AI-Augmented logistics and strategic evaluation
an extended ELECTRE Tri-Neutrosophic framework for DHL’s global transformation
DOI:
https://doi.org/10.14488/BJOPM.2661.2026Keywords:
AI-augmented logistics, ELECTRE Tri, Neutrosophic sets, DHL transformation, Multi-criteria decision-makingAbstract
Goal: The objective of this research is to evaluate DHL’s transformation into an AI-augmented logistics provider. The study aims to determine how strategic initiatives can be prioritized under uncertainty, ensuring resilience, scalability, sustainability, and workforce adaptation in global logistics operations.
Design / Methodology / Approach: The study applies the Extended ELECTRE Tri method enriched with neutrosophic uncertainty modeling. Techniques include entropy-based weighting, neutrosophic closeness, outranking flows, and Monte Carlo sensitivity analysis. Expert judgments were collected and modeled using Interval-Valued Neutrosophic Sets to capture hesitation, divergence, and indeterminacy in evaluations.
Results: The analysis classified cybersecurity platforms, autonomous orchestration, and sustainability routing as high-priority initiatives, demonstrating stability under uncertainty and alignment with operational and ecological goals. Predictive workforce augmentation and agentic AI communications were assessed as moderate priority, reflecting contextual dependence and variability in expert judgments.
Limitations of the investigation: The study focuses exclusively on DHL, limiting generalizability across the logistics sector. Reliance on expert judgments introduces potential bias, and the criteria set reflects DHL priorities rather than evolving conditions. Proprietary data constraints restricted empirical validation, and classification outcomes remain sensitive to threshold selection despite robustness checks.
Practical implications: The findings provide DHL and similar operators with a structured framework to selectively scale robust initiatives while cautiously adapting those with weaker stability. The approach supports evidence-based decision-making for digital transformation strategies in logistics under uncertainty.
Originality / Value: This research contributes to logistics literature by advancing multi-criteria decision-making through the integration of neutrosophic uncertainty modeling. It demonstrates the applicability of the Extended ELECTRE Tri framework to real-world logistics transformation, offering a replicable model for global operators navigating AI adoption and digital resilience.
Downloads
References
Agrawal, N. (2021) « Multi-criteria decision-making toward supplier selection:
exploration of PROMETHEE II method », Benchmarking: An International Journal, 29(7), p. 2122‑2146. https://doi.org/10.1108/BIJ-02-2021-0071
Al-Hourani, S. and Weraikat, D. (2025) « A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions », Sustainability, 17(14), p. 6591. https://doi.org/10.3390/su17146591
Balan, G.S., Kumar, V.S. and Raj, S.A. (2025) « Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery », Supply Chain Analytics, 10, p. 100121. https://doi.org/10.1016/j.sca.2025.100121
Barua, D.A., Sami, S.A. and Barua, L. (2025) « Leveraging artificial intelligence for smart production management in industry 4.0 », Scientific Reports, 15(1), p. 41559. https://doi.org/10.1038/s41598-025-25413-6
Baseer, M. et al. (2023) « pELECTRE-Tri: Probabilistic ELECTRE-Tri Method—Application for the Energy Renovation of Buildings », Energies, 16(14), p. 5296. https://doi.org/10.3390/en16145296
Cerchione, R., Passaro, R. and Tavano, M. (2026) « The Integration of AI, Blockchain and IoT for the Sustainable Development of the Logistics Service Industry: Insights From a PRISMA-Based Analysis », Sustainable Development. https://doi.org/10.1002/sd.70745
Dias, L. and Clímaco, J. (2000) « ELECTRE TRI for Groups with Imprecise Information on Parameter Values », Group Decision and Negotiation, 9(5), p. 355‑377. https://doi.org/10.1023/A:1008739614981
Dwivedi, Y.K. et al. (2025) « Artificial intelligence agents and agentic systems in hospitality and tourism: challenges, opportunities and research agenda », International Journal of Contemporary Hospitality Management, 38(1), p. 27‑52. https://doi.org/10.1108/IJCHM-02-2025-0287
Ezell, B., Lynch, C.J. and Hester, P.T. (2021) « Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques », Applied Sciences, 11(21), p. 10397. https://doi.org/10.3390/app112110397
Fernández, E. et al. (2017) « ELECTRE TRI-nB: A new multiple criteria ordinal classification method », European Journal of Operational Research, 263(1), p. 214‑224. https://doi.org/10.1016/j.ejor.2017.04.048
Greco, S. et al. (2019) « On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness », Social Indicators Research, 141(1), p. 61‑94. https://doi.org/10.1007/s11205-017-1832-9
Hetmanczyk, M.P. (2024) « A Method for Evaluating the Maturity Level of Production Process Automation in the Context of Digital Transformation—Polish Case Study », Applied Sciences, 14(11), p. 4380. https://doi.org/10.3390/app14114380
Himeur, Y. et al. (2023) « AI-big data analytics for building automation and management systems:
a survey, actual challenges and future perspectives », Artificial Intelligence Review, 56(6), p. 4929‑5021. https://doi.org/10.1007/s10462-022-10286-2
Huge-Brodin, M., Sweeney, E. and Evangelista, P. (2020) « Environmental alignment between logistics service providers and shippers – a supply chain perspective », The International Journal of Logistics Management, 31(3), p. 575‑605. https://doi.org/10.1108/IJLM-04-2019-0101
Ibrahim, M.D., Pereira, M.A. and Caldas, P. (2024) « Efficiency analysis of the innovation-driven sustainable logistics industry », Socio-Economic Planning Sciences, 96, p. 102050. https://doi.org/10.1016/j.seps.2024.102050
