Inteligentna logistyka. Nowoczesne technologie i innowacyjne strategie

Spis treści:

Wstęp
Wprowadzenie
A. Wprowadzenie do inteligentnej logistyki
B. Znaczenie technologii w zarządzaniu łańcuchem dostaw
C. Kluczowe wyzwania i kierunki rozwoju

Część I. PODSTAWY INTELIGENTNEJ LOGISTYKI

1. Definicja i kluczowe koncepcje
1.1. Co to jest inteligentna logistyka?
1.2. Różnice między tradycyjną a inteligentną logistyką
1.3. Modele zarządzania nowoczesnym łańcuchem dostaw
2. Technologie napędzające inteligentną logistykę
2.1. Sztuczna inteligencja (AI) w optymalizacji procesów
2.2. Internet rzeczy (IoT) i monitorowanie ładunków
2.3. Blockchain – przejrzystość i bezpieczeństwo danych
2.4. Big data i analityka predykcyjna w logistyce
3. Automatyzacja i robotyzacja
3.1. Autonomiczne pojazdy i drony w logistyce
3.2. Roboty magazynowe i systemy automatycznego składowania (AS/RS)
3.3. Inteligentne systemy sortowania i kompletacji zamówień
3.4. Cyfrowi asystenci i chatboty w obsłudze klienta

Część II. INTELIGENTNE ZARZĄDZANIE ŁAŃCUCHEM DOSTAW

4. Inteligentne systemy zarządzania magazynem (WMS)
4.1. Nowoczesne systemy magazynowe
4.2. Optymalizacja zapasów i dynamiczne zarządzanie stanami magazynowymi
4.3.  Integracja IoT i sztucznej inteligencji w zarządzaniu magazynem
5. Inteligentny transport i logistyka miejska
5.1. Optymalizacja tras dostaw przy użyciu AI 
5.2. Ekologiczne i autonomiczne pojazdy w transporcie
5.3. Mikrologistyka i dostawy ostatniej mili
5.4. Inteligentne zarządzanie flotą
6. Integracja i cyfrowa transformacja łańcucha dostaw
6.1. Przemysł 4.0 a logistyka 
6.2. Cyfrowe bliźniaki (digital twin) w zarządzaniu logistyką
6.3. Chmura obliczeniowa i systemy ERP w łańcuchu dostaw

Część III. WYZWANIA I PRZYSZŁOŚĆ INTELIGENTNEJ LOGISTYKI

7. Cyberbezpieczeństwo i ryzyko w inteligentnej logistyce
7.1. Zagrożenia cybernetyczne w logistyce
7.2. Ochrona danych i transakcji w ekosystemie logistycznym
7.3. Strategie zarządzania ryzykiem
8. Zrównoważona inteligentna logistyka
8.1. Green Logistics – technologie zmniejszające ślad węglowy
8.2. Inteligentne opakowania i recykling w logistyce
8.3. Ekologiczne strategie transportowe
9. Przyszłość inteligentnej logistyki
9.1. Logistyka 5.0 – nowa era automatyzacji
9.2. Wpływ sztucznej inteligencji i robotyki na rynek pracy
9.3. Nowe modele biznesowe i trendy w inteligentnej logistyce

Zakończenie
Bibliografia

[1] Akdemir,  D.M.,  Bulut,  Z.A.  (2024).  Business  and  Customer-Based  Chatbot  Activities:  
The Role of Customer Satisfaction in Online Purchase Intention and Intention to Reuse 
Chatbots.  Journal  of  Theoretical  and  Applied  Electronic  Commerce  Research,  19(4), 
2961–2979.
[2] Ali,  A.A.A.,  Fayad,  A.A.S.,  Alomair,  A.,  Al  Naim,  A.S.  (2024).  The  Role  of  Digital  
Supply Chain on Inventory Management Effectiveness within Engineering Companies in 
Jordan. Sustainability, 16(18), 8031.
[3] Annarita D.M., Gianpaolo G., Demetrio L., Emanuele M. (2024). Sustainable last-mile 
distribution with autonomous delivery robots and public transportation. Transportation 
Research Part C: Emerging Technologies, Volume 163.
