The timing is hardly accidental. Supply chains now generate torrents of data—point-of-sale records, sensor readings from trucks and containers, weather feeds, supplier dashboards—and the complexity of the decisions made on top of that data has grown in step. Machine learning, in which algorithms learn statistical patterns from historical data, has become central to demand forecasting, inventory optimization, supplier selection, and dynamic pricing. Deep learning, its more powerful cousin, stacks artificial neurons into deep neural networks capable of digesting unstructured inputs such as images, audio, and raw text, enabling tasks like visual inspection of products, natural-language processing of contracts and demand signals, and anomaly detection across sprawling logistics networks. Researchers have reported gains across every link of the chain, from factory floor to last-mile delivery. But the authors of the new paper argue that the field’s obsession with a particular family of performance statistics has created a blind spot at precisely the moment executives need clarity most.

That blind spot involves the metrics everyone cites. Accuracy, precision, sensitivity, recall, and the F1 score are the standard currencies of machine learning evaluation. Accuracy measures the share of predictions a model gets right; precision captures how many of its positive predictions were actually correct; sensitivity—also called recall—reveals how many true cases the model managed to catch; and the F1 score blends precision and recall into a single harmonic mean. These numbers are excellent for comparing two algorithms against the same dataset. What they cannot do, AbdEllatif and Abdel Aal contend, is tell a procurement director, a logistics manager, or a shareholder whether deploying a given technique improved the organization in ways those stakeholders actually care about. A model can post a stellar F1 score while failing to reduce costs, accelerate deliveries, or ease workloads. The study therefore shifts the question from “how well does the algorithm perform?” to “how much benefit does the integration of machine learning or deep learning with supply chain management tasks deliver, according to the people who must live with the results?”

To capture those human judgments, the researchers reached for one of the most exotic toolkits in modern decision science: neutrosophic numbers. Fuzzy sets, introduced by Lotfi Zadeh in 1965, let an observation belong partially to a category—for instance, judging a forecasting model’s benefit as “0.7 good.” Krassimir Atanassov’s intuitionistic fuzzy sets added a second degree of freedom, allowing experts to state both how true and how false a claim feels, with the two summing to at most one. Neutrosophic logic, proposed by mathematician Florentin Smarandache in the mid-1990s, goes further still: truth, indeterminacy, and falsehood become three fully independent membership degrees, each ranging between zero and one. That third channel—indeterminacy—is the crucial one. A supply chain expert asked whether a deep learning system will deliver benefits might genuinely not know, and neutrosophic mathematics can encode that hesitancy instead of forcing artificial precision. The study employs single-valued trapezoidal neutrosophic numbers, which attach a four-point interval to each judgment, letting evaluators express ranges of belief rather than single brittle values.

The centerpiece of the framework is an aggregation operator the authors call the Single-Valued Trapezoidal Neutrosophic Number Weighted Arithmetic Average, or SVTNNWAA. In essence, the method gathers evaluations from multiple stakeholders, each expressed as a trapezoidal neutrosophic number carrying its own truth, indeterminacy, and falsity components, and fuses them into a single collective rating. Because the averaging is weighted, judgments tied to more important criteria exert greater influence on the final score. The mathematics preserves all three neutrosophic components throughout the computation, so the uncertainty voiced by experts is not laundered away in the process. Once the aggregated values are computed, techniques drawn from the fuzzy-numbers literature—including the graded mean integration representation, a standard defuzzification approach that converts an interval judgment into a crisp, comparable figure—translate the results into benefit rates that can be ranked and visualized. The output is a league table of machine learning and deep learning techniques ordered not by benchmark accuracy but by their expected organizational payoff.

Assigning those weights is where the second half of the framework comes in: the Full Consistency Method, or FUCOM. Classical weighting techniques such as the Analytic Hierarchy Process require experts to compare every criterion against every other one, producing n(n−1)/2 judgments for n criteria—a burden that balloons quickly and invites inconsistency. FUCOM, introduced in 2018 by operational researchers led by Dragan Pamučar, slashes the workload to roughly n−1 comparisons. Experts rank the criteria in order of priority and then compare the top-ranked criterion against each of the others, yielding a compact set of ratio judgments. The method then solves an optimization problem that minimizes the maximum deviation from perfect consistency, exploiting the transitivity of the comparisons to guarantee mathematically coherent weights. In the new study, FUCOM supplies the consistent weighting structure that the SVTNNWAA operator requires, ensuring that the aggregated stakeholder scores rest on a defensible foundation rather than on arbitrarily chosen priorities. Fewer comparisons also mean less fatigue for busy executives—a practical virtue in corporate settings where evaluation panels have limited patience for lengthy questionnaire exercises.