Iqbal, T. and Ahmad, S. (2022) « Transparency in humanitarian logistics and supply chain:
the moderating role of digitalisation », Journal of Humanitarian Logistics and Supply Chain Management, 12(3), p. 425‑448. https://doi.org/10.1108/JHLSCM-04-2021-0029
Kadmiri, W.E. et al. (2026) « Towards resilient and sustainable smart warehousing: A systematic literature review and multi-dimensional cross-functional framework », Cleaner Logistics and Supply Chain, 19, p. 100340. https://doi.org/10.1016/j.clscn.2026.100340
Khan, S. et al. (2026) « Green AI techniques for reducing energy consumption in AI systems », Array, 29, p. 100652. https://doi.org/10.1016/j.array.2025.100652
Kumar, A., Mishra, S. and Lamba, K. (2025) « Sustainable synchromodal logistics: unveiling key enablers through neutrosophic interpretive structural modeling analysis », Industrial Management & Data Systems, 126(1), p. 288‑322. https://doi.org/10.1108/IMDS-11-2024-1090
Lee, A.T., Ramasamy, R.K. and Subbarao, A. (2025) « Barriers to and Facilitators of Technology Adoption in Emergency Departments: A Comprehensive Review », International Journal of Environmental Research and Public Health, 22(4), p. 479. https://doi.org/10.3390/ijerph22040479
Malik, M.A.B., Brandão, M. and Coopamootoo, K. (2026) « Towards Worker-Centered Warehouse Robots: A User Study on Privacy, Inclusivity and Safety », International Journal of Social Robotics, 18(2), p. 26. https://doi.org/10.1007/s12369-026-01359-1
Misbauddin, S.M. et al. (2023) « Exploring the Antecedents of Supply Chain Viability in a Pandemic Context: An Empirical Study on the Commercial Flower Supply Chain of an Emerging Economy », Sustainability, 15(3), p. 2146. https://doi.org/10.3390/su15032146
Nguyen, P. et al. (2026) « AI-driven digital twin-based security orchestration, automation and response for critical infrastructures », Automated Software Engineering, 33(2), p. 61. https://doi.org/10.1007/s10515-026-00612-1
Osa, N. et al. (2026) « Beyond Cobots: Designing Collaborative Applications through Human-Centred Design ». Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.6337964
Petropoulos, F. et al. (2026) « Operations & supply chain management: principles and practice », International Journal of Production Research, 64(1), p. 330‑513. https://doi.org/10.1080/00207543.2025.2555531
Rahmati, M. (2025) « Dynamic role-adaptive collaborative robots for sustainable smart manufacturing: an AI-driven approach », Journal of Intelligent Manufacturing and Special Equipment, 6(2), p. 101‑115. https://doi.org/10.1108/JIMSE-01-2025-0001
Raina, K. et al. (2026) « Artificial intelligence-driven management: Bridging innovation, knowledge creation, and sustainable business practices », Journal of Innovation & Knowledge, 11, p. 100860. https://doi.org/10.1016/j.jik.2025.100860
Ramli, A.M. et al. (2026) « The urgency of regulating artificial intelligence in relation to cybersecurity and cyber resilience », Cogent Social Sciences, 12(1), p. 2632977. https://doi.org/10.1080/23311886.2026.2632977
Ren, H. et al. (2024) « Resilience strategies in an intertwined supply network: Mitigating the vulnerability under disruption ripple effects », International Journal of Production Economics, 278, p. 109419. https://doi.org/10.1016/j.ijpe.2024.109419
Shen, Y. and Zhang, X. (2024) « The impact of artificial intelligence on employment: the role of virtual agglomeration », Humanities and Social Sciences Communications, 11(1), p. 122. https://doi.org/10.1057/s41599-024-02647-9
Sista, E. and De Giovanni, P. (2021) « Scaling Up Smart City Logistics Projects: The Case of the Smooth Project », Smart Cities, 4(4), p. 1337‑1365. https://doi.org/10.3390/smartcities4040071
Smyth, C. et al. (2024) « Artificial intelligence and prescriptive analytics for supply chain resilience:
a systematic literature review and research agenda », International Journal of Production Research, 62(23), p. 8537‑8561. https://doi.org/10.1080/00207543.2024.2341415
Song, Z. et al. (2026) « Large language models in supply chain management: a systematic literature review and application framework », International Journal of Production Research, 0(0), p. 1‑41. https://doi.org/10.1080/00207543.2026.2641103
Taherdoost, H. and Madanchian, M. (2023) « Multi-Criteria Decision Making (MCDM) Methods and Concepts ». Rochester, NY: Social Science Research Network.
Toderas, M. (2025) « Artificial Intelligence for Sustainability: A Systematic Review and Critical Analysis of AI Applications, Challenges, and Future Directions », Sustainability, 17(17), p. 8049. https://doi.org/10.3390/su17178049
Valencia-Arias, A. et al. (2025) « Industrial applications of generative artificial intelligence: transformations in processes, design, and production », Discover Artificial Intelligence, 5(1), p. 327. https://doi.org/10.1007/s44163-025-00557-6
Walter, A., Ahsan, K. and Rahman, S. (2025) « Application of artificial intelligence in demand planning for supply chains: a systematic literature review », The International Journal of Logistics Management, 36(3), p. 672‑719. https://doi.org/10.1108/IJLM-02-2024-0120
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Fadoua Tamtam, Amina Tourabi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors must have a written permission from any third-party materials used in the article, such as figures and graphics. The permission must explicitly allow authors to use the materials. The permission should be submitted with the article, as a supplementary file.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after BJO&PM publishes it (See The Effect of Open Access).
