Performance Assessment Model for Petrol Stations Using a Multi-Criteria Condition Rating Index

Abstract

Saudi Arabia’s rapid urbanization and economic growth have increased the number of petrol stations crucial to national infrastructure. Despite oversight from seven local authorities, many stations fail to meet Ministry of Municipal and Rural Affairs (MMRA) standards due to decentralized management. This study develops a Condition Rating Index (CRI) for petrol stations, designed to serve as the backbone of a comprehensive decision support system for the operation and rehabilitation processes of petrol stations in Saudi Arabia. It is based on dividing petrol stations into four key areas: refueling tanks, pump stations, car service buildings, and commercial spaces. Performance factors for each area are identified and categorized hierarchically into main and sub-factors. The Analytical Hierarchy Process (AHP) is used to determine relative importance weights for these factors, while Multi-Attribute Utility Theory (MAUT) is used to assign utility scores (1–10 scale) reflecting performance levels, where 1 is poor, and 10 is optimal. The overall CRI for each petrol station is calculated by aggregating the scores of all four spaces, combining their relative importance weights with the specific CRI scores aligned with each factor’s utility level. These space-specific CRI scores reveal particular performance levels and rehabilitation needs for each area. The developed CRI functions as a transparent, integrated tool for effectively communicating performance levels and rehabilitation needs among all stakeholders. It serves as an effective decision support tool for prioritizing rehabilitation interventions based on performance levels and budget constraints, offering a comprehensive approach for enhancing petrol station management across Saudi Arabia. This paper develops a transparent and adaptable Condition Rating Index (CRI) that bridges gaps in petrol station management and aligns with sustainability goals.

1. Introduction

Petrol stations have evolved from simple fuel dispensers into multifunctional service and commercial hubs, offering a wide range of services such as car maintenance, retail outlets, and restaurants. In Saudi Arabia, where approximately 10,000 petrol stations operate, these facilities are managed by seven distinct governmental entities, including the Ministry of Municipal and Rural Affairs. However, this fragmented management framework, characterized by standalone systems for each entity, often leads to inefficiencies, conflicting directives, and disputes among stakeholders. A recent KAPSARC report highlighted that 440 out of 1219 petrol stations along Saudi highways failed to meet operational and safety standards, further exposing the vulnerabilities in the current regulatory system. These issues underscore the need for a unified and transparent framework to enhance regulatory compliance and operational efficiency [1,2].

Despite efforts to manage these facilities effectively, the absence of an integrated management platform has resulted in significant operational inconsistencies. Many petrol stations rely on localized practices and managerial experience rather than standardized frameworks aligned with international best practices. This has led to critical safety concerns and inefficiencies. Studies indicate that a significant number of stations fail to meet essential safety and operational standards, with incidents such as fires in Madinah, Tabuk, and along the Makkah-Jeddah expressway emphasizing the vulnerabilities of the current system. Furthermore, the lack of integrated safety protocols and preventive maintenance practices exacerbates these challenges, while environmental sustainability measures remain inadequately addressed [3,4,5,6].

Historically, most management systems developed for petrol stations have focused on safety issues, including fire risk prevention, workplace hazards, and compliance with safety regulations. Studies by [7,8] emphasize the critical role of decision-support systems (DSS) in standardizing safety practices and operational compliance. However, relatively few studies address maintenance procedures and effective asset management. Refs. [6,9] highlight the value of adopting condition-based maintenance frameworks and computational tools for optimizing resource allocation and addressing operational inefficiencies. The growing complexity and multifunctional nature of petrol stations demand an integrated approach encompassing safety, operational, and environmental standards.

To address these challenges, this study developed a Condition Rating Index (CRI) using the Analytical Hierarchy Process (AHP) integrated with Multi-Attribute Utility Theory (MAUT). The CRI is a comprehensive decision-support system, enabling stakeholders to evaluate petrol station performance transparently and holistically. The AHP is particularly effective in scenarios involving inconsistent or limited data quality, as it integrates both qualitative and quantitative factors into the decision-making process.

The CRI enhances transparency and management efficiency and aligns with the United Nations Sustainable Development Goals (SDGs). It supports SDG 7 (affordable and clean energy) by promoting energy-efficient operations and incorporating renewable energy solutions in petrol stations. By fostering innovative infrastructure and sustainable urban development, the CRI also aligns with SDG 9 (industry, innovation, and infrastructure) and SDG 11 (sustainable cities and communities). Furthermore, it contributes to SDG 12 (responsible consumption and production) by encouraging resource efficiency and waste reduction. Enhanced safety measures and risk management protocols address SDG 3 (good health and well-being), while sustainable fuel practices and reduced environmental impacts support SDG 13 (climate action) [10,11,12]. By integrating these goals into the management of petrol stations, the CRI demonstrates a commitment to sustainable economic growth, environmental stewardship, and improved operational standards across Saudi Arabia.

2. Background and Motivation

Petrol stations are essential elements of modern infrastructure, serving as critical nodes in transportation networks and providing the fuel necessary for economic activities. These facilities have evolved from basic fuel dispensers into multifunctional service hubs offering amenities such as car maintenance, retail outlets, and restaurants, significantly contributing to urban connectivity and economic development. Their role extends beyond vehicle operational support, including their contribution to local economies, infrastructure resilience, and overall urban development [8,13]. The importance of petrol stations is particularly evident in rapidly growing economies, where effective management of these facilities can substantially enhance economic output and quality of life.

Proper management of petrol stations is vital for ensuring operational efficiency, safety, and environmental sustainability. However, the management of these facilities is often fragmented, with localized practices prevailing over standardized approaches. This lack of consistency leads to inefficiencies, operational risks, and heightened safety concerns, as demonstrated by fire incidents and environmental challenges associated with mismanagement [14,15,16,17]. Effective management frameworks are required to mitigate these risks, ensuring petrol stations operate safely and sustainably while meeting global operational benchmarks [2,4].

