The provision of an efficient water supply service (WSS) is crucial for the well-being of citizens and the sustainability of cities. This study aims to evaluate the performance of WSS using the results of a household survey and the ranking of performance indicators (PIs) by the analytic hierarchy process method. The methodology developed was tested for the case of the city of Taoura (Algeria). A survey was carried out among 340 residents of the city. The survey results showed that the majority of respondents (70%) were relatively dissatisfied with the quantity of water provided and 67% of households surveyed rated the quality of service as poor. Then, the performance was evaluated according to 5 decision criteria and 20 PIs. The results of the evaluation of the relative weights of the criteria are as follows: the ‘Financial and economic’ criterion plays the most important role, with a relative weight of 38.61%, followed by the ‘Operational’ criterion (24.7%) and the criterion ‘Physics’ (17.32%). The methodology used in this study can be a reliable tool for evaluating the performance of WSS in developing countries.

  • Development of a methodological framework to identify and classify performance indicators.

  • Water supply services are facing issues in achieving sustainability objectives.

  • Five criteria and 20 performance indicators were identified for assessment.

  • Weights were generated using the analytic hierarchy process.

  • Calculate the benefits for households and water companies with the new water tariff estimated by the willingness to pay (WTP).

The management of water resources in cities will become increasingly critical following several constraints such as climate change, water stress, economic development and rapid urbanization (Chang et al. 2020; Bulti & Yutura 2023). Sustainable cities require a supply of drinking water sufficient in quantity and quality (Priadi et al. 2024). Due to water scarcity conditions, citizens of most cities in developing countries receive intermittent water supplies (Dadras et al. 2023). As a result, most cities in developing countries experience difficulties in effectively and sustainably managing their water resources (Senna et al. 2023). To resolve these difficulties, it is necessary to improve the quality of the daily management of water supply services (WSSs) in these cities (Mian et al. 2023).

Sustainable management of WSS poses major challenges on a global scale. To this end, it is essential to analyze the performance of WSS (Sakai 2024). WSS management is one of the most serious problems facing cities in most developing countries (Molinos-Senante et al. 2023). WSS managers face various challenges, such as (i) insufficient water resources (Emamjomehzadeh et al. 2023), (ii) poor water quality (Chathuranika et al. 2023), (iii) high rate water losses (Zyoud & Fuchs-Hanusch 2020), (iv) customer dissatisfaction (Abubakar 2019), (v) excessive energy consumption (Rodriguez-Merchan et al. 2021) and (vi) economic deficiency (Boukhari et al. 2020). All of these challenges represent serious threats to the sustainability of cities.

According to the scientific literature, there are several methods and tools to evaluate the performance of a WSS (Tekile & Legesse 2023). In general, the performance of WSS is based on the three dimensions of sustainability (economic viability, environmental sustainability and social equity) (Dadebo et al. 2023) and the technical dimension (Alegre et al. 2017). The economic (financial) dimension determines cost management by calculating the difference between expenses (investment costs and operating costs) and WSS revenues. The environmental dimension leads to the reduction of negative impacts on the environment and the protection of natural resources (water and energy resources). The social dimension can be described as ensuring customer satisfaction and protecting their health and safety. On the other hand, technical performance analyzes loss rates and evaluates the operational mode of networks (Zenelabden & Dikgang 2022).

Most previous studies on WSS performance covered only one of the four dimensions of sustainability, (i) economic (Moosavian et al. 2022), (ii) environmental (Pinto et al. 2017), (iii) social (Maziotis et al. 2016) and (iv) technical (Suárez-Varela et al. 2017). Other studies have evaluated the performance of WSS according to a set of performance indicators (PIs) (Berg & Danilenko 2011; Vilanova et al. 2015; Kaur et al. 2021). PIs are the most used tools to evaluate the performance of WSS management. The application of PIs can help WSS managers identify financial and operational problems and set objectives to improve the daily management of WSS. PIs are practical tools for analyzing the operational, financial, environmental and social aspects of WSS (Sakai 2024). One of the major challenges faced by WSS managers in implementing PIs is the lack of standardized measures to accurately assess their performance and make informed decisions. The success of a WSS is measured by its ability to meet the needs and expectations of citizens. On the other hand, the number of studies on the sustainability of WSS management performance by applying a hybrid PI-multi-criteria decision-making (MCDM) method is limited. Other researchers have developed hybrid PI-MCDM methods in various sectors. Nam et al. (2019) used a set of PIs coupled with the analytic hierarchy process (AHP) method to evaluate the sanitary sewer system. (Alhumid et al. 2019).