[4] Aron, C., Sgarbossa, F. (2023). The Physical Internet as an Approach for Resilient Logistics 
Practices: Literature Review and Future Research Avenues, IFAC-PapersOnLine, Volume 
56, Issue 2.
[5] Arpita  A.,  Saurabh  B.  (2024).  Chatbots’  effectiveness  in  service  recovery .  International 
Journal of Information Management, Volume 76.
[6] Association  of  American  Railroads.  (2020).  The  Positive  Environmental  Effects  of  
Increased Freight by Rail Movements in America.
[7] Ballot, E., Montreuil, B., Zacharia, Z.G. (2021). Physical Internet: First results and next 
challenges. Journal of Business Logistics, 42(1), 101-107.
[8] Banaszyk, P., Kauf, S., Szołtysek, J. (2021). Logistyka jako czynnik dobrostanu nowej gene-
racji. Gospodarka Materiałowa i Logistyka. 09. 12-18.
[9] Barzizza,  E.,  Biasetton,  N.,  Ceccato,  R.,  Salmaso,  L.  (2023).  Big  Data  Analytics  and  
Machine Learning in Supply Chain 4.0: A Literature Review. Stats, 6(2), 596-616.
[10]  Bnouachir, H., Meriyem, C., Zegrari, M., Aziza, C., Deshayes, L., Medromi, H. (2022). 
Smart Fleet Management System Based on Multi-agent Systems: Mining Context.
[11]  Bo, F., Qiwen, Y. (2021). Operations management of smart logistics: A literature review 
and future research, Front. Eng. Manag, 8(3): 344-355.
[12]  Bortolini, M., Calabrese, F., Galizia, F.G. (2022). Crowd Logistics: A Survey of Success-
ful Applications and Implementation Potential in Northern Italy. Sustainability, 14(24), 
16881.230 Bibliografia
[13]  Büyüközkan, G., Ilıcak, Ö.. (2021). Smart urban logistics: Literature review and future 
directions. Socio-Economic Planning Sciences. 81.
[14]  Ceccato, R., Gastaldi, M.. (2023). Last mile distribution using cargo bikes: a simulation 
study in Padova, European Transport.
[15]  Chen, W., Men, Y., Fuster, N., Osorio, C., Juan, A.A. (2024). Artificial Intelligence in 
Logistics Optimization with Sustainable Criteria: A Review. Sustainability, 16(21), 9145.
[16]  Chen, Y., Wang, H., Liu, J. (2023). Automation in Logistics: A Review of Current Technol-
ogies and Future Trends. Journal of Logistics Research, 15(2), 45-60. 
[17]  Cheung,  K.-F.,  Bell,  M.,  Bhattacharjya,  J.  (2021).  Cybersecurity  in  logistics  and  supply 
chain management: An overview and future research directions. Transportation Research 
Part E Logistics and Transportation Review. 146.
[18]  Chin-Lung H., Judy Chuan-Chuan L.. (2023). Understanding the user satisfaction and 
loyalty of customer service chatbots. Journal of Retailing and Consumer Services, Volume 
71, 2023.
[19]  Coelho, P., Corona, B., ten Klooster, R., Worrell, E. (2020). Sustainability of reusable pack-
aging – Current situation and trends. Resources, Conservation & Recycling: X. 6.
[20]  Cramer,  F.  Fikar,  C.  (2025).  Modeling  sustainable  crowd  logistics  delivery  networks:  
A scoping systems thinking review, Sustainability Analytics and Modeling, Volume 5.
[21]  Digital Twin in Logistics Market Size – By Component (Software, Services), Deployment 
Model (Cloud-based, On-premises), Application (Route Optimization, Warehouse and 
Inventory Management, Predictive Maintenance, Asset Tracking), End User & Forecast, 
2024–2032, 2024.
[22]  El Hamdi, S., Abouabdellah, A. (2022). Logistics: Impact of Industry 4.0. Applied Scienc-
es, 12(9), 4209.
[23]  Fatorachian, H. (2025). Enhancing Smart City Logistics Through IoT-Enabled Predictive 
Analytics: A Digital Twin and Cybernetic Feedback Approach. Smart Cities, 8(2), 56 .
[24]  Feng, B., Ye, Q. (2021). Operations management of smart logistics: A literature review and 
future research. Frontiers of Engineering Management, 8(3), 344-355.