Asset management is a systematic approach to optimizing the lifecycle performance of assets while minimizing risks and maximizing efficiency. Advanced asset management tools, including condition-based maintenance (CBM), IoT-enabled monitoring systems, and digital twins, have been widely adopted in sectors such as petrochemicals, oil and gas, and the built environment. These tools enable real-time performance tracking, proactive decision-making, and resource optimization, enhancing the efficiency and sustainability of asset operations [18,19,20].

Adopting such tools in the context of petrol stations can address unique challenges, such as fire hazards, operational inefficiencies, and environmental risks. Studies have highlighted the potential for integrating CBM and decision-support tools to enhance petrol station operations, reduce costs, and align with sustainability goals [2,21]. However, despite the availability of advanced asset management models, petrol stations often lag behind in implementing these solutions, underscoring the need for tailored frameworks [14,22,23,24,25].

Research on petrol station management predominantly addresses safety and maintenance issues. Safety-focused studies emphasize the importance of hazard identification, fire risk reduction, and occupational health measures to protect workers and customers. For example [5,14], explored fire prevention protocols and hazard mitigation strategies, while [4,26,27] discussed comprehensive occupational health and safety frameworks. These studies underscore the critical need for robust safety protocols to minimize risks associated with petrol station operations.

Maintenance-focused studies highlight the value of condition-based maintenance CBM models in extending asset lifecycles and enhancing operational reliability. For instance, refs. [25,28,29] demonstrated the effectiveness of CBM systems in optimizing resource allocation and maintenance schedules. Total Productive Maintenance (TPM) methodologies, as discussed by [16,24,30,31], further improve overall performance by minimizing downtime and ensuring equipment reliability. Despite these advances, most existing models lack integration across safety, maintenance, and environmental dimensions, limiting their applicability to the comprehensive management of petrol stations [8,21,29,32].

While existing research provides valuable insights into safety and maintenance practices, it lacks a unified framework that integrates safety, operational performance, and sustainability. This gap highlights the need for a comprehensive tool to address the multifaceted challenges of petrol station management. Current asset management models often focus narrowly on specific issues, failing to provide a holistic approach that aligns with global standards and sustainability objectives [2,19].

To address this gap, this study aims to develop a Condition Rating Index (CRI) designed for petrol stations to address key management gaps, enhance safety, improve operational efficiency, and align with sustainability goals.

The proposed CRI will serve as a transparent decision-support tool for stakeholders involved in managing petrol stations. By providing clear performance metrics, the CRI aims to facilitate better communication, improve decision-making, and enhance coordination among regulatory bodies, operators, and other stakeholders. This model is designed to streamline operational practices, ensure compliance with safety standards, and support environmental sustainability initiatives.

Development and Significance of the CRI

The developed CRI is based on using the Analytical Hierarchy Process (AHP) integrated with Multi-Attribute Utility Theory (MAUT) due to data availability problems in addition to the capability of this method to accommodate quantitative and qualitative data types, which will overcome data issues to evaluate petrol station performance comprehensively. This framework incorporates qualitative and quantitative data, addressing the limitations of existing models and ensuring a holistic assessment. Additionally, the CRI aligns with the United Nations Sustainable Development Goals (SDGs), supporting SDG 3 (Good Health and Well-being), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action) [12,33,34,35]. By promoting safety, operational efficiency, and sustainability, the developed CRI represents a transformative approach to petrol station management and creates a transparent, effective tool among all stakeholders for better collaboration and better decision-making aligned with rehabilitation objectives and budget constraints.

3. Methodology

Petrol stations are complex facilities composed of four main spaces: pump station areas, refueling tank areas, commercial buildings, and car service buildings [3,4,8]. Each space has unique functions and requirements, and effective management is essential for safe and efficient operation. These spaces are illustrated in Figure 1. This study is focused on developing a condition rating model for each space. This model will provide decision-makers and stakeholders with a unified, integrated, and transparent tool for a detailed understanding of the status of different elements within these assets, enabling them to make effective, optimized operational and rehabilitation decisions within their budget constraints.

The research methodology of this study is divided into three phases, as illustrated in Figure 2.

• Identify factors affecting petrol station operation and maintenance: This phase will involve a comprehensive review of local and international standards and consultation with experts in the field. The goal is to identify all factors that can significantly impact the operation and maintenance of petrol stations.
• Organize factors into a hierarchical order: Once all factors have been identified, they will be organized into a hierarchical order. This will involve grouping similar factors and creating a hierarchy of main criteria and sub-factors. This phase aims to develop a structured framework for evaluating the overall performance of petrol stations.
• Extract attributes and calculate CRI: In the final phase, attributes will be extracted for each factor identified in Phase 1. These attributes will be based on petrol station operation standards, best practices, and expert input.
  The attributes will then be aggregated with their weighted factors to calculate each petrol station’s integrated condition rating index (CRI). The CRI will provide a comprehensive measure of the overall condition of a petrol station.

3.1. Data Collection and Analysis for Phase One

The initial phase involved an extensive and comprehensive data collection effort to identify different factors affecting the performance of petrol stations. This comprehensive examination systematically identified the primary factors influencing petrol station performance through literature, best practices, safety standards, local and international operation and maintenance norms, and the Ministry of Municipal and Rural Affairs standards and guidelines. Local experts and stakeholders meticulously analyzed and categorized these factors into main and sub-main categories to ensure a robust foundation. The hierarchy of these factors for each space is illustrated in Figure 3.

