Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 17, No. 2, 2024 362 Reduction of Transportation Cost: Examinig the Moderating Role of Artificial Intelligence (AI) Implementation in Bangladesh Hossain Md Anwar School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 410006, China Abstract: This study investigates the role of logistics strategies Route and Node Optimization, Shipment Consolidation, and Demand Forecasting in reducing transportation costs in the logistics sector of Bangladesh. It also explores the moderating effect of Artificial Intelligence (AI) on the effectiveness of these strategies. With the increasing complexity of logistics operations and the growing need for cost-efficient practices, this research highlights how traditional logistics methods can be augmented with advanced AI technologies to optimize operations and achieve significant cost reductions. Data were collected through structured surveys involving 300 respondents from logistics companies in Bangladesh. Using Principal Component Analysis (PCA) and Multiple Regression Analysis, the relationships between the independent variables (logistics strategies), the moderating variable (AI implementation), and the dependent variable (Transportation Cost Reduction) were evaluated. The results reveal that Shipment Consolidation had the most significant impact on transportation cost reduction, followed by Demand Forecasting and Route and Node Optimization. AI implementation was found to positively moderate these relationships, enhancing the efficiency of each strategy. The findings underscore the transformative potential of AI in logistics operations, particularly in developing economies. The study contributes to both theory and practice by providing actionable recommendations for integrating AI into logistics to achieve cost efficiency. Future research directions are suggested, including exploring longitudinal impacts and expanding the study scope to other regions. Keywords: Transportation Cost Reduction, Route and Node Optimization, Shipment Consolidation, Demand Forecasting, Artificial Intelligence (AI), Logistics Strategies, Cost Efficiency, AI Implementation, Logistics Sector. 1. Introduction The logistics sector serves as a fundamental pillar for economic development, enabling the efficient movement of goods and services across industries and markets. In Bangladesh, logistics plays a vital role in supporting the nation's integration into global supply chains, yet the sector faces significant challenges. Transportation costs, which comprise a substantial portion of total logistics expenses, are a critical factor for businesses striving to maintain profitability and competitiveness. Inefficient logistics operations, coupled with infrastructural limitations such as poor road networks, traffic congestion, and outdated practices, often inflate transportation costs, hindering the growth and efficiency of the logistics industry [1]. With globalization intensifying competition, the complexity of supply chains has grown exponentially. Traditional logistics practices, such as static route planning and manual shipment management, have become insufficient for addressing modern logistical demands. Inefficiencies in route optimization, underutilization of transportation resources, and the inability to predict fluctuating demand patterns exacerbate transportation costs [2]. To combat these challenges, businesses must adopt innovative solutions that go beyond conventional approaches, ensuring streamlined logistics operations and significant cost reductions. Artificial Intelligence (AI) has emerged as a transformative technology capable of revolutionizing logistics practices. By leveraging AI-powered solutions, businesses can achieve real-time route optimization, dynamic shipment consolidation, and accurate demand forecasting. AI enables companies to process large datasets, predict customer behavior, and make data-driven decisions that enhance operational efficiency. Its potential to bridge the gap between traditional inefficiencies and modern demands makes AI a crucial tool for achieving sustainable cost efficiency in logistics. This study focuses on three essential logistics strategies: Route and Node Optimization, Shipment Consolidation, and Demand Forecasting. Each of these strategies has been widely recognized for its potential to reduce transportation costs. However, their true value lies in their integration with AI technologies, which can amplify their effectiveness. While these strategies have been extensively studied in developed economies, there is limited research on their combined impact with AI in