Supply chain integration in the industry 4.0 era: a systematic literature review
Integração da cadeia de suprimentos na era industrial 4.0: uma revisão sistemática da literatura
DOI:
https://doi.org/10.34117/bjdv8n10-178Keywords:
supply chain integration, industry 4.0, vertical integrationAbstract
From an Industry 4.0 perspective, supply chain actors (suppliers, manufacturers, retailers, and third-party logistics operators) are integrated into a collaborative network based on information-sharing to improve the overall supply chain performance. Real data can be captured and systematically processed into information, hence dealing with uncertainty. Poor integration may lead to supply chain disruptions. This paper examines the role of Industry 4.0 (I4.0) for integrating the supply chain, to which a systematic literature review (SLR) has been applied. First, according to the research questions, the I4.0 technologies adopted for supply chain integration were identified. Second, the approaches for integrating the supply chain at I4.0 were examined and classified by strategy (vertical, horizontal, and end-to-end integration). Third, the functional and cross-functional approaches for supply chain integration at I4.0 were also examined. Finally, it was discussed which traditional SCI approaches can be upgraded to the Industry 4.0 era and the future research directions.
References
Abreu, A., & Alcântara, R. (2017). Internal integration in supply chain integration: a systematic literature review. Revista Reuna, 22(4), 40 – 64. https://revistas.una.br/reuna/article/view/938
Adriano, D. D., Novaes, A. G., & Wangham, M. S. (2019, November). Providing a Dynamic Milk-Run Vehicle Routing Using Vehicular Ad hoc Networks. 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), Miami Beach, United States. https://doi.org/10.1145/3345838.3356006
Alfalla-luque, R., Medina-Lopez, C., & Dey, P. (2013). Supply chain integration framework using literature review. Production Planning & Control, 24(8-9), 800 – 817. https://doi.org/10.1080/09537287.2012.666870
Ali, M., Patel, P., & Breslin, J. (2019, May). Middleware for Real-Time Event Detection and Predictive Analytics in Smart Manufacturing. 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece. https://doi.org/10.1109/DCOSS.2019.00079
Angeles, R. (2005). RFID technologies: supply-chain applications and implementation issues. Information systems management, 22(1), 51 – 65. https://doi.org/10.1201/1078/44912.22.1.20051201/85739.7
Anton, F., Borangiu, T., Răileanu, S., Anton, S., Ivănescu, N., & Iacob, I. (2020). Secure Sharing of Robot and Manufacturing Resources in the Cloud for Research and Development. In K. Berns, & D. Görges (Eds.), Advances in Service and Industrial Robotics (pp. 535 – 543). Cham: Springer. https://doi.org/10.1007/978-3-030-19648-6_61
Bag, S., Telukdarie, A., Pretorius, J., & Gupta, S. (2018). Industry 4.0 and supply chain sustainability: framework and future research directions. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-03-2018-0056
Bányai, T., Illés, B., & Bányai, A. (2018). Smart scheduling: An integrated first mile and last mile supply approach. Complexity. https://doi.org/10.1155/2018/5180156
Barata, J., Da Cunha, P., & Stal, J. (2018). Mobile supply chain management in the Industry 4.0 era. Journal of Enterprise Information Management, 31(1), 173 – 193. https://doi.org/10.1108/JEIM-09-2016-0156
Beheshtinia, M. A., Ghasemi, A., & Farokhnia, M. (2018). Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company). Journal of Modelling in Management, 13(1), 27 – 49. https://doi.org/10.1108/JM2-10-2016-0094
Bocewicz, G., Nielsen, P., & Banaszak, Z. (2019). Declarative Modeling of a Milk-Run Vehicle Routing Problem for Split and Merge Supply Streams Scheduling. In J. Świątek, L. Borzemski, & Z. Wilimowska (Eds.), Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 (pp. 157 – 172). Cham: Springer. https://doi.org/10.1007/978-3-319-99996-8_15
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 8(1), 37 – 44.
