A Quali-Quantitative Analysis Model Integrating Fuzzy Analytical Hierarchy Process and Cost–Benefit Analysis for Optimizing KPI Implementation: Insights from a Practical Case Study Application

Abstract

In today’s competitive industrial landscape, effective performance measurement is crucial for achieving operational success. Key Performance Indicators (KPIs) are widely used to track progress, but their implementation often lacks a comprehensive framework that considers both financial outcomes and managerial insights. A quali-quantitative analysis model is introduced to optimize the implementation of KPIs in industrial settings, demonstrated through a case study of a Cambodian charcoal factory. By integrating Cost–Benefit Analysis (CBA) and Fuzzy Analytic Hierarchy Process (FAHP), the model combines both quantitative financial analysis and qualitative managerial evaluations to assess and rank a selected set of KPIs. This dual approach ensures a more comprehensive understanding of KPI impacts, enabling informed decision-making. The results highlight the critical need for balancing measurable financial benefits with qualitative insights, particularly in industries within developing nations that are forced to compromise in constrained environments, and where both economic outcomes and strategic considerations are essential for sustainable growth. Furthermore, the proposed model has universal applicability across different industrial contexts, providing a flexible and adaptable framework for KPI selection beyond the specific case study analyzed.

1. Introduction

In today’s highly competitive industrial landscape, effective performance measurement is essential for achieving operational and strategic success. Key Performance Indicators (KPIs) play a central role in tracking progress and guiding decision-making. However, their selection and implementation often present challenges, especially in resource-constrained environments where financial limitations and operational efficiency must be carefully balanced [1]. Existing methods for KPI selection predominantly focus on either quantitative or qualitative evaluations, limiting their ability to address the complex trade-offs that decision-makers face in practice.

Quantitative methods like Cost–Benefit Analysis (CBA) provide valuable insights into financial outcomes by systematically converting costs and benefits into monetary terms. While widely used for evaluating project feasibility, CBA often neglects intangible factors such as strategic alignment, stakeholder preferences, and long-term sustainability [2,3]. On the other hand, qualitative tools like the Fuzzy Analytic Hierarchy Process (FAHP) excel in capturing subjective judgments and managerial priorities but lack the financial rigor necessary for comprehensive decision-making [4]. While some studies suggest that integrated frameworks combining qualitative and quantitative methodologies already exist—especially in production systems inspired by the Toyota Production System (TPS)—the application of such integrated models remains limited in the context of KPI selection for industrial environments, particularly in developing economies [5]. Goti et al. [5] illustrate how company-specific production systems (XPS) incorporate both qualitative and quantitative tools to optimize KPI implementation, particularly through data quality management, IT infrastructure, and sustainability-oriented performance monitoring. However, existing studies primarily focus on either financial impact assessments or managerial decision-making processes, rather than establishing a structured approach that optimally balances both aspects. This highlights a need for further exploration of how different integration methods can enhance KPI selection, uncovering additional benefits that arise when alternative techniques are applied.

To address this gap, this paper proposes a quali-quantitative analysis model that integrates CBA and FAHP. This dual approach enables decision-makers to evaluate KPIs based on both economic impact and qualitative considerations, offering a comprehensive framework for optimizing KPI selection in industrial contexts. The model is demonstrated through a case study of a Cambodian charcoal factory, providing practical insights into its applicability and value. Furthermore, the model’s structure ensures universal applicability, making it suitable for industries beyond those examined in this study.

This study aims to answer the following research questions:

• RQ1: How can qualitative and quantitative aspects of KPI selection be integrated through FAHP and CBA in constrained industrial environments?
• RQ2: What benefits does this integrated approach offer over traditional, single-method approaches?

This research is significant as it bridges the gap between financial feasibility and strategic alignment, offering a novel framework that can be applied in various industrial settings. The paper is organized as follows: Section 2 reviews the relevant literature on CBA and FAHP, identifying gaps and opportunities. Section 3 outlines the methodology used to integrate these approaches. Section 4 applies the proposed model to a real-world case study, while Section 5 presents the results and their implications. Section 6 discusses the findings in relation to the literature, and Section 7 concludes with key insights, limitations, and directions for future research.

