ISBN: 978-83-8419-118-7 ISBN (online): 978-83-8419-119-4 ISSN: 0860-2751 OAI
DOI: 10.18276/978-83-8419-119-4
Issue archive / T. (MDVIII) 1434
| Year: | 2026 |
| Field: | Field of Social Sciences |
| Discipline: | management and quality sciences |
| Keywords: | supply chain management optimization computer simulation artificial intelligence large language models SCM 5.0 |
| Authors: |
Tomasz
Wiśniewski
Uniwersytet Szczeciński |
Information
Electronic version of the publication available under CC BY-SA 4.0 license after 12 months from the date of release: May 2026
Funded by the Ministry of Science within the framework of the Program "Regional Initiative of Excellence"
Printed version of the publication can be purchased in the store of the University of Szczecin Press: wn.usz.edu.pl/sklep/
Abstract
A hybrid approach to optimizing supply chain management using computer simulation and artificial intelligence
The monograph explores the integration of optimization methods, computer simulation, and artificial intelligence in supply chain management, aligning with the Supply Chain Management 5.0 paradigm. In the face of growing environmental complexity and uncertainty, advanced analytical technologies have become essential for building flexible, resilient, and efficient supply chains. The study identifies a research gap concerning the full integration of AI tools – particularly large language models – with simulation-based modeling in optimization processes, despite their widely recognized theoretical potential.
The monograph adopts a theoretical-empirical approach. The theoretical part systematizes the scientific literature on optimization methods, computer simulation, and artificial intelligence, and includes a bibliometric analysis revealing the limited presence of hybrid approaches in existing research. The empirical part comprises a survey of Polish managers and case studies demonstrating practical applications of AI–simulation integration in areas such as inventory management, production scheduling, and procurement logistics. The results indicate a low level of adoption of advanced tools among Polish enterprises, mainly due to knowledge gaps and competency barriers.
The proposed hybrid approach shows that combining computer simulation with large language models can significantly simplify modeling and optimization, making these techniques more accessible to practitioners. The monograph highlights the potential of such solutions to reduce the gap between theory and practice.
Bibliography
| 1. | Ahuja, R.K., Ergun, O., Orlin, J.B., Punnen, A.P. (2002). A survey of very large scale neighbourhood search techniques. Discrete Applied Mathematics, 123, 75–102. |
| 2. | Aamer, A., Eka Yani, L., Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1–13. |
| 3. | Abbasi, B., Babaei, T., Hosseinifard, Z., Smith-Miles, K., Dehghani, M. (2020). Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management. Computers and Operations Research, 119, 104941. |
| 4. | Agalianos, K., Ponis, S.T., Aretoulaki, E., Plakas, G., Efthymiou, O. (2020). Discrete event simulation and digital twins: Review and challenges for logistics. Procedia Manufacturing, 51, 1636–1641. |
| 5. | Ahi, P., Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. |
| 6. | Ahmadini, A.A.H., Modibbo, U.M., Shaikh, A.A., Ali, I. (2021). Multi-objective optimization modelling of sustainable green supply chain in inventory and production management. Alexandria Engineering Journal, 60(6), 5129–5146. |
| 7. | Akbari, M., Do, T.N.A. (2021). A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking, 28(10), 2977–3005. |
| 8. | Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., Edinbarough, I. (2022). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 5(1), 27. |
| 9. | Alawneh, A., Alrefaei, M., Diabat, A., Al-Aomar, R., Faisal, M.N. (2014). An LP model for optimizing a supply chain management system for steel company. W: Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2014, IMECS 2014, 12–14 March, 2014, Hong Kong (s. 1162–1164). Newswood Limited. |
| 10. | Albayrak Ünal, Ö., Erkayman, B., Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering, 30(4), 2605–2625. |