The data collection for this phase was conducted through a three-part survey involving 200 experienced stakeholders, including owners, safety engineers, maintenance engineers, and experienced operators. After organizing and confirming the factor hierarchy, their relative weights were assessed using the AHP method. This method facilitated pairwise comparisons to assign relative importance weights for the identified factors. Despite applying revised survey modifications, conducting personal interviews, and engaging in extensive phone calls to aid data collection, the survey complexity limited the responses to 120 stakeholders. These participants met the reliability and consistency criteria, with all considered responses achieving a consistency index below 10%. The process incorporated both qualitative and quantitative perspectives to ensure accuracy in evaluating the importance of the factors.

Data reliability using Cronbach’s alfa (Cα) was calculated along with the coefficient of variance for all factors and subfactors. Cronbach’s Alpha is a number between 0 and 1 that shows data reliability calculated using Equation (1); the higher values indicate more dependable data. The typical Cα values and their interpretation are shown in

Table 1. Cronbach’s Alpha values and their interpretations [36].

Cronbach’s Alpha Values	“Interpretation”
0.9 and greater	“High reliability”
0.80–0.89	“Good reliability”
0.70–0.79	“Acceptable reliability”
0.65–0.69	“Marginal reliability”
0.50–0.64	“Minimal reliability”

. The coefficient of variation (CoV) evaluates relative variability through standard deviation compared to the mean. Calculating relative importance weights measures data stability and dispersion for each factor, revealing variability and consistency. Higher CoV signifies more variability, while lower suggests greater consistency relative to the mean. The relative weights of main factors and subfactors, along with their reliability and CoV, are presented in

Table 2. Relative importance weights for petrol station main spaces.

Petrol Station Spaces	Relative Weight	COV	Cronbach Alpha
Refueling Tanks Area	0.54	0.11	0.931
Pump Station Area	0.26	0.13
Car Service Area	0.12	0.14
Commercial Building	0.08	0.13

.

$$\mathrm{C}\mathsf{\alpha }={\displaystyle \frac{n}{n-1}}(1-{\displaystyle \frac{\mathsf{\Sigma }Vi}{\overline{V}}})$$

(1)

where:

• Cα: Is the Cronbach’s Alpha;
• n: Is the number of points;
• Vi: Is the variance of scores for each point;
• $\overline{V}$: Is the total variance of overall points.

The relative importance weights for these spaces were calculated using the first part of the survey using the eigenvector AHP technique and listed in Table 2. The relative importance weights showed that the Refueling tank tare has the highest relative importance weight of 54%, followed by the pump station area with 26%, which reflects the importance of these two spaces in the functionality of the site petrol stations, compared to the care service are and commercial buildings with relative weights 12% and 8%, respectively, as shown in Table 2.

3.2. Data Collection and Analysis for the Second Two

In the second phase, the factors and subfactors affecting the performance of each space were organized in the hierarchy shown in Figure 4a,b. Factors for the four spaces considered in this study were classified into physical, operational, environmental, and fire protection factors for the refueling tanks and pumping station areas. Meanwhile, the commercial buildings and car services areas were categorized as physical, operational, and environmental factors, with fire protection systems evaluated under electromechanical factors.

The relative weights for main factors and sub-factors, in addition to the relative importance weight for each space in identified spaces within the petrol station, were identified, as presented in

Table 3. Reliability and CoV and relative importance weights for different factors.

Criteria Level	Functional Space	Final Relative Weights	COV	Cronbach’s Alpha
Overall Functional Spaces
1	Refueling Tanks Area	0.54	0.11	0.931
	Pump Station Area	0.26	0.13
	Car Service Area	0.12	0.14
	Commercial Buildings Area	0.08	0.13
Refueling Tank Area
2	Physical	0.36	0.14	0.861
	Operational	0.13	0.13
	Environmental	0.17	0.31
	Fire Protection	0.34	0.21
3	Architecture	0.41	0.13	0.765
	Infra.	0.59	0.16
4	Entrance and Exit	0.55	0.18	0.839
	Floor Finish	0.21	0.16
	Fueling Area Standard	0.25	0.16
5	Tank	0.44	0.21	0.869
	Pipe	0.20	0.28
	Pump	0.36	0.13
6	Operational Age	0.38	0.16	0.910
	Operation Management	0.62	0.13
7	Weather	0.39	0.16	0.875
	Humidity	0.61	0.16
8	Fire Fighting Protection	0.24	0.23	0.855
	Safety Procedure	0.36	0.19
	Fuel Evaporation Control System	0.40	0.2
Pump Station Area
9	Physical	0.39	0.21	0.839
	Operational	0.13	0.28
	Environmental	0.15	0.13
	Fire Protection	0.33	0.16
10	Infrastructure	0.40	0.13	0.711
	Architecture	0.60	0.16
11	Entrance and Exit	0.40	0.16	0.834
	Floor Finish	0.21	0.23
	Fueling Area Standard	0.39	0.19
12	Operational Age	0.48	0.2	0.894
	Operation Management	0.52	0.18
13	Weather	0.55	0.16	0.943
	Humidity	0.45	0.16
14	Fire Fighting Protection	0.36	0.23	0.895
	Safety Procedure	0.32	0.19
	Fuel Evaporation Control System	0.32	013
Car Services Buildings
15	Physical	0.47	0.11	0.785
	Operational	0.27	0.25
	Environmental	0.27	0.2
16	Structure	0.24	0.18	0.751
	Architecture	0.36	0.19
	MEP	0.40	0.26
17	Mechanical	0.24	0.25	0.762
	Electrical	0.18	0.19
	Plumping	0.13	0.26
	Fire Protection	0.45	0.25
18	Operational Age	0.38	0.19	0.784
	Operation Management	0.62	0.23
19	Weather	0.45	0.18	0.859
	Humidity	0.55	0.19
Commercial Buildings
20	Physical	0.61	0.25	0.811
	Operational	0.25	0.17
	Environmental	0.14	0.19
21	Structure	0.18	0.2	0.851
	Architecture	0.40	0.18
	MEP	0.42	0.16
22	Mechanical	0.19	0.16	0.845
	Electrical	0.17	0.23
	Plumping	0.12	0.19
	Fire Protection	0.52	0.2
23	Operational Age	0.38	0.18	0.823
	Operation Management	0.62	0.13
24	Weather	0.39	1.15	0.743
	Humidity	0.61	0.16

. The relative importance weights for each space’s main factors and sub-factors were obtained using AHP through the pairwise comparison matrix. The data were collected through the second part of the survey, designed to collect the relative importance weights for different factors of the four spaces identified for the petrol station.