2. Literature Review

Cost–Benefit Analysis (CBA) is widely regarded as a reliable method for evaluating the economic feasibility of projects, including in KPI implementation. The strength of CBA lies in its ability to convert both costs and benefits into a common financial measure, allowing decision-makers to assess the net economic impact of different KPI alternatives. The approach is frequently cited in the literature for its capacity to generate key financial indicators, such as Net Present Value (NPV), Internal Rate of Return (IRR), and Benefit–Cost Ratio (BCR), which can also be useful in determining the profitability and viability of KPI implementation projects [2]. In the context of industrial manufacturing, CBA has been extensively used to evaluate KPIs related to productivity improvements, cost reductions, and waste minimization. Studies by Drèze and Stern [6] highlight the critical role of CBA in capital investment decisions and performance management frameworks, demonstrating its effectiveness in aligning KPIs with financial goals. However, the literature also acknowledges that CBA is limited in its ability to account for intangible factors such as employee morale, customer satisfaction, and alignment with long-term strategic objectives [3,7]. These qualitative factors, though harder to quantify, play a crucial role in KPI selection, particularly in industries where long-term sustainability and stakeholder alignment are paramount. Recent studies have extended the application of CBA by integrating it with multi-criteria decision-making techniques. For instance, ref. [8] reviewed how combining CBA with Multi-Criteria Decision Analysis (MCDA) could address sustainability challenges by incorporating non-monetary criteria into transport project evaluations. Similarly, in other works authors proposed a framework that integrates CBA with the Analytic Hierarchy Process (AHP) to better balance conflicting goals in road safety projects [9,10]. While CBA provides a solid quantitative foundation, the literature identifies a clear gap in integrating qualitative considerations into the evaluation process [5,7]. The focus on monetary metrics in CBA often results in an oversight of critical qualitative factors that influence the long-term success of KPI implementation. This limitation is addressed in part by integrating advanced methods such as the Weighted Fuzzy Assessment Method (WFAM), as demonstrated [11], or multi-criteria decision-making (MCDM) tools such as the Fuzzy Analytic Hierarchy Process (FAHP) [12]. FAHP, an extension of the Analytic Hierarchy Process (AHP), incorporates fuzzy logic to handle uncertainty and subjective judgments, making it particularly useful in environments where precise data are unavailable or preferences are imprecise. FAHP has been particularly useful in KPI selection within complex decision-making environments, such as those found in manufacturing. Studies by Mikhailov and Tsvetinov [4] show how FAHP can rank KPIs by capturing both tangible and intangible factors, such as process flexibility, employee satisfaction, and environmental impact. FAHP has been applied in a variety of industries to prioritize KPIs based on qualitative judgments, providing a structured approach to align KPIs with organizational strategies [13,14]. Despite the demonstrated utility of FAHP in capturing subjective and strategic considerations, its application remains limited in certain contexts, particularly in developing nations. Studies by Alemayehu Shume and Mitikie [15] have shown the effectiveness of combining FAHP with the Delphi method to address these challenges in contractor selection, illustrating its potential adaptability for industries with limited resources. Similarly, ref. [16] integrated FAHP with the TOPSIS algorithm to optimize project selection decisions, further highlighting the method’s flexibility and robustness. Ultimately, many studies, including those by Mangla [17] and Mikhailov and Tsvetinov [4], acknowledge that FAHP is underutilized in industries with resource constraints and less-developed infrastructures. In these environments, the need for advanced decision-making tools like FAHP is critical, but the adoption of such methods is hindered by a lack of expertise and technology. This gap presents a significant opportunity for research to explore how FAHP can be adapted or simplified for use in industries that lack access to advanced decision-making frameworks.

The Need for an Integrated Models

The separation of quantitative and qualitative approaches in the literature presents a clear research gap, with existing literature largely treating these methods in isolation and therefore limiting their effectiveness in environments where both financial viability and strategic alignment are essential. The existing body of literature on KPI implementation has laid important groundwork through the use of both qualitative and quantitative methodologies. However, significant gaps remain in the integration of these approaches and their application in constrained environments. This paper aims to address these gaps by proposing a comprehensive framework that combines CBA and FAHP, offering a more holistic approach to KPI selection. This integrated model is poised to provide valuable contributions to both academia and industry by offering a structured way to prioritize KPIs based on both economic impact and qualitative considerations.

3. Methodology

The methodology used for this study combines the two well-established techniques previously discussed: Cost–Benefit Analysis (CBA) and the Fuzzy Analytic Hierarchy Process (FAHP). Together, these approaches are used to form a comprehensive evaluation that considers both quantitative and qualitative factors regarding the implementation of Key Performance Indicators (KPIs). This dual approach enables a more balanced assessment of KPIs, providing actionable insights that account for both economic outcomes and managerial priorities.

3.1. Cost–Benefit Analysis

Cost–Benefit Analysis is, both in the literature and in current practice, one of the most widely used methodologies to highlight the feasibility of an investment project. This analysis process systematically compares the aggregate anticipated costs of a project against its projected benefits, quantifying the net value in financial terms. The primary objective of CBA is to assist decision-makers in assessing the viability of a project from economic, financial, and social perspectives [18], ultimately facilitating optimal resource allocation within an organization. According to Thomas and Chindarkr [19], the CBA process serves two primary aims:

• To determine the viability of an investment project, assessing whether it constitutes a sound investment.
• To compare multiple competing investment projects, identifying the most feasible option.

This approach enables decision-makers to monetize both positive and negative outcomes, facilitating direct comparisons between diverse projects or alternatives and guiding them towards options that maximize net benefits. Boardman et al. [2] thoroughly describe in their works the cost–benefit analysis methodology, and in it they proceed to identify six fundamental steps that are typically used to perform CBA:

• Scope Definition: Clearly outline the scope, objectives, and time frame of the project or decision to be evaluated. This step sets the foundation for the analysis by establishing what is being assessed and over what period.
• Alternatives Identification: Consider all viable alternatives (including the “do nothing” option) to ensure that the best course of action is chosen. This step involves exploring different approaches or projects that could meet the same objectives.
• Cost and Benefit Identification: List all potential costs (e.g., capital, operational, maintenance) and benefits (e.g., revenue generation, social or environmental improvements) associated with each alternative. Note that in this step it is important to consider both direct and indirect impacts of all alternatives.
• Monetization: Convert all identified costs and benefits into monetary terms, typically using a common unit (e.g., local currency). This often includes discounting future values to present values, to account for the time value of money.