[25]  Ghazvinian, A., Feng, B., Feng, J., Talebzadeh, H., Dzikuć, M. (2024). Lean, agile, re-
silient, green, and sustainable (LARGS) supplier selection using multi-criteria structural 
equation modeling under fuzzy environments. Sustainability, 16(4), 1594.
[26]  Global  Logistics-as-a-Service  Markets,  2020-2024  &  2025-2030:  Expand  Geograph-
ical Presence & Diversifying Service Offerings Recommended by Leading Analysts –  
ResearchAndMarkets.com, 2024.
[27]  GSC ARR (2024). AI-driven warehouse automation: A comprehensive review of systems. 
GSC Advanced Research and Reviews, 18(2), 272-282.
[28]  Gunes, S. (2021). Micro-consolidation practices in urban delivery systems: Compara-
tive evaluation of last mile deliveries using e-cargo bikes and microhubs. University of  
Washington.
[29]  Herburger, M., Wieland, A., Hochstrasser, C. (2024). Building Supply Chain Resilience to 
Cyber Risks: A Dynamic Capabilities Perspective. Supply Chain Management: An Inter-
national Journal, 29(7), 28-50.Bibliografia 231
[30]  Hofmann, F.. (2020). Order picking optimisation on a unidirectional cyclical picking line, 
Stellenbosch University.
[31]  Hoffmann, T., Prause, G. (2018). On the Regulatory Framework for Last-Mile Delivery 
Robots. Machines, 6(3), 33.
[32]  Holubčík, M., Koman, G., Soviar, J.. (2021). Industry 4.0 in Logistics Operations, Trans-
portation Research Procedia, Volume 53.
[33]  International  Transport  Forum,  Managing  the  Transition  to  Driverless  Road  Freight 
Transport.
[34]  Ivanov,  D.,  Dolgui,  A.  (2020).  Viability  of  intertwined  supply  networks:  Extending  the 
supply chain resilience angles towards survivability. International Journal of Production 
Research, 58(10), 2904-2915.
[35]  Jacobs, F. (2020). Order picking accounts for 50 proc. to 65 proc. of total DC operating 
expenses. International Journal of Logistics Management. (znaczenie kosztowe procesu 
kompletacji zamówień).
[36]  Jahangir, M.F., Schultz, C.P.L., Kamari, A. (2024). A review of drivers and barriers of Digi-
tal Twin adoption in building project development processes. Journal of Information Tech-
nology in Construction, 29, 141–178.
[37]  Jamarani,  A.,  Haddadi,  S.,  Sarvizadeh,  R.  (2024).  Big  data  and  predictive  analytics:  
A systematic review of applications. Artif Intell Rev 57, 176.
[38]  Kalkha,  H.,  Khiat,  A.,  Bahnasse,  A.,  Hassan,  O.  (2023).  The  Rising  Trends  of  Smart 
E-Commerce Logistics. IEEE Access. PP. 1-1. 10.1109/ACCESS.2023.3252566.
[39]  Karaoulanis, A. (2024). The Role of Micro Fulfilment Centers in Alleviating, in a Sustain -
able Way, the Urban Last Mile Logistics Problem: A Systematic Literature Review. Sustain-
ability, 16(20), 8774.
[40]  Kapoor,  G.,  Sang  Lee,  Y.,  Sikora,  R.,  Piramuthu,  S..  (2024).  Drone-based  warehouse 
inventory management of perishables, International Journal of Production Economics, 
Volume 278.
[41]  Kayhan, H. (2022). Ensuring Trust in Pharmaceutical Supply Chains by Data Protection by 
Design Approach to Blockchains. Blockchain in healthcare today, 5.
[42]  Khan, N., Solvang, W.D., Yu, H., Rolland, B.E. (2024). Towards the design of a smart 
warehouse management system for spare parts management in the oil and gas sector, Fron-
tiers in Sustainability, Vol. 5.
[43]  Khan,  Z.,  Ahmed,  M.B.  (2025).  Advancements  in  Automated  Storage  and  Retrieval  
Systems: A Comprehensive Review. Robotics & Automation Engineering Journal, 6(2), 
001–010.
