INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
The Effect of Ai-Route Optimization on Supply Chain Performance  
among Large Supermarkets in Nairobi City County, Kenya  
Eben Ezer Amani Kazadi, Reuben Musyoka Mwove, PhD  
School of Business and Economics, Department of Commerce, Daystar University, P O Box 44400- 00100  
Nairobi, Kenya  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100292  
Received: 27 November 2025; Accepted: 01 December 2025; Published: 08 December 2025  
ABSTRACT  
Supermarkets increasingly face logistical challenges such as fluctuating demand patterns, rising distribution  
costs, and the need for timely and efficient product delivery. Artificial Intelligence (AI) has emerged as a critical  
tool for improving logistics decision-making, particularly through route optimization systems that enhance  
delivery speed, reduce transportation costs, and improve overall supply chain responsiveness. This study  
examined the effect of AI-Route Optimization on supply chain performance among large supermarkets in  
Nairobi City County, Kenya. The study was grounded in the Hybrid Intelligence Model, Triple Triangle  
Constraint Theory, and Technology Acceptance Theory to explain the integration and influence of AI  
technologies on supply chain outcomes. Adescriptive research design was adopted, targeting employees working  
in the supply chain departments of 10 large supermarkets within Nairobi City County. A sample of 70  
respondents was drawn from this population, and data were collected using structured questionnaires. Reliability  
was tested through a pretest involving seven respondents from selected Naivas branches in Kiambu County.  
Descriptive statistics, including means and standard deviations, were used to summarize the data, while  
inferential statistics such as correlation and regression analysis were applied to determine the relationship  
between AI-route optimization and supply chain performance using SPSS version 30. The findings indicated that  
AI-Route Optimization had a statistically significant effect on supply chain performance (M = 3.57, SD = 0.43),  
with the regression model showing strong explanatory power (R² = 0.79) and statistical significance (F =  
222.015, ρ < 0.01). The study concluded that AI-driven route optimization positively enhances supply chain  
performance by improving delivery efficiency, minimizing transport costs, and strengthening service reliability.  
The study recommends that supermarket supply chain managers invest in advanced AI-based routing tools,  
integrate real-time data sources such as GPS and traffic feeds, and enhance staff capacity to effectively utilize  
these systems. Further research is recommended in other geographical contexts to compare the role of AI-route  
optimization across different retail and logistics environments.  
INTRODUCTION  
In today’s highly competitive retail environment, supply chain performance has become a core driver of  
organizational success, influencing delivery reliability, customer satisfaction, and overall operational efficiency  
(Kalaitzi et al., 2019). Over the years, supply chains have evolved from basic production–distribution systems  
into sophisticated, technology-supported networks that require advanced tools to manage growing complexity  
(Alomar, 2022). The emergence of Artificial Intelligence (AI) has accelerated this transformation by enabling  
firms to leverage data-driven insights, real-time analytics, and intelligent decision-making to streamline supply  
chain operations (Zong & Guan, 2024).  
AI has been particularly influential in enhancing logistics activities such as transportation and distribution, which  
are central to ensuring timely product availability in supermarkets (Muthuswamy & Ali, 2023). As global and  
local retail markets become more dynamic, large supermarkets face increasing pressure to improve delivery  
accuracy, minimize lead times, and reduce operational costs (Hove-Sibanda et al., 2021). Traditional routing  
practices often struggle with challenges such as traffic congestion, unpredictable travel times, fuel inefficiencies,  
and limited visibility across delivery routes, which undermine overall supply chain performance (Kazim, 2018).  
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AI-based route optimization has emerged as a transformative solution to these challenges. By integrating real-  
time traffic data, historical route patterns, and delivery constraints, AI systems can recommend the most efficient  
delivery paths, thereby improving fleet utilization, reducing transportation costs, and enhancing delivery  
reliability (Helo et al., 2022). Studies have demonstrated that AI-driven routing tools significantly enhance  
logistics efficiency, though results vary across contexts due to differences in technological readiness and  
infrastructure quality (Oosthuizen et al., 2021).  
