INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
Analysis of Factors Affecting the Application of Ai in Supply Chain  
Operations  
Ha Minh Hieu  
Vietnam Aviation Academy, Vietnam  
DOI: https://doi.org/10.47772/IJRISS.2025.914MG00249  
Received: 14 December 2025; Accepted: 22 December 2025; Published: 29 December 2025  
ABSTRACT  
In the era of global connectivity, supply chain management has emerged as a significant challenge. AI holds  
the promise to revolutionize the approach and handling of supply chain management. Market transformations  
underscore the need for agility. Incorporating AI into supply chain management rapidly caters to optimization  
and adaptability. This research zeroes in on the positive facets of AI within supply chain management. The  
outcomes bolster AI implementation, propel higher-level administration, and foster the development of  
advanced supply chain models.  
Keywords: Supply chain management; AI; Optimization; Adaptability; Factor  
INTRODUCTION  
In the era of digitalization and global connectivity, supply chain management has evolved into a critical  
cornerstone of business success (Abubakar et al., 2019). The intricate interplay of factors and the constant  
transformation of the business landscape present substantial challenges to sustaining efficient supply chain  
operations (Ali et al., 2018). As organizations navigate this complex environment, Artificial Intelligence (AI)  
has emerged as an indispensable instrument, holding the potential to revolutionize how we approach and  
execute supply chain management (Agarwal et al., 2019).  
The contemporary market landscape is characterized by swift and unpredictable changes—ranging from shifts  
in customer preferences to the fluid dynamics of the global supply network. This rapidly evolving context  
underscores the necessity for supply chain management to be highly agile, responsive, and adaptable (Allen,  
1998). To effectively address the dynamic demands and maintain competitiveness, organizations are earnestly  
exploring advanced methodologies that can finely tune supply chain processes. Against this backdrop, the  
harnessing of Artificial Intelligence's capabilities in supply chain management emerges as a strategic necessity,  
offering the promise of substantial advantages (Austin, 2013).  
AI has the potential to bolster supply chain efficiency by analyzing massive datasets, forecasting trends,  
optimizing operations, and automating routine tasks. This not only mitigates the risk of errors but also allows  
human resources to focus on more strategic, value-added activities (Abubakar et al., 2019). Furthermore, AI's  
ability to process real-time information allows organizations to respond promptly to disruptions, minimize  
downtime, and enhance overall customer satisfaction (Diabat et al, 2015).  
This research endeavors to delve into the intricacies of AI's positive impact on supply chain management. By  
scrutinizing and dissecting these influential factors, we can not only direct improvements in the efficacy of  
supply chain management but also propel the widespread adoption and integration of AI technologies in this  
domain (ICAP, 2025). The outcomes of this study hold the potential to yield profound insights, contributing to  
a comprehensive understanding of AI's capabilities. Moreover, these insights have the power to stimulate  
heightened managerial acumen, paving the way for a new era of strategic decision-making Allcott et al., 2014).  
In this pursuit, organizations are presented with an opportunity to optimize resource allocation, streamline  
processes, and fortify their competitive edge. As AI empowers supply chain managers to make data-driven  
decisions in real time, it lays the foundation for predictive and proactive strategies, reducing operational risks  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
and enhancing overall organizational resilience (World Bank, 2025). The findings from this research can  
catalyze a paradigm shift in supply chain management, urging businesses and entities to craft advanced supply  
chain models that effectively navigate the challenges and seize the opportunities presented by the 21st century  
(Dubey et al., 2017).  
LITERATURE REVIEW  
AI (artificial intelligence)  
AI boasts a history that extends further than its conventional understanding, spanning realms from science and  
philosophy, tracing back to ancient Greece. However, its modern incarnation owes much to Alan Turing and  
the 1956 Dartmouth College conference, where the term "Artificial Intelligence" was officially coined by John  
McCarthy, defined as the "science and engineering of making intelligent machines." This event is often  
regarded as the "birth of artificial intelligence." (Dejoux et al., 2018).  