To demonstrate that the machinery works outside of theory, the researchers applied the method in a practical case study. The results show that it enables decision-makers to assess and visualize the benefit rates of machine learning and deep learning techniques, indicating which approach is most suitable for more effective supply chain management. Just as importantly, the evaluation incorporates stakeholders’ viewpoints directly: the benefit of integrating a given AI technique with supply chain tasks is scored through the eyes of the people responsible for the chain’s performance, rather than inferred from technical benchmarks alone. Because the entire calculation runs in a neutrosophic environment, the framework absorbs the uncertainty and indeterminacy that inevitably accompany judgments about emerging technology—situations where experts hold partial knowledge, evidence conflicts, or outright indecision reigns. The case study thus functions as a proof of concept that neutrosophic multi-criteria decision-making can move from academic journals into the meeting rooms where technology investments are actually argued over.

The broader significance lies in bridging two communities that often talk past each other. Data scientists publish benchmark results; operations managers ask what those results mean for costs, service levels, and resilience. By fusing FUCOM-derived weights with neutrosophic aggregation, the new framework gives both sides a shared language. It belongs to the growing field of multi-criteria decision-making in supply chain management, a discipline that has previously transformed how firms select suppliers and prioritize risks with tools such as the Analytic Hierarchy Process and TOPSIS. What distinguishes this contribution is its explicit targeting of AI integration decisions—an area where enthusiasm routinely outruns evidence. Frameworks like this one could help executives determine where a deep learning investment beats a simpler machine learning model, and where neither justifies the disruption. They also create an auditable record of why a technology was chosen, which matters as organizations face growing scrutiny over algorithmic procurement decisions.

The study, published open access in the Journal of Big Data, a Springer Nature title, appeared on 5 August 2026 after a peer-review journey that began with submission on 18 July 2025 and acceptance on 12 July 2026. Springer is releasing the paper early as a citable, peer-reviewed accepted version carrying a permanent DOI, ahead of the final Version of Record. The work was funded by the University of Jeddah under grant number UJ-23-DR-67, with the authors thanking the university for its technical and financial support. AbdEllatif is affiliated with the university’s College of Business, while Abdel Aal is based at the Faculty of Computers and Informatics at Zagazig University in Egypt. Published under a Creative Commons license that permits sharing with appropriate credit, the paper falls squarely within the journal’s research area of multi-criteria decision-making in supply chain management. Both authors declare no competing interests, and the study required no ethical approval.

As global supply chains strain under geopolitical shocks, climate disruption, and relentless consumer expectations, corporations are expected to keep escalating their AI spending—and every one of those dollars will eventually face a boardroom reckoning. Tools that convert human judgment into transparent, uncertainty-aware rankings may prove as consequential as the algorithms they evaluate. The authors’ approach is not limited to logistics; any domain where experts must weigh emerging technologies under uncertainty—from healthcare informatics to smart manufacturing—could, in principle, adopt the same neutrosophic machinery. For now, the study stands as a reminder that the hardest part of artificial intelligence is not teaching machines to learn. It is teaching organizations to know, with confidence, whether the machines are actually helping. With this framework, the answer arrives as a number—one that finally accounts for doubt.

The concept of last-mile delivery often refers to the final stage of the logistics process— where goods are delivered from a transportation hub to their final destination. While it may seem straightforward, this segment can account for a significant portion of logistics costs and is notoriously complex. In megacities where infrastructure is underdeveloped and traffic conditions are unpredictable, this final leg presents unique challenges. As urban populations swell, so do the demands for efficient, timely deliveries. Understanding how technological innovation can help optimize this process is of paramount importance.

One of the prime innovations in the study revolves around the use of reinforcement learning, a branch of machine learning where algorithms learn to make decisions through trial and error. By simulating various traffic scenarios, delivery routes, and urban challenges, reinforcement learning can help develop smarter delivery strategies that dynamically adapt to ever-changing conditions. The researchers employed advanced algorithms that not only learn from past data but also from real-time inputs, making adjustments instantly based on current traffic situations and delivery needs.