Despite growing global adoption, empirical research on AI-route optimization within the Kenyan retail sector  
remains limited. While large supermarkets in Nairobi continue to expand their distribution networks, little is  
known about the extent to which AI-enabled routing systems have been implemented or the magnitude of their  
impact on supply chain performance (Charles et al., 2021). This gap highlights the need for context-specific  
evidence on how AI-driven routing influences transportation efficiency, delivery reliability, and overall supply  
chain outcomes. Therefore, this study examined the effect of AI-Route Optimization on supply chain  
performance among large supermarkets in Nairobi City County, Kenya, generating insights that are essential for  
retailers, policymakers, and technology providers seeking to strengthen logistics capabilities within the  
supermarket sector.  
Research Hypothesis  
AI-Route optimization has no statistically significant effect on supply chain performance among large  
supermarkets in Nairobi city county, Kenya.  
Conceptual Framework  
According to Burns and Burns (2013), a conceptual framework is a tool that researchers use to direct a research  
project's studies, plans, practices, thinking, and execution. The complete study process is reflected in it (Kivunja,  
2018). According to Kivunja (2018), it is a diagrammatic flow chart that illustrates or explains the connections  
between the elements and variables that have been recognized as pertinent to the study. The main purpose of  
the conceptual framework is to assist researchers in connecting their goals with the body of existing literature.  
Source: Researcher (2025)  
Figure 1: Conceptual Framework  
LITERATURE REVIEW  
Theoretical Literature Review  
The Technology Acceptance Theory (TAT), developed by Fred Davis in 1989, explains how individuals adopt  
and use new technologies based on two primary perceptions: perceived usefulness and perceived ease of use.  
According to the model, users are more willing to embrace a technology when they believe it will enhance their  
performance and when it is simple to operate (Ammenwerth, 2019). These perceptions shape a user's attitude,  
intention, and actual behavior toward adopting a technological innovation.  
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TAT has been widely applied in understanding technology adoption across various sectors, including artificial  
intelligence. As AI tools increasingly support forecasting, decision-making, and automation, users’ acceptance  
depends largely on their belief that such systems are beneficial and user-friendly (Unal & Uzun, 2021). However,  
scholars note that while the model offers a strong foundation for analyzing technology adoption, it has  
limitations. It focuses primarily on individual perceptions and does not sufficiently account for broader  
influences such as organizational culture, social pressure, infrastructure readiness, or financial barriers (Kim &  
Wang, 2021; Dutot et al., 2019). These omissions highlight the need for contextual considerations when applying  
TAT in complex organizational settings.  
Despite these limitations, TAT remains relevant in studies examining technological integration within supply  
chains. Effective implementation of AI tools, including route optimization systems, depends on employees’  
willingness to interact with and trust the technology (Liu et al., 2022). When users perceive AI-driven routing  
platforms as beneficial, reliable, and easy to use, adoption rates rise, leading to improvements in operational  
efficiency, reduced costs, and better decision-making.  
In the context of this study, TAT provides a valuable lens for understanding how perceptions of AI-route  
optimization influence its acceptance and subsequent impact on supply chain performance. The theory helps  
explain how the independent variable (AI-route optimization) aligns with user behavior and ultimately influences  
the dependent variable (supply chain performance) within large supermarkets.  
Empirical Literature Review  
Studies across different countries consistently show that AI-route optimization enhances supply chain  
performance, though the extent varies by context and industry. In the USA, Vaka (2024) found that AI improved  
delivery efficiency and customer satisfaction among e-commerce firms, though the study’s geographical and  
sectoral focus limits its applicability to Kenyan supermarkets. Similarly, Khadem et al. (2023) reported that AI  
improved efficiency and route decision-making in India’s manufacturing sector, but with a modest explanatory  
power (R² = 0.052), indicating the need for further sector-specific research.  
In Pakistan, Modgil et al. (2022) demonstrated that AI strengthens supply chain resilience through improved  
visibility and last-mile delivery, although the study did not analyze specific AI tools such as route optimization.  
Experimental findings by Hassouna et al. (2022) in Egypt showed that AI-based systems identified optimal  
transportation routes that minimized cost and time (F = 1.38, ρ < 0.05). However, this research was limited to  
the transport industry rather than retail.  
Existing studies agree that AI contributes positively to logistics efficiency, but they reveal contextual,  
methodological, and geographical gaps. Most research has focused on manufacturing, transport, or e-commerce  
sectors outside Africa. The current study seeks to address these gaps by examining AI-Route Optimization and  
supply chain performance in large supermarkets in Nairobi City County, Kenya.  