Initially, AI was conceptualized around high-level cognition, not merely recognizing concepts or executing  
complex motor skills, but engaging in multi-step reasoning, comprehending natural language, devising  
innovative artifacts, formulating novel plans, and even reasoning about its own reasoning. This holistic human-  
like intelligence was termed "strong AI." The prevailing approach to strong AI centered on symbolic  
reasoning, considering computers as more than just numeric calculators but general symbol manipulators. Yet,  
while this approach displayed early promise, it faced significant challenges, leading many AI branches to  
diverge from it due to its complexity and limited progress as the 21st century unfolded. The realization of  
strong AI remains an ongoing uncertainty (Elbegzaya, 2020).  
The distinction between weak AI and strong AI also delves into rule adherence—how machines interact with  
rules. Wolfe differentiates rule-based decision-making, where machines strictly adhere to developer-set rules,  
from rule-following decision-making, where machines follow rules not explicitly defined to them. Rule-based  
decision-making aligns with weak AI, while rule-following decision-making leans towards strong AI  
aspirations. Neural networks (NN), exemplifying rule-following decision-making, allow algorithms to self-  
learn. However, the stage of machines autonomously creating and following their rules, characteristic of strong  
AI, has yet to be attained (Exxact, 2023).  
AI's trajectory since its inception in the 1950s has witnessed ebbs and flows, commonly known as AI's  
"summers and winters." From 2010 onward, AI has resurged into a "summer" period, attributed largely to  
notable enhancements in computer processing power and the availability of vast data reserves. This AI  
renaissance stems from three pivotal breakthroughs:  
(1) Introduction of more sophisticated algorithms.  
(2) The emergence of affordable graphics processors capable of swift calculations.  
(3) Availability of large, accurately annotated databases, facilitating advanced intelligent system learning  
(Exxact, 2023).  
Supply chain concept and supply chain management  
Competing successfully in any business environment today requires businesses to engage in the business of its  
suppliers as well as its customers. This requires businesses when providing products or services that customers  
need to pay more attention to the flow of materials, how the supplier's products and services are designed and  
packaged, how the finished product is transported and stored, and what the end consumer or customer actually  
requires (for example, many businesses may not know how their product is being used). the creation of the  
final product used by the customer). Moreover, in the context of fierce competition in the global market today,  
the introduction of new products with increasingly shorter life cycles, along with the increasing level of  
customer expectations have spurred businesses to must invest, and focus heavily on its supply chain. This,  
coupled with continued advancements in communications and transportation technologies (e.g., mobile  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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communications, the Internet and overnight delivery), has fueled the continued evolution of supply chains and  
techniques to manage it (Fong et al., 2025)  
In a typical supply chain, materials are purchased from one or more suppliers; Parts are manufactured in one or  
more factories, then shipped to warehouses for intermediate storage and finally to retailers and customers.  
Therefore, to reduce costs and improve service levels, effective supply chain strategies must consider  
interactions at different levels in the supply chain. The supply chain, also referred to as the logistics network,  
includes suppliers, manufacturing centers, warehouses, distribution centers, and retail stores, as well as raw  
materials and inventory (Fan, 2024). during production and the finished product moves between facilities.  
Some of the supply chain concepts include:  
“A supply chain is an association of companies that bring a product or service to market” *– Fundamentals of  
Logistics Management of Douglas M. Lambert, James R. Stock and Lisa M. Ellram (Diabat et al, 2015).  
“Supply chain includes all stages involved, directly or indirectly, in meeting customer needs. The supply chain  
includes not only manufacturers and suppliers, but also carriers, warehouses, retailers and customers” **–  
Supply Chain Management: strategy, planning and operation of Chopra Sunil and Peter Meindl (Chi et al,  
2020).  
“A supply chain is a network of production and distribution options that perform the functions of procuring  
raw materials, converting raw materials, selling products and finished products, and distributing them to  
customers” ***- An introduction to supply chain management – Ganesham, Ran and Terry P.Harrision (Diabat  
et al, 2015).  