Cagliano, R., Caniato, F., & Spina, G. (2006). The linkage between supply chain integration and manufacturing improvement programmes. International Journal of Operations & Production Management,26(3), 282 – 299. https://doi.org/10.1108/01443570610646201
Cao, B., Wang, Z., Shi, H., & Yinet, Y. (2015, November). Research and practice on Aluminum Industry 4.0. Sixth International Conference on Intelligent Control and Information Processing (ICICIP), Wuhan, China. https://doi.org/10.1109/ICICIP.2015.7388226
Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., & Alcalá, S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Chaudhary, K., Singh, M., Tarar, S., Chauhan, D., & Srivastava, M. (2018). Machine Learning Based Adaptive Framework for Logistic Planning in Industry 4.0. In M. Singh, P. Gupta, V. Tyagi, J. Flusser, & T. Ören (Eds.) Advances in Computing and Data Sciences (pp. 431 – 438). Singapore: Springer. https://doi.org/10.1007/978-981-13-1810-8_43
Cheng, B., Leung, J. Y. T., Li, K., & Yang, S. (2019). Integrated optimization of material supplying, manufacturing, and product distribution: Models and fast algorithms. European Journal of Operational Research, 277(1), 100 – 111. https://doi.org/10.1016/j.ejor.2019.02.024
Cooper, M., Lambert, D., & Pagh, J. (1997). Supply chain management: more than a new name for logistics. The international journal of logistics management, 8, (1), 1 – 14. https://doi.org/10.1108/09574099710805556
Dallasega, P., Rauch, E., & Linder, C. (2018). Industry 4.0 as an enabler of proximity for construction supply chains: A systematic literature review. Computers in Industry, 99, 205 – 225. https://doi.org/10.1016/j.compind.2018.03.039
Dev, N., Shankar, R., & Qaiser, F. (2020). Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, 104583. https://doi.org/10.1016/j.resconrec.2019.104583
Ding, K., & Jiang, P. (2016). A Graphical Formalized Deduction for Integrated Production and Logistic Service Flow Monitoring in Social Manufacturing. In E. Qi (Eds.), Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation (pp. 603 – 613). Paris: Atlantis Press. https://doi.org/10.2991/978-94-6239-145-1_57
Ding, K., Jiang, P., & Su, S. (2018). RFID-enabled social manufacturing system for inter-enterprise monitoring and dispatching of integrated production and transportation tasks. Robotics and Computer-Integrated Manufacturing, 49, 120 – 133. https://doi.org/10.1016/j.rcim.2017.06.009
Eroglu, D. Y., Cagliano, A. C., & Rafele, C. (2017). A multi-capacity integrated vehicle routing and loading model for pickup milk run. International Journal of Mechanics and Control, 18(2), 107 – 122.
Flynn, B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of operations management, 28(1), 58 – 71. https://doi.org/10.1016/j.jom.2009.06.001
Fontes, D., & Homayouni, S. (2019). Joint production and transportation scheduling in flexible manufacturing systems. Journal of Global Optimization, 74(4), 879 – 908. https://doi.org/10.1007/s10898-018-0681-7
Frazzon, E., Albrecht, A., Hurtado, P., Silva, L., & Pannek, J. (2015). Hybrid modelling approach for the scheduling and control of integrated production and logistic processes along export supply chains. Ifac Papersonline, 48(3), 1521 – 1526. https://doi.org/10.1016/j.ifacol.2015.06.302
Freitas, A. et al. (2017, October). Savings in internal logistics using a RFID-based software system in a lean context. Proceedings of the 47th International Conference on Computers and Industrial Engineering (CIE47), Lisbon, Portugal.