the context of developing countries like Bangladesh [3]. This gap highlights the need to explore how AI can be leveraged to optimize logistics operations and reduce transportation costs in resource-constrained settings. The key objectives of this research are: (1) To evaluate the effectiveness of Route and Node Optimization, Shipment Consolidation, and Demand Forecasting in reducing transportation costs in Bangladesh's logistics sector. (2) To analyze the moderating role of AI implementation in enhancing the efficiency of these logistics’ strategies. This study fills an important research gap by examining the combined impact of logistics strategies and AI in a developing economy. While most studies focus on AI's standalone benefits or the independent effects of logistics strategies, this research integrates these elements into a unified framework. Furthermore, the study emphasizes the unique challenges and opportunities present in Bangladesh, offering insights into how businesses can overcome infrastructural and technological limitations to achieve cost efficiencies [4]. 363 The findings of this study hold significant implications for both academia and industry. For academics, the study contributes to the literature on AI and logistics by demonstrating the synergistic potential of combining AI with traditional logistics strategies. For practitioners, it provides actionable recommendations for adopting AI technologies to optimize logistics operations and achieve cost reductions. Additionally, the research offers insights for policymakers, highlighting the need for investments in infrastructure and technology to support AI adoption in logistics. This research seeks to provide a comprehensive understanding of how logistics strategies and AI can work together to transform transportation cost efficiency in Bangladesh. By addressing the challenges of cost reduction and operational inefficiencies, the study offers a roadmap for innovation and competitiveness in the logistics sector [5]. The subsequent sections of this paper detail the literature review, methodology, empirical findings, and practical implications, paving the way for future advancements in logistics research and practice [6, 7]. 2. Literature Review The logistics industry is a cornerstone of economic activities, enabling the efficient movement of goods, enhancing supply chain reliability, and contributing significantly to national and global economic growth. However, as supply chains become increasingly complex and globalized, the costs associated with transportation have emerged as a pressing concern for logistics companies. This chapter reviews existing literature on transportation cost reduction strategies, the role of Artificial Intelligence (AI) in logistics, and the interplay between these elements in the context of developing economies like Bangladesh. 2.1. Transportation Cost Reduction Transportation costs represent a significant portion of total logistics expenses, often accounting for up to 50% of a company's total operating costs in the supply chain. Reducing these costs is essential for businesses to remain competitive in a globalized market [8, 9]. Effective cost reduction requires a comprehensive approach that addresses inefficiencies in routing, shipment consolidation, and demand forecasting. Key strategies for reducing costs include improving vehicle utilization, minimizing empty miles, and optimizing warehouse locations. In the Bangladeshi context, transportation inefficiencies are exacerbated by infrastructural challenges, such as poor road conditions, traffic congestion, and limited technological adoption. The businesses in Bangladesh often face higher transportation costs compared to regional competitors due to these barriers [10]. This necessitates the adoption of innovative solutions to enhance operational efficiency and reduce costs. 2.2. Route and Node Optimization Route and Node Optimization focuses on designing the most efficient transportation routes and strategically placing warehouses or distribution nodes. These strategies aim to minimize travel distances, reduce fuel consumption, and ensure timely delivery of goods. Demonstrated that optimized routes could lead to a reduction of up to 20% in transportation costs [11]. Recent advancements in AI have revolutionized route optimization by incorporating real-time data, such as traffic conditions, weather forecasts, and delivery deadlines. AI- based systems, such as dynamic routing algorithms, enable businesses to adjust routes proactively, thereby reducing delays and fuel wastage [12, 13]. Node optimization, on the other hand, involves selecting optimal locations for warehouses and distribution centers to streamline the flow of goods. Emphasize that strategic node placement can significantly reduce last-mile delivery costs, which are often the most expensive segment of the supply chain [14]. 