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.

As global trade patterns evolve due to technological advancements, the research reveals that artificial intelligence is not merely a technological tool but a significant driver of economic transformation. It emphasizes the growing importance of AI in enhancing productivity and efficiency across several sectors, from manufacturing to agriculture and services. By leveraging AI technologies, countries can optimize their supply chains, predict market trends, and ultimately increase their export competitiveness, thus leading to a more strategic engagement in international trade.

One of the key findings of this research is the disparity in AI adoption rates among different sectors within Turkey and the BRICS nations. In Turkey, for instance, the manufacturing sector stands out as the primary beneficiary of AI integration, with firms utilizing advanced algorithms for process optimization and predictive maintenance. This not only improves production efficiency but also minimizes costs, allowing Turkish manufacturers to compete more effectively in global markets. Conversely, sectors such as agriculture are identified as lagging in AI adoption, highlighting the need for targeted policies to bridge this gap.

The BRICS economies present a diverse landscape regarding AI impact on trade. In China, state-backed initiatives and substantial investments in AI research have propelled the nation to the forefront of technological innovation. Chinese companies are encountering unprecedented gains in manufacturing and e-commerce, significantly affecting their trade balances. The study underscores how such advancements can reshape trade relationships and create competitive advantages, as China continues to challenge traditional powerhouses in the global market.

In India, the emergence of AI-driven startups is transforming its services sector, particularly in information technology and business process outsourcing. As Indian firms harness AI to enhance service delivery and reduce operational costs, they are starting to attract more significant international investment. This growth trajectory indicates a potential shift in India’s trade profile, from being a service-oriented economy to a technology-driven powerhouse, which could substantially alter its interactions in the global trade arena.

The research highlights that while AI presents a host of opportunities for economic growth, it also raises critical questions regarding workforce displacement and the need for reskilling. The automation of tasks traditionally performed by humans can lead to job losses in sectors like manufacturing and agriculture. Policymakers are urged to consider these dynamics when devising strategies aimed at maximizing the benefits of AI, while also protecting vulnerable segments of the labor force.

Moreover, the study reveals that cross-border collaborations in AI development can significantly enhance international trade prospects. Countries that prioritize partnerships in AI research and development can create synergies that facilitate knowledge transfer and innovation. This cooperation could be particularly advantageous for smaller economies that may lack the resources to compete directly with larger markets. By fostering international alliances, these nations can cultivate a more robust presence in the global trade landscape.

Cybersecurity concerns also emerge as a significant theme in the context of AI and international trade. The study points out that as AI systems become more integrated into trade operations, the risks of cyberattacks increase correspondingly. Countries must prioritize enhancing their cybersecurity frameworks to protect critical trade infrastructures from potential threats. Such vigilance is essential to maintain trust and reliability in AI-powered trade systems, ensuring that the benefits of automation do not come at the cost of security and stability.

As the research progresses, it highlights the necessity for regulatory frameworks to govern the use of AI in international trade. Countries are urged to develop comprehensive policies that address ethical considerations, data privacy, and the need for transparency in AI algorithms. Adopting robust regulations not only ensures fairness in trade practices but also promotes a level playing field for all participants in the international market, facilitating trust and confidence in technological advancements.

The findings underline that nations must adopt a strategic approach to integrating AI into their economic frameworks. Countries like Turkey and the BRICS economies have the potential to significantly influence global trade dynamics by embracing AI technologies. However, this requires a coordinated effort involving government, industries, and educational institutions to foster innovation, provide necessary resources for research, and develop a skilled workforce prepared for the future.

The research calls attention to the role of tech giants in shaping international trade policies. Major corporations at the forefront of AI development have the power to influence how different nations navigate the global economy. By engaging in conversations about the ethical use of AI and its implications for trade, these companies can help foster an environment that balances profit motives with social responsibility.

In conclusion, Çüneyt’s findings present a nuanced view of the sectoral impacts of AI on international trade. The study illuminates how AI can be a potent catalyst for economic growth and competitiveness, particularly in Turkey and the BRICS economies, while also addressing the multifaceted challenges that accompany its integration. Policymakers, business leaders, and educators must work collaboratively to harness the potential of AI, ensuring that its benefits are widely distributed across society and that the transition towards a tech-driven economy is managed responsibly.

In a world increasingly defined by technological advancements, understanding the intersection of AI and international trade will be crucial for all stakeholders involved. The insights drawn from this research can serve as a foundation for future initiatives aimed at leveraging AI for sustainable and inclusive economic development across borders.

Subject of Research: The impact of artificial intelligence on international trade in Turkey and BRICS economies

Article Title: Sectoral analysis of the impact of artificial intelligence on international trade in Turkey and the BRICS economies

Article References:

Cüneyt, Ç. Sectoral analysis of the impact of artificial intelligence on international trade in Turkey and the BRICS economies.
Discov Artif Intell 5, 275 (2025). https://doi.org/10.1007/s44163-025-00575-4

Image Credits: AI Generated

DOI: 10.1007/s44163-025-00575-4

Keywords: Artificial Intelligence, International Trade, Turkey, BRICS Economies, Economic Development, Cybersecurity, Workforce Reskilling, Policy Frameworks, Global Market Dynamics.