Guo, Z., Zhang, Y., Zhao, X., & Song, X. (2017). A timed colored petri net simulation-based self-adaptive collaboration method for production-logistics systems. Applied Sciences, 7(3). https://doi.org/10.3390/app7030235
Güner, A. R., Murat, A., & Chinnam, R. B. (2017). Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks. Transportation Research Part E: Logistics and Transportation Review, 97, 251-267. https://doi.org/10.1016/j.tre.2016.10.014
He, Z., Guo, Z., & Wang, J. (2019). Integrated scheduling of production and distribution operations in a global MTO supply chain. Enterprise Information Systems, 13(4), 490-514. https://doi.org/10.1080/17517575.2018.1428770
Hegedűs, C., Frankó, A., & Varga, P. (2019, May). Asset and production Tracking through Value Chains for Industry 4.0 using the Arrowhead Framework. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). Taipei, Taiwan, Taiwan. https://doi.org/10.1109/ICPHYS.2019.8780381
Hou, L., & Qiu, W. (2008). Coordinating the three-level supply chain with combined contracts under demand uncertainty. Journal of Beijing University of Aeronautics and Astronautics, 21(1), 1 – 5.
Huang, M., Yang, J., Ma, T., Li, X., & Wang, T. (2017). The modeling of milk-run vehicle routing problem based on improved CW algorithm that joined time window. Transportation Research Procedia, 25, 716 – 728. https://doi.org/10.1016/j.trpro.2017.05.453
i-Scoop. Industry 4.0: the fourth industrial revolution – guide to Industrie 4.0. (2016). https://www.i-scoop.eu/industry-4-0/#Vertical_integration_in_Industry_40 Accessed April 23 2020.
Islam, M. A., Jantan, A. H., Hashim, H., Chong, C. W., Abdullah, M. M., & Abdul Hamid, A. B. (2018). Fourth Industrial Revolution in Developing Countries: A Case on Bangladesh. Journal of Management Information and Decision Sciences, 21(1). https://ssrn.com/abstract=3417813
Jalil, S. A., Hashmi, N., Asim, Z., & Javaid, S. (2019). A decentralized bi-level multi-objective model for integrated production and transportation problems in closed-loop supply chain networks. International Journal of Management Science and Engineering Management, 14(3), 206 – 217. https://doi.org/10.1080/17509653.2018.1545607
Jia, Z. H., Zhuo, X. X., Leung, J. Y., & Li, K. (2019). Integrated production and transportation on parallel batch machines to minimize total weighted delivery time. Computers & Operations Research, 102, 39-51. https://doi.org/10.1016/j.cor.2018.07.026
Jiang, Y., Zhou, X., & Xu, Q. (2019). Scenario Analysis–Based Decision and Coordination in Supply Chain Management with Production and Transportation Scheduling. Symmetry, 11(2). https://doi.org/10.3390/sym11020160
Juhász, J., & Bányai, T. (2018). What Industry 4.0 Means for Just-In-Sequence Supply in Automotive Industry? In K. Jármai, & B. Bolló (Eds.), Vehicle and Automotive Engineering 2 (pp. 226 – 240). Cham: Springer. https://doi.org/10.1007/978-3-319-75677-6_19
Kamal, M., & Irani, Z. (2014). Analysing supply chain integration through a systematic literature review: a normative perspective. Supply Chain Management: An International Journal, 19(5-6), 523 – 557. https://doi.org/10.1108/SCM-12-2013-0491
Khanuja, A., & Jain, R. (2019). Supply chain integration: a review of enablers, dimensions and performance. Benchmarking: An International Journal, 27(1), 264 – 301. https://doi.org/10.1108/BIJ-07-2018-0217
Lacomme, P., Moukrim, A., Quilliot, A., & Vinot, M. (2015, October). A graspxels approach for the resolution of the integrated production and transportation scheduling problem. Proceedings of the 45th International Conference on Computers and Industrial Engineering (CIE45). Metz, France.
Lacomme, P., Moukrim, A., Quilliot, A. & Vinot, M. (2016). The Integrated Production and Transportation Scheduling Problem based on a GRASPxELS resolution scheme. Ifac Papersonline, 49(12), 1466 – 1471.
