(1):230–266.
- Kamble S, Gunasekaran A, Dhone NC. Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. Int J P Res. 2020;58(5):1319–1337.
- Ghobakhloo M. Industry 4.0, digitization, and opportunities for sustainability. J Clean Prod. 2020;252:119869.
- Nara EOB, Da Costa MB, Baierle IC, et al. Expected impact of Industry 4.0 technologies on sustainable development: a study in the context of Brazil’s plastic industry. Sustainable Prod Consumption. 2021;25:102–122.
- Rosa P, Sassanelli C, Urbinati A, et al. Assessing relations between Circular Economy and Industry 4.0: a systematic literature review. Int J P Res. 2020;58(6):1662–1687.
- Oztemel E, Gursev S. Literature review of Industry 4.0 and related technologies. J Intell Manuf. 2020;31(1):127–182.
- Bui TD, Tsai FM, Tseng ML, et al. Sustainable supply chain management towards disruption and organizational ambidexterity: a data driven analysis. Sustainable Prod Consumption. 2021;26:373–410.
- Bui TD, Tsai FM, Tseng ML, et al. Identifying sustainable solid waste management barriers in practice using the fuzzy Delphi method. ResouConserv Recycl. 2020;154:104625.
- Tseng ML, Bui TD, Lim MK, et al. Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis. Ind Manage Data Syst. 2021;121(3):657–700.
- Tseng ML, Tran TPT, Wu KJ, et al. Exploring sustainable seafood supply chain management based on linguistic preferences: collaboration in the supply chain and lean management drive economic benefits. Int J Logistics Res Appl. 2020;1–23.
- Zupic I, Čater T. Bibliometric methods in management and organization. Organizational Res Methods. 2015;18(3):429–472.
- Rejeb MA, Simske S, Rejeb K, et al. Internet of Things Research in Supply Chain Management and Logistics: a Bibliometric Analysis. Internet Things. 2020;12:100318.
- Buer SV, Strandhagen JO, Chan FT. The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. Int J P Res. 2018;56(8):2924–2940.
- Sony M, Naik S. Industry 4.0 integration with socio-technical systems theory: a systematic review and proposed theoretical model. Technol Soc. 2020;61:101248.
- Khanchanapong T, Prajogo D, Sohal AS, et al. The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance. Int J Prod Econ. 2014;153:191–203.
- Tortorella GL, Giglio R, Van Dun DH. Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. Int J Oper Prod Manage. 2019;39(6/7/8):860–886.
- Shahin M, Chen FF, Bouzary H, et al. Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. Int J Adv Manuf Technol. 2020;107, 2927–2936.
- Rosin F, Forget P, Lamouri S, et al. Impacts of Industry 4.0 technologies on Lean principles. Int J P Res. 2020;58(6):1644–1661.
- Kamble SS, Belhadi A, Gunasekaran A, et al. A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technol Forecasting Social Change. 2021;165:120567.
- Majeed A, Zhang Y, Ren S, et al. A big data-driven framework for sustainable and smart additive manufacturing. Rob Comput Integr Manuf. 2021;67:102026.
- Wang Y, Wang S, Yang B, et al. Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: architecture, control mechanism, application scenario and benefits. J Clean Prod. 2020;248:119299. ( Article in Press). .
- Srinivasan R, Giannikas V, Kumar M, et al. Modelling food sourcing decisions under climate change: a data-driven approach. Comput Ind Eng. 2019;128:911–919.
- Zhou Z, Zhao L. Cloud computing model for big data processing and performance optimization of multimedia communication. Comput Commun. 2020;160:326–332.
- Hajjaji Y, Boulila W, Farah IR, et al. Big data and IoT-based applications in smart environments: a systematic review. Comput Sci Rev. 2021;39:100318.
- Lau BPL, Marakkalage SH, Zhou Y, et al. A survey of data fusion in smart city applications. Inf Fusion. 2019;52:357–374.