[44]  Konecka,  S.,  Bentyn,  Z.  (2024).  Cyberattacks  as  Threats  in  Supply  Chains,  European  
Research Studies Journal Volume XXVII Issue 3, 778–796.
[45]  Kong, J., Xie, M., Wang, H. (2025). Integrating Autonomous Vehicles and Drones for Last-
Mile Delivery: A Routing Problem with Two Types of Drones and Multiple Visits. Drones,  
9(4), 280.
[46]  Koreis, J. (2025). Human-robot vs. human-manual teams: Understanding the dynamics 
of  experience  and  performance  variability  in  picker-to-parts  order  picking,  Computers  
& Industrial Engineering, Vol. 200.232 Bibliografia
[47]  Lee, I., Mangalaraj, G. (2022). Big Data Analytics in Supply Chain Management: A Sys-
tematic  Literature  Review  and  Research  Directions.  Big  Data  and  Cognitive  Comput-
ing, 6(1), 17.
[48]  Lemardelé, C., Estrada, M., Pagès, L., Bachofner, M. (2021). Potentialities of drones and 
ground autonomous delivery devices for last-mile logistics. Transportation Research Part 
E: Logistics and Transportation Review, 149, 102325.
[49]  Li, X., Zhang, Y., Zhao, L. (2022). Blockchain Applications in Supply Chain Management: 
A Review. International Journal of Supply Chain Management, 11(3), 12-25. 
[50]  London Freight Consolidation Study – Final Report, 2019.
[51]  Loop. (2021). Case study: Loop – Packaging reuse model. Circular X.  
[52]  Lukyanova, I., Haddud, A., Khare, A. (2022). Types of ERP Systems and Their Impacts on 
the Supply Chains in the Humanitarian and Private Sectors. Sustainability, 14(20), 13054.
[53]  Machado, N., Taboada Rodriguez, C.M. (2025). A Logistics 5.0 maturity model: a hu-
man-centric and sustainable approach for the supply chain of the future. ITEGAM-JE-
TIA, 11(51), 164-170.
[54]  Mekala, V., Abinaya, S., Abinivesh, M.R., Surya, B. (2023). Cargo Monitoring and Track-
ing Based on IoT. 1-5.
[55]  Mittal, M. (2024). Colonial Pipeline Cyberattack Drives Urgent Reforms in Cybersecurity 
and Critical Infrastructure Resilience. International Journal of Oil, Gas and Coal Engi-
neering, 12(5), 106-119.
[56]  Moghadam, A., Salhi, A., Yang, X., Nguyen, T., Pourhejazy, P. (2025). A multi-objective 
approach for the integrated planning of drone and robot assisted truck operations in last-
mile delivery. Expert Systems with Applications. 269. 126434.
[57]  Nafi, Z., Benmoussa, R., Elharouni, F. (2025). Digital Twin for Supply Chain Improve-
ment: A Case Study of a Building Company. In: Benmoussa, R., Benazzouz, T., Dahbi, S. 
(eds) Industrial and Logistics Systems Design and Efficient Operation. SIL 2024. Lecture 
Notes in Networks and Systems, vol 1332. Springer, Cham.
[58]  Ocean Spray & Environmental Defense Fund (EDF). (2010). Case Studies in Carbon- 
Efficient Logistics   . MIT Center for Transportation & Logistics.
[59]  Ojo, T.P. (2025). AI-driven cyber threat detection for global logistics in United States. Inter-
national Journal of Science and Research Archive. 14. 1360–1367.
[60]  Oladele, O.K. (2024). Internet of Things (IoT) in Logistics: Transforming Supply Chains 
with Connected Devices and Data Analytics. 
[61]  Oluwademilade Sodiya, E., Joseph Umoga, U., Oladipupo Amoo, O., Atadoga, A. (2024). 
AI-driven  warehouse  automation:  A  comprehensive  review  of  systems,  GSC  Advanced  
Research and Reviews, 18(02), 272–282.
[62]  Ouahbi, Y., Lagmiri, N., Ziti, S.. (2025). Advancing Supply Chain Management Through 
Artificial Intelligence: A Systematic Literature Review . International Journal of Electrical 
Engineering and Computer Science. 38, 321–332.