All products reach the consumer through some form of supply chain, some larger and some much more  
complex. With this supply chain idea, it is easy to see that there is only one source of profit for the whole  
chain, which is the end customer. When individual firms in the supply chain make business decisions without  
regard to other members of the chain, this ultimately leads to very high prices for the final customer, high  
levels of supply chain service response is low and this results in low end consumer demand (Nguyen et al.,  
2025).  
So, what is supply chain management? We consider the following definition:  
Supply chain management is a set of practices that integrate and efficiently use suppliers, manufacturers,  
warehouses, and stores to deliver manufactured goods to the right locations. with the right quality  
requirements, with the aim of minimizing system-wide costs while satisfying service level requirements.  
Or: Supply chain management is the coordination of production, inventory, location and transportation among  
participants in the supply chain to meet the needs of the market smoothly and efficiently (Nguyen et al., 2025).  
Research models  
After studying the factors influencing AI and the decision-making process for its integration into supply chain  
management, the authors decided to select 8 factors to construct a research model for exploring the impact of  
these factors on the application of AI in supply chain management.  
H1: Enhanced Accurate Predictions: AI possesses the capability to analyze large datasets and thereby generate  
precise forecasts about product demand, market transformations, and supply chain conditions. This aids in  
optimizing planning and resource allocation.  
H2: Process Optimization: AI can optimize transportation processes, production scheduling, inventory  
management, and other supply chain activities, reducing waste and enhancing efficiency.  
H3: Forecasting Incidents and Risks: AI can identify potential scenarios that might lead to incidents or risks  
within the supply chain, enabling organizations to take preventive or responsive measures promptly.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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H4: Inventory Optimization: AI can predict optimal inventory levels based on historical data and demand  
forecasts, minimizing the risks of shortages or excess stock.  
H5: Real-Time Responsiveness: AI can process data swiftly and provide real-time management insights,  
enabling supply chain managers to make quicker and more flexible decisions.  
H6: Transport Routing Optimization: AI can optimize route and transportation schedules, saving time, energy,  
and overall costs.  
H7: Enhanced Market Forecasting: AI can analyze data from various sources to predict market trends, enabling  
businesses to proactively prepare for and respond to shifts in demand.  
H8: Integration of Diverse Information: AI has the ability to process and analyze information from various  
sources, including production, transportation, planning, and market data, allowing decisions to be made with a  
more comprehensive perspective.  
Figure 1. The proposed research model assesses the impact of influencing factors on the utilization of AI in  
supply chain operation.  
METHODS  
Qualitative method  
In this study, the authors conducted a preliminary investigation based on various sources such as scientific  
articles, undergraduate theses, master's theses, and scientific journals within the country. As a result, the initial  
research aimed to identify potential new variables or unsuitable variables and validate the semantic accuracy of  
measurement scales to align with the Vietnamese context. Subsequently, the authors will refine, eliminate, and  
supplement as necessary to construct a comprehensive questionnaire for the formal research phase.  
Quantitative methods  
The authors employed statistical methods including descriptive analysis, Cronbach's Alpha analysis,  
Exploratory Factor Analysis, Correlation Analysis, Linear Regression Analysis, Normality, Testing, and  
ANOVA Analysis to analyze and process the data gathered from 280 survey responses collected from  
Logistics businesses and customers utilizing smart logistics services.  
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RESULTS  
Check the reliability of the scale (Cronbach Alpha analysis)  
Before analyzing the EFA exploratory factor, we conduct an assessment of the reliability of the scale of all  
factors to eliminate those factors with low reliability. Reliability represents the relationship of observed  
variables in the same scale, so the correlation coefficient between them must be high.  
Criteria to evaluate the reliability of the scale:  
- Type of observed variables with variable correlation coefficient - sum less than 0.3  
- Type of scale when Cronbach's Alpha reliability is less than 0.6  
- Eliminate observed variables when the reliability of Cronbach's Alpha is greater than the reliability of  
Cronbach's Alpha of the whole scale.  
