تعیین سطح و تحلیل فرآیندهای سیستم لجستیک متناسب با نسل 4 صنعت (مورد مطالعه: مراکز لجستیک در ایران)
الموضوعات :
zahra Rahimi
1
(Ph.D Student in Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Habibollah Javanmard
2
(departement of industrial management
Islamic azad university
arak, iran)
Amir Azizi
3
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Seyad Esmail Najafi
4
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
تاريخ الإرسال : 01 الجمعة , جمادى الأولى, 1444
تاريخ التأكيد : 17 السبت , رمضان, 1444
تاريخ الإصدار : 01 الإثنين , ذو الحجة, 1444
الکلمات المفتاحية:
اجزای فرایند,
صنعت نسل 4,
تکامل,
لجستیک,
مراکز لجستیک,
ملخص المقالة :
برای ارتقاء سطح اجزای لجستیک، متناسب با صنعت نسل 4 لازم است وضعیت فرایندهای لجستیک، اندازه‌گیری شود. برای اندازه‌گیری، اولین گام تعیین سطح مورد نیاز تکامل اجزاست و لازم است، ابتدا فرایندها و اجزای لجستیک تعیین گردند، سپس مراحل تکامل مشخض شده و در انتها اندازه تکامل مورد نیاز آنها بدست آید. هدف این مقاله تعیین سطح مورد نیاز اجزای سیستم لجستیک در مراکز لجستیک ایران متناسب با صنعت نسل 4 است. روش تحقیق توصیفی-کاربردی و گردآوری داده‌ها، میدانی است، جامعه آماری دوگروه است، گروه اول ده تن از خبرگان لجستیک هستند که نظر آنها برای تعیین فرایندها و اجزای لجستیک استفاده شده، گروه دوم مدیران در مراکز لجستیک ایران به تعداد 102 نفر هستند، که با روش سرشماری نظرات تعداد 63 نمونه مورد استفاده قرار گرفته‌است. با ابزار مصاحبه تخصصی از خبرگان، فرایندها و اجزای لجستیک به تعداد چهار فرایند و سیزده جزء اجرایی شناسایی و دسته‌بندی شده اند. براساس نظر مدیران لجستیک، وضعیت موجود اجزاء در هر مرکز لجستیک، ارائه شده‌است. با استفاده از میانگین اجزای سیستم لجستیک و درجه سازگاری با نرم‌افزارهای SPSS , Excel وضعیت تکامل اجزای لجستیک برای مراکز، سنجش شد. نتایج نشان داد که فرایند حمل و نقل تکامل کمتری نسبت به سایر فرایندها دارد زیرا وابستگی به سیستم‌های خارجی دارد. فرایند مدیریت اطلاعات بواسطه نرم‌افزاری بودن و وابستگی کمتر به سیتم‌های خارجی، سطح تکامل بیشتری دارد. براساس تحلیل‌های انجام شده، پیشنهادات به مراکز لجستیک بعنوان بهره‌وران خاص و تحقیقات آتی ارائه شده اند.
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Agdas, D., and R. D. Ellis. (2010). The Potential of XML Technology as an Answer to the Data Interchange Problems of the Construction Industry. Construction Management and Economics 28 (7): 737–746.
Alex, V. B. (2006). A Parametric Analysis of Heuristics for the Vehicle Routing Problem with Side-Constraints. European Journal of Operational Research 137 (2): 348–370.
Angreani, L.S, Annas Vijaya, A. Wicaksono, H, (2020). Systematic Literature Review of Industry 4.0 Maturity Model for Manufacturing and Logistics Sectors, Procedia Manufacturing 52:337–343.
Arrais-Castro, A., R. Maria Leonilde, G. D. Varela, R. A. Putnik, J. M. Ribeiro, and L. Ferreira. (2018). Collaborative Framework for Virtual Organisation Synthesis Based on a Dynamic Multi-Criteria Decision Model. International Journal of Computer Integrated Manufacturing 31 (9): 857–868.
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.
Bag, S., Yadav, G., Wood, L.C, Dhamija P, Joshi, S. (2020). Industry 4.0 and the circular economy: Resource melioration in logistics. Resources Policy 68, 101776.
Ballou, R.H. (2007). The evolution and future of logistics and supply chain management, European Business Review, 19 (4): 332-348.
Barrera, M. M., Mario, and O. Cruz-Mejia. (2014). Reverse Logistics of Recovery and Recycling of Non-Returnable Beverage Containers in the Brewery Industry: A Profitable Visit Algorithm. International Journal of Physical Distribution & Logistics Management 44 (7): 577–596.
Barreto, L., Amaral, A., Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13: 1245–1252.
Battista C, Fumi A, Schiraldi M.M. (2012), The Logistics Maturity Model: guidelines for logistic processes continuous improvement, Proceedings of the XXIII World POMS Conference, 20-23 April; Chicago (USA. confpapers/025/025-1329.pdf
Battista, C, Schirald, M. M. (2013). The Logistic Maturity Model: Application to a Fashion Company. International Journal of Engineering Business Management, 5, 29-38.
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Bloss, R. (2011). Automation Meets Logistics at the Promat Show and Demonstrates Faster Packing and Order Filling. Assembly Automation, 31 (4): 315–318.
Bogataj, D., M. Bogataj, and D. Hudoklin. (2017). Mitigating Risks of Perishable Products in the Cyber-Physical Systems Based on the Extended MRP Model. International Journal of Production Economics, 193: 51–62.
Boysen, N., S. Schwerdfeger, and F. Weidinger. (2018). Scheduling Last-Mile Deliveries with Truck-Based Autonomous Robots. European Journal of Operational Research, 271 (3): 1085–1099.