2.3. Shipment Consolidation Shipment Consolidation involves combining multiple smaller shipments into larger ones to maximize transportation efficiency. This strategy reduces the number of trips, optimizes the use of vehicles, and lowers fuel consumption. Demonstrated that effective shipment consolidation could reduce transportation costs by up to 15%, especially in fragmented supply chains [15]. AI has played a transformative role in improving shipment consolidation by dynamically grouping shipments based on real-time data. Machine learning models analyze factors such as shipment volume, delivery deadlines, and vehicle capacities to create optimal consolidation plans. In the Bangladeshi logistics sector, where supply chains often involve multiple small suppliers and retailers, shipment consolidation offers a viable solution for reducing costs while improving delivery reliability [16, 17]. 2.4. Demand Forecasting Demand Forecasting is a critical logistics strategy that involves predicting future demand for goods to optimize inventory levels and logistics planning. Accurate demand forecasting minimizes the risks of stockouts and overstocking, which often lead to higher transportation costs due to expedited or unnecessary shipments [18]. Traditional forecasting methods rely on historical data and statistical models, but these often fail to account for dynamic market trends and external disruptions. AI-based forecasting models, such as neural networks and time-series analysis, have demonstrated superior accuracy by incorporating diverse data sources, including market trends, customer preferences, and economic indicators [19]. In developing countries like Bangladesh, AI-powered demand forecasting could help businesses navigate fluctuating market conditions and improve supply chain resilience. 2.5. The Role of Artificial Intelligence in Logistics Artificial Intelligence has emerged as a game-changer in the logistics industry, offering solutions that address inefficiencies and enhance decision-making capabilities. AI technologies, such as machine learning, predictive analytics, and robotics, enable businesses to automate complex processes, optimize resource utilization, and improve operational efficiency [20, 21]. In the context of transportation cost reduction, AI enhances logistics operations in three key areas: Dynamic Route Optimization: AI algorithms process real- time data to identify the most efficient routes, reducing fuel consumption and delivery times. Shipment Consolidation Automation: AI systems dynamically group shipments to maximize vehicle utilization and minimize transportation frequency. Enhanced Demand Forecasting: AI improves forecasting 364 accuracy by analyzing large datasets, enabling better alignment of supply with demand. Despite these benefits, the adoption of AI in logistics is not without challenges. The barriers such as high implementation costs, data quality issues, and resistance to change within organizations [22]. Overcoming these challenges requires strategic investments in technology and capacity-building initiatives. 2.6. Challenges in Implementing AI in Logistics While the benefits of AI in logistics are well-documented, its implementation faces several challenges, particularly in developing economies like Bangladesh. Key barriers include: Technological Infrastructure: Limited access to advanced technological infrastructure hinders AI adoption in logistics operations. Data Quality and Availability: AI systems rely on high- quality data, which is often lacking in resource-constrained settings. Cost of Implementation: The high upfront investment required for AI technologies poses a significant barrier for small and medium-sized enterprises (SMEs). Workforce Resistance: Employees often resist adopting AI- driven systems due to fears of job displacement and lack of understanding. Addressing these challenges requires collaborative efforts from businesses, policymakers, and technology providers. Investments in infrastructure, training programs, and government incentives can facilitate the integration of AI into logistics operations [23, 24]. 