The relative weights for the main criteria and sub-criteria are represented in Figure 5. It shows that the factors of petrol stations are categorized into main and sub-factors for each of the four spaces examined. The table indicates the following impact values: The Refueling Tank Area (0.54), central to the station, is influenced by the Physical Layout (0.36) and Architecture (0.41), highlighting the need for an efficient and appealing design. Infrastructure (0.59) emphasizes aesthetic, functional, and durable components, while Entrance and Exit (0.55) emphasizes smooth vehicle movement. Operational aspects (0.13) are less prioritized than Operation Management (0.62), emphasizing managerial oversight.

The environmental concerns are mainly represented by General Environmental Impact (0.17), suggesting a lesser direct focus on environmental issues. Nonetheless, the role of weather (0.39), especially humidity (0.61), indicates its effect on fuel storage, particularly surface structures. Safety Protocols are characterized by Fire Protection (0.34), Fire Fighting Protection (0.24), and Fuel Evaporation Control System (0.4), emphasizing safety and fuel quality. The Pump Station Area (0.26), although less prioritized than the refueling tank, is predominantly affected by Operation Management (0.52) and Humidity (0.45). In the Car Services Area (0.12), the major influencing factors are Operation Management (0.62) and humidity (0.55). Commercial Buildings (0.08) have a subsidiary role in petrol stations. When present, they are chiefly influenced by Physical aspects (0.61), Operation Management (0.62), and humidity (0.61). The results show that operational management and humidity are paramount across different station areas. However, the significance of other factors fluctuates. This reveals the complex nature of station performance and the necessity for tailored management strategies.

3.3. Data Collection and Analysis for Phase Three

The data collection for the third phase employed the Multi-Attribute Utility (MAUT) technique, which quantifies the performance level of each factor by its utility score, resembling its performance level within an identified utility system for each factor. In this phase, all factors identified for all identified spaces are linked with specific utility scoring systems obtained from operation standards, best practices and operation manuals, and experts. These utility values provide a comprehensive, systematic, and consistent tool to evaluate diverse attributes and alternative utility scores for each identified factor. The utility values are interpreted to an actual performance level, which, when aggregated with other factors with their relative importance weight, provide an overall performance score reflecting the performance of each space within the petrol station in addition to the overall performance of the whole petrol station.

3.4. Assigning Utility Scores

The utility values for different factors of the four main paces considered in this study for petrol stations were of two main types: functional utility attributes derived from standards, literature, and best practice guidelines, and expert-based utility values obtained through the third part of the survey. The MAUT approach was used because of its advantage of handling both quantitative and qualitative values to evaluate and quantify the status of each factor, allowing for the accommodation of continuous future upgrades. The Utility value score U serves as a reflection of the status of each factor and, when combined with its relative importance, contributes to the computation of the overall Condition Rating Index (CRI). In expert-based Utility value U, experts were requested to provide preference utility values on a 1–10 scale for various attributes affecting each factor, while standard Utility values are based on standard performance level. A utility value of 10 indicates the highest performance and best preference score.

Table 4. (a) Utility values for architecture factors. (b) Utility values for infrastructure factors. (c) Utility values for operational factors. (d) Utility values for environment and fire protection factors.