- Tseng ML, Wu KJ, Lim MK, et al. Data-driven sustainable supply chain management performance: a hierarchical structure assessment under uncertainties. J Clean Prod. 2019;227:760–771.
- Bag S, Yadav G, Dhamija P, et al. Key resources for Industry 4.0 adoption and its effect on sustainable production and circular economy: an empirical study. J Clean Prod. 2021;281:125233. ( Article in Press). .
- Okpoti ES, Jeong IJ. A reactive decentralized coordination algorithm for event-driven production planning and control: a cyber-physical production system prototype case study. J Manuf Syst. 2021;58:143–158.
- Tao F, Qi Q, Wang L, et al. Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0: correlation and comparison. Engineering. 2019;5(4):653–661.
- Ying KC, Pourhejazy P, Cheng CY, et al. Cyber-physical assembly system-based optimization for robotic assembly sequence planning. J Manuf Syst. 2021;58:452–466.
- Weber KM, Gudowsky N, Aichholzer G. Foresight and technology assessment for the Austrian parliament — finding new ways of debating the future of Industry 4.0. Futures. 2019;109:240–251.
- Tuptuk N, Hailes S. Security of smart manufacturing systems. J Manuf Syst. 2018;47:93–106.
- Kavallieratos G, Katsikas S, Gkioulos V. SafeSec Tropos: joint security and safety requirements elicitation. Comp Stand Interfaces. 2020;70:103429.
- Lee J, Cameron I, Hassall M. Improving process safety: what roles for Digitalization and Industry 4.0? Process SafEnviron Prot. 2019;132:325–339.
- Khalid A, Kirisci P, Khan ZH, et al. Security framework for industrial collaborative robotic cyber-physical systems. Comput Ind. 2018;97:132–145.
- Ogonji MM, Okeyo G, Wafula JM. A survey on privacy and security of Internet of Things. Comput Sci Rev. 2020;38:100312.
- Bhushan B, Sinha P, Sagayam KM, et al. Untangling blockchain technology: a survey on state of the art, security threats, privacy services, applications and future research directions. Comput Electr Eng. 90(2020);106897. ( Article in Press).
- Neumann WP, Winkelhaus S, Grosse EH, et al. Industry 4.0 and the human factor – a systems framework and analysis methodology for successful development. Int J Prod Econ. 2021;233:107992. ( Article in Press). .
- Kadir BA, Broberg O. Human-centered design of work systems in the transition to industry 4.0. Appl Ergon. 2021;92:103334.
- Liu J, Chang H, Y-l FJ, et al. Influence of artificial intelligence on technological innovation: evidence from the panel data of China’s manufacturing sectors. Technol Forecasting Social Change. 2020;158:120142.
- Mao S, Wang B, Tang Y, et al. Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry. Engineering. 2019;5(6):995–1002.
- Nishant R, Kennedy M, Corbett J. Artificial intelligence for sustainability: challenges, opportunities, and a research agenda. Inter J Inf Manage. 2020;53:102104.
- Loureiro SMC, Guerreiro J, Tussyadiah I. Artificial intelligence in business: state of the art and future research agenda. J Bus Res. 2020;129:911–926.
- Jimeno-Morenilla A, Azariadis P, Molina-Carmona R, et al. Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: a review. Comput Ind. 2021;125:103390.
- Lenoir D, Schramm KW, Lalah JO. Green Chemistry: some important forerunners and current issues. Sustainable Chem Pharm. 2020;18:100313. ( Article in Press). .
- Lenz J, MacDonald E, Harik R, et al. Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life. J Manuf Syst. 2020;57:274–286.
- Duan Y, Luo Y, Li W, et al. A collaborative task-oriented scheduling driven routing approach for industrial IoT based on mobile devices. Ad Hoc Netw. 2018;81:86–99.
- Frank AG, Dalenogare LS, Ayala NF. Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ. 2019;210:15–26.
- Ahmad T, Zhang D, Huang C, et al. Artificial intelligence in sustainable energy industry: status Quo, challenges and opportunities. J Clean Prod. 2021;289:125834. ( Article in Press). .