Lacomme, P., Moukrim, A., Quilliot, A., & Vinot, M. (2018). Supply chain optimisation with both production and transportation integration: multiple vehicles for a single perishable product. International Journal of Production Research, 56(12), 4313 – 4336. https://doi.org/10.1080/00207543.2018.1431416
Lallas, E., Xenakis, A., & Stamoulis, G. (2019, September). A generic framework for a Peer to Peer Blockchain based Fog Architecture in Industrial Automation. 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). Piraeus, Greece. https://doi.org/10.1109/SEEDA-CECNSM.2019.8908360
Lambert, D., & Cooper, M. (2000). Issues in supply chain management. Industrial marketing management, 29(1), 65 – 83.
Lee, H. (2016). A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0. International Journal of Production Research, 55(2), 593 – 605. https://doi.org/10.1080/00207543.2016.1221162
Lee, H. (2019). Effective Dynamic Control Strategy of a Key Supplier with Multiple Downstream Manufacturers Using Industrial Internet of Things and Cloud System. Processes, 7(3), 172 – 190. https://doi.org/10.3390/pr7030172
Longo, F., Nicoletti, L., Padovano, A., D'Atri, G., & Forte, M. (2019). Blockchain-enabled supply chain: An experimental study. Computers & Industrial Engineering, 136, 57 – 69. https://doi.org/10.1016/j.cie.2019.07.026
Luo, H, Chen, J., & Huang, G. (2016). IoT Enabled Production-Logistic Synchronization in Make-to-Order Industry. In E. Qi, J. Shen, & R. Dou (Eds.), Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015 (pp. 527 – 538). Paris: Atlantis Press. https://doi.org/10.2991/978-94-6239-180-2_51
Luo, H., Wang, K., Kong, X., Lu, S., & Qu, T. (2017). Synchronized production and logistics via ubiquitous computing technology. Robotics and Computer-Integrated Manufacturing, 45, 99 – 115. https://doi.org/10.1016/j.rcim.2016.01.008
Mageed, A., & Rupasinghe, D. (2017). 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, 6(1), 25 – 40.
Mantravadi, S., Moller, C., & Christensen, M. (2018, October). Perspectives on Real-Time Information Sharing through Smart Factories: Visibility via Enterprise Integration. 2018 International Conference on Smart Systems and Technologies (SST). Osijek, Croatia. 10.1109/SST.2018.8564617
Marques, M., Agostinho, C., Zacharewicz, G., & Jardim-Gonçalves, R. (2017). Decentralized decision support for intelligent manufacturing in Industry 4.0. Journal of Ambient Intelligence and Smart Environments, 9(3), 299 – 313. https://doi.org/10.3233/AIS-170436
Masdefiol, M., & Stävmo, F. (2016). Industry 4.0–Only designed to fit the German automotive industry: A multiple case study on the feasibility of Industry 4.0 to Swedish SMEs. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-30238
Neubauer, M., & Krenn, F. (2017). Subject-oriented design of smart hyper-connected logistics systems. In M. Mühlhäuser, & C. Zehbold (Eds.), Proceedings of the 9th Conference on Subject-oriented Business Process Management (pp. 1 – 8). New York: Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3040565.3040572
Nguyen, T., Splieger, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254 – 264. https://doi.org/10.1016/j.cor.2017.07.004
Novais, L., Maqueira, J., Ortiz-Bas, Á. (2019). A systematic literature review of cloud computing use in supply chain integration. Computers & Industrial Engineering, 129, 296 – 314. https://doi.org/10.1016/j.cie.2019.01.056
Nukala, R., Panduru, K., Shields, A., Riordan, D., Doody, P., & Walsh, J. (2016, June). Internet of Things: A review from 'Farm to Fork'. 27th Irish Signals and Systems Conference (ISSC). Londonderry, United Kingdom. https://doi.org/10.1109/ISSC.2016.7528456
Oesterreich, T., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121 – 139. https://doi.org/10.1016/j.compind.2016.09.006
Pinto, J., & Diemer, A. (2020). Supply chain integration strategies and circularity in the European steel industry. Resources, Conservation and Recycling, 153, 104517. https://doi.org/10.1016/j.resconrec.2019.104517
Pires, M., Frazzon, E., Danielli, A., Kuck, M., & Freitag, M. (2018). Towards a simulation-based optimization approach to integrate supply chain planning and control. Procedia Cirp, 72, 520 – 525.