After the EFA analysis, the hypotheses are posed as all independent variables including: Enhanced Accurate  
Predictions; Process Optimization; Forecasting Incidents and Risks; Inventory Optimization; Real-Time  
Responsiveness; Transport Routing Optimization; Enhanced Marketing Forecasting; Integration of Diverse  
Information all influence the use of AI in supply chain management. the model is calibrated as follows:  
Hypothesis testing  
In this section, the author will determine the relationship between 9 factors, in which there are 8 independent  
variables: Enhanced Accurate Predictions (EAP), Process Optimization (PO), Forecasting Incidents and Risks:  
(FIR), Inventory Optimization (IO), Real-Time Responsiveness (RTR), Transport Routing Optimization  
(TRO), Enhanced Marketing Forecasting (EMF), Integration of Diverse Information (IDI) and 1 dependent  
variable is: Utilizing AI in supply chain management (UAI). Pearson correlation was used to test the strong  
linear relationship between the dependent variable and the independent variable. Because the condition for  
regression is first to be correlated. In addition, it is necessary to identify the problem of multicollinearity  
between the independent variables.  
The independent variables Enhanced Accurate Predictions (EAP), Process Optimization (PO), and Integration  
of Diverse Information (IDI) significantly influence using AI in supply chain management, as indicated by Sig  
values below 0.05 (we reject the null hypothesis H0: There is no relationship between the two variables).  
Furthermore, these correlation coefficients fall within the range of 0.2 – 0.4, indicating a moderate correlation  
between the independent variables and the dependent variable. Specifically, the correlation coefficient for  
Enhanced Accurate Predictions (EAP) is 0.367, for Process Optimization (PO) is 0.333, for Forecasting  
Incidents and Risks (FIR) is 0.216, and for Transport Routing Optimization (TRO) is 0.268. On the other hand,  
the independent variables Inventory Optimization (IO), Real-Time Responsiveness (RTR), Enhanced  
Marketing Forecasting (EMF), and Integration of Diverse Information (IDI) have Sig values greater than 0.05.  
Therefore, we conclude that these independent variables are not significantly correlated with the dependent  
variable, the usage of AI in supply chain management.  
In this case, it's not advisable to immediately exclude variables after the Pearson correlation test, as this is just  
a necessary condition in regression analysis. Some cases might exhibit non-significant Pearson correlations,  
but regression can still be effective, and the tests can yield statistically significant results. Hence, a regression  
analysis should be conducted to make conclusive determinations.  
Table1 Results of linear correlation test  
Correlations  
PHT  
1
HT  
DT  
TC  
-,056  
CTDT  
-,081  
TKCCDG  
,181**  
QM  
-,012  
CSVC  
,106  
HL  
EAP  
Pearson  
,249**  
,179**  
,367**  
Correlation  
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Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
Pearson  
Correlation  
Sig. (2-tailed)  
N
,000  
265  
1
,003  
265  
,366  
265  
,015  
,191  
265  
-,060  
,003  
265  
,848  
265  
-,005  
,085  
265  
,000  
265  
265  
PO  
,249**  
,193**  
,457**  
,149*  
,333**  
,000  
265  
,002  
265  
1
,813  
265  
,119  
,327  
265  
-,052  
,000  
265  
,939  
265  
-,116  
,015  
265  
,000  
265  
265  
FIR  
IO  
,179**  
,193**  
,161**  
,256**  
,216**  
,003  
265  
-,056  
,002  
265  
,015  
,054  
265  
1
,399  
265  
,040  
,009  
265  
-,013  
,060  
265  
,003  
,000  
265  
,000  
265  
-,028  
265  
,119  
,122*  
,366  
265  
-,081  
,813  
265  
-,060  
,054  
265  
-,052  