The case of Algeria is almost the same as in other developing countries. WSS management faces several problems, such as financial insufficiency following lack of cost recovery, high rates of non-revenue water (NRW) and the quality of water distributed to citizens (Boukhari & de Miras 2019). The guiding vision of the water company (Algérienne Des Eaux: ADE) is to exploit and manage water resources at the municipal level in a sustainable manner and with financial self-sufficiency (Boukhari et al. 2011). Monitoring WSS performance is an essential step to achieve better daily management of the service offering to ADE subscribers.

This study aims to develop a methodology to evaluate the performance of WSS in Algeria, by applying a hybrid method based on IPs and AHP to provide information on the current performance of WSS. The selected PIs aim to evaluate the performance of WSS in Algeria. Next, the AHP method will rank the PIs according to their impacts on the technical performance of the WSS by providing valuable feedback on areas that need improvement. The proposed methodology is then applied to evaluate the performance of the WSS of the city of Taoura (Souk-Ahras Department). ADE managers provide their subscribers with intermittent distribution of drinking water (a few hours every 3 days) (ADE 2023). This situation is generally due to the following two constraints: lack of produced water (decrease in flow rates produced by drilling following the low precipitation in recent years) and the high rate of losses in the distribution systems (almost 50% of lost water) (ADE 2023). The Taoura ADE also suffers from a financial deficit like all other cities in the Souk-Ahras department (Boukhari & de Miras 2019). This financial insufficiency has led to a reduction in investments for the maintenance of the drinking water supply system and a poor quality of service provided to citizens.

Presentation of the study area

The city of Taoura is located in the department of Souk-Ahras (North-East of Algeria). This city has an area of 31 ha and a population of 18,933 inhabitants (according to the latest population census in 2021).

The drinking water service managed by ADE has 4,806 domestic subscribers (households). The drinking water supply is provided via a distribution network made up of pipes of different diameters (between 40 and 315 mm) and different materials (Polyvinyl Chloride (PVC), High-Density Polyethylene (HDPE) and steel). The network is supplied by 01 borehole and 05 storage structures. The total length of the distribution network is 60 km (ADE 2023). The rate of water loss in the distribution network is more than 40%.

Research methodology

The methodology developed in this study began by carrying out a diagnosis of the current situation of WSS management performance. In this step, a household satisfaction survey and a technical analysis of the performance of the WSS were carried out to define the inadequacies of the current management of the WSS in the study area. Then, a literature review was conducted to identify the criteria and PIs, as well as the most used methods for evaluating the performance of WSS. After the choice of the method and the selection of evaluation criteria and PIS, experts were consulted to calculate the relative weights of the elements (criteria and PIs) and to classify the PIs which have a great influence on the performance of the WSS. The methodology developed in this study is composed of three main steps (Figure 1).
Figure 1

The process to propose.

Figure 1

The process to propose.

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Step 1. Diagnosis of the current WSS situation

Collection of data

Data collection is a crucial step in carrying out this study. The data (volume produced, distributed, invoiced, number of leaks repaired and number of subscribers) were collected during the period between 2018 and 2022. By analyzing the data collected, areas of concern were identified, allowing criticism of the quality of service to be seen.

Citizen satisfaction survey

Citizen satisfaction was estimated by the results of a household survey. The objective of this survey is to identify priorities for improving service performance (Boukhari et al. 2023). The survey will make it possible to collect information in addition to that obtained directly from the ADE operating services.