Qu, T., Chen, Y., Wang, Z., Nie, D., Luo, H., & Huang, K. (2015, April). Internet-of-Things-based just-in-time milk-run logistics routing system. IEEE 12th International Conference on Networking, Sensing and Control. Taipei, Taiwan, Taiwan. https://doi.org/10.1109/ICNSC.2015.7116045
Raschinger, M., Kipouridis, O., & Gunthner, W. (2016, May). A service-oriented cloud application for a collaborative tool management system. 2016 International Conference on Industrial Engineering, Management Science and Application (ICIMSA). Jeju, South Korea. https://doi.org/10.1109/ICIMSA.2016.7503987
Rodríguez, J., Contreras, L., & Rivas, E. (2018). Industry Knowledge Management Model 4.0. In Á. Rocha, & T. Guarda (Eds.), Proceedings of the International Conference on Information Technology & Systems (pp. 275 – 283). Cham: Springer. https://doi.org/10.1007/978-3-319-73450-7_27
González, G., Gonzalez, J., & Méndez, J. (2020). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing, 31, 1257 – 1273. https://doi.org/10.1007/s10845-019-01510-y
Rotella, P. Is Data The New Oil? (2012). https://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/#899cd67db3dd Accessed February 20 2020.
Sainathuni, B., Parikh, P., Zhang, X., & Kong, N. (2014). The warehouse-inventory-transportation problem for supply chains. European Journal of Operational Research, 237(2), 690 – 700. https://doi.org/10.1016/j.ejor.2014.02.007
Santos, N. et al. (2018, September). UH4SP: a software platform for integrated management of connected smart plants. 2018 International Conference on Intelligent Systems (IS). Madeira, Portugal. https://doi.org/10.1109/IS.2018.8710468
Scholz-Reiter, B., Makuschewitz, T., Novaes, A., Frazzon, E., & Lima Jr, O. (2011). An approach for the sustainable integration of production and transportation scheduling. International Journal of Logistics Systems and Management, 10(2), 158 – 179. https://doi.org/10.1504/IJLSM.2011.042626
Schulz, K., & Freund, D. (2019). A multichain architecture for distributed supply chain design in industry 4.0. In W. Abramowicz, & A. Paschke (Eds.), Business Information Systems Workshops, BIS 2018 (pp. 277 – 288). Cham: Springer. https://doi.org/10.1007/978-3-030-04849-5_25
Shou, Y., Li, Y., Park, Y., & Kang, M. (2017). The impact of product complexity and variety on supply chain integration. International Journal of Physical Distribution & Logistics Management, 47(4), 297 – 317. https://doi.org/10.1108/IJPDLM-03-2016-0080
Singh, A., Mishra, N., Ali, S., Shukla, N., & Shankar, R. (2015). Cloud computing technology: Reducing carbon footprint in beef supply chain. International Journal of Production Economics, 164, 462 – 471. https://doi.org/10.1016/j.ijpe.2014.09.019
Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536 – 541. https://doi.org/10.1016/j.procir.2016.01.129
Sun, J., Yamamoto, H., & Matsui, M. (2020). Horizontal integration management: An optimal switching model for parallel production system with multiple periods in smart supply chain environment. International Journal of Production Economics, 221, 107475. https://doi.org/10.1016/j.ijpe.2019.08.010
Sundarakani, B., Kamran, R., Maheshwari, P., & Jain, V. (2019). Designing a hybrid cloud for a supply chain network of Industry 4.0: a theoretical framework. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-04-2018-0109
