تعیین سطح و تحلیل فرآیندهای سیستم لجستیک متناسب با نسل 4 صنعت (مورد مطالعه: مراکز لجستیک در ایران)
محورهای موضوعی :
مدیریت صنعتی
zahra Rahimi
1
,
Habibollah Javanmard
2
,
Amir Azizi
3
,
Seyad Esmail Najafi
4
1 - Ph.D Student in Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - departement of industrial management
Islamic azad university
arak, iran
3 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
تاریخ دریافت : 1401/09/04
تاریخ پذیرش : 1402/01/19
تاریخ انتشار : 1402/03/29
کلید واژه:
اجزای فرایند,
صنعت نسل 4,
تکامل,
لجستیک,
مراکز لجستیک,
چکیده مقاله :
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.
چکیده انگلیسی:
In order to improve the level of logistics components in accordance with the industry 4.0, it is necessary to increase situation of logistics processes. To increase the level of logistics processes, the first step is to measure growth rate and evolution. To measure the evolution of the logistics system, processes and components of the logistics and evolution stages of them must determin. The purpose of this paper is to determine the level of the logistics system components based on industry 4.0 in Iran’s logistics centers. The research method is descriptive – applied. Samples includes two groups; the first group are ten logistics experts. Their knowledge has used for determining the processes and also the maturity level. The second group are 102 managers and decision makers in logistics centers, they provide information for the components validation and maturity assessment based on the present situation of components and logistics processes. Using the expert interviews, processes and components of logistics have been identified and classified into four processes and thirteen components. By mean of components status and the use of degree of adaptive method in Excel and SPSS software, the maturity of logistics has been measured. The results showed the transportation process is less developed because has more dependence on the external system and transport has not evolved in general. The process of information management has higher levels, because of less dependence on external systems. Based on the results recommendation for the large assembly industry and future researches are presented
منابع و مأخذ:
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.
Becker, J., Knackstedt, R., P¨oppelbuß, J. (2009). Developing maturity models for IT management. Inf. Syst. Eng. 1: 213–222.
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.
Domingues, P. Sampaio, P. Arezes, P, M. (2016). Integrated management systems assessment: a maturity model proposal, Journal of Cleaner Production, 124 (2016) 164-174.
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.
Fawcett, S. E., and M. A. Waller. (2014). Supply Chain Game Changers-Mega, Nano, and Virtual Trends-And Forces that Impede Supply Chain Design (I.e., Building a Winning Team). Journal of Business Logistics, 35 (3): 157–164.
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869
Giusti, R., Manerba, D., Bruno, G., Tadei, R. (2019b). Synchro-modal logistics: an overview of critical success factors, enabling technologies, and open research issues. Transportation Research Part E: Logistics and Transportation Review, 129, 92–110.
Glistau E., Machado N. I. C. (2018). Logistics 4.0 and the Revalidation of Logistics Concepts and Strategies, Available at: htILs: //www.researchgate.net/publication/ 327417565, (Accessed 25 February 2019).
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.
Nitsche, B. (2021). Exploring the Potentials of Automation in Logistics and Supply Chain Management: Paving the Way for Autonomous Supply Chains, journal of Logistics,5(3), 51-63.
Oleśków-Szłapka, J. Wojciechowski, H., Domański, R. (2019). Logistics 4.0 Maturity Levels Assessed Based on GDM (Grey Decision Model) and Artificial Intelligence in Logistics 4.0 -Trends and Future Perspective, Procedia Manufacturing 39 1734–1742.
Proença, D, Borbinha, J. (2016). Maturity Models for Information Systems - A State of the Art, Procedia Computer Science 100, 1042 – 1049.
Phuong Vu, T, Grant, D.B, Menachof, D.A, (2021). Exploring logistics service quality in Hai Phong, Vietnam, The Asian Journal of Shipping and Logistics, 36, 54–64.
Ramos, L.F.P., Louresa E. F. R., Deschamps F. (2021). An Analysis of Maturity Models and Current State Assessment of Organizations for Industry 4.0 Implementation, Procedia Manufacturing 51, P.1098–1105.
Ranjbar, R, Mohammadi, A, Hamidi, N. (2018). Increasing the level of invincibility and reducing the cost of supply chain based on radio frequency identification technology, Journal of strategic management in industrial systems, 13 (46). P.14-29(in persian)
Rashidi torbati, SH. Rdfar, R, Pilevari, N. (2021). Supply Chain Intelligence with IoT Approach (Case study: Companies active in the field of information and communication technology in Tehran province), Journal of strategic management in industrial systems, 16 (58). P.14-29(in persian).
