Abstract
Recent technological advances in capturing real-time and large amounts of data provide an opportunity for production logistics systems to enhance their performance. Transitioning to a data-driven system is a complex and multifaceted process. While many articles highlight the benefits of data-driven applications, there are few empirical studies on how production logistics are influenced at the system level. This paper examines the potential benefits of transitioning to a data-driven production logistics system through two case studies. The first case involves an internal material flow between two departments in a large Swedish heavy automotive manufacturer with complex multi-product internal logistic flows. The second case involves the cross-docking flow of pallets in one terminal of a major courier industry player in the Nordic region with diverse customers. The current situation in each case was compared to a smart data-driven state through discrete event simulation, expert interviews, observations, and document review. The analysis shows that a seamless data flow can improve the overall performance of the systems in terms of lead-time, resource utilization efficiency, responsiveness, and space efficiency. The study provides insights towards creating a roadmap for the transition to a data-driven production logistics system.
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References
Cao, X., Li, T., Wang, Q.: RFID-based multi-attribute logistics information processing and anomaly mining in production logistics. Int. J. Prod. Res. 57(17), 5453–5466 (2019). https://doi.org/10.1080/00207543.2018.1526421
Zafarzadeh, M., Wiktorsson, M., Baalsrud Hauge, J.: A systematic review on technologies for data-driven production logistics: their role from a holistic and value creation perspective. Logistics 5(2), 24 (2021). https://doi.org/10.3390/logistics5020024
Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48(January), 157–169 (2018). https://doi.org/10.1016/j.jmsy.2018.01.006
Yin, S., Kaynak, O.: Big data for modern industry: challenges and trends [point of view]. Proc. IEEE 103(2), 143–146 (2015)
Dai, H.-N., Wang, H., Xu, G., Wan, J., Imran, M.: Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Enterp. Inf. Syst. 14(9–10), 1279–1303 (2020). https://doi.org/10.1080/17517575.2019.1633689
Hopkins, J., Hawking, P.: Big data analytics and IoT in logistics: a case study. IJLM 29(2), 575–591 (2018). https://doi.org/10.1108/IJLM-05-2017-0109
Zhang, Y., Cheng, Y., Wang, X.V., Zhong, R.Y., Zhang, Y., Tao, F.: Data-driven smart production line and its common factors. Int. J. Adv. Manuf. Technol. 103(1–4), 1211–1223 (2019). https://doi.org/10.1007/s00170-019-03469-9
Liu, S., Wang, L., Wang, X.V., Wiktorsson, M.: A framework of data-driven dynamic optimisation for smart production logistics. In: IFIP, pp. 213–221 (2020)
Müller, J.M., Kiel, D., Voigt, K.I.: What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability 10(1) (2018)
Hofmann, E., Rüsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 89, 23–34 (2017). https://doi.org/10.1016/j.compind.2017.04.002
Fatorachian, H., Kazemi, H.: A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Prod. Planning Control 29(8), 633–644 (2018). https://doi.org/10.1080/09537287.2018.1424960
Meredith, J.: Building operations management theory through case and field research. J. Oper. Manag. 16(4), 441–454 (1998)
Robinson, S.: Simulation: The Practice of Model Development and Use. Palgrave Macmillan, Basingstoke (2014)
Chung, C.A.: Simulation Modeling Handbook: A Practical Approach. CRC Press Inc., Boca Raton (2004)
Kvale, S.: Doing Interviews. SAGE Publications Ltd. (2007)
Karlsson, C.: Research Methods for Operations Management, Second. Routledge, New York (2016)
Zhang, Y., Guo, Z., Lv, J., Liu, Y.: A framework for smart production-logistics systems based on CPS and industrial IoT. IEEE Trans. Ind. Inf. 14(9), 4019–4032 (2018). https://doi.org/10.1109/TII.2018.2845683
Ren, S., Zhao, X., Huang, B., Wang, Z., Song, X.: A framework for shopfloor material delivery based on real-time manufacturing big data. J. Ambient Intell. Human Comput. 10(3), 1093–1108 (2019). https://doi.org/10.1007/s12652-018-1017-7
Cheng, J., Chen, W., Tao, F., Lin, C.-L.: Industrial IoT in 5G environment towards smart manufacturing. J. Ind. Inf. Integr. 10, 10–19 (2018). https://doi.org/10.1016/j.jii.2018.04.001
Meng, Z., Wu, Z., Gray, J.: RFID-based object-centric data management framework for smart manufacturing applications. IEEE Internet Things J. 6(2), 2706–2716 (2019). https://doi.org/10.1109/JIOT.2018.2873426
Egger, J., Masood, T.: Augmented reality in support of intelligent manufacturing – a systematic literature review. Comput. Ind. Eng. 140, 106195 (2020). https://doi.org/10.1016/j.cie.2019.106195
Dotoli, M., Fay, A., Miśkowicz, M., Seatzu, C.: An overview of current technologies and emerging trends in factory automation. Int. J. Prod. Res. 57(15–16), 5047–5067 (2019). https://doi.org/10.1080/00207543.2018.1510558
Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D., Prakash, A.: Impact of big data and predictive analytics capability on supply chain sustainability. IJLM 29(2), 513–538 (2018). https://doi.org/10.1108/IJLM-05-2017-0134
Kalaiarasan, R., Olhager, J., Agrawal, T.K., Wiktorsson, M.: The ABCDE of supply chain visibility: a systematic literature review and framework. Int. J. Prod. Econ. 248, 108464 (2022). https://doi.org/10.1016/j.ijpe.2022.108464
Balaska, V., Folinas, D., Konstantinidis, F.K., Gasteratos, A.: Smart counting of unboxed stocks in the Warehouse 4.0 ecosystem. In: Proceedings of the 2022 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6. IEEE, Kaohsiung, June 2022. https://doi.org/10.1109/IST55454.2022.9827758
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Zafarzadeh, M., Wiktorsson, M., Hauge, J.B. (2023). Data-Driven Production Logistics: Future Scenario in Two Swedish Companies Based on Discrete Event Simulation. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-43670-3_48
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