Step 2. Identification and selection of criteria and performance indicators

According to the scientific literature, there is no standard set of PIs to assess the quality of services or systems (Loureiro et al. 2023). The selection of criteria and PIs was carried out on the basis of a literature review and the prior request of experts in the field of WSS management. In this study, 5 evaluation criteria and 20 PIs were chosen.

Step 3. Application of the AHP method

MCDM methods are used to identify and quantify the considerations of decision-makers and managers regarding various factors in order to compare alternative courses of action (Huang et al. 2011). According to the scientific literature, there are around 10 MCDM methods. Consequently, the most used methods in the field of water science and technology are AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), ELimination Et Choix Translating REality (ELECTRE) and Preference Ranking Organization Method for Enrichment Evaluations (ROMETHEE) (Khan et al. 2018; Garai & Garg 2022; Taherdoost & Madanchian 2023). The AHP is one of the most commonly used approaches to MCDM methods due to its simplicity and transparency (Singh et al. 2024). The AHP was developed in the 1980s by Thomas Saaty. It has been applied in different fields such as medicine (Santa Barletta et al. 2023), project selection in the context of sustainable development (Jurík et al. 2022), nuclear power plant (Mahmudah et al. 2024) and energy (Ahadi et al. 2023). The AHP method has been frequently used to evaluate performance of water distribution network management (Kilinç et al. 2018), irrigation (Veisi et al. 2022), identification of areas vulnerable to flooding (Burayu et al. 2023), management of water services (Boukhari et al. 2023), groundwater (Shekar & Mathew 2023), water quality (Huang et al. 2023) and rehabilitation of water distribution networks (Hassoun Nedjar et al. 2023).

In this study, the AHP method was selected due to its flexibility and mathematical simplicity (Singh et al. 2024) and its ability to integrate quantitative and qualitative factors obtained through expert opinions. The AHP methodology involves simplifying complex and sometimes contradictory decision problems (Saaty 1980) by breaking it down into a multi-level hierarchical structure (Zyoud et al. 2016). The application of the AHP includes the following steps.

Define the problem and determine its objective

The first step is to define the decision problem and then determine the objective through the implementation of the AHP.

Selection of evaluation elements

Before starting AHP procedures, decision criteria and indicators must be selected based on the problem of the study area and the opinion of experts.

Structuring the decision problem in the hierarchy

The construction of the hierarchical structure includes several levels of evaluation. However, the process will decompose the complex decision problem into a simple hierarchical structure. This structure includes all elements that are grouped by level. Then, the items are rated independently to facilitate evaluation.

Pairwise comparisons

The goal of this step is to establish the decision matrices of all the elements for each level. However, the judgment of different experts made it possible to determine the decision matrices for each level of the hierarchy. Each expert must complete a questionnaire by applying the Saaty scale (Table 1) to assign weights from 1 to 9 to all the elements identified in the study. This procedure is the basis for creating decision matrices by comparing the preference of each item over another item (pairwise comparison) to determine relative weights.

Table 1

Numerical comparison scale (Saaty 1980)

Numerical scaleDegree of preference
1.0 Equal importance of both elements 
3.0 One element is a little more important than the other 
5.0 One element is more important than the other 
7.0 One element is much more important than the other 
9.0 One element is absolutely more important than the other 
2.0, 4.0, 6.0, 8.0 Intermediate values between two judgments, used to refine the judgment 
Numerical scaleDegree of preference
1.0 Equal importance of both elements 
3.0 One element is a little more important than the other 
5.0 One element is more important than the other 
7.0 One element is much more important than the other 
9.0 One element is absolutely more important than the other 
2.0, 4.0, 6.0, 8.0 Intermediate values between two judgments, used to refine the judgment 

In this step, a pairwise comparison matrix ‘A’ was defined.
formula
(1)

A is the decision matrix, aij are the pairwise comparisons between elements i and j for i, j ∈ {1, 2,…, n} and aii = 1 and aij = 1/aji where n is the number of each element in the decision matrix.