2.7. Research Gap While extensive research exists on logistics strategies and AI's standalone benefits, studies integrating these elements remain limited, particularly in the context of developing economies. Most existing studies focus on developed markets, where technological infrastructure and resources are abundant. This creates a gap in understanding how AI can be effectively integrated with logistics strategies in resource- constrained settings like Bangladesh. Additionally, there is limited empirical evidence on the moderating role of AI in enhancing the impact of logistics strategies. This study aims to address these gaps by examining the combined effect of Route and Node Optimization, Shipment Consolidation, and Demand Forecasting with AI on transportation cost reduction. 3. Methodology This chapter outlines the research design, data collection methods, and analytical techniques used to investigate the role of logistics strategies Route and Node Optimization, Shipment Consolidation, and Demand Forecasting and the moderating effect of Artificial Intelligence (AI) in reducing transportation costs. The methodology was designed to provide reliable and actionable insights into the logistics practices of companies in Bangladesh. 3.1. Research Framework The study employed a quantitative research approach to examine the relationships between logistics strategies, AI implementation, and transportation cost reduction. A cross- sectional design was chosen, as it allows for data collection at a single point in time, ensuring efficiency and consistency in analysis. The research model integrated independent variables (Route and Node Optimization, Shipment Consolidation, Demand Forecasting), the moderating variable (AI implementation), and the dependent variable (Transportation Cost Reduction). The conceptual framework was developed based on an extensive review of the literature, identifying key variables and their hypothesized relationships. Hypotheses were formulated to test the direct effects of logistics strategies on transportation cost reduction and the moderating influence of AI. The conceptual framework for this study is presented in Figure 1, which illustrates the relationships between the independent variables (Route and Node Optimization, Shipment Consolidation, and Demand Forecasting), the moderating variable (AI Implementation), and the dependent variable (Transportation Cost Reduction). Figure1. Research Model 3.2. Data Collection Data were collected using structured surveys, which were distributed both physically and online to logistics professionals across Bangladesh. The survey instrument consisted of multiple sections, each focusing on one of the research variables: Route and Node Optimization: Four questions measuring the extent of optimized transportation routes and node placement. Shipment Consolidation: Four questions assessing practices for combining shipments. Demand Forecasting: Four questions evaluating the accuracy and use of demand forecasting techniques. AI Implementation: Four questions addressing the adoption and utilization of AI technologies in logistics operations. Transportation Cost Reduction: Four questions measuring the effectiveness of cost-saving strategies. The survey used a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) to capture participants’ perceptions. This scaling method allowed for quantitative measurement of subjective responses, facilitating statistical analysis. 3.3. Validity and Reliability To ensure the validity and reliability of the data collection instrument: Content Validity: The survey questions were reviewed by logistics experts to confirm their relevance to the research objectives. Construct Validity: A pilot test was conducted with 30 respondents to refine the survey and ensure clarity. Reliability Analysis: Cronbach’s Alpha was used to assess 365 the internal consistency of the survey items. A Cronbach’s Alpha value of 0.920 confirmed the instrument’s reliability. 3.4. Data Analysis The data collected were analyzed using IBM SPSS 26.0, employing both descriptive and inferential statistical techniques to test the hypotheses. The analysis was conducted in several stages: Descriptive Statistics: Used to summarize demographic variables (gender, age, education, and salary) and provide an overview of the data. Principal Component Analysis (PCA): Applied to reduce data dimensionality and identify key components influencing transportation cost reduction. Multiple Regression Analysis: Conducted to evaluate the relationships between the independent variables and the dependent variable, as well as the moderating effect of AI. Moderation Analysis: Interaction terms were created to test AI’s role in moderating the relationship between logistics strategies and transportation cost reduction. 