To further optimize the delivery process, the researchers incorporated social media data as an auxiliary resource for traffic prediction. Underdeveloped megacities often suffer from outdated traffic systems and limited data availability. However, social media is a treasure trove of real-time information. By analyzing geotagged posts, tweets, and other social media signals, the algorithms can gain insights into traffic trends, public events causing congestion, and even potential disruptions due to weather conditions. This integration allows for a more holistic approach to traffic prediction and situational awareness, enhancing the accuracy of the proposed delivery models.

Moreover, the study doesn’t merely focus on the technical capabilities of reinforcement learning and social media data. It also emphasizes equitable access to delivery services. In urban hubs where delivery access can be limited for vast segments of the population, equitable logistics become crucial. The research explores strategies that ensure delivery routes accommodate underserved areas, promoting social equity while maximizing operational efficiency. This inclusive approach not only improves service quality but also empowers communities that may otherwise be neglected in urban logistics.

The results from Rabelo and his team demonstrate a considerable improvement in delivery times and operational costs. In controlled simulations, the application of these advanced models outperformed traditional routing methods significantly. The combination of reinforcement learning and real-time data feed not only predicts traffic more accurately but also allows for proactive adjustments to delivery routes. As a result, deliveries could be completed more efficiently, often arriving ahead of customer expectations.

Despite its success, the application of these technologies raises pertinent questions about scalability and implementation in real-world scenarios. Underdeveloped megacities come with various infrastructural, societal, and technological limitations that may hinder the widespread adoption of such advanced logistics solutions. Stakeholders—including local governments, tech companies, and logistics providers—must collaborate to create frameworks that facilitate the integration of these technologies into existing systems. This multi-faceted collaboration is essential for overcoming the barriers posed by insufficient infrastructure.

Furthermore, this research presents an interesting cross-section of urban planning and transportation logistics. As cities evolve and face increasing strain from population growth, integrating AI-driven tactics for last-mile delivery could transform urban landscapes. Smart cities of the future may rely heavily on such innovations, combining various forms of transportation and delivery, ranging from electric vehicles to drones, all coordinated through sophisticated AI algorithms that account for real-world conditions.

Another remarkable aspect of Rabelo et al.’s study is its potential applicability beyond urban delivery scenarios. The methodology employed could inform other logistic challenges across different contexts, including rural areas or emergency response situations. As technological advancements continue to flourish, harnessing them for practical applications has far-reaching implications—extending the benefits of smart logistics to varied geographic and socio-economic contexts.

To conclude, the exploration presented in this research not only exemplifies the capabilities of modern AI technology in addressing age-old logistical challenges but also underscores a significant shift in how we envision urban delivery systems. By employing reinforcement learning and leveraging social media data, one can foster more efficient, equitable, and responsive delivery models. As urbanization continues to expand in the global landscape, approaches such as these may very well be the cornerstone for shaping the future of logistics in megacities.

In sum, Rabelo, Rincón-Guio, and Laynes’s groundbreaking study not only paves the way for smarter logistics in underdeveloped megacities but also serves as a clarion call for future research that embraces innovation and inclusivity in urban logistics. The crossroads of technology and social equity presents both challenges and opportunities, and this study is an important step in harnessing those possibilities for better urban living experiences.

Subject of Research: Last-mile delivery optimization using reinforcement learning and social media-based traffic prediction in underdeveloped megacities.

Article Title: Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities.

Article References: Rabelo, L., Rincón-Guio, C., Laynes, V. et al. Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities. Discov Cities 2, 69 (2025). https://doi.org/10.1007/s44327-025-00112-z

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s44327-025-00112-z

Keywords: Last-mile delivery, reinforcement learning, urban logistics, social media data analysis, traffic prediction, megacities, underdeveloped regions, equitable access, AI technologies.

A recent study spearheaded by Challouf, Alhloul, and Nemeth, titled “Mapping the role of industry 4.0 technologies in green supply chain management,” delves into this intersection with a bibliometric analysis that enriches our understanding of current trends and outlines the potential pathways for future research. It uncovers the profound impact that technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics are having on supply chains that prioritize ecological integrity while striving for efficiency.

The crux of the research lies in its systematic approach, employing bibliometric tools to analyze a plethora of academic literature. This method not only aids in identifying key publications but also highlights influential authors and prevailing research themes within the domain. The findings illuminate a significant correlation between the adoption of Industry 4.0 technologies and the advancement of green practices in supply chains. As companies gear up to integrate these technologies, they unlock the potential for variant strategies that can mitigate environmental impacts.