Calculate relative weights

The calculation of relative weights must be carried out for all elements (criteria and indicators) of the hierarchy. To calculate the relative weights, the following steps are used:

  • Calculate the sum of each column of matrix ‘A’.

  • Devise each element of matrix ‘A’ by the total of the column and we will obtain the normalized matrix ‘B’.

  • Calculate the average of each row of the matrix ‘B’ we obtain the vector wi or i = 1, …, n.

Check the consistency ratio for each element
This step is essential to check the consistency or inconsistency of the decision matrix. According to Saaty, the consistency ratio (CR) must be equal to or less than 10% (Saaty 1980). The calculation of the CR is carried out by comparing a coherence index (CI) to that of a random consistency index (RI). In general, the coherence of each matrix was calculated from Equation (2):
formula
(2)

With:

  • CI is calculated from Equation (3):
    formula
    (3)
  • RI is given in Table 2.

  • λmax is the largest eigenvalue. λ is obtained by calculating the scalar product of the main eigenvector and the vector of column sums of the matrix.

  • n is the size of each matrix.

Table 2

Random consistency index (RI)

n123456789101112131415
RI 0.52 0.89 1.12 1.25 1.35 1.40 1.45 1.49 1.52 1.54 1.56 1.58 1.59 
n123456789101112131415
RI 0.52 0.89 1.12 1.25 1.35 1.40 1.45 1.49 1.52 1.54 1.56 1.58 1.59 

Calculate overall weight

The calculation of the overall weight is carried out by multiplying the relative weights of the criteria by the relative weights of the PIs associated with each criterion:
formula
(4)

The goal of calculating the overall weight is to classify the PIs according to their importance. By ranking PIs by their importance using the AHP method, WSS managers can better understand the strengths and weaknesses of their services provided to citizens.

Diagnosis of the current WSS situation

Analysis of collected data

After collecting data on volumes produced and invoiced for the period between 2018 and 2022, an estimation of the NRW ratio was carried out. The results are presented in Figure 2.
Figure 2

Estimated NRW between 2018 and 2022.

Figure 2

Estimated NRW between 2018 and 2022.

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Figure 2 illustrates that there is an annual increase in the NRW ratio (it reaches 48.75% for the year 2022). There is a need to look for short-term alternatives to reduce high levels of water loss in distribution networks (for example, it is important to quickly repair leaks and combat illegal connections).

Simulation result

The hydraulic simulation of the water distribution network of the city of Taoura has been shown in Figure 3.
Figure 3

Result of the hydraulic simulation by Epanet.

Figure 3

Result of the hydraulic simulation by Epanet.

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According to Figure 3, the hydraulic simulation for the distribution network of the city of Taoura by the Epanet 2.0 software showed that there are high pressures. The most dominant type of material in the network is PVC (ADE 2023). Most of the problems concerning PVC pipes are at the connection level between pipes, the dislodging of pipes is often the origin of the leaks recorded on the distribution network. With an average of six leaks repaired every day, more than 70% of these leaks occur in PVC pipes (ADE 2023). The use of HDPE pipes is highly recommended (corrosion resistant, flexible and its lightness which facilitates pipe installation).

Household survey results

The city of Taoura is made up of four distribution sectors and each sector is made up of 3–6 sub-sectors (sectorization). Households in all sub-sectors have the same characteristics (number of hours of drinking water distribution and type of housing: for example, collective or individual and social housing estates). Then, we took a random sample of more than 7% of the number of subscribers from each sub-sector provided that the households surveyed were scattered in the same sub-sector (see table in Appendix A1). To this end, the survey affected almost the entire territory of the city.

In total, 340 surveys were carried out for the case of citizens of the city of Taoura. The aim of the survey is to find out the opinions of citizens on satisfaction with the quality of service provided by the managers of the ADE of Taoura. The socio-economic results of the survey among subscribers are presented in Table 3.