RESEARCH METHODOLOGY  
The study adopted a descriptive research design, which was appropriate for examining how AI-enabled route  
optimization influenced supply chain performance among supermarkets in Nairobi City County. The target  
population consisted of logistics, procurement, and operations personnel drawn from major supermarket chains  
operating within the county. A census approach was used for supermarkets, while purposive sampling was  
applied to select respondents directly involved in logistics decision-making.  
Primary data were collected using structured questionnaires, which contained both closed-ended and Likert-scale  
items aligned with the study variables. The instrument’s reliability was confirmed through Cronbach’s alpha,  
where all constructs exceeded the acceptable threshold. Validity was ensured through expert review and piloting.  
Data were analyzed using descriptive statistics, including means, frequencies, and standard deviations, to  
summarize the characteristics of the variables. Inferential analysis, specifically regression analysis, was used to  
determine the effect of AI-enabled route optimization on supply chain performance. Ethical considerations  
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including informed consent, confidentiality, and voluntary participation were also strictly observed throughout  
the research process.  
Data Processing and Analysis  
To examine the relationship between AI-Route Optimization and supply chain performance, correlation analysis  
was first employed to determine the strength and direction of the association between the independent and  
dependent variables. Following this, regression analysis was conducted to assess the predictive influence of AI-  
Route Optimization on supply chain performance. The regression model used in the study was specified as:  
=
+
+
0
1
1
In this model, represents the dependent variable, supply chain performance, while 1denotes the independent  
variable, AI-Route Optimization. The coefficient 0represents the intercept, 1measures the magnitude and  
direction of the effect of AI-Route Optimization on supply chain performance, and captures the random error  
term. This approach enabled the study to quantify the impact of AI-based route optimization on the operational  
efficiency, delivery reliability, and overall performance of large supermarket supply chains in Nairobi City  
County.  
RESEARCH FINDINGS  
The study assessed the impact of AI-Route Optimization on supply chain performance in large supermarkets in  
Nairobi City County. Data collected from 61 respondents, representing supply chain managers, supervisors, and  
officers, revealed that AI-Route Optimization significantly influenced supply chain performance, particularly in  
delivery reliability, cost efficiency, and operational effectiveness.  
Demographics and Familiarity with AI  
The majority of respondents were male (68.9%) and aged between 41–50 years (65.6%), indicating a mature  
workforce with substantial professional experience. Most respondents held at least a bachelor’s degree (59%) or  
postgraduate qualification (31.1%), suggesting strong educational backgrounds suited to adopting AI  
technologies. In terms of work experience, 36.1% had 6–10 years, and 26.2% had 11–15 years of experience,  
reflecting considerable exposure to supermarket operations and supply chain practices. The mix of managers,  
supervisors, and officers provided diverse perspectives on the use of AI in routing and logistics.  
Impact of AI-Route Optimization  
Analysis of AI-Route Optimization indicated that respondents strongly agreed that AI tools enhanced delivery  
efficiency, reduced operational costs, and improved real-time decision-making in route planning. Descriptive  
statistics showed a high mean score (M = 3.57, SD = 0.43), reflecting general consensus on the positive impact  
of AI-route tools. Regression analysis confirmed that AI-Route Optimization had a statistically significant effect  
on supply chain performance (R² = 0.79, F = 222.015, ρ < 0.01).  
Table 1: AI-Route Optimization and Supply Chain Performance  
Variable  
Number of Items Mean SD R²  
F
Significance (ρ)  
AI-Route Optimization 15  
Source: Primary Data (2025)  
3.57 0.43 0.79 222.02 <0.01  
The findings indicate that AI-Route Optimization significantly contributed to improved supply chain  
performance in large supermarkets by enabling more efficient delivery routes, reducing transport costs, and  
enhancing the reliability of product distribution. This supports the integration of AI-based routing systems as a  
strategic tool for optimizing logistics operations in the Kenyan retail sector.  
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Descriptive Statistics: AI-Route Optimization and Supply Chain Performance  
The study assessed the impact of AI-Route Optimization on supply chain performance in large supermarkets in  
Nairobi City County across three dimensions: delivery time reduction, cost efficiency, and real-time adaptability.  
The respondents’ feedback was analyzed using mean scores and standard deviations to determine the level of  
agreement on each indicator.  