[63]  Ouajji, H., (2023). The Rising Trends of Smart E-Commerce Logistics. Proceedings of IEEE 
ICDT.Bibliografia 233
[64]  Ouyang, Q., Zheng, J., Wang, S. (2019). Investigation of the construction of intelligent lo-
gistics system from traditional logistics model based on wireless network technology. EUR-
ASIP Journal on Wireless Communications and Networking. 2019.
[65]  Patdono. S., Lisda, P., Ahmed R.B., Ahmad, R.. (2023) Predictive Analytics to Improve In-
ventory Performance: A Case Study of an FMCG Company, Operations and Supply Chain 
Management, Vol. 16, No. 2, pp. 293–310.
[66]  Rad, F.F., Oghazi, P., Onur, İ. (2025). Adoption of AI-based order picking in warehouse: 
benefits, challenges, and critical success factors. Rev Manag Sci.
[67]  Ramos De Santis, P. (2024). Customer satisfaction in logistics: an analysis of chatbots in 
the leading companies of Colombia, Peru, and Ecuador. Retos Revista de Ciencias de la 
Administración y Economía, 14(27), 111-125.
[68]  Rojek,  I.,  Jasiulewicz-Kaczmarek,  M.,  Piszcz,  A.,  Galas,  K.,  Mikołajewski,  D.  (2024).  
Review  of  the  6G-Based  Supply  Chain  Management  within  Industry  4.0/5.0  Para-
digm. Electronics, 13(13), 2624.
[69]  Roman, E.-A., Stere, A.-S., Roșca, E., Radu, A.-V., Codroiu, D., Anamaria, I. (2025). State 
of the Art of Digital Twins in Improving Supply Chain Resilience. Logistics, 9(1), 22.
[70]  Roshid, M.M., Waaje, A., Meem, T.N., & Sarkar, A. (2024). Logistics 4.0: A comprehensive 
literature review of technological integration, challenges, and future prospects of implemen-
tation of Industry 4.0 technologies. International Journal of Technology, Knowledge and 
Society, 20(1), 65-85.
[71]  Savushkin,  N.  (2024).  Warehouse  automation  in  logistics:  Case  study  of  Amazon  and  
Ocado, Metropolia University of Applied Sciences.
[72]  Scrioșteanu, A., Criveanu, M.M. (2023). Reverse Logistics of Packaging Waste under the 
Conditions of a Sustainable Circular Economy at the Level of the European Union States. 
Sustainability, 15(20), 14727  .
[73]  Sergi, I., Montanaro, T., Benvenuto, F.L., Patrono L. (2021). A Smart and Secure Logistics 
System Based on IoT and Cloud Technologies. Sensors (Basel). 2021 Mar 23;21(6):2231.
[74]  Shuaibu, A.S., Mahmoud, A.S., Sheltami, T.R. (2025). A Review of Last-Mile Delivery 
Optimization:  Strategies,  Technologies,  Drone  Integration,  and  Future  Trends.  Drones,   
9(3), 158.
[75]  Sim, S., Kim, D., Park, K., Bae, H. (2024). Artificial Intelligence-based Smart Port Logistics 
Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case 
Study of Busan Port.
[76]  Siqi, W., Weng, M.L., Jun-Hwa, C., Xin-Jean, L. (2025). Working with robots: Trends and 
future directions, Technological Forecasting and Social Change, Volume 212, 2025.
[77]  Sun,  X.,  Yu,  H.,  Solvang,  W.D.  (2022).  The  application  of  Industry  4.0  technologies  in  
sustainable logistics: a systematic literature review (2012–2020) to explore future research 
opportunities. Environ. Sci. Pollut. Res. 29, 9560-9591.
[78]  Syed, Z., Dapaah, E., Mapfaza, G., Remias, T., Mupa, M.N. (2024). Enhancing Supply 
Chain Resilience with Cloud-Based ERP Systems. 8. 106-128.
[79]  Taj, S., Imran, A., Kastrati, Z., Daudpota, S., Memon, R., Ahmed, J. (2023). IoT-based 
supply chain management: A systematic literature review. Internet of Things. 24. 100982.234 Bibliografia
[80]  Tinnes, E., Perez, F., Kandel, M. (2024). Decarbonizing logistics: Charting the path ahead. 