Table 3

Household survey results

VariablesNumberPercentage
Type of habitat 
 Villa 101 29.71 
 Apartment 184 54.12 
 Collective house 47 13.82 
 Others 2.35 
Type of occupation 
 Owner 226 66.47 
 Tenant 114 33.53 
Number of families per dwelling 
 1 315 92.65 
 2 19 5.59 
 3 1.76 
 More than 3 
Number of people per home 
 2 16 4.71 
 3 37 10.88 
 4 53 15.59 
 5 78 22.94 
 6 86 25.29 
 7 39 11.47 
 More than 7 31 9.12 
Assessment of the quality of service 
 Good 64 18.82 
 Average 115 33.82 
 Poor 161 47.36 
Assessment of water quality 
 Yes 132 38.82 
 No 208 61.18 
VariablesNumberPercentage
Type of habitat 
 Villa 101 29.71 
 Apartment 184 54.12 
 Collective house 47 13.82 
 Others 2.35 
Type of occupation 
 Owner 226 66.47 
 Tenant 114 33.53 
Number of families per dwelling 
 1 315 92.65 
 2 19 5.59 
 3 1.76 
 More than 3 
Number of people per home 
 2 16 4.71 
 3 37 10.88 
 4 53 15.59 
 5 78 22.94 
 6 86 25.29 
 7 39 11.47 
 More than 7 31 9.12 
Assessment of the quality of service 
 Good 64 18.82 
 Average 115 33.82 
 Poor 161 47.36 
Assessment of water quality 
 Yes 132 38.82 
 No 208 61.18 

According to the results of the survey, 67% of respondents are dissatisfied with the quality of service provided by the local ADE. The majority of the population surveyed (70%) was relatively dissatisfied with the quantity of water supplied by the Taoura ADE.

The survey results showed that only 41% of households drink tap water directly. Participants said they had to treat their tap water before drinking it, usually by boiling it (11%) and 8% added chlorine to the water they dispensed. On the other hand, 49% of households use spring water, 31% buy bottled water and 1% of respondents buy water from water sellers by tanker truck. Twenty-three percent of households reported that the water had a bad smell, 38% reported that the water tasted bad and 4% reported that the water was cloudy. The results reveal that it is necessary to improve drinking water treatment processes to meet national standards for distributed water quality.

The survey results indicate that the majority of respondents are dissatisfied with the quantity of water and quality of service, because the distribution of drinking water is carried out once in 3 days for an average 3 h. Moreover, to make up for the lack of water supply in the other 2 days, most households store water in water tanks (Boukhari et al. 2023). Water stored in tanks which are generally installed on the roofs of houses loses its quality due to storage conditions (Slavik et al. 2020).

Selection of criteria and performance indicators

After an extensive literature review, the criteria and IPs are identified and then selected to be applied in the study area. The criteria and selected PIs were presented in Table 4.

Table 4

The criteria and PIs selected

CriteriaPIsSymbols
Financial and economic (C1) Recovery of operating expenses PI 1.1 
Recovery of investment costs PI 1.2 
Full cost recovery PI 1.3 
Existence of a social group PI 1.4 
Physical (C2) Knowledge of the linearity of adduction and distribution networks PI 2.1 
Knowledge of the age of pipes PI 2.2 
Meter coverage PI 2.3 
Knowledge of the type of pipe materials PI 2.4 
Operational (C3) Water quality monitoring PI 3.1 
Quality of maintenance work PI 3.2 
Number of failures PI 3.3 
Water loss rate PI 3.4 
Staff (C4) Existence of staff training PI 4.1 
Staff balance by function PI 4.2 
Staff qualification PI 4.3 
Staff health and safety PI 4.4 
Quality of service (C5) Service coverage PI 5.1 
Customer complaints PI 5.2 
Compliance rate of distributed water quality analysis PI 5.3 
Power pressure and continuity PI 5.4 
CriteriaPIsSymbols
Financial and economic (C1) Recovery of operating expenses PI 1.1 
Recovery of investment costs PI 1.2 
Full cost recovery PI 1.3 
Existence of a social group PI 1.4 
Physical (C2) Knowledge of the linearity of adduction and distribution networks PI 2.1 
Knowledge of the age of pipes PI 2.2 
Meter coverage PI 2.3 
Knowledge of the type of pipe materials PI 2.4 
Operational (C3) Water quality monitoring PI 3.1 
Quality of maintenance work PI 3.2 
Number of failures PI 3.3 
Water loss rate PI 3.4 
Staff (C4) Existence of staff training PI 4.1 
Staff balance by function PI 4.2 
Staff qualification PI 4.3 
Staff health and safety PI 4.4 
Quality of service (C5) Service coverage PI 5.1 
Customer complaints PI 5.2 
Compliance rate of distributed water quality analysis PI 5.3 
Power pressure and continuity PI 5.4 