Table: Descriptive Statistics for AI-Route Optimization  
Dimension  
Mean Standard  
Deviation (SD)  
Interpretation  
Delivery  
Time 3.66 0.43  
Agreed – AI reduces delivery time by optimizing routes  
Reduction  
Cost Efficiency  
3.73 0.73  
3.32 0.69  
3.57 0.62  
Agreed – AI improves cost efficiency through route planning  
and resource optimization  
Real-Time  
Adaptability  
Neutral – AI provides some adaptability but respondents were  
less certain about effectiveness in unexpected situations  
Overall Average  
Agreed – AI-Route Optimization positively influences supply  
chain performance  
Source: Primary Data (2025)  
The findings indicate that AI-Route Optimization had a notable positive effect on supply chain performance,  
particularly in reducing delivery time and enhancing cost efficiency. Delivery time reduction had the highest  
agreement among respondents, confirming that AI-enabled route planning significantly streamlines operations.  
Cost efficiency also showed strong positive effects, reflecting savings in fuel, travel time, and driver productivity.  
Real-time adaptability received a slightly lower mean, suggesting that while AI contributes to flexibility in  
dynamic conditions, respondents perceived some limitations in its ability to handle unexpected disruptions fully.  
The descriptive statistics confirm that AI-Route Optimization is an effective tool for improving key aspects of  
supply chain performance in large supermarkets in Nairobi City County.  
Correlation Analysis: AI-Route Optimization and Supply Chain Performance  
The study examined the relationship between AI-Route Optimization and various dimensions of supply chain  
performance, including cost reduction, improved efficiency, and real-time decision-making. Pearson correlation  
coefficients were computed, and the results are presented below:  
Table 2: AI-Route Optimization and Supply Chain Performance  
Variables  
AI-Route  
Optimization  
Cost  
Reduction  
Improved  
Efficiency  
Real-Time  
Making  
Decision  
AI-Route Optimization  
Cost Reduction  
1
0.877**  
1
0.802**  
0.946**  
1
0.818**  
0.646**  
0.757**  
1
0.877**  
0.802**  
Improved Efficiency  
0.946**  
0.646**  
Real-Time Decision Making 0.818**  
0.757**  
Source: Primary Data (2025)  
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The findings show strong positive correlations between AI-Route Optimization and all measured dimensions of  
supply chain performance. AI-Route Optimization exhibited the highest correlation with cost reduction (r =  
0.877), followed by real-time decision-making (r = 0.818) and improved efficiency (r = 0.802). All correlations  
are statistically significant at the 0.01 level, indicating that as AI-Route Optimization improves, key aspects of  
supply chain performance such as cost efficiency, operational efficiency, and timely decision-making also  
improve.  
This confirms that implementing AI-based route optimization significantly contributes to enhancing overall  
supply chain performance in large supermarkets in Nairobi City County.  
Regression Analysis: AI-Route Optimization and Supply Chain Performance  
The study examined the effect ofAI-Route Optimization on supply chain performance among large supermarkets  
in Nairobi City County using a simple linear regression model expressed as:  
=
+
+
0
1
3
Where represents supply chain performance, 3denotes AI-Route Optimization, 0is the regression intercept,  
1is the coefficient of AI-Route Optimization, and represents the error term.  
Model Summary  
The regression model (Table 38) showed a strong relationship between AI-Route Optimization and supply chain  
performance. The coefficient of determination (Adjusted R² = 0.786) indicated that approximately 78.6% of the  
variation in supply chain performance was explained by AI-Route Optimization, while the remaining 21.4% was  
attributed to other unmeasured factors. The standard error of the estimate was 0.271, indicating a reasonably  
good fit.  
Table 3: Model Summary  
Model R  
R²  
Adjusted R² Std. Error of Estimate  
0.27081  
1
0.889 0.79 0.786  
Source: Primary Data (2025)  
ANOVA  
The ANOVA results (Table 39) confirmed the model’s overall significance, with F(1, 59) = 222.015, p < 0.01,  
indicating that AI-Route Optimization is a significant predictor of supply chain performance. This demonstrates  
that the regression model is suitable for understanding and predicting changes in supply chain performance based  
on AI-route optimization.  
Table 4: ANOVA  
Model  
Sum of Squares df Mean Square F  
Sig.  
Regression 16.282  
1
16.282  
222.015 0.000  
Residual  
Total  
4.327  
59 0.073  
60  
20.609  
Source: Primary Data (2025)  
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Regression Coefficients  
The coefficient analysis (Table 40) revealed that AI-Route Optimization had a positive and statistically  
significant effect on supply chain performance (β = 1.202, t = 14.9, p < 0.01). The regression intercept was -  
0.574, which is not statistically significant at the 1% level (p = 0.053).  