Suri, K., Gaaloul, W., Cuccuru, A., & Gerard, S. (2017, June). Semantic framework for internet of things-aware business process development. IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). Poznan, Poland. https://doi.org/10.1109/WETICE.2017.54
Telukdarie, A., Buhulaiga, E., Bag, S., Gupta, S., & Luo, Z. (2018). Industry 4.0 implementation for multinationals. Process Safety and Environmental Protection, 118, 316 – 329. https://doi.org/10.1016/j.psep.2018.06.030
Teng, L., Zhang, Z., Li, P., & Gong, D. (2019). Integrated Inventory-Transportation Problem in Vendor-Managed Inventory System. IEEE Access, 7, 160324 – 160333. https://doi.org/10.1109/ACCESS.2019.2950036
Thoben, K., Wiesner, S., & Wuest, T. (2017). "Industrie 4.0" and smart manufacturing-a review of research issues and application examples. International journal of automation technology, 11(1), 4 – 16. https://doi.org/10.20965/ijat.2017.p0004
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207 – 222. https://doi.org/10.1111/1467-8551.00375
Ud-Din, F., Henskens, F., Paul, D., & Wallis, M. (2019). Agent-Oriented Smart Factory (AOSF): An MAS Based Framework for SMEs Under Industry 4.0. In G. Jezic, YH. Chen-Burger, R. Howlett, L. Jain, L. Vlacic, & R.Šperka (Eds.), Agents and Multi-Agent Systems: Technologies and Applications 2018 (pp. 44 – 54). Cham: Springer. https://doi.org/10.1007/978-3-319-92031-3_5
Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: managing the digital transformation. Springer. https://doi.org/10.1007/978-3-319-57870-5
Van Der Vaart, T.,Van Donk, D. (2008). A critical review of survey-based research in supply chain integration. International journal of production economics, 111, 42 – 55. https://doi.org/10.1016/j.ijpe.2006.10.011
Vieira, A., Dias, L., Santos, M., Pereira, G., & Oliveira, J. (2019). Simulation of an automotive supply chain using big data. Computers & Industrial Engineering, 137, 106033, https://doi.org/10.1016/j.cie.2019.106033
Wakenshaw, S., Maple, C., Chen, D., & Micillo, R. (2017, September). An IoT-enabled supply chain integration framework: empirical case studies. 15th International Conference on Manufacturing Research (ICMR 2017). London, United Kingdom. https://doi.org/10.3233/978-1-61499-792-4-263
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158 – 168. https://doi.org/10.1016/j.comnet.2015.12.017
Wang, S., Ouyang, J., Li, D., & Liu, C. (2017). An integrated industrial ethernet solution for the implementation of smart factory. IEEE Access, 5, 25455 – 25462. https://doi.org/10.1109/ACCESS.2017.2770180
Wang, L., & Wang, G. (2016). Big data in cyber-physical systems, digital manufacturing and industry 4.0. International Journal of Engineering and Manufacturing, 6(4), 1-8. https://doi.org/10.5815/ijem.2016.04.01
Wu, X., Zhang, R., Zeng, B., & Zhou, S. (2013). A trust evaluation model for cloud computing. Procedia Computer Science, 17, 1170 – 1177. https://doi.org/10.1016/j.procs.2013.05.149
Yi, J., & Su, Z. (2017). A Study on a Specific Vehicle Routing Problem Under the Cargo Loading and Stacking by Milk-run Operations. Journal of Wuhan University of Technology. https://doi.org/10.3963/j.issn.2095-3844.2017.02.001
Zhang, Y., Liu, S., Liu, Y., Yang, H., Li, M., Huisingh, D., & Wang, L. (2018). The 'Internet of Things' enabled real-time scheduling for remanufacturing of automobile engines. Journal of cleaner production, 185, 562 – 575. https://doi.org/10.1016/j.jclepro.2018.02.061