,518  
265  
1
,835  
265  
,015  
,967  
265  
,047  
265  
-,044  
,654  
265  
,039  
265  
,040  
RTR  
TRO  
EMF  
IDI  
,287**  
,191  
265  
,327  
265  
,399  
265  
,518  
265  
-,013  
,812  
265  
1
,000  
265  
-,009  
,476  
265  
,524  
265  
265  
,015  
,181**  
,457**  
,161**  
,167**  
,268**  
,003  
265  
-,012  
,000  
265  
-,005  
,009  
265  
-,116  
,835  
265  
,003  
,812  
265  
,882  
265  
1
,007  
265  
,000  
265  
-,081  
265  
-,009  
,287**  
-,150*  
,848  
265  
,106  
,939  
265  
,060  
265  
,967  
265  
,000  
265  
-,044  
,882  
265  
,014  
265  
1
,190  
265  
,085  
265  
,149*  
,256**  
,122*  
,167**  
-,150*  
,085  
265  
,015  
265  
,000  
265  
,047  
265  
-,028  
,476  
265  
,039  
,007  
265  
,014  
265  
-,081  
,168  
265  
1
265  
,085  
UAI  
Pearson  
Correlation  
Sig. (2-tailed)  
N
,367**  
,333**  
,216**  
,268**  
,000  
265  
,000  
265  
,000  
265  
,654  
265  
,524  
265  
,000  
265  
,190  
265  
,168  
265  
265  
**. Correlation is significant at the 0,01 level (2-tailed).  
*. Correlation is significant at the 0,05 level (2-tailed).  
Multivariate regression method will be used to analyze the relationship between variables:  
+ Dependent variables: Utilizing AI in supply chain management (UAI)  
+ Independent variable: Enhanced Accurate Predictions: EAP, Process Optimization: PO, Forecasting  
Incidents and Risks: FIR, Inventory Optimization: IO, Real-Time Responsiveness: RTR, Transport Routing  
Optimization: TRO, Enhanced Marketing Forecasting: EMF, Integration of Diverse Information: IDI.  
The regression model will have the following form:  
UAI = po + pi* EAP + p2*PO + p3*FIR + p4*IO + p5*RTR +  
p6*TRO + p7*EMF + p4*IDI.  
Including:  
1. Enhanced Accurate Predictions: EAP  
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2. Process Optimization: PO  
3. Forecasting Incidents and Risks: FIR  
4. Inventory Optimization: IO  
5. Real-Time Responsiveness: RTR  
6. Transport Routing Optimization: TRO  
7. Enhanced Marketing Forecasting: EMF  
8. Integration of Diverse Information: IDI  
Based on the analysis results, we observe that the regression model with the 8 independent variables has a  
relatively low adjusted coefficient of determination (Adjusted R square) of 0.212 (Table 2). However, the level  
of significance of the F-test value (Sig = 0.000) is less than 0.05 (null hypothesis Ho is rejected), indicating  
that the combined effect of the variables present in the model can explain the variation in the application of AI  
in supply chain management. Therefore, we conclude that our constructed model fits the dataset appropriately.  
In other words, the independent variables explain approximately 21.2% of the variance in the dependent  
variable.  
The results from the table above demonstrate that the Enhanced Accurate Predictions (EAP) aspect and the  
Process Optimization (PO) aspect have a level of significance with a Sig value less than 0.05. Thus, we reject  
the null hypothesis H0, which states that there is no linear relationship between the independent variables and  
the dependent variable, implying that these independent variables and the dependent variable have a linear  
relationship.  
The remaining variables, namely Forecasting Incidents and Risks (FIR), Inventory Optimization (IO), Real-  
Time Responsiveness (RTR), Transport Routing Optimization (TRO), Enhanced Marketing Forecasting  
(EMF), and Integration of Diverse Information (IDI), all have Sig > 0,05.  
Therefore, we accept the null hypothesis (H0) that there is no linear relationship between the independent  
variables and the dependent variable, indicating that these independent variables have no linear relationship  
with the dependent variable.  