According to Table 4, each criterion is defined by four PIs. For example, the financial and economic criterion (C1) is determined by the four PIs (PI 1.1, PI 1.2, PI 1.3 and PI 1.4).

  • Financial and economic

    • Recovery of operating costs (PI 1.1): This indicator measures the recovery of operating costs (Opex) such as maintenance and repair costs, energy, personnel and others.

    • Recovery of investment charges (PI 1.2): Investment costs (Capex capital charges) represent expenses incurred to acquire, construct or rehabilitate the infrastructure necessary for the operation of the WSS.

    • Financial cost recovery (PI 1.3): PI 1.3 represents the difference between WSS expenses and revenues.

    • Existence of a social band (PI 1.4): It represents the affordability of the pricing applied. To this end, we must check whether there is a tariff and a social bracket in the current pricing.

  • Physical

    • Knowledge of the linearity of the supply and distribution networks (PI 2.1): It is also important to ensure that the total length of the distribution network is sufficient to serve all consumers efficiently.

    • Knowledge of the age of pipes (PI 2.2): This indicator presents the aging of drinking water networks.

    • Meter coverage (PI 2.3): PI 2.3 makes it possible to measure the proportion of customers who have access to billing based on actual water consumption.

    • Knowledge of the type of pipe materials (PI 2.4): The objective of this indicator is to know whether or not ADE managers have knowledge of the type of pipe materials.

  • Operational

    • Water quality monitoring (PI 3.1): Water quality standards can vary depending on national regulations, so it is important to ensure that testing meets the appropriate standards for assessment accuracy of WSS performance.

    • Quality of maintenance work (PI 3.2): This indicator measures the quality of maintenance of the physical assets of the drinking water distribution system. The number of incidents represents the number of breakdowns or technical problems occurring on the distribution network.

    • Number of failures (PI 3.3): PI 3.3 measures the quality of the installation of equipment in the drinking water distribution system.

    • Water loss rate (PI 3.4): IP 3.4 represents the difference between the volume produced and the volume billed.

  • Staff

    • Existence of staff training (PI 4.1): One of the management objectives is to maintain well-trained staff.

    • Staff balance by function (PI 4.2): This indicator makes it possible to assess the importance of the number of employees in each function.

    • Personnel qualification (PI 4.3): One of the essential management objectives is to have qualified personnel to properly carry out the required tasks.

    • Personnel health and safety (PI 4.4): The objective of PI 4.4 is to take into account aspects of employee health and safety. Negligence in implementing effective safety regulations would increase absenteeism and workplace accidents.

  • Quality of service

    • Service coverage (PI 5.1): PI 5.1 determines the service coverage provided to different types of subscribers (households, businesses, businesses, industries, tourism, etc.).

    • Customer complaints (PI 5.2): The number and type of customer complaints are a good indicator for assessing the level of quality achieved by a company. The number of complaints represents the number of complaints filed by customers for issues related to the daily management of WSS.

    • Compliance rate of analysis of the quality of the water distributed (PI 5.3): It evaluates compliance with the regulatory limits for the quality of the water distributed to the user concerning the physico-chemical parameters and the bacteriological parameters (the presence of pathogenic bacteria in water). Water quality non-compliances may include test results exceeding regulatory limits for contaminants such as lead, bacteria, nitrates, pesticides, etc.

