IDEAS home Printed from https://ideas.repec.org/r/eee/proeco/v165y2015icp260-272.html
   My bibliography  Save this item

A big data approach for logistics trajectory discovery from RFID-enabled production data

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ahmed Musa & Al-Amin Abba Dabo, 2016. "A Review of RFID in Supply Chain Management: 2000–2015," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 17(2), pages 189-228, June.
  2. Wang, Hui & Gong, Qiguo & Wang, Shouyang, 2017. "Information processing structures and decision making delays in MRP and JIT," International Journal of Production Economics, Elsevier, vol. 188(C), pages 41-49.
  3. Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
  4. Robert Karaszewski & Paweł Modrzyński & Gözde Türkmen Müldür & Jacek Wójcik, 2021. "Blockchain Technology in Life Cycle Assessment—New Research Trends," Energies, MDPI, vol. 14(24), pages 1-13, December.
  5. Weihua Liu & Shangsong Long & Yanjie Liang & Jinkun Wang & Shuang Wei, 2023. "The influence of leadership and smart level on the strategy choice of the smart logistics platform: a perspective of collaborative innovation participation," Annals of Operations Research, Springer, vol. 324(1), pages 893-935, May.
  6. Pei-Ju Wu, 2016. "Logistics business analytics for achieving environmental sustainability," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 2(6), pages 264-269.
  7. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
  8. Riccardo Brozzi & David Forti & Erwin Rauch & Dominik T. Matt, 2020. "The Advantages of Industry 4.0 Applications for Sustainability: Results from a Sample of Manufacturing Companies," Sustainability, MDPI, vol. 12(9), pages 1-19, May.
  9. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
  10. Yun Liu & Zhe Yan & Yijie Cheng & Xuanting Ye, 2018. "Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry," Sustainability, MDPI, vol. 10(1), pages 1-23, January.
  11. Niebel, Thomas & Rasel, Fabienne & Viete, Steffen, 2017. "BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation," ZEW Discussion Papers 17-053, ZEW - Leibniz Centre for European Economic Research.
  12. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
  13. Vecchio, Pasquale Del & Secundo, Giustina & Maruccia, Ylenia & Passiante, Giuseppina, 2019. "A system dynamic approach for the smart mobility of people: Implications in the age of big data," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
  14. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
  15. Judit Nagy & Judit Oláh & Edina Erdei & Domicián Máté & József Popp, 2018. "The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary," Sustainability, MDPI, vol. 10(10), pages 1-25, September.
  16. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
  17. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
  18. Yingfeng Zhang & Sichao Liu & Yang Liu & Rui Li, 2016. "Smart box-enabled product–service system for cloud logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 54(22), pages 6693-6706, November.
  19. Juan J. Lull & Roberto Cervelló-Royo & José Luis Galdón, 2024. "Crossroads between Big Data and entrepreneurship: current key trends," International Entrepreneurship and Management Journal, Springer, vol. 20(4), pages 2763-2790, December.
  20. Mariani, Marcello & Borghi, Matteo, 2019. "Industry 4.0: A bibliometric review of its managerial intellectual structure and potential evolution in the service industries," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
  21. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
  22. Benjamin Ohene Kwapong Baffoe & Wenping Luo, 2021. "South African Executives Propensity to Use, Diffuse, and Adopt the Humanitarian Logistics Digital Business Ecosystem (HLDBE)," SAGE Open, , vol. 11(3), pages 21582440211, September.
  23. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
  24. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
  25. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
  26. Halawa, Farouq & Dauod, Husam & Lee, In Gyu & Li, Yinglei & Yoon, Sang Won & Chung, Sung Hoon, 2020. "Introduction of a real time location system to enhance the warehouse safety and operational efficiency," International Journal of Production Economics, Elsevier, vol. 224(C).
  27. Iryna Lukyanova & Abubaker Haddud & Anshuman Khare, 2022. "Types of ERP Systems and Their Impacts on the Supply Chains in the Humanitarian and Private Sectors," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
  28. Hao Liu & Haodong Chen & Hengyi Zhang & Haibin Liu & Xingwang Yu & Shiqing Zhang, 2022. "Contract Design of Logistics Service Supply Chain Based on Smart Transformation," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
  29. Weichang Kong & Fei Qiao & Qidi Wu, 2020. "Real-manufacturing-oriented big data analysis and data value evaluation with domain knowledge," Computational Statistics, Springer, vol. 35(2), pages 515-538, June.
  30. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
  31. Chung, Leanne & Lo, Carlos Wing-Hung & Li, Pansy Hon Ying, 2016. "The interaction effects of institutional constraints on managerial intentions and sustainable performance," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 374-383.
  32. John A. Aloysius & Hartmut Hoehle & Soheil Goodarzi & Viswanath Venkatesh, 2018. "Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes," Annals of Operations Research, Springer, vol. 270(1), pages 25-51, November.
  33. Sanjeev Yadav & Sunil Luthra & Dixit Garg, 2022. "Internet of things (IoT) based coordination system in Agri-food supply chain: development of an efficient framework using DEMATEL-ISM," Operations Management Research, Springer, vol. 15(1), pages 1-27, June.
  34. Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
  35. Kozjek, Dominik & Vrabič, Rok & Eržen, Gregor & Butala, Peter, 2018. "Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources," International Journal of Production Economics, Elsevier, vol. 200(C), pages 181-191.
  36. Julian Marius Müller & Daniel Kiel & Kai-Ingo Voigt, 2018. "What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability," Sustainability, MDPI, vol. 10(1), pages 1-24, January.
  37. Daniel Kiel & Julian M. Müller & Christian Arnold & Kai-Ingo Voigt, 2017. "Sustainable Industrial Value Creation: Benefits And Challenges Of Industry 4.0," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(08), pages 1-34, December.
  38. Hendrik S. Birkel & Johannes W. Veile & Julian M. Müller & Evi Hartmann & Kai-Ingo Voigt, 2019. "Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers," Sustainability, MDPI, vol. 11(2), pages 1-27, January.
  39. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
  40. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.