Looking at the standardized beta coefficients, the Enhanced Accurate Predictions (EAP) factor has a p-value of  
0.286, and the Process Optimization (PO) factor has a p-value of 0.202. This indicates that the Enhanced  
Accurate Predictions and Process Optimization factors have a positive impact on the utilization of AI in supply  
chain management (Table 4).  
Regarding multicollinearity, in the table above, we observe that none of the variables exhibit multicollinearity  
(the VIF values are all less than 2).  
Table 2. Model overview  
Model Summary  
Model  
1
R
R Square  
,236  
Adjusted R Square  
,212  
Std, Error of the Estimate  
,50185  
,485a  
a. Predictors: (Constant), EAP, PO, FIR, IO, RTR, TRO, EMF, IDI  
Table 3 F test  
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ANOVAa  
Model  
1
Sum of Squares  
19,874  
df  
Mean Square  
2,484  
F
Sig,  
Regression  
Residual  
Total  
8
9,864  
,000b  
64,475  
256  
264  
,252  
84,349  
a. Dependent Variable: UAI  
b. Predictors: (Constant), EAP, PO, FIR, IO, RTR, TRO, EMF, IDI  
Table 4 Statistical values of the model  
Coefficientsa  
Model  
Unstandardized  
Coefficients  
Standardized  
Coefficients  
t
Sig.  
Collinearity Statistics  
B
Std. Error  
Beta  
Tolerance  
VIF  
1
(Constant)  
EAP  
PO  
1,687  
,283  
,188  
,104  
-,019  
,060  
,096  
-,060  
-,027  
,346  
,057  
,059  
,053  
,038  
,032  
,055  
,036  
,054  
4,868  
4,974  
3,190  
1,941  
-,490  
1,857  
1,732  
-1,679  
-,501  
,000  
,000  
,002  
,053  
,625  
,064  
,084  
,094  
,617  
,286  
,202  
,113  
-,027  
,107  
,108  
-,097  
-,029  
,904  
,748  
,876  
,966  
,905  
,772  
,891  
,889  
1,106  
1,337  
1,141  
1,035  
1,105  
1,296  
1,123  
1,125  
FIR  
IO  
RTR  
TRO  
EMF  
IDI  
a. Dependent Variable: UAI  
RECOMMENDATIONS AND LIMITATIONS  
The purpose of the research is to make recommendations to increase the application of AI in supply chain  
management for the factors affecting the application of AI or in other words increase the Mean value of non-  
variable variables, academic. Through the discussion process in qualitative research and self-inquiry. The  
authors will provide solutions for those variables that have Mean < 3.5.  
Enhanced Accurate Predictions: While AI enhances prediction accuracy, it relies on historical data and  
patterns. Unforeseen disruptions or rapid market shifts may challenge its accuracy, necessitating continuous  
monitoring and adaptation.  
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Process Optimization: AI-driven process optimization requires meticulous data integration and model  
calibration. Implementation complexities may arise, requiring expertise to fine-tune algorithms and ensure  
seamless integration across diverse processes.  
Forecasting Incidents and Risks: While AI aids in incident prediction, it might not anticipate entirely  
unprecedented events. The effectiveness heavily depends on the quality of data fed into the model and the  
ability to encompass novel scenarios.  
Inventory Optimization: AI's ability to optimize inventory is reliant on accurate demand forecasts. It might  
struggle with sudden changes in demand patterns or disruptions that affect the supply chain, necessitating  
complementary risk mitigation strategies.  
Real-Time Responsiveness: AI's real-time insights depend on data availability and processing speeds.  
Inadequate data quality or delays can impact the responsiveness of the system.  
Transport Routing Optimization: AI's transport routing optimization is grounded in available data and  
historical patterns. Real-time conditions like traffic or weather could lead to deviations from the optimized  
routes, requiring on-the-fly adjustments.  