(a)
Factor	Sub-Factor	Utility Score	Utility Description
Architectural Factors	Entrance and exit	8–10	Efficient flow, clear signs, safety measures, and vehicle access for all.
		6–8	Modest efficient flow, clear signs, safety measures, and vehicle access for all.
		4–6	Acceptable efficient flow, clear signs, safety measures, and vehicle access for all.
		2–4	Inefficient flow, no clear signs, no or inadequate safety measures, and restricted vehicle access.
	Floor Finish	8–10	Highly durable, slip-resistant, chemically resistant, and compliant for easy maintenance.
		6–8	Moderate durable, slip-resistant, chemically resistant, and compliant for easy maintenance.
		4–6	Acceptable: durable, slip-resistant, chemically resistant, and compliant for easy maintenance.
		2–4	Non-durable, slip-resistant, chemically resistant, and compliant for easy maintenance.
	Fueling Area Design and Standards	8–10	Fully available functioning safety measures, fuel pump integrity, environmental protection, and certified operational requirements.
		6–8	Available functioning safety measures, acceptable fuel pump integrity, environmental protection available systems, and certified operational requirements.
		4–6	Available functioning safety measures with minor incidence, acceptable fuel pump integrity records, semi-functional environmental protection available systems, and certified operational requirements.
		2–4	Available functioning safety measures with many incidents, bad fuel pump integrity, semi-functional environmental protection systems, and operational requirements violations.
(b)
Factor	Sub-factor	Utility score	Utility Description
Infrastructure	Tanks	8–10	Available leak detection and prevention systems; excellent structural integrity; available installation records; available corrosion protection measures.
		6–8	Available leak detection and prevention systems; good structural integrity; available installation records; available corrosion protection measures.
		4–6	Available leak detection and prevention systems; acceptable structural integrity; semi-available installation and maintenance records; available corrosion protection measures.
		2–4	Available but defective leak detection and prevention systems; inadequate to critical structural integrity; messy or unavailable maintenance records; visible corrosion in the tanks.
	Pipes	8–10	Excellent pipe material with no corrosion, full compliance with safety and environment standards, pressure and flow are satisfied, available maintenance records with updates.
		6–8	Excellent pipe material with no corrosion, partial compliance with safety and environment standards, pressure and flow are satisfied with minor downtime, partially updated maintenance records with updates.
		4–6	Acceptable pipe material with minor corrosion, low compliance with safety and environment standards, pressure, and flow are satisfied with clear downtime and updated maintenance.
		2–4	Clearly visible pipe corrosion, low compliance with safety and environment standards, pressure and flow are satisfied with clear downtime and updated maintenance.
	Pumps	8–10	Calibrated flow rate, ensured no leaks, compliance with safety and environmental standards, and updated maintenance records.
		6–8	Calibrated flow rate with minor errors, ensured no leaks, no downtime, Acceptable compliance with safety and environmental standards, and available maintenance records.
		4–6	Calibrated flow rate with clear errors, ensured no leaks or downtime, had acceptable compliance with safety and environmental standards, and had incomplete maintenance records.
		2–4	Major flow rate calibration errors, some leaks, non-acceptable downtime, questioned compliance with safety and environmental standards, and incomplete maintenance records.
(c)
Factor	Sub-factor	Utility score	Utility Description
Operational	Operational Age	8–10	0–5	(New units fully operation conditions)
		6–8	5–10	(Good operating condition and proper maintenance)
		4–6	10–15	(Aging units in acceptable operating conditions)
		2–4	15–20	(Approaching end of service life with faulty operation)
		1	>20	End of service life (depends on the unit’s useful life)
	Operation Management Standards	8–10	Fully trained workers, full compliance with safety regulations, standard customer service and marketing practices, excellent equipment condition and maintenance records, applying fuel quality control and inventory management, continuous employee training, and upgrades programs.
		6–8	Good level of trained workers, full compliance with safety regulations, standard customer service and marketing practices, good equipment condition and maintenance records, applying fuel quality control and inventory management, available employee training and upgrades programs.
		4–6	Acceptable level of trained workers, good compliance with safety regulations, local customer service and marketing practices, acceptable equipment condition and maintenance records, applying acceptable fuel quality control and inventory management, weak employee training and upgrades programs.
		2–4	Unacceptable level of trained workers, barely compliance with safety regulations, local customer service and marketing practices, non-acceptable equipment condition and maintenance records, missing good fuel quality control and inventory management, non-employee training and upgrades programs.
(d)
Factor	Sub-factor	Utility score	Utility Description
Environmental	Weather	8–10	Moderate weather all year has mild or no influence on petrol station performance, which depends on demand, footfall, operations, infrastructure, and safety.
		6–8	Moderate to severe weather yearly cycles have a noticeable impact and need proper maintenance programs to keep good performance and not affect operations, safety, and supply chain.
		4–6	Moderate to severe weather almost all year has a noticeable impact on petrol stations’ infrastructure, personnel safety, operation disruption, and failing supply chain operations.
		2–4	Extreme weather can influence petrol station performance by affecting fuel demand, footfall, operations, infrastructure, safety, and supply chain management.
	Humidity	8–10	Mild to low humidity in weather has minimal impact on fuel quality, low impact on equipment corrosion, and no impact on the safety procedures or operation practices.
		6–8	Seasonal humidity cycles in weather remarkably affect fuel quality, accelerate equipment corrosion, raise safety concerns, and impact vapor recovery systems.
		4–6	Daily humidity cycles and high temperatures with salty weather severely affect equipment, accelerate equipment corrosion, raise safety concerns, jeopardize vapor recovery systems, and affect fuel quality.
		2–4	Sever high humidity in weather, including temperature daily and seasonal cycles, in addition to salty vapors, affects fuel quality, accelerates equipment corrosion, raises safety concerns, and damages vapor recovery systems.
Fire Protection and Safety		8–10	Petrol stations are fully equipped with safety measures like fire extinguishers, automatic fire suppression systems, and emergency shutdown systems to mitigate fire risks. In addition, there are good records for regular inspections, maintenance, and staff training for compliance and preparedness.
		6–8	Petrol stations are fully equipped with safety measures like fire extinguishers, automatic fire suppression systems, and emergency shutdown systems to mitigate fire risks. In addition, there are some records for regular inspections, maintenance, and acceptable staff training.
		4–6	Petrol stations are partially equipped with some safety measures like fire extinguishers. However, automatic fire suppression systems are missing, and emergency shutdown systems are manually operated only. They lack inspection and maintenance records, in addition to primitive staff training.
		2–4	Petrol stations are partially equipped with some safety measures like fire extinguishers. However, automatic fire suppression systems are missing, and there are no emergency shutdown systems, no recodes of inspection maintenance, and no staff training program.

a–d summarizes the utility scores for factors affecting petrol station performance. These scores, derived using the MAUT method, systematically evaluate physical, operational, environmental, and fire protection aspects. The scoring reflects the current condition and performance levels, guiding stakeholders in identifying areas for improvement.

3.5. The AHP-MAUT CRI Model

To determine the Condition Rating Index (CRI) for each space, the scores of all factors and sub-factors were aggregated, considering their corresponding utility values that represent the status of each space, as shown in Figure 6. Integrating AHP and MAUT strengthened the decision-making process, ensuring a thorough and objective evaluation of the factors and sub-factors significantly impacted by petrol station performance within the proposed CRI. The overall CRI for the petrol station was obtained by aggregating the CRIs of individual spaces, considering their respective relative importance weights. This comprehensive approach ensures a robust evaluation of the petrol station’s condition. The CRI values were computed using Equation (2).

$$CRI=\sum _{i=1}^n\sum _{j=1}^m{w}_i·v_{ij}·U{v}_{ij}$$

(2)

In which

• $w_i$: Relative weight of each main factor (criterion)
• i: Relative weight of each sub-factor
• $v_{ij}$: Criterion j within factor i, and
• $U{v}_{ij}$: The sub-factor preference utility value scored out of 10.