[81]  Tubis, A.A., Rohman, J. (2023). Intelligent Warehouse in Industry 4.0-Systematic Litera-
ture Review. Sensors (Basel, Switzerland), 23(8), 4105.
[82]  Ugbebor, F., Adeteye, M., Ugbebor, J.. (2024). Automated Inventory Management Sys-
tems with IoT Integration to Optimize Stock Levels and Reduce Carrying Costs for SMEs: 
A  Comprehensive  Review.  Journal  of  Artificial  Intelligence  General  science  (JAIGS) 
ISSN:3006-4023. 6. 306-340.
[83]  van der Valk, H., Haße, H., Möller, F., Otto, B. (2022). Archetypes of Digital Twins. Busi-
ness & Information Systems Engineering.
[84]  Vicente, J.J., Neves, L., Bernardo, I. (2024). The potential of Logistics 4.0 technologies:  
a case study through business intelligence framing by applying the Delphi method, Frontiers 
in Artificial Intelligence, Volume 7.
[85]  Wan B., Wan W., Hanif N., Ahmed Z. (2022) Logistics performance and environmental 
sustainability: Do green innovation, renewable energy, and economic globalization mat-
ter?. Front. Environ. Sci. 10:996341.
[86]  Wang, X., Dong, X., Zhang, Z., Wang, Y. (2024). Transportation carbon reduction tech-
nologies: A review of fundamentals, application, and performance. Journal of Traffic and 
Transportation Engineering (Eng. Ed.), 11(6), 1160-1185 .
[87]  Winardi, S., Ng, P.W., Arifin, H., Apriyanto Megawan, S. (2024). Enhancing Warehouse 
Inventory Management through IoT Tools for Monitoring Stock Items. 1-6. 
[88]  Winkelhaus, S., Grosse, E. (2020). Logistics 4.0: a systematic review towards a new logistics 
system. International Journal of Production Research. 58. 18-43.
[89]  Wodnicka, M., Chomicz, G., Jaksik, D. (2024). Logistyka i jej inteligentny wymiar w roz- 
woju  smart  city.  W:  Wielowymiarowość  współczesnych  wyzwań  logistyki  i  transportu  
(s. 109-134). Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
[90]  World Economic Forum, Transforming Urban Logistics: Sustainable and Efficient Last-
Mile Delivery in Cities, 2024.
[91]  Wu, X., Mai, J., Zhou, J., Jiang, M., Wang, K. (2020). Concept and Key Technologies of 
Intelligent Logistics. Journal of Physics: Conference Series. 
[92]  Xia, J., Li, H., He, Z. (2023). The Effect of Blockchain Technology on Supply Chain Collab -
oration: A Case Study of Lenovo. Systems, 11(6), 299.
[93]  Xin, W., Xiuping, D., Zihe, Z., Yuanqing, W. (2024). Transportation carbon reduction 
technologies: A review of fundamentals, application, and performance, Journal of Traffic 
and Transportation Engineering (English Edition), Volume 11, Issue 6.
[94]  Ye, A., Cai, J., Yang, Z., Deng, Y., Li, X. (2025). The impact of intelligent logistics on logistics 
performance improvement. Sustainability, 17(2), 659.
[95]  Zaman, J., Shoomal, A., Jahanbakht, M., Ozay, D. (2025). Driving Supply Chain Trans-
formation with IoT and AI Integration: A Dual Approach Using Bibliometric Analysis and 
Topic Modeling. IoT. 6. 21.
[96]  Zeenat, K., Mohsin, B.A. (2025). Advancements in Automated Storage and Retrieval Sys-
tems: A Comprehensive Review, Robotics and Automation Engineering Journal.Bibliografia 235
[97]  Zero Waste Europe. (2022). The environmental benefits of reusable packaging systems . 
[98]  Zhang,  D.,  Pee,  L.G.,  Cui,  L.  (2021).  Artificial  intelligence  in  E-commerce  fulfillment:  
A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal 
of Information Management. 57. 102304.
[99]  Zheng, G., Kong, L., Brintrup, A. (2023). Federated machine learning for privacy pre-
serving,  collective  supply  chain  risk  prediction.  International  Journal  of  Production  
Research, 61(23), 8115-8132.