    • Pressure and continuity of supply (PI 5.4): This indicator evaluates the pressure in the network and the continuity of the drinking water supply.

Result of applying the AHP method

Selection of evaluation elements

Five decision criteria and 20 PIs were supported for the evaluation of the performance of WSS in the city of Taoura.

Development of the hierarchical structure

In the case of this study, three levels were taken into account in the AHP: the first level is dedicated to the evaluation objective (level 0), on the other hand, the other two levels are assigned to the two elements (criteria and PIs). The hierarchical structure is defined in Figure 4. For the second level of criteria (C), five decision criteria are taken into account: financial and economic (C1), physical (C2), operational (C3), personnel (C4) and quality of service (C5). In the third level, 20 PIs were applied for the evaluation of the higher level criteria, with four PIs for each criterion.
Figure 4

Graphical representation of the proposed hierarchical structure.

Figure 4

Graphical representation of the proposed hierarchical structure.

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Establishment of the decision matrix

To establish the decision matrix, 30 experts were consulted to determine their contributions and preferences according to the AHP methodology. The panel of experts is made up of three groups of 10 experts (10 university researchers, 10 decision-makers from the water resources department and 10 managers from the local ADE). These experts were chosen based on their professional and scientific experience. Each of the academic experts (university researchers) holds a doctorate in science and has at least one scientific publication on the problem of water services management in Algeria. On the other hand, professional experts have an engineering degree and have more than 10 years of experience within the ADE or the water resources department. A short training session was carried out with the experts to briefly explain the AHP method and the scoring principle for pairwise comparisons between all assessment items. Then, a questionnaire was given to each expert to construct the decision matrices according to the Saaty scale for each level of the hierarchy (criterion and PIs). Therefore, each expert completed six decision matrices (one matrix for the criteria and five matrices for the PIs). After checking the consistency of each matrix (12 experts redid their judgments following the non-consistency of the decision matrices), the results were grouped into a single matrix for the criteria [M1] and five matrices for the PIs [M2.1–M2.5].

  • Pairwise comparison of criteria:

To carry out pairwise comparisons for the five criteria, the judgment results of the 30 experts were listed in a single decision matrix [M1]. Each expert makes his judgment using the Saaty scale and delivers his own pairwise comparison matrix. Then, the AHP is applied.
formula

Calculation of the priority vector

  • Calculate the sum of each column of the matrix [M1]

C1C2C3C4C5
C1 
C2 1/3 1/2 
C3 ½ 
C4 1/3 1/3 1/2 
C5 ¼ 1/2 1/4 1/3 
Sum 2.42 6.83 4.25 9.33 14.00 
C1C2C3C4C5
C1 
C2 1/3 1/2 
C3 ½ 
C4 1/3 1/3 1/2 
C5 ¼ 1/2 1/4 1/3 
Sum 2.42 6.83 4.25 9.33 14.00 

  • Divide each element of the matrix [M1] by the total of the column and we will obtain the normalized matrix [B1]
    formula
  • Calculate the average of each row of the matrix [B1] to have the relative weight vector {w1}
    formula

Verification: 0.386 + 0.173 + 0.247 + 0.125 + 0.069 = 1.000.

Checking the CR

  • Calculate the eigenvalue λmax

To calculate the eigenvalue λmax, the vector {C} is calculated first. It is calculated from the following equation:
formula
The calculation of the eigenvector {λ} is carried out by the following equation:
formula

λmax is the large value of the vector {λ} and λmax = 5.414.

  • Determine the value of the random coherence index (RI)

According to the RI table, for n = 5 → RI = 1.12

  • Calculate the CI
    formula
  • Calculate CR
    formula
  • Check CR

The CR value for this matrix is 0.0924 (9.24%), which is less than 10%. Therefore, the matrix is considered coherent.

The results of calculating the relative weights of the criteria matrix [M1] are illustrated in Figure 5.
Figure 5

Allocation of relative weights of criteria.