The calculated CRI was then reviewed by operators, stakeholders, and managers to assess current facility performance and identify necessary interventions and alternatives according to the desired performance level and available maintenance budget. The interpretation of the CRI for petrol stations is presented in

Table 5. Overall utility scores.

Condition Score	Condition	Description
9–10	Excellent	The facility is in excellent condition; no rehabilitation is needed.
8–<9	Good	The facility is in good system condition; minor routine maintenance is needed.
7–<8	Acceptable	The facility is in acceptable condition, but essential maintenance is needed to keep it functional.
5–<7	Bad	The facility needs immediate intervention to keep working. Otherwise, the facility must be closed since its performance is below acceptable level.
3–4	Critical	The facility is non-functional and needs major rehabilitation or replacement.
1–2	Severe	Unsafe immediate closure is enforced.

.

4. CRI Implementation

The developed CRI was implemented as a case study on a real petrol station. As mentioned earlier, seven authorities are currently responsible for managing and controlling petrol stations in Saudi Arabia. Applying the developed CRI to a real case study to quantify its performance and pinpoint the level of performance for each unit within the petrol station will ensure that all these authorities will have the same vision about the petrol station and these authorities will be able to understand and quantify the performance of this petrol stations based on its four spaces discussed earlier.

The case study was a selected petrol station located in the Eastern Province. The name of this petrol station is kept confidential, and the collected data was assured to be used for demonstration and academic purposes only. This petrol station was selected because it has four spaces. The system followed in this study is a refueling tank area, pump station, car service area, and commercial building. It was built in 2015. The breakdown of areas for the petrol station is the following: 3200 m² for the total station; the refueling tank area is about 120 m²; the pump station area is 480 m²; the car service building is about 450 m² (including car wash and changing tires and oil); and the commercial building is 415 m² (including supermarket, coffee shop, and restaurant). The remaining 1700 m² represents the fenced area, including car pathways and the parking area, as illustrated in Figure 7.

The developed Condition Rating Index was implemented on the four spaces at the petrol station outlined in the study. The application revealed the ability to discern the performance level of each space using its specific parameters, enabling precise fault detection and evaluation, which in turn facilitates the prioritization of essential operation and maintenance interventions. The CRI for the petrol spaces and the main criteria used in this study are shown in

Table 6. CRI for main criteria and sub-criteria for petrol station spaces.

H. Level	Criteria Main Factor & Subfactors	Relative Weight wt	Utility Score U	Condition Linguistic Description	wt xU	Aggerated Score	Petrol Station Spaces CR	Petrol Station Overall CR
Space 1	Refueling tank area	0.54	6.8	Acceptable	6.23		7.9	7.6
1.1	Physical	0.36	8	Good	8	7.9
1.1.1	Architecture	0.41	8	Good	7.87
1.1.1.1	Entrance & Exit	0.55	8	Good	4.4
1.1.1.2	Floor Finish	0.21	7	Acceptable	1.47
1.1.1.3	Fueling area standard	0.25	8	Good	2
1.1.2	Infrastructure	0.59	8	Good	7.92
1.1.2.1	Tank	0.44	7	Acceptable	3.08
1.1.2.2	Pipe	0.2	8	Good	1.6
1.1.2.3	Pump	0.36	9	Excellent	3.24
1.2	Operational	0.13	7.5	Acceptable	7.62	7.62
1.2.1	Age	0.38	7	Acceptable	2.66
1.2.2	Operation management	0.62	8	Good	4.96
1.3	Environmental	0.17	4	Critical	3.56	3.52
1.3.1	Weather	0.39	6	Bad	2.34
1.3.2	Humidity	0.61	2	Sever	1.22
1.4	Fire Protection	0.34	7	Acceptable	6.88	6.88
1.4.1	Fire Fighting Protection	0.24	8	good	1.92
1.4.2	Safety procedure	0.36	6	Bad	2.16
1.4.3	Fuel evaporation ctrl. sys	0.4	7	Acceptable	2.8
Space 2	Pump station area	0.26	7	Acceptable	7		8
2.1	Physical	0.39	8	Good	8	8
2.1.1	Infrastructure	0.4	8	Good	3.2
2.1.2	Architecture	0.6	8	Acceptable	7.81
2.1.2.1	Entrance & Exit	0.4	7	Good	2.8
2.1.2.2	Finish	0.21	9	Excellent	1.89
2.1.2.3	Pump station standard	0.39	8	Good	3.12
2.2	Operational	0.13	8	Acceptable	7.52	7.52
2.2.1	Age	0.48	7	Acceptable	3.36
2.2.2	Operation management	0.52	8	Good	4.16
2.3	Environmental	0.15	7	Bad	4.2	4.2
2.3.1	Weather	0.55	6	Bad	3.3
2.3.2	Humidity	0.45	2	Sever	0.9
2.4	Fire Protection	0.33	7	Acceptable	7.04	7.04
2.4.1	Fire Fighting Protection	0.36	8	good	2.88
2.4.2	Safety procedure	0.32	6	Bad	1.92
2.4.3	Fuel evaporation ctrl. Sys	0.32	7	Acceptable	2.24
Space 3	Car Services Area	0.12	5	Bad	5.22		6.0
3.1	Physical	0.47	6	Bad	2.82	5.97
3.1.1	Structure	0.24	7	Acceptable	7
3.1.2	Architecture	0.36	6	Bad	6
3.1.3	MEP	0.4	6	Bad	5.33
3.1.3.1	Mechanical	0.24	6	Bad	1.44
3.1.3.2	Electrical	0.18	8	Good	1.44
3.1.3.3	Plumping	0.13	5	Bad	0.65
3.1.3.4	Fire Protection	0.45	4	Critical	1.8
3.2	Operational	0.27	6	Bad	5.14	5.14
3.2.1	Age	0.38	7	Acceptable	2.66
3.2.2	Operation management	0.62	4	Critical	2.48
3.3	Environmental	0.27	5	Bad	3.8	3.8
3.3.1	Weather	0.45	6	Bad	2.7
3.3.2	Humidity	0.55	2	Sever	1.1
Space 4	Commercial Buildings	0.08	6	Bad	6		7.3
4.1	Physical	0.61	7	Acceptable	7	7.4
4.1.1	Structure	0.18	7	Acceptable	7
4.1.2	Architecture	0.4	6	Bad	7
4.1.3	MEP	0.42	8	Acceptable	7.95
4.1.3.1	Mechanical	0.19	9	Excellent	1.71
4.1.3.2	Electrical	0.17	8	Good	1.36
4.1.3.3	Plumping	0.12	6	Bad	0.72
4.1.3.4	Fire Protection	0.52	8	Good	4.16
4.2	Operational	0.25	6	Bad	6	5.14
4.2.1	Age	0.38	7	Acceptable	2.66
4.2.2	Operation management	0.62	4	Critical	2.48
4.3	Environmental	0.14	8	Acceptable	8	3.56
4.3.1	Weather	0.39	6	Bad	2.34
4.3.2	Humidity	0.61	2	Sever	1.22