Źródła internetowe
[100]  https://www.mecalux.com/blog/warehouse-robotics
[101]  https://www.bestpractice.ai/ai-case-study-best-practice/ups_saves_over_10_million_
gallons_of_fuel_and_up_to_%  24400m_in_costs_annually_with_advanced_telemat-
ics_and_analysis
[102]  https://www.iotforall.com/how-intermodal-shipment-tracking-systems-work
[103]  https://www.dhl.com/global-en/delivered/innovation/the-value-of-iot-in-supply-
chains.html
[104]  https://www.maersk.com/news/articles/2022/11/29/maersk-and-ibm-to-discontinue-
tradelens
[105]  https://www2.deloitte.com/us/en/pages/operations/articles/blockchain-supply- 
chain-innovation.html
[106]  https://sssagarsahoo.medium.com/big-data-analytics-at-ups-a-case-study-eb1e87be-
|965e
[107]  https://transportgeography.org/contents/geography-city-logistics/autonomous- 
vehicles-urban-deliveries
[108]  https://www.businessinsider.com/amazon-warehouse-robots-expansion-proteus- 
hercules-jobs-workers-2024-5
[109]  https://www.businesswire.com/news/home/20201023005182/en/Global-Warehouse-
Automation-Market-Report-2020-with-Post-Pandemic-COVID-19-Impact-Insights-
AGVAMR-ASRS-Conveyors-Sortation-Order-Picking-AIDC-Palletizing-and-WMS
[110]  https://www.supermarketnews.com/grocery-technology/inside-look-at-kroger-s-first-
ocado-robotic-warehouse
[111]  https://addverb.com/case-studies/dhl-automated-sortation-system/
[112]  https://press.siemens.com/global/en/pressrelease/maximum-transparency- 
siemens-shows-holistic-digital-twin-logistics-center
[113]  https://www.anylogistix.com/case-studies/digital-twin-for-supply-chain-design-and-
cost-reduction
[114]  https://socradar.io/top-10-cyber-attacks-targeting-e-commerce-industry/
[115]  https://www.supplychainbrain.com/blogs/1-think-tank/post/32877-what-2020-
taught-us-about-e-commerce-and-sharing-information-online
[116]  https://digitaldefynd.com/IQ/supply-chain-cybersecurity-case-studies/
[117]  https://digital-strategy.ec.europa.eu/en/policies/nis2-directive
[118]  https://www.cisa.gov/news-events/news/attack-colonial-pipeline-what-weve-learned-236 Bibliografia
what-weve-done-over-past-two-years
[119]  https://climate.mit.edu/explainers/freight-transportation
[120]  https://www.iea.org/energy-system/transport/trucks-and-buses
[121]  https://ctl.mit.edu/case-studies-carbon-efficient-logistics
[122]  https://www.dhl.com/discover/en-global/logistics-advice/sustainability-and-green-lo-
gistics/what-is-green-logistics
[123]  https://ctl.mit.edu/case-studies-carbon-efficient-logistics
[124]  https://www.circularx.eu/en/cases/49/loop-packaging-reuse-model
[125]  https://environment.ec.europa.eu/topics/waste-and-recycling/packaging-waste_en
[126]  https://dmnews.com/global-logistics-automation-market-to-reach-147b-by-2030/
[127]  https://www.businessinsider.com/amazon-warehouse-robots-expansion-proteus-her-
cules-jobs-workers-2024-5?IR=T
[128]  https://www.linkedin.com/pulse/ai-driven-agentification-work-impact-jobs- 
20242030-poweredbywiti-zbyfc
[129]  https://www.govtech.com/fs/infrastructure-can-limit-delivery-robots-as-much-as-tech
[130]  https://matrackinc.com/self-driving-trucks/
[131]  https://siliconcanals.com/sales-of-service-robots-up-30-worldwide-ifr-reports/
[132]  https://www.brookings.edu/articles/navigating-the-future-of-work-a-case-for-a-ro-
bot-tax-in-the-age-of-ai/
[133]  https://datainnovation.org/2023/06/taxing-robots-would-hurt-not-help-american-
workers/
[134]  https://www.dhl.com/us-en/home/innovation-in-logistics/logistics-trend-radar/
metaverses-supply-chain.html
[135]  https://adnavem.com/blog/digital-future-logistics