Caiado, R.G. Scavarda L.F, Gavi˜ao, L.C, Ivson P, Nascimento D.L, Garza-Reyes. J.A. (2021). A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management, J. Production Economics, 231, 1-21.
Carvalho, J, Rocha A, Abreu, A. (2016). Maturity Models of Healthcare Information Systems and Technologies: a Literature Review, Review of Managerial Science, 5(2):9
Choy, K. (2002). An Intelligent Supplier Management Tool for Benchmarking Suppliers in Outsource Manufacturing. Expert Systems with Applications, 22 (3): 213–224.
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Essaadi, I, Grabot, B, Fénies, P. (2016). Location of logistics hubs at national and subnational level with consideration of the structure of the location choice, IFAC-PapersOnLine, 49-31: 155–160.
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Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90-98.
Hercko, J., Botka, M., (2017). Intelligent logistic management, in Next Generation Logistics: Technologies and Applications. In: Drašković, V. (Ed.). SPH – The Scientific Publishing, Velje, Denmark, pp. 1–18.
Home-Ortiza, J M., Pourakbari-Kasmaei, M, Lehtonen, M, (2019). Optimal location-allocation of storage devices and renewable-based DG in distribution systems. Electric Power Systems Research 172, 11–21. DOI: 1016/j.epsr.2019.02.013
Hou, J.-L., W. Nathan, and W. Yu-Jen. (2009). A Job Assignment Model for Conveyor-Aided Picking System. Computers & Industrial Engineering 56 (4): 1254–1264.
Jahn, C., Kersten, W. and Ringle, C. M. (2018), Logistics 4.0 and sustainable supply chain management: innovative solutions for logistics and sustainable supply chain management in the context of industry 4.0. In: Hamburg International Conference of Logistics (HICL).
Javanmard, H, (2017), Logistics and supply chain management. Arak Branch, Iran. Publication of Islamic azad university. (In Persian)
Javanmard, H. (2008). Using Degree of Adaptive (DOA) Model for Partner Selection in Supply Chain. International Journal of Mechanical, Industrial and Aerospace Sciences. 1.0 (4).
Junge, A. L., Verhoeven, P., Reipert, J. and M. Mansfeld. (2019). Pathway of Digital Transformation in Logistics: Best Practice Concepts and Future Developments.” Edited by Frank Straube. In Scientific Series Logistics at the Berlin Institute of Technology. Special Edition Berlin: Universitätsverlag der TU Berlin. https://depositonce.tu-berlin.de/handle/11303/9446.
Kersten, W., M. Seiter, V. S. Birgit, N. Hackius, and T. Maurer. (2017). Trends and Strategies in Logistics and Supply Chain Management: Digital Transformation Opportunities. Journal of Logistics Research and Applications 13 (1): 13–39.
Kiil, K., Dreyer, H. C., Hvolby H.-H., and Chabada, L. (2018). Sustainable Food Supply Chains: The Impact of Automatic Replenishment in Grocery Stores. Production Planning & Control 29 (2): 106–116.
Kim, B. I., Graves R. J., Heragu, S. S., Onge, A. S. (2009). Intelligent Agent Modeling of an Industrial Warehousing Problem.” IIE Transactions 34 (7): 601–612.
Kochak, A., Sharma, S. (2015). Demand Forecasting Using Neural Network for Supply Chain Management. International Journal of Mechanical Engineering and Robotics Research 4 (1): 96–104.
Kostrzewski, M, Filina-Dawidowicz, L, Walusiak, M, (2021). Modern technologies development in logistics centers: the case study of Poland, Transportation Research Procedia 55, P- 268–275
Li, R, Chen, H, 2022, Research on Automation Control of University Logistics Management System Based on Wireless Communication Network, Wireless Communications and Mobile Computing, Article ID 1939434, 8.
Lin, B. Liua, S. Linb, R. Wang, J. Sun, M. Wang, X, Liu, C, Wu, J. Xiao, J, (2019), The location-allocation model for multi-classification-yard location problem, Transportation Research Part E 122, 283–308.
Lindstrom, V, Winroth, M, (2010), Aligning manufacturing strategy and levels of automation: A case study, Journal of Engineering Technology Management. 27 148–159.
Liu, W, Wang, S, Lin, Y, Xie, D, Zhang, J, (2020), Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies, Transportation Research Part E, 137, 101928.
Lizarralde D. R., Ganzarain, E. López C. Serrano L.I. (2020), An Industry 4.0 maturity model for machine tool companies, Technological Forecasting & Social Change 159, P. 1-13.
Marchet, G., Melacini, M. Perotti, S. Tappia, E. (2013). Development of a Framework for the Design of Autonomous Vehicle Storage and Retrieval Systems. International Journal of Production Research 51 (14): 4365–4387.
Mittal, S. Muztoba, A.K Romero, D. Wuest, T. (2018). Critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems. 49, October, P. 194-214.
Mori, J., Kajikawa, Y. Kashima, H. Sakata. I. (2012). Machine Learning Approach for Finding Business Partners and Building Reciprocal Relationships. Expert Systems with Applications 39 (12): 10402–10407.
Myers, M. B., Daugherty, P. J. Autry, C.W. (2000). The Effectiveness of Automatic Inventory Replenishment in Supply Chain Operations: Antecedents and Outcomes. Journal of Retailing 76 (4): 455–481.
Nikolopoulos, K. I., Zied Babai, M. Bozos. K. (2016). Forecasting Supply Chain Sporadic Demand with Nearest Neighbor Approaches. International Journal of Production Economics 177: 139–148.
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