3.5. Research Hypotheses Based on the research framework, the following hypotheses were tested: H1: Route and Node Optimization positively influences Transportation Cost Reduction. H2: Shipment Consolidation positively influences Transportation Cost Reduction. H3: Demand Forecasting positively influences Transportation Cost Reduction. H4a: AI Implementation positively moderates the relationship between Route and Node Optimization and Transportation Cost Reduction. H4b: AI Implementation positively moderates the relationship between Shipment Consolidation and Transportation Cost Reduction. H4c: AI Implementation positively moderates the relationship between Demand Forecasting and Transportation Cost Reduction. 3.6. Ethical Considerations Ethical protocols were strictly followed throughout the study. Respondents were assured of the confidentiality and anonymity of their responses. Participation in the survey was voluntary, and informed consent was obtained before data collection. The research complied with ethical guidelines for social science research, ensuring integrity and transparency. 3.7. Limitations of the Methodology While the methodology was robust, several limitations should be acknowledged: Cross-sectional Design: The data capture a snapshot in time and may not reflect changes or trends in logistics practices over time. Non-Probability Sampling: The use of convenience sampling may limit the generalizability of the findings to the broader population. Self-Reported Data: The reliance on participants' perceptions introduces the potential for response bias. 4. Analysis The study based on the data collected from 300 respondents working in logistics companies across Bangladesh. The results are organized to address the research objectives and hypotheses, focusing on the impact of logistics strategies on transportation cost reduction and the moderating role of Artificial Intelligence (AI). Statistical analyses, including descriptive statistics, Principal Component Analysis (PCA), and multiple regression analysis, were employed to derive meaningful insights. 4.1. Descriptive Statistics Table 1. Demographic Statistics Category Subcategory Frequency Percent Valid Percent Cumulative Percent Gender Male 144 48 48 48 Gender Female 156 52 52 100 Age 18-25 12 4 4 4 Age 25-35 135 45 45 49 Age 36-45 91 30.3 30.3 79.3 Age 45-55 54 18 18 97.3 Age 55+ 8 2.7 2.7 100 Education High school and below 38 12.7 12.7 12.7 Education Diploma 48 16 16 28.7 Education Bachelor's 119 39.7 39.7 68.3 Education Master's 56 18.7 18.7 87 Education PhD 39 13 13 100 Salary <15k 56 18.7 18.7 18.7 Salary 15,001-20k 83 27.7 27.7 46.3 Salary 20,001-25k 91 30.3 30.3 76.7 Salary 25,001-30k 55 18.3 18.3 95 Salary >30k 15 5 5 100 Descriptive statistics provide an overview of the demographic characteristics of the respondents and summarize the central tendencies and distributions of the variables. The demographic profile revealed the following: Gender: The sample consisted of 48% male and 52% female respondents. Age: The majority of participants were aged between 25– 35 years (45%), followed by 35–45 years (30.3%), indicating a relatively young workforce. Education: Most respondents held a bachelor's degree 366 (39.7%), while a significant portion had completed master's (18.7%) or diploma programs (16%). Salary: The highest percentage of respondents earned between 20,000–25,000 BDT (30.3%). Descriptive statistics for the independent variables (Route and Node Optimization, Shipment Consolidation, Demand Forecasting), the moderating variable (AI Implementation), and the dependent variable (Transportation Cost Reduction) showed consistent responses, with mean scores ranging between 2.4 and 2.6 on a 5-point Likert scale. This indicates moderate agreement with the effectiveness of these strategies. 4.1.1. Principle Component Analysis (PCA) Table 2. KMO and Bartlett's Test Results Test Statistic Value Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy 0.896 Bartlett’s Test of Sphericity Approx. Chi-Square 6364.638 Degrees of Freedom (df) 190 Significance (Sig.) 0 Table 3. Total Variance Explained Com pone nt Initial Eigenva lues: Total % of Varianc e Cumulat ive % Extraction Sums of Squared Loadings: Total % of Variance (Extraction) Cumulative % (Extraction) Rotation Sums of Squared Loadings: Total % of Variance (Rotation) Cumulativ e % (Rotation) 1 9.835 49.174 49.174 9.835 49.174 49.174 3.364 16.818 16.818 2 1.859 9.293 58.467 1.859 9.293 58.467 3.355 16.776 33.594 3 1.833 9.165 67.631 1.833 9.165 67.631 3.348 16.742 50.336 4 1.653 8.265 75.897 1.653 8.265 75.897 3.262 16.311 66.647 5 1.397 6.986 82.883 1.397 6.986 82.883 3.247 16.236 82.883 6 0.66 3.301 86.184 7 0.618 3.088 89.272 8 0.565 2.823 92.095 9 