One of the standout findings from this analysis is the critical role that IoT plays in enhancing real-time tracking and monitoring of resources along the supply chain. IoT facilitates the collection of vast amounts of data, enabling businesses to refine their operations and eliminate waste. This tech-centric approach not only fosters a culture of transparency but also encourages collaborations among supply chain partners to contribute collectively towards achieving sustainability targets.

Moreover, the integration of AI presents another dimension to this transformative phase. AI-powered systems can predict demand more accurately, thereby reducing overproduction and minimizing excess waste. Intelligent algorithms can analyze consumption patterns and optimize logistics, ensuring that goods are produced and delivered in alignment with genuine market needs. This demand-driven supply chain model is an essential strategy in reducing carbon footprints and enhancing operational performance.

Additionally, big data analytics serves as the backbone of data-driven decision-making. Through meticulous data analysis, organizations are equipped to identify inefficiencies and streamline operations, creating models that foster the circular economy — striving for ways to repurpose materials and prolong product life cycles. Such practices not only significantly lower environmental impact but also can result in cost savings, creating a compelling business case to adopt these technologies.

The research further underscores the importance of collaboration across various stakeholders in the supply chain. By leveraging technological advancements, companies can establish more cohesive partnerships, working together towards common eco-friendly goals. Enhanced communication through Industry 4.0 technologies breaks down silos and promotes a robust information share that is essential for coordinated efforts in reducing resource consumption and waste.

Moreover, this study highlights the necessity for companies to rethink their traditional value propositions. As consumers become more environmentally conscious, the demand for sustainably sourced products is growing. Businesses that adapt to these shifting paradigms will not only gain a competitive edge but will also contribute positively to global sustainability efforts. Embracing green supply chain practices propelled by Industry 4.0 technologies represents an opportunity for businesses to lead by example.

The implications of this research extend beyond immediate operational efficiencies. The findings further indicate a shift in the educational landscape within supply chain management. Educational institutions are beginning to re-align their curricula to include a stronger focus on sustainability and technological proficiency, preparing future leaders who can navigate the complexities of modern supply chains. This evolution is crucial as it builds a workforce equipped with the necessary skills to leverage technology in achieving sustainability targets.

While the potential of these technologies is expansive, the research also highlights various challenges that organizations encounter in their pursuit of integrating technology with sustainable practices. A significant barrier remains the lack of standardized protocols across industries, which can hinder the effective adoption of Industry 4.0 technologies. There is a pressing need for regulatory frameworks that can guide businesses in implementing these innovations responsibly while ensuring environmental safeguards.

Another challenge documented in this study is the investment barrier associated with transitioning to advanced technologies. The initial costs of implementing IoT, AI, and other Industry 4.0 technologies may deter some businesses, especially small and medium-sized enterprises (SMEs). However, the long-term savings and increased profitability linked to operational efficiencies may outweigh these initial investments. Hence, a strategic approach is necessary for organizations to comprehend and navigate the cost-benefit landscape of these transformative technologies.

In conclusion, the research conducted by Challouf and colleagues is instrumental in mapping the intricate relationship between Industry 4.0 technologies and green supply chain management. The findings underscore the transformative potential of these technologies to drive sustainability while enhancing operational efficiencies. As organizations forge ahead in this technological era, the insights derived from this study serve as a guiding light, illuminating the pathways for businesses to innovate responsibly and contribute positively to sustainability goals.

Balancing technological advancement with ecological responsibility is not merely a challenge but an opportunity for growth and leadership in today’s interconnected world.

Subject of Research:
The role of Industry 4.0 technologies in enhancing green supply chain management and sustainability.

Article Title:
Mapping the role of industry 4.0 technologies in green supply chain management: a bibliometric and structured text analysis.

Article References:
Challouf, K., Alhloul, A. & Nemeth, N. Mapping the role of industry 4.0 technologies in green supply chain management: a bibliometric and structured text analysis. Discov Sustain 6, 949 (2025). https://doi.org/10.1007/s43621-025-01827-0

Image Credits:
AI Generated

DOI:
10.1007/s43621-025-01827-0

Keywords:
Industry 4.0, Green Supply Chain Management, Sustainability, IoT, Artificial Intelligence, Big Data Analytics, Circular Economy, Collaboration, Operational Efficiency.