Reay, J. H., Colaianni, A. J., Harleston, E. F., Maletic, A., Marcus, J. G. (2006). Logistics maturity evaluator (Report No. IR509R1). LMI Research Institute. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a457193.pdf.
Richards, G. Grinsted, S. (2013). The Logistics and Supply Chain Toolkit: Over 90 Tools for Transport, Warehousing and Inventory Management, USA, Kogan Page Publishers.
Sakai, T, Beziat, A, Heitz, A, (2020). Location factors for logistics facilities: Location choice modeling considering activity categories. Journal of Transport Geography85:102710.
Sanae, Y., Faycal, F. Ahmed M., (2019). A Supply Chain Maturity Model for automotive SMEs: a case study, International Federation of Accountants. 52-13, P. 2044–2049.
Van der Laan, E.A. Brito, M.P. Vermaesen, S.C. (2007). Logistics Information and Knowledge Management Issues in Humanitarian Aid Organizations ERIM Report Series Reference No. ERS-2007-026-LIS, Available at SSRN: https://ssrn.com/abstract=985724
Villegas, M. A., Pedregal, D. J. (2019). Automatic Selection of Unobserved Components Models for Supply Chain Forecasting. International Journal of Forecasting 35 (1): 157–169.
Wen, J., Li, H. Zhu. F. (2018). Swarm Robotics Control and Communications: Imminent Challenges for Next Generation Smart Logistics. IEEE Communications Magazine 56 (7): 102–107.
Werner-Levandoska, K, Kosacka-Olejnik, M. (2018). Lgistics Maturity Model for Service Company- Theorical Background. Procedia Manufacturing 17, P. 791-802.
Werner-Lewandowska, M, Olejnik K, (2019), Logistics 4.0 Maturity in Service Industry: Empirical Research Results. Procedia Manufacturing 38, Pages 1058-1065.
Werner-Lewandowska, M, Olejnik K, (2020), How to improve logistics maturity? – A roadmap proposal for the service industry, Procedia Manufacturing 51, 1650–1656.
Williams, J. A. S. (2007). A Review of Research Towards Computer Integrated De-manufacturing for Materials Recovery. International Journal of Computer Integrated Manufacturing 20 (8): 773–780.
Willner, O, Gosling, J, Schönsleben, P. (2016). Establishing a maturity model for design automation in sales-delivery, processes of ETO products, Computers in Industry 82, 57–68.
Woschank M, Dallasega, P. (2021). The Impact of Logistics 4.0 on Performance in Manufacturing Companies: A Pilot Study, Procedia Manufacturing 55, 487–491.
Yadas, G., Luthra, S., Jakhar, S. K., Mangla, S. K., & Rai, D. P. (2020). A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case. Journal of Cleaner Production, 254-267.
Yavas, V. Ozkan-Ozenb, Y.D (2020), Logistics centers in the new industrial era: A proposed framework for logistics center 4.0, Transportation Research Part B. 101864, P 1-18.
Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Information systems research, 21(4), 724-735.
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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.
Becker, J., Knackstedt, R., P¨oppelbuß, J. (2009). Developing maturity models for IT management. Inf. Syst. Eng. 1: 213–222.
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.
Domingues, P. Sampaio, P. Arezes, P, M. (2016). Integrated management systems assessment: a maturity model proposal, Journal of Cleaner Production, 124 (2016) 164-174.
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.
Fawcett, S. E., and M. A. Waller. (2014). Supply Chain Game Changers-Mega, Nano, and Virtual Trends-And Forces that Impede Supply Chain Design (I.e., Building a Winning Team). Journal of Business Logistics, 35 (3): 157–164.
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869
Giusti, R., Manerba, D., Bruno, G., Tadei, R. (2019b). Synchro-modal logistics: an overview of critical success factors, enabling technologies, and open research issues. Transportation Research Part E: Logistics and Transportation Review, 129, 92–110.
Glistau E., Machado N. I. C. (2018). Logistics 4.0 and the Revalidation of Logistics Concepts and Strategies, Available at: htILs: //www.researchgate.net/publication/ 327417565, (Accessed 25 February 2019).
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.
Nitsche, B. (2021). Exploring the Potentials of Automation in Logistics and Supply Chain Management: Paving the Way for Autonomous Supply Chains, journal of Logistics,5(3), 51-63.