Table 5: Regression Coefficients  
Model  
Unstandardized  
Coefficients  
Standardized  
Coefficients  
t
Sig.  
B
Std. Error  
0.291  
Beta  
1
(Constant)  
-0.574  
-1.973  
14.9  
0.053  
0
AI-Route Optimization 1.202  
0.081  
0.889  
a Dependent Variable: Supply Chain Performance  
Source: Primary Data (2025)  
The resulting regression equation is:  
= −0.574 + 1.202  
+
3
The results indicate that for every one-unit increase in AI-Route Optimization, supply chain performance is  
expected to increase by 1.202 units, holding other factors constant. The null hypothesis (Ho3), which stated that  
AI-Route Optimization has no statistically significant effect on supply chain performance, was therefore rejected.  
This finding aligns with previous studies, such as Bello et al. (2024), which found that AI-driven route  
optimization improves efficiency, reduces operational costs, enhances delivery reliability, and strengthens supply  
chain resilience.  
AI-Route Optimization significantly enhances supply chain performance in large supermarkets in Nairobi City  
County. The results underscore the importance of integrating AI-driven routing systems into logistics  
management to improve operational efficiency, reduce costs, and optimize delivery processes.  
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS  
Summary  
The study examined the effect ofAI-Route Optimization on supply chain performance among large supermarkets  
in Nairobi City County, Kenya, focusing on delivery time reduction, cost efficiency, and real-time adaptability.  
The correlation analysis indicated a strong positive relationship between AI-Route Optimization and supply  
chain performance, suggesting that improvements in route optimization enhance operational outcomes.  
Regression analysis confirmed this relationship, with a significant coefficient estimate of β = 1.202 (p = 0.000 <  
0.01). This demonstrates that AI-Route Optimization significantly contributes to the improvement of supply  
chain performance, explaining a substantial portion of the variation in performance among the supermarkets  
studied.  
Conclusion  
The findings of the study show that AI-Route Optimization positively and significantly affects supply chain  
performance in large supermarkets in Nairobi City County. By dynamically optimizing delivery routes,  
supermarkets achieved reductions in delivery times, enhanced cost efficiency, and improved real-time decision-  
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making. The results confirm that adopting AI-driven route optimization strengthens overall supply chain  
efficiency, reliability, and resilience, enabling supermarkets to meet customer demands more effectively.  
Recommendation  
To fully leverage the benefits of AI-Route Optimization, supply chain managers should prioritize the selection  
of AI platforms that provide real-time visibility of routes, improve delivery reliability, and reduce operational  
costs through fuel and maintenance savings. They should invest in robust data infrastructure and skilled  
personnel to ensure the effective implementation and maintenance of AI systems. Integration of AI tools with  
existing supply chain execution systems is crucial to enable accurate, real-time decision-making. Managers  
should also promote collaboration across departments to align AI solutions with strategic business objectives.  
Furthermore, it is essential to maintain enabling systems, including reliable hardware, software, and internet  
connectivity, to allow supermarkets to adapt to changing operational demands without compromising supply  
chain performance.  
Statement Of The Problem  
Efficient route planning was essential for supermarkets to achieve timely and cost-effective deliveries, yet large  
supermarkets in Nairobi City County continued to experience transport inefficiencies such as fuel wastage,  
delays, unreliable delivery times, and high logistics costs. Although AI-based route optimization had proven  
effective in improving distribution efficiency in global retail supply chains, evidence from previous studies  
remained mixed, with some reporting significant performance gains while others noted high integration costs  
and limited effectiveness. In Kenya, existing research had largely concentrated on general technology adoption  
in supply chains, leaving minimal empirical evidence on the use and impact of AI-route optimization in the retail  
sector. It was unclear whether supermarkets in Nairobi used AI-driven routing tools, how effectively they had  
been applied, and the extent to which they influenced supply chain performance outcomes such as delivery  
reliability, cost efficiency, and service responsiveness. This gap in contextualized knowledge formed the basis  
of the study, which sought to assess the effect of AI-Route Optimization on supply chain performance among  
large supermarkets in Nairobi City County, Kenya.  
Objective Of The Study  
The purpose of the study was to examine the effect of AI-Route Optimization on supply chain performance  
among large supermarkets in Nairobi City County, Kenya.  
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