Figure 5

Allocation of relative weights of criteria.

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The experts judged that the ‘Financial and economic’ criteria plays the most important role with a relative weight of 38.61% followed by the ‘Operational’ criteria (24.7%) and the ‘Physical’ criteria (17.32%) in the management of WSS. Most water companies in Algeria suffer from financial insufficiency following the water pricing applied and have not changed since 2005. This financial insufficiency has a negative impact on the improvement of operational performance (the reduction of water losses and the application of new modern technologies for the maintenance of drinking water supply systems) and physical (the replacement of obsolete pipes with HDPE pipes).

  • Pairwise comparison of PIs:

The relative weights of the PIs are calculated in relation to the matrices M2.1M2.5. The pairwise comparison process for the case of the PIs level is the same as that of the criteria. For this purpose, the priority vectors are calculated in the same way as for the other level. Matrices M2.1M2.5 are presented in Supplementary material. The results of calculating the relative weights for each PI are presented in Figure 6.
Figure 6

Calculation of relative weights of PIs.

Figure 6

Calculation of relative weights of PIs.

Close modal

Calculation of overall weights

The overall weight of the indicators is calculated by multiplying its local priority vector by the corresponding relative weight of the criterion and the PIs. The overall weights of the indicators are synthesized to establish overall priorities for the selection of PIs that have a large influence on the performance of WSS management. The distribution of the overall weightings obtained from the 20 PIs is presented in Figure 7. For this purpose, the PIs of the financial and economic criterion have relatively high weightings, which implies that the tasks of this group are more priorities. Then, the overall prioritization of the 20 IPs is summarized based on their ranking, as shown in Supplementary material.
Figure 7

Calculation of overall weights of PIs.

Figure 7

Calculation of overall weights of PIs.

Close modal

The four PIs of the criterion (financial and economic), representing 38.61% of all scores, were classified as the most important for the sustainability of WSS. This indicates that experts judged that economic viability is essential for good WSS management performance. The three indicators PI1.3 (financial cost recovery), PI1.1 (recovery of operating costs) and PI3.2 (quality of maintenance work) reaching almost 50% of all PIs. To improve the performance of WSS in the city of Taoura or in all other Algerian cities, ADE managers must improve the recovery of financial and operating costs by applying new water pricing (tariff reform) and by reducing high rates of water loss in drinking water distribution networks.

The main objective of this study is to evaluate the performance of WSS management in Algeria. The methodology used is based on a household satisfaction survey and a classification of IPs using the AHP method. Then, it is tested for the case of the town of Taoura (Department of Souk-Ahras, Algeria). During this study, 5 decision criteria and 20 PIs were chosen for the evaluation of the performance of WSS. The AHP method was applied to classify the PIs according to their importance. The results of calculating the relative weights of the criteria showed that the ‘Financial and economic’ criterion plays the most important role with a relative weight of 38.61% followed by the ‘Operational’ criterion (24.7%) and ‘Physical’ (17.32%). On the other hand, the results of the calculation of the overall weights showed that the three PIs ‘Total cost recovery’, ‘Recovery of operating costs’ and ‘Quality of maintenance work’ reached almost 50% of all PI scores. The role of PIs is to help WSS managers define gaps in the operations of daily WSS management.

In conclusion, the methodology developed in this study can play a crucial role for the assessment of WSS management. It provides valuable insights into WSS quality performance by enabling informed decision-making for continuous improvement. Taking into account several criteria and PIs in the decision-making process can help WSS managers make sustainable choices to improve the effectiveness of WSS. However, it is important to note that achieving these goals requires continuous monitoring and fine-tuning of criteria and PIs.

This study has some limitations. First, the number of criteria and PIs is low to assess the sustainability of WSS management in large cities. Second, uncertainty was not taken into account in the calculation of weights carried out by the AHP method. To this end, future research should take into consideration the resolution of uncertainty using other approaches such as the fuzzy-AHP method. The results of this research can improve the existing literature on WSS performance evaluation in Algeria and developing countries.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Supplementary data