| 11. | Alejandro-Chable, J.D., Salais-Fierro, T.E., Saucedo-Martínez, J.A., Cedillo-Campos, M.G. (2024). A new lean logistics management model for the modern supply chain. Mobile Networks and Applications, 29(1), 70–81. |
| 12. | Ali, S.B. (2022). Industrial revolution 4.0 and supply chain digitization: Future of supply chain management. South Asian Journal of Social Review, 1(1), 21–41. |
| 13. | Alpaydin, E. (2021). Machine learning. MIT Press. |
| 14. | Alsadi, M., Arshad, J., Ali, J., Prince, A., Shishank, S. (2023). TruCert: Blockchain-based trustworthy product certification within autonomous automotive supply chains. Computers and Electrical Engineering, 109, 108738. |
| 15. | Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., Zhang, M. (2019). A survey on evolutionary machine learning. Journal of the Royal Society of New Zealand, 49(2), 205–228. |
| 16. | Amini, M., Wakolbinger, T., Racer, M., Nejad, M.G. (2012). Alternative supply chain production- sales policies for new product diffusion: An agent-based modeling and simulation approach. European Journal of Operational Research, 216(2), 301–311. |
| 17. | Ante, L. (2021). Digital twin technology for smart manufacturing and industry 4.0: A bibliometric analysis of the intellectual structure of the research discourse. Manufacturing Letters, 27, 96–102. |
| 18. | AnyLogic. https://www.anylogic.com (dostęp 15.01.2025). |
| 19. | Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J. (2006). The traveling salesman problem: A computational study. Princeton University Press. |
| 20. | ARENA. https://www.rockwellautomation.com/es-mx/products/software/arena-simulation. html (dostęp 18.01.2025). |
| 21. | Aria, M., Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007. |
| 22. | Asha, L.N., Dey, A., Yodo, N., Aragon, L.G. (2022). Optimization approaches for multiple conflicting objectives in sustainable green supply chain management. Sustainability, 14(19), 12790. |
| 23. | Babbie, E. (2008). Podstawy badań społecznych. Wydawnictwo Naukowe PWN. |
| 24. | Babulak, E., Wang, M. (2010). Discrete event simulation. W: A. Goti (red.), Discrete event simulations (s. 1–24). Sciyo. |
| 25. | Bai, G., Chai, Z., Ling, C., Wang, S., Lu, J., Zhang, N., Shi, T., Yu, Z., Zhu, M., Zhang, Y. i in. (2024). Beyond efficiency: A systematic survey of resource-efficient large language models. https://arxiv.org/abs/2401.00625. |
| 26. | Baker, K.R. (2007). Introduction to sequencing and scheduling. Wiley. |
| 27. | Bal, M., Pawlicka, K. (2021). Supply chain finance and challenges of modern supply chains. LogForum, 17(1), 71–82. |
| 28. | Ballou, R.H. (2004). Business logistics/supply chain management (wyd. 5). Pearson Prentice Hall. |
| 29. | Banaszyk, P., Kauf, S., Szołtysek, J. (2021). Logistyka jako czynnik dobrostanu. Polskie Wydawnictwo Ekonomiczne. |
| 30. | Bandyopadhyay, S., Bhattacharya, R. (2014). Discrete and continuous simulation: Theory and practice. CRC Press. |
| 31. | Banks, J., Carson, J.S., Nelson, B.L. (2005). Discrete-event system simulation (wyd. 4). Pearson Prentice Hall. |
| 32. | Barata, J., Cardoso, J.C., Cunha, P.R. (2023). Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering. Systems Engineering, 26(6), 715–727. |
| 33. | Barcik, R., Odlanicka-Poczobutt, M. (2020). Logistyka 4.0 – wybrane zastosowania. Wydawnictwo Dom Organizatora. |
| 34. | Baryannis, G., Validi, S., Dani, S., Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57, 2179–2202. |
| 35. | Batterton, K.A., Hale, K.N. (2017). The Likert scale: What it is and how to use it. Phalanx, 50(2), 32–39. |
| 36. | Bauer, V.I., Bazanov, A.V., Kozin, E.S., Nemkov, V.M., Mukhortov, A.A. (2019). Optimization of technological transport sets using AnyLogic simulation environment. Journal of Mechanical Engineering Research and Developments, 42(2), 41–43. |
| 37. | Bedyńska, S., Cypryańska, M. (2013). Statystyczny drogowskaz 1. Wydawnictwo Akademickie SEDNO. |
| 38. | Bell, J.E., McMullen, P.R. (2004). Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics, 18(1), 41–48. |
| 39. | Bertsimas, D., Tsitsiklis, J.N. (1993). Simulated annealing. Statistical Science, 8(1), 10–15. |
| 40. | Bérubé, M., Giannelia, T., Vial, G. (2021). Barriers to the implementation of AI in organizations: Findings from a Delphi study. W: Proceedings of the 54th Hawaii International Conference on System Sciences (s. 6702–6711). https://doi.org/10.24251/HICSS.2021.805. |