- Sarja M, Onkila T, Mäkelä M. A systematic literature review of the transition to the circular economy in business organizations: obstacles, catalysts and ambivalences. J Clean Prod. 286(2020);125492.
- Centobelli P, Cerchione R, Chiaroni D, et al. Designing business models in circular economy: a systematic literature review and research agenda. Business Strat Environ. 2020;29(4):1734–1749.
- Dutta G, Kumar R, Sindhwani R, et al. Digital transformation priorities of India’s discrete manufacturing SMEs–a conceptual study in perspective of Industry 4.0. Compet Rev. 2020;30(3):289–314. .
- Fernández-Rovira C, Valdés JÁ, Molleví G, et al. The digital transformation of business. Towards the datafication of the relationship with customers. Technol Forecasting Social Change. 2021;162:120339.
- Chen Y, Lin Z. Business intelligence capabilities and firm performance: a study in China. Inter J Inf Manage. 2021;57:102232.
- Papananias M, McLeay TE, Obajemu O, et al. Inspection by exception: a new machine learning-based approach for multistage manufacturing. Appl Soft Comput. 2020;97:106787.
- Injadat M, Moubayed A, Nassif AB, et al. Machine learning towards intelligent systems: applications, challenges, and opportunities. Artif Intell Rev. 2021;54, 3299–3348.
- Sharp M, Ak R, Hedberg T Jr. A survey of the advancing use and development of machine learning in smart manufacturing. J Manuf Syst. 2018;48:170–179.
- Goh GD, Sing SL, Yeong WY. A review on machine learning in 3D printing: applications, potential, and challenges. Artif Intell Rev. 2020;54, 63–94.
- Gunasekaran A, Subramanian N, Tiwari MK, et al. Information sharing in supply chain of agricultural products based on the Internet of Things. Ind Manage Data Syst. 2016;116(7):1397–1416.
- Lin SY, Du Y, Ko PC, et al. Fog Computing Based Hybrid Deep Learning Framework in effective inspection system for smart manufacturing. Comput Commun. 2020;160:636–642.
- Wang J, Ma Y, Zhang L, et al. Deep learning for smart manufacturing: methods and applications. J Manuf Syst. 2018;48:144–156.
- Guo Z, Zhou D, Zhou Q, et al. A hybrid method for evaluation of maintainability towards a design process using virtual reality. Comput Ind Eng. 2020;140:106227.
- De Regt A, Barnes SJ, Plangger K. The virtual reality value chain. Bus Horiz. 2020;63(6):737–748.
- Malik AA, Masood T, Bilberg A. Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace. Int J Comput Integr Manuf. 2020;33(1):22–37.
- Luo Y, Song K, Ding X, et al. Environmental sustainability of textiles and apparel: a review of evaluation methods. Environ Impact Assess Rev. 2021;86:106497.
- Beier G, Niehoff S, Ziems T, et al. Sustainability aspects of a digitalized industry–A comparative study from China and Germany. Int J Precis Eng Manuf Green Technol. 2017;4(2):227–234.
- Gobbo JA, Busso CM, Gobbo SCO, et al. Making the links among environmental protection, process safety, and Industry 4.0. Process SafEnviron Prot. 2018;117:372–382.
- Fu Y, Zhu J. Trusted data infrastructure for smart cities: a blockchain perspective. Build Res Inf. 2021;49(1):21–37.
- Abbate T, Cesaroni F, Cinici MC, et al. Business models for developing smart cities. A fuzzy set qualitative comparative analysis of an IoT platform. Technol Forecasting Social Change. 2019;142:183–193.
- Cha J, Singh SK, Kim TW, et al. Blockchain-empowered cloud architecture based on secret sharing for smart city. J Inf Secur Appl. 2021;57:102686.
- Nižetić S, Djilali N, Papadopoulos A, et al. Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management. J Clean Prod. 2019;231:565–591.
- Silva BN, Khan M, Han K. Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities Soc. 2018;38:697–713.