Oleśków-Szłapka, J. Wojciechowski, H., Domański, R. (2019). Logistics 4.0 Maturity Levels Assessed Based on GDM (Grey Decision Model) and Artificial Intelligence in Logistics 4.0 -Trends and Future Perspective, Procedia Manufacturing 39 1734–1742.
Proença, D, Borbinha, J. (2016). Maturity Models for Information Systems - A State of the Art, Procedia Computer Science 100, 1042 – 1049.
Phuong Vu, T, Grant, D.B, Menachof, D.A, (2021). Exploring logistics service quality in Hai Phong, Vietnam, The Asian Journal of Shipping and Logistics, 36, 54–64.
Ramos, L.F.P., Louresa E. F. R., Deschamps F. (2021). An Analysis of Maturity Models and Current State Assessment of Organizations for Industry 4.0 Implementation, Procedia Manufacturing 51, P.1098–1105.
Ranjbar, R, Mohammadi, A, Hamidi, N. (2018). Increasing the level of invincibility and reducing the cost of supply chain based on radio frequency identification technology, Journal of strategic management in industrial systems, 13 (46). P.14-29(in persian)
Rashidi torbati, SH. Rdfar, R, Pilevari, N. (2021). Supply Chain Intelligence with IoT Approach (Case study: Companies active in the field of information and communication technology in Tehran province), Journal of strategic management in industrial systems, 16 (58). P.14-29(in persian).
Reay, J. H., Colaianni, A. J., Harleston, E. F., Maletic, A., Marcus, J. G. (2006). Logistics maturity evaluator (Report No. IR509R1). LMI Research Institute. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a457193.pdf.
Richards, G. Grinsted, S. (2013). The Logistics and Supply Chain Toolkit: Over 90 Tools for Transport, Warehousing and Inventory Management, USA, Kogan Page Publishers.
Sakai, T, Beziat, A, Heitz, A, (2020). Location factors for logistics facilities: Location choice modeling considering activity categories. Journal of Transport Geography85:102710.
Sanae, Y., Faycal, F. Ahmed M., (2019). A Supply Chain Maturity Model for automotive SMEs: a case study, International Federation of Accountants. 52-13, P. 2044–2049.
Van der Laan, E.A. Brito, M.P. Vermaesen, S.C. (2007). Logistics Information and Knowledge Management Issues in Humanitarian Aid Organizations ERIM Report Series Reference No. ERS-2007-026-LIS, Available at SSRN: https://ssrn.com/abstract=985724
Villegas, M. A., Pedregal, D. J. (2019). Automatic Selection of Unobserved Components Models for Supply Chain Forecasting. International Journal of Forecasting 35 (1): 157–169.
Wen, J., Li, H. Zhu. F. (2018). Swarm Robotics Control and Communications: Imminent Challenges for Next Generation Smart Logistics. IEEE Communications Magazine 56 (7): 102–107.
Werner-Levandoska, K, Kosacka-Olejnik, M. (2018). Lgistics Maturity Model for Service Company- Theorical Background. Procedia Manufacturing 17, P. 791-802.
Werner-Lewandowska, M, Olejnik K, (2019), Logistics 4.0 Maturity in Service Industry: Empirical Research Results. Procedia Manufacturing 38, Pages 1058-1065.
Werner-Lewandowska, M, Olejnik K, (2020), How to improve logistics maturity? – A roadmap proposal for the service industry, Procedia Manufacturing 51, 1650–1656.
Williams, J. A. S. (2007). A Review of Research Towards Computer Integrated De-manufacturing for Materials Recovery. International Journal of Computer Integrated Manufacturing 20 (8): 773–780.
Willner, O, Gosling, J, Schönsleben, P. (2016). Establishing a maturity model for design automation in sales-delivery, processes of ETO products, Computers in Industry 82, 57–68.
Woschank M, Dallasega, P. (2021). The Impact of Logistics 4.0 on Performance in Manufacturing Companies: A Pilot Study, Procedia Manufacturing 55, 487–491.
Yadas, G., Luthra, S., Jakhar, S. K., Mangla, S. K., & Rai, D. P. (2020). A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case. Journal of Cleaner Production, 254-267.
Yavas, V. Ozkan-Ozenb, Y.D (2020), Logistics centers in the new industrial era: A proposed framework for logistics center 4.0, Transportation Research Part B. 101864, P 1-18.
Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Information systems research, 21(4), 724-735.