| 41. | Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7, 58–66. https://doi.org/10.47672/ejt.1473. |
| 42. | Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8, 239–287. |
| 43. | Bielecki, M. (2022). Total Logistic Management. Logistyka i łańcuchy dostaw 4.0. Wydawnictwo Uniwersytetu Łódzkiego. |
| 44. | Bier, T., Lange, A., Glock, C.H. (2020). Methods for mitigating disruptions in complex supply chain structures: A systematic literature review. International Journal of Production Research, 58(6), 1835–1856. |
| 45. | Birta, L.G., Arbez, G. (2013). Modelling and simulation. Springer. |
| 46. | Biswas, S., Narahari, Y. (2004). Object oriented modeling and decision support for supply chains. European Journal of Operational Research, 153(3), 704–726. |
| 47. | Bixby, R.E. (2002). The solution of mixed integer programming problems. Operations Research, 50(3), 308–326. |
| 48. | Blum, C., Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308. |
| 49. | Bolatan, G.I.S. (2021). From Logistics 4.0 to Logistics 5.0 logistics for digital society. Academic Studies in Humanities and Social Sciences, 191. |
| 50. | Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl_3), 7280–7287. |
| 51. | Bonabeau, E., Dorigo, M., Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Oxford University Press. |
| 52. | Bossel, H. (2018). Modeling and simulation. AK Peters/CRC Press. |
| 53. | Boudouaia, M.A., Ouchani, S., Qaisar, S.M., Almaktoom, A.T. (2024). Supply Chain 5.0: Vision, challenges, and perspectives. W: 2024 21st Learning and Technology Conference (L&T) (s. 203–208). IEEE. |
| 54. | Bowersox, D.J., Closs, D.J., Cooper, M.B., Bowersox, J.C. (2020). Supply chain logistics management. McGraw-Hill. |
| 55. | Bozarth, C.B., Handfield, R.B. (2017). Wprowadzenie do zarządzania operacjami i łańcuchem dostaw. Helion. |
| 56. | Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D. (2020). Language models are few-shot learners. https://arxiv.org/abs/2005.14165. |
| 57. | Buchari, E., Sattar, E., Sumantri, I., Novelo, R. (2019, October). Developing information system to improve hinterland productivity and rubber multimodal transportation in South Sumatera Indonesia. W: IOP Conference Series: Materials Science and Engineering (Vol. 615, No. 1, 012127). IOP Publishing. DOI: 10.1088/1757-899X/615/1/012127. |
| 58. | Bukłaha E. (2012). Sukces, skuteczność i efektywność w zarządzaniu projektami, Studia i prace Kolegium Zarządzania i Finansów. Zeszyt naukowy, 113, 24–35. |
| 59. | Campos, T., Carvalho, M.S., Oliveira, J.A., Silva, P.V., Machado, T. (2017). Using discrete simulation to support internal logistics process design. W: 47th International Conference on Computers & Industrial Engineering 2017 (CIE47): How digital platforms and industrial engineering are transforming industry and services, 11–13 October 2017, Lisbon, Portugal (s. 1–9). CIE, Curran Associates. |
| 60. | Campos Zabala, F.J. (2023). The barriers for implementing AI. W: Grow your business with AI: A first principles approach for scaling artificial intelligence in the enterprise (s. 85– 110). Apress. |
| 61. | Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A., Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi. org/10.1016/j.ijinfomgt.2019.03.004. |
| 62. | Cerquides, J., Farinelli, A., Meseguer, P., Ramchurn, S.D. (2014). A tutorial on optimization for multi-agent systems. The Computer Journal, 57(6), 799–824. |
| 63. | Ceylan, Z., Tozan, H., Bulkan, S. (2021). A coordinated scheduling problem for the supply chain in a flexible job shop machine environment. Operational Research, 21, 875–900. |
| 64. | Chaciński Ł. (2025), Wykorzystanie sztucznej inteligencji przez przedsiębiorstwa w gospodarce cyfrowej. W: M. Kapela, A. Stępniak-Kucharska (red.), Współczesne problemy gospodarcze – Wyzwania gospodarki cyfrowej, Kolegium Nauk Ekonomicznych i Społecznych, Politechnika Warszawska, Płock, s. 115–125. |
| 65. | Chatterjee, S., Chaudhuri, R. (2022). Supply chain sustainability during turbulent environment: Examining the role of firm capabilities and government regulation. Operations Management Research, 15(3), 1081–1095. |