0.202 1.012 93.106 10 0.182 0.909 94.015 11 0.155 0.773 94.788 12 0.142 0.712 95.501 13 0.138 0.688 96.189 14 0.133 0.666 96.855 15 0.123 0.613 97.468 16 0.119 0.595 98.063 17 0.109 0.543 98.606 18 0.105 0.525 99.131 19 0.095 0.477 99.608 20 0.078 0.392 100 Table 4. Rotated Component Matrix Variable Component 1 Component 2 Component 3 Component 4 Component 5 RNO1 0.849 RNO2 0.828 RNO3 0.848 RNO4 0.844 CS1 0.803 CS2 0.844 CS3 0.825 CS4 0.813 DF1 0.86 DF2 0.81 DF3 0.837 DF4 0.841 AI1 0.846 AI2 0.837 AI3 0.827 AI4 0.835 TCR1 0.822 TCR2 0.818 TCR3 0.796 TCR4 0.823 The results demonstrate the robustness of the data for factor analysis, supported by a Kaiser-Meyer-Olkin (KMO) value of 367 0.896, indicating high sampling adequacy. Additionally, Bartlett’s Test of Sphericity was highly significant (Chi- Square = 6364.638, df = 190, p < 0.001), confirming the data’s suitability for further analysis. Five components were extracted, collectively explaining 82.883% of the variance, with the first component alone accounting for 49.174%. The rotated component matrix revealed strong loadings for all variables on their respective components, validating the constructs. For example, variables such as RNO1–RNO4 loaded strongly onto Component 1 (0.828–0.849), while CS1–CS4 showed high loadings on Component 2 (0.803– 0.844). The consistent and distinct loading patterns affirm the validity of the theoretical framework, providing a solid foundation for subsequent analyses. 4.2. Reliability Analysis The reliability analysis confirmed excellent internal consistency for all constructs in the study, with Cronbach’s Alpha values exceeding 0.9. Transportation Cost Reduction (TCR), Route and Node Optimization (RNO), Shipment Consolidation (CS), Demand Forecasting (DF), and AI Implementation (AI) demonstrated strong reliability, indicating that the survey items effectively measured their respective constructs. The Cronbach’s Alpha for the entire questionnaire was 0.937, further validating the overall reliability of the instrument. These results confirm that the data collected is consistent and reliable for further statistical analysis. Table 5. Reliability Statistics Statistic Value Cronbach's Alpha 0.945 Cronbach's Alpha Based on Standardized Items 0.945 N of Items 20 4.3. Correlation Analysis The correlation analysis confirms the validity of the study's constructs, showing significant positive relationships among all variables at the 0.01 level. Key findings include Transportation Cost Reduction (TCR) positively correlating with Route and Node Optimization (r = 0.460), Shipment Consolidation (r = 0.535), Demand Forecasting (r = 0.515), and AI Implementation (r = 0.507). Moderate correlations among independent variables suggest discriminant validity, while strong correlations with TCR indicate convergent validity. These results support the constructs' validity and provide a solid foundation for further analysis. Table 6. Correlation Matrix Variable Media nTCR: Pearso n Correl ation Med ianT CR: Sig. (2- taile d) Med ianT CR: N Media nRNO : Pearso n Correl ation Med ianR NO: Sig. (2- taile d) Med ianR NO: N Media nCS: Pearso n Correl ation Med ian CS: Sig. (2- taile d) Med ianC S: N Media nDF: Pearso n Correl ation Medi anDF : Sig. (2- tailed ) Me dia nD F: N Media nAI: Pearso n Correl ation Med ian AI: Sig. (2- taile d) Med ian AI: N MedianT CR 1 300 0.46 0 300 0.535 0 300 0.515 0 300 0.507 0 300 MedianR NO 0.46 0 300 1 300 0.475 0 300 0.461 0 300 0.458 0 300 MedianCS 0.535 0 300 0.475 0 300 1 300 0.449 0 300 0.494 0 300 MedianD F 0.515 0 300 0.461 0 300 0.449 0 300 1 300 0.46 0 300 MedianAI 0.507 0 300 0.458 0 300 0.494 0 300 0.46 0 300 1 300 4.4. Multiple Regression Analysis The multiple regression analysis highlights the significant positive impact of Route and Node Optimization (RNO), Shipment Consolidation (CS), and Demand Forecasting (DF) on Transportation Cost Reduction (TCR). The model explains 40.1% of the variance in TCR (R² = 0.401), with an F-ratio of 66.181 (p < 0.001), confirming its overall significance. Individually, RNO (B = 0.193, p = 0.001), CS (B = 0.367, p < 0.001), and DF (B = 0.308, p < 0.001) all contribute significantly to reducing transportation costs. These results validate the study’s hypotheses and underscore the importance of effective logistics strategies in cost optimization. Table 7. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.634 0.401 0.395 0.86908 Table 8. Coefficients Mod el Unstand ardized Coeffici ents: B Unstandardi zed Coefficients : Std. Error Standardiz ed Coefficien ts: Beta t Sig. 1 0.398 0.163 2.437 0.015 1 0.193 0.06 0.173 3.218 0.001 1 0.367 0.061 0.322 6.027 0 1 0.308 0.056 0.29 5.472 0 4.5. Moderating Efficiency Test The analysis highlights that Artificial Intelligence (AI) significantly enhances the effectiveness of logistics strategies in reducing transportation costs. AI acts as a moderator, with a moderate positive impact on the relationship between Route and Node Optimization (Beta = 0.150, p = 0.013), a strong effect on Shipment Consolidation (Beta = 0.214, p = 0.001), and a substantial positive influence on Demand Forecasting (Beta = 0.182, p = 0.005). These findings emphasize that integrating AI into logistics operations strengthens the efficiency of individual strategies, particularly Shipment Consolidation, and underscores the importance of AI-driven technologies for achieving cost optimization and improved 368 logistics performance. Table 9. Moderating Efficiency Effect Predictor Variables Unstandardized Coefficients (B) Std. Error Standardized Coefficients (Beta) t-value Sig. (p- value) Constant 2.404 0.063 - 38.157 0.000 Interaction_RNO_AI (Route and Node Optimization × AI) 2.875E-6 0.000 0.150 2.500 0.013 Interaction_CS_AI (Shipment Consolidation × AI) 5.549E-6 0.000 0.214 3.421 0.001 Interaction_DF_AI (Demand Forecasting × AI) 3.348E-6 0.000 0.182 2.800 0.005 4.6. Hypothesis Testing The ANOVA table indicates that the regression model is statistically significant in explaining Transportation Cost Reduction (TCR). The F-statistic of 66.181 and the p-value of 0.000 suggest that the independent variables (Route and Node Optimization, Shipment Consolidation, and Demand Forecasting) together have a significant impact on TCR. The Sum of Squares shows that the model explains a substantial portion of the variation in TCR (149.961 for Regression), while the remaining variation (223.569 for Residual) is not explained by the model. The high F-statistic and low p-value confirm that the model is a good fit for the data, demonstrating that these three factors play a crucial role in reducing transportation costs. Table 10. ANOVA Model Sum of Squares df Mean Square F Sig. Regression 149.961 3 49.987 66.181 0.000 Residual 223.569 296 0.755 Total 373.53 299 5. Discussion The study highlights AI’s transformative role in enhancing logistics strategies, with Shipment Consolidation showing the strongest impact on transportation cost reduction. AI significantly improved the efficiency of Route and Node Optimization and Demand Forecasting, addressing key logistical challenges in Bangladesh. Despite the benefits, adoption barriers like limited infrastructure and workforce resistance remain. The findings provide a scalable framework for integrating AI in logistics, with implications for cost efficiency in similar developing economies. Future research should explore long-term impacts and broader applications. 6. Conclusion The study confirmed that logistics strategies Route Optimization, Shipment Consolidation, and Demand Forecasting significantly reduce transportation costs, with AI playing a critical role in enhancing their effectiveness. Shipment Consolidation had the strongest impact, while AI strengthened the efficiency of all strategies. The research contributes theoretically by validating AI’s moderating role in logistics management and practically by offering actionable insights for logistics companies in developing countries like Bangladesh. Despite challenges such as limited data access, technological barriers, and resistance to change, the study achieved its objectives, addressing the research questions effectively. Recommendations include investing in AI-driven tools, supporting digital infrastructure, and exploring AI’s role in other logistics areas. Limitations, such as the small sample size and geographic focus, point to future research opportunities, including longitudinal studies and broader geographic analysis. Acknowledgements I would like to express my sincere gratitude to my supervisor, Dr. Dong Ding, for their invaluable guidance and support throughout this research. I am thankful to Chongqing University of Posts and Telecommunications and the Department of Management Science and Engineering for providing resources and an encouraging academic environment. Special thanks to the respondents from logistics companies in Bangladesh whose participation made this study possible. I am also grateful to my friends and colleagues for their encouragement and feedback. Lastly, heartfelt appreciation goes to my family for their unwavering love and support, which has been my source of strength throughout this journey. References [1] Guan, X., Zhang, X., & Sun, Y. (2020). 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