and plotted in Figure 8. The utility score of each criterion was assessed for each space based on the utility level scoring system shown in Table 4.

Applying the developed CRI on the selected petrol station gave a comprehensive evaluation of its current performance and rehabilitation needs derived from this model, as shown in Table 6. The CRI for each space in the studied petrol station where are as follows: The refueling tank area scored and pump station areas, which are the most important spaces in the petrol station spaces, scored 8, which is good performance according to the developed scale model shown in Table 5. The car service area scored 6, which is bad on the developed scale. The overall performance of the petrol station studied is 7, which is satisfactory. The detailed score for all factors is shown in Table 6, which reveals the following:

The refueling tank area was well-designed especially the entrance, exit, and fueling zones. The infrastructure, especially the pumps, stood out in terms of quality and efficiency. Despite its age, the tank area’s operation was satisfactory. Challenges posed by environmental conditions, especially surface equipment such as fuel dispensers, were identified, pinpointing the importance of frequent preventive maintenance to overcome severe weather conditions. However, severe weather affects buried infrastructures like the tank and pipes less. In the fire protection area for the refueling zone, the efficacy of the firefighting and fuel evaporation control systems was validated. Yet, a need for enhancement in safety protocols was pinpointed, showing that workers need to be trained. Training programs and certification must be considered to keep up with the required standards to prevent possible incidents and better manage these incidents.

The pump station was in good shape, with its infrastructure rated highly. Its above-ground parts are more exposed to environmental wear than underground ones. Improved fire safety measures are also required here. The car services area showed some weaknesses, especially in architecture and mechanical and electrical works (MEP). It needs better upkeep due to weather exposure. The station’s commercial buildings had mixed results. While some parts, like the heating and cooling system (HVAC), worked well, the buildings’ design and operational management could be improved. Buildings exposed to the environment, like roofs, need special attention. Implementing the developed CRI revealed critical safety concerns, emphasized the urgency to reinforce outdoor structures against adverse environmental conditions, and highlighted the significance of enhancing MEP systems. Furthermore, potential avenues to refine the station’s architectural design were identified. This model collectively provides a blueprint for reinforcing the petrol station’s performance and operational efficiency.

5. Discussion

Asset management for petrol stations aims to optimize asset performance, minimize downtime and disruptions, ensure safety and environmental compliance, and maximize the value and lifespan of assets. It requires a holistic and proactive approach that integrates various operational, safety, and regulatory aspects to achieve efficient and sustainable operations. These stations are considered one of the vital infrastructures for cities’ efficient transportation systems needed for a sustainable economy. To keep these stations functioning effectively, they are challenged to meet not only local and international operation and maintenance standards. Effective asset management for petrol stations encompasses key elements such as asset inventory and condition assessment, maintenance and repair programs, asset performance monitoring, compliance with regulatory requirements, and financial planning for asset replacement.

The developed CRI in this study highlights a critical gap in the existing body of literature regarding the comprehensive performance evaluation of petrol stations. Previous research has predominantly focused on safety concerns, such as fire prevention and occupational hazards, or has examined commercial and regulatory issues while largely neglecting petrol stations’ broader performance and maintenance needs. For example, refs. [5,37] emphasized the importance of fire safety measures and their impacts on workers and customers. Similarly, ref. [2] investigated asset management strategies within the industry’s dynamic context but did not comprehensively address maintenance processes. Ref. [17] analyzed asset management under fluctuating economic conditions but failed to propose a detailed framework for evaluating performance. These findings underscore the need for a more integrated approach to petrol station management.

Technological innovations have begun to address some of these deficiencies. For instance, ref. [38] examined the application of IoT-enabled sensors for real-time performance monitoring in petrol stations, demonstrating their potential to enhance operational efficiency and minimize maintenance delays. Likewise, ref. [20] highlighted the transformative potential of AI in optimizing asset management through predictive analytics, cost reduction, and risk mitigation. However, while these advancements are promising, their scope is often limited to specific technological solutions, lacking the holistic integration of safety, maintenance, and operational metrics necessary for comprehensive performance evaluation.