| 66. | Chen, H., Chen, Z., Lin, F., Zhuang, P. (2021). Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access, 9, 36008– 36018. |
| 67. | Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.D.O., Kaplan, J., (…), Zaremba, W. (2021). Evaluating large language models trained on code. https://arxiv.org/abs/2107.03374. |
| 68. | Cheng, C.Y., Chen, T.L., Chen, Y.Y. (2014). An analysis of the structural complexity of supply chain networks. Applied Mathematical Modelling, 38(9–10), 2328–2344. |
| 69. | Chomiak-Orsa, I., Hauke, K., Perechuda, K., Pondel, M. (2023a). The use of Digital Twin in the sustainable development of the city on the example of managing parking resources. Procedia Computer Science, 225, 2183–2193. |
| 70. | Chomiak-Orsa, I., Greńczuk, A., Łuczak, K., Piwoni-Krzeszowska, E. (2023b). AI in accelerating the creation of renewable energy sources. Bibliometric analysis. W: European Conference on Artificial Intelligence (s. 155–162). Springer Nature Switzerland. |
| 71. | Chong, E.K.P., Żak, S.H. (2022). An introduction to optimization (wyd. 5). Wiley. |
| 72. | Chopra, S., Meindl, P. (2016). Supply chain management: Strategy, planning, and operation (wyd. 6). Pearson Education. |
| 73. | Christopher, M. (2016). Logistics & supply chain management (wyd. 5). Pearson Education. |
| 74. | Christos, K., Dimitrios, B., Dimitrios, A. (2016). Agent-based simulation for modeling supply chains: A comparative case study. International Journal of New Technology and Research, 2, 263416. |
| 75. | Chwiłkowska-Kubala, A., Cyfert, S., Malewska, K., Mierzejewska, K., Szumowski, W. (2023). The impact of resources on digital transformation in energy sector companies. The role of readiness for digital transformation. Technology in Society, 74, 102315. |
| 76. | Chybalski, F. (2017). O uogólnianiu wyników analiz ilościowych w naukach o zarządzaniu. Zeszyty Naukowe. Organizacja i Zarządzanie, 67(1214), 5–18. |
| 77. | Cigolini, R., Pero, M., Rossi, T., Sianesi, A. (2014). Linking supply chain configuration to supply chain performance: A discrete event simulation model. Simulation Modelling Practice and Theory, 40, 1–11. |
| 78. | Coolican, H. (2017). Research methods and statistics in psychology. Psychology Press. |
| 79. | Copacino, W.C. (2019). Supply chain management: The basics and beyond. Routledge. |
| 80. | Cordeau, J.F., Gendreau, M., Laporte, G. (2001). A tabu search heuristic for the vehicle routing problem with multiple depots. Networks, 38(4), 193–208. |
| 81. | Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. |
| 82. | Cyfert, S., Dyduch, W., Latusek-Jurczak, D., Niemczyk, J., Sopińska, A. (2014). Subdyscypliny w naukach o zarządzaniu – logika wyodrębnienia, identyfikacja modelu koncepcyjnego oraz zawartość tematyczna. Organizacja i Kierowanie, 161, 37–49. |
| 83. | Cyplik, P. (2003). Przegląd metod sterowania zapasami. Logistyka, 1, 23–27. |
| 84. | Cyplik, P. (2005). Zastosowanie klasycznych metod zarządzania zapasami do optymalizacji zapasów magazynowych – case study. LogForum, 1(3), 4. |
| 85. | Czakon, W. (2005). Procesowe podejście do badania efektywności. Wydawnictwo Akademii Ekonomicznej w Katowicach. |
| 86. | D’Angelo, G., Palmieri, F. (2021). GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Information Sciences, 547, 136–162. |
| 87. | Dantzig, G.B. (1963). Linear programming and extensions. Princeton University Press. |
| 88. | Dasgupta, D., Michalewicz, Z. (red.) (2013). Evolutionary algorithms in engineering applications. Springer Science & Business Media. |
| 89. | De la Torre, R., Corlu, C.G., Faulin, J., Onggo, B.S., Juan, A.A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability (Switzerland), 13(3), 1–21. |
| 90. | Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley. |
| 91. | Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. |
| 92. | Debta, M.K., Mishra, S.S., Mishra, D. (2017). Simulation of a real life problem in flexible manufacturing system without breakdown and having alternate machines using SIMUL8. International Research Journal of Engineering and Technology, 4, 1267–1274. |
| 93. | Dehghanimohammadabadi, M., Keyser, T.K. (2017). Intelligent simulation: Integration of SIMIO and MATLAB to deploy decision support systems to simulation environment. Simulation Modelling Practice and Theory, 71, 45–60. |