Enhanced Market Forecasting: While AI can enhance market forecasting, it may not consider complex  
macroeconomic factors or unforeseeable geopolitical events, necessitating a blend of AI insights and human  
judgment.  
Integration of Diverse Information: Integrating diverse data sources requires robust data governance and  
quality assurance. Inaccurate or conflicting data could lead to biased decisions, emphasizing the importance of  
data validation.  
Overall, these factors demonstrate AI's potential to transform supply chain management, but they should be  
integrated thoughtfully considering contextual constraints and potential limitations to ensure effective  
utilization.  
REFERENCES  
1. Abubakar, A. M., Behravesh, E., Rezapouraghda, H., & Yildiz, S. B. (2019). Applying artificial  
intelligence technique to predict knowledge hiding behavior. International Journal of Information  
Management, 49, 45–57.  
2. Allcott, H., & Rogers, T. (2014). The short-run and long-run effects of behavioral interventions:  
Experimental evidence from energy conservation. American Economic Review, 104(10), 3003–3037.  
https://doi.org/10.1257/aer.104.10.3003  
3. Agarwal, S., Kumar, S., & Goel, U. (2019). Stock market response to information diffusion through  
internet sources: A literature review. International Journal of Information Management, 45, 118–131.  
4. Ali, O., Shrestha, A., Soar, J., & Wamba, S. F. (2018). Cloud computing-enabled healthcare  
opportunities, issues, and applications: A systematic review. International Journal of Information  
Management, 43, 146–158.  
5. Allen, J. F. (1998). AI growing up: The changes and opportunities. AI Magazine, 19(4), 13–23.  
6. Austin, P., Tu, J., Ho, J., Levy, D., & Lee, D. (2013). Using methods from the data-mining and  
machine-learning literature for disease classification of heart failure subtypes. J. Clin. Epidemiol., 398–  
407..  
7. Chi, M., Huang, R., & George, J. F. (2020). Collaboration in demand-driven supply chain: Based on a  
perspective of governance and IT-business strategic alignment. International Journal of Information  
Management, 52, Article 102062.  
8. Dejoux, C., & L ́eon, E. (2018). M ́etamorphose des managers (1st ed.). France: Pearson. Dennehy, D.  
(2020). Ireland post-pandemic: Utilizing AI to kick-start economic recovery.  
9. Diabat, A., Al-Salem, M. (2015), ‘An integrated supply chain problem with environmental  
considerations’, International Journal of Production Economics, 164, 330–338.  
Page 3248  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
10. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Wamba, S. F. (2017), ‘The impact of  
big data on world-class sustainable manufacturing’, International Journal of Advanced Manufacturing  
Technology, 84(1–4), 631–645.  
11. Elbegzaya, T. (2020). Application AI in Traditional Supply Chain Management Decision-Making.  
12. Exxact (2023). 5 Ways To Use AI For Supply Chain Management. Www.exxactcorp.com. Retrieved  
Exxact  
(2023).  
from  
https://www.exxactcorp.com/blog/Deep  
Learning/ai-for-supply-chain-  
management  
13. Fan, M. (2024), ‘Research on Human Resource Management in Improving Supply Chain Efficiency’,  
Advances in Economics, Management and Political Sciences, 75, 17-21.  
14. Fong, W.K., Zhou, Y. (2025), ‘Emerging economies can lead the way on green logistics. Experts  
explain why’. Climate Action and Waste Reduction. World Economic Forum. www.weforum.org  
15. ICAP – International Carbon Action Partnership. (2025, July 2). Vietnam issues rules for pilot ETS,  
launching August 2025.  
16. Nguyen, V.L., & Pham, T.H. (2025), ‘Green supply chain in Vietnam’s agricultural export activities’,  
International Journal of Innovation, Responsibility and Social Sciences, 8(3).  
17. World Bank. (2025). State and trends of carbon pricing 2025. World Bank. https://doi.org/10.1596/978-  
1-4648-2255-1  
Page 3249  
www.rsisinternational.org