Research focused on maintenance practices has also revealed a limited, unidimensional perspective. Ref. [3] emphasized the integration of maintenance systems into operational processes to improve efficiency but did not propose an overarching evaluation framework. Similarly, refs. [21,29,30,39] explored Total Productive Maintenance (TPM) and Condition-Based Maintenance (CBM), focusing on preventive strategies without addressing other critical dimensions, such as environmental sustainability and transparency among stakeholders.

In response to these gaps, this study introduces a robust Condition Rating Index (CRI), which serves as a decision-support tool for evaluating the performance of all operational units within petrol stations. The CRI is designed to address the multifaceted challenges of petrol station management by integrating qualitative and quantitative data through the Analytical Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT). These methodologies enable the CRI to comprehensively evaluate performance levels across various spaces, including refueling tanks, pump stations, car service areas, and commercial buildings.

Unlike previous models that focus on isolated aspects of performance, the CRI offers a transparent and systematic framework for identifying deficiencies, prioritizing maintenance, and aligning rehabilitation efforts with organizational goals and financial constraints. By quantifying performance based on detailed criteria for each unit, the CRI empowers stakeholders to make informed and collaborative decisions. Additionally, incorporating IoT and AI tools within this framework facilitates real-time data collection and predictive analytics, significantly enhancing the ability to preempt potential issues and streamline maintenance planning.

Integrating AHP ensures a systematic weighting of diverse qualitative and quantitative factors, reflecting their relative importance. Simultaneously, MAUT provides a utility scoring system that offers a nuanced understanding of technical and judgmental performance metrics on a 1–10 scale. Together, these methodologies create a versatile and effective tool for performance assessment, encompassing surface and subsurface elements uniquely influenced by operational and environmental conditions.

The developed CRI bridges these evident gaps in the literature, providing a comprehensive, transparent, and effective decision-support system for petrol station management. By addressing safety, maintenance, and operational dimensions while leveraging advanced technologies, the CRI represents a significant advancement in the field, promoting sustainable, efficient, and informed management practices.

The developed CRI already has several United Nations Sustainable Development Goals (SDGs) by enhancing safety measures (SDG 3), protecting the environment through effective waste management (SDG 6), ensuring safe fuel storage and distribution (SDG 7), fostering economic growth and job creation (SDG 8), and supporting urban sustainability (SDG 11) and efficient resource utilization (SDG 12). By incorporating advancements demonstrated in recent studies on Multi-Criteria Decision-Making (MCDM) techniques—such as evaluating materials for hybrid vehicle batteries [40] and optimizing manufacturing processes for thermoplastics [41], the CRI could be enhanced to address the evolving infrastructure needs of hybrid and electric vehicles.

Integrating these methodologies into the CRI framework would allow petrol stations to adapt to modern technological demands, such as accommodating diverse vehicle types and advanced materials. This evolution would further align the CRI with additional SDGs, including innovation in industry and infrastructure (SDG 9) and fostering global partnerships (SDG 17) while reinforcing its contributions to sustainability, safety, and efficiency across petrol station operations.

Future work could expand this study to incorporate advancements in vehicle technology and their corresponding infrastructure needs in petrol stations. For instance, the CRI could be modified to evaluate the readiness of petrol stations to support hybrid and electric vehicles, ensuring that evolving energy demands and sustainability goals are met. This would provide a forward-looking approach to adapting petrol station management to the changing automotive landscape.

6. Conclusions

The study adopted factors and utility values to quantify their performance and contribution within petrol stations. This study developed a comprehensive CRI for petrol stations by dividing these assets into four standard spaces. The relative weight for each space is identified in this study. All major factors affecting the performance of each space are identified and organized in a hierarchal order, organized into main and sub-main factors. The relative importance weight for each factor and sub-factor was identified in this study using the AHP approach. Moreover, the utility value for each factor was also identified in this study, which makes it a comprehensive, transparent tool that can be used by different stakeholders, especially if different stakeholders manage each space within the petrol station.

In Saudi Arabia, there are seven authorities responsible for managing the operations and performances of petrol stations. A transparent and unified decision support system (DSS) among these authorities empowers petrol stations to identify deficiencies, optimize resource allocation, and enhance overall performance. Developing a Condition Rating Index for petrol stations serves as the backbone of a structured decision support system. This index plays a pivotal role in quantifying the performance of key units within petrol stations and providing an overall assessment of station performance; it can be used as the backbone for such DSS. Implementing the CRI in the case study showed that it can easily pinpoint the performance of different units and parameters affecting the performance of different operating units in different spaces within the petrol station. This will identify the performance level of each unit and space and facilitate addressing responsibilities among decision-makers and stakeholders. An effective decision support system for asset management in petrol stations should provide essential information on assets inventory, value, condition rating, costs of rehabilitation interventions, prioritization of interventions, cost optimization, and the consequences of deferring maintenance interventions. The developed CRI can be improved by adopting more specific experimental tools and techniques to describe the utility score of different impacts better. This continuous improvement will provide more realistic prediction models to help better plan maintenance interventions and budget allocations.

The CRI developed in this study represents a major step forward in petrol station management. It offers a transparent, systematic framework for evaluating performance across operational units. By integrating hierarchical modeling with advanced technologies such as IoT and AI, the CRI enables data-driven, proactive maintenance and operational planning. This comprehensive tool addresses existing gaps, helping stakeholders optimize station performance, prioritize rehabilitation efforts, and align management practices with sustainability goals.

This study recognizes certain limitations. The CRI was developed and validated using data specific to Saudi Arabia, which may restrict its direct application in regions with different regulatory or environmental contexts. Future research should aim to adapt the CRI framework for broader use by recalibrating weights and incorporating local standards to reflect regional differences. Furthermore, integrating advanced technologies like IoT and AI can enhance the model’s capabilities, enabling real-time monitoring and predictive maintenance to support more dynamic and responsive management practices.