| 94. | Demizu, T., Fukazawa, Y., Morita, H. (2023). Inventory management of new products in retailers using model-based deep reinforcement learning. Expert Systems with Applications, 229, 120256. |
| 95. | Dendera-Gruszka, M., Kuklińska, E., Masłowski, D. (2017). Efektywność łańcucha dostaw. Gospodarka Materiałowa i Logistyka, 11, 12–25. |
| 96. | Dev, N.K., Shankar, R., Qaiser, F.H. (2020). Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, 104583. |
| 97. | Dhamija, P., Bag, S. (2020). Role of artificial intelligence in operations environment: A review and bibliometric analysis. TQM Journal, 32(4), 869–896. |
| 98. | Dhanya, D., Satheesh Kumar, S., Thilagavathy, A., Prasad, D.V.S.S.S.V., Boopathi, S. (2023). Data analytics and artificial intelligence in the circular economy: Case studies. W: Intelligent Engineering Applications and Applied Sciences for Sustainability (s. 40–58). IGI Global. |
| 99. | Ding, H., Benyoucef, L., Xie, X. (2005). A simulation optimization methodology for supplier selection problem. International Journal of Computer Integrated Manufacturing, 18(2–3), 210–224. |
| 100. | Ding, H., Benyoucef, L., Xie, X., Hans, C., Schumacher, J. (2004). “One” a new tool for supply chain network optimization and simulation. W: Proceedings of the 2004 Winter Simulation Conference (s. 1404–1411). IEEE. |
| 101. | Diwekar, U.M. (2020). Introduction to applied optimization. Springer Nature. |
| 102. | Dorigatti, M., Guarnaschelli, A., Chiotti, O., Salomone, H.E. (2016). A service-oriented framework for agent-based simulations of collaborative supply chains. Computers in Industry, 83, 92–107. |
| 103. | Dorigo, M., Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344, 243–278. |
| 104. | Dorigo, M., Stützle, T. (2004). Ant colony optimization. MIT Press. |
| 105. | Dorigo, M., Birattari, M., Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. |
| 106. | Drożdż, W., Redżeb, K. (2024). Possibilities of Using AI in DSO Sector. W: European Conference on Artificial Intelligence (s. 52–68). Springer Nature Switzerland. |
| 107. | Duarte, S., Cruz-Machado, V. (2018). Exploring linkages between lean and green supply chain and the Industry 4.0. W: Proceedings of the Eleventh International Conference on Management Science and Engineering Management (s. 1–12). Springer. |
| 108. | Durán, J.M. (2018). Computer simulations in science and engineering. Springer. |
| 109. | Dyczkowska, J. (2012). Logistyka zaopatrzenia i produkcji – wpływ na logistykę dystrybucji. Prace Naukowe Politechniki Warszawskiej, 84, 19–28. |
| 110. | Dyduch, W. (2012). Współczesne dylematy zarządzania pomiarem efektywności organizacyjnej. W: T. Dudycz, G. Osbert-Pociecha, B. Brycz (red.), Efektywność – konceptualizacja i uwarunkowania (s. 86–95). Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu. |
| 111. | Ewees, A.A., Elaziz, M.A., Houssein, E.H. (2018). Improved grasshopper optimization algorithm using opposition-based learning. Expert Systems with Applications, 112, 156–172. ExtendSim. https://extendsim.com/ (dostęp 15.01.2025). |
| 112. | Faramarzi-Oghani, S., Dolati Neghabadi, P., Talbi, E.G., Tavakkoli-Moghaddam, R. (2023). Meta-heuristics for sustainable supply chain management: A review. International Journal of Production Research, 61(6), 1979–2009. |
| 113. | Fechner, I. (2007). Zarządzanie łańcuchem dostaw. Wyższa Szkoła Logistyki. |
| 114. | Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., Miranda, S. (2017). The role of uncertainty in supply chains under dynamic modeling. International Journal of Industrial Engineering Computations, 8(1), 119–140. |
| 115. | Fiedler, A., Sackmann, D., Haasis, H.D. (2020). Multi-agent systems in supply chain management. Industrie 4.0 Management, 36(5), 43–47. |
| 116. | Field, A. (2013). Discovering statistics using IBM SPSS statistics (wyd. 4). Sage. |
| 117. | Fisher, M.L. (1997). What is the right supply chain for your product? Harvard Business Review, 75(2), 105–116. |
| 118. | FlexSim. https://www.flexsim.com/ (dostęp 15.01.2025). |
| 119. | Floridi, L. (2016). Faultless responsibility: On the nature and allocation of moral responsibility for distributed moral actions. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160112. |