The Present Values of Costs and Benefits can be calculated as illustrated below:

$$PV\left(C\right)={\sum }_{t=0}^n{\displaystyle \frac{C_t}{{(1+r)}^t}}$$

(1)

$$PV\left(B\right)={\sum }_{t=0}^n{\displaystyle \frac{B_t}{{(1+r)}^t}}$$

(2)

where C_t and B_t are the relative costs and benefits expected for the year t, considering a discount rate r over a lifespan of n years of the project.

These values are then used to compare the different alternatives quantitatively.

5.

Alternative Comparison: In this step, metrics like Net Present Value (NPV) and Benefit–Cost Ratio (BCR) are used to assess the financial viability of the project. NPV measures the difference between discounted benefits and costs, while BCR compares the benefits to the costs as a ratio.

6.

Sensitivity Analysis: Perform sensitivity analysis to account for uncertainties in the data or assumptions, allowing decision-makers to see how changes in key variables (e.g., discount rates or cost estimates) affect the outcomes.

The final phase of Cost–Benefit Analysis (CBA) involves evaluating the alternatives to determine the optimal solution for the given problem or objective. This evaluation typically compares the total monetized costs against the total monetized benefits. However, various methods exist in the literature for this comparison and for assessing the efficiency of project alternatives [19]. Ortega [20] reports some of the most popular methods:

•

Net Present Value (NPV): This metric calculates the difference between the present value of a project’s cash inflows and outflows, using a discount rate to account for the time value of money.

$$\mathrm{NPV}=PV\left(B\right)-PV\left(C\right)$$

(3)

A positive NPV indicates that the project’s returns exceed its costs, making it a favorable investment. Therefore, NPV is an essential indicator for determining the overall profitability of long-term projects.

•

Benefit–Cost Ratio (BCR): BCR compares the present value of total benefits to total costs.

$$\mathrm{BCR}={\displaystyle \frac{PV\left(B\right)}{PV\left(C\right)}}$$

(4)

Based on the Benefit–Cost Ratio (BCR), the decision-making process regarding a project’s feasibility can be summarized as follows:

•

BCR < 1.0: In this case, the project’s costs outweigh its benefits, indicating that the project is not economically viable. Consequently, the project should be rejected based on this result.

•

BCR = 1.0: Here, the costs and benefits are equal, implying that the alternative is marginally viable. While in this case the alternative may be implemented, one should do so with caution, as the potential for economic success is limited.

•

BCR > 1.0: In this case, the benefits exceed the costs, signifying that the alternative is indeed economically favorable and should be allowed to proceed.

Therefore, BCR enables the ranking of projects in terms of profitability and can be particularly useful for comparing alternatives of different scales [2].

•

Internal Rate of Return (IRR): IRR calculates the discount rate that brings a project’s NPV to zero, effectively representing the expected rate of return as a percentage. A project is generally considered acceptable if its IRR exceeds the required rate of return or cost of capital.

In the context of Key Performance Indicator (KPI) selection and implementation, CBA can serve as a critical tool for prioritization. It enables organizations to evaluate the costs associated with adopting specific KPIs against their anticipated benefits. The application of CBA in KPI selection promotes resource allocation to high-impact indicators, potentially leading to enhanced operational efficiency, waste reduction, and quality improvements.

3.2. Fuzzy Analytic Hierarchy Process

Fuzzy Analytic Hierarchy Process (FAHP) is an extension of the traditional Analytic Hierarchy Process (AHP), a popular multi-criteria decision-making method [21]. AHP is widely used for complex decision-making scenarios where multiple factors must be considered and compared [22,23,24]. However, in many cases, decision-makers face uncertainty or ambiguity when making judgments about the importance of certain criteria. FAHP incorporates fuzzy logic to handle this inherent uncertainty, allowing for degrees of truth rather than strict true/false (or 0/1) comparisons, and therefore enabling more nuanced and flexible evaluations [24,25].

FAHP was chosen for its ability to handle uncertainty in KPI selection while maintaining methodological simplicity. Although it requires an increasing number of pairwise comparisons as alternatives grow, this was not an issue in our study, which analyzed only five KPIs. In cases with a larger set of alternatives, hierarchical structuring or hybrid approaches could mitigate scalability concerns. While more modern MCDM methods like BWM, ANP, or TOPSIS exist, they present trade-offs. BWM reduces the number of comparisons but lacks FAHP’s ability to handle uncertainty effectively [26,27]. ANP captures interdependencies among criteria but is computationally intensive and complex to implement in resource-limited environments [28]. TOPSIS is useful for ranking alternatives based on their proximity to an ideal solution but does not integrate expert judgments with the same structured approach as FAHP [29].

FAHP provides a balanced trade-off between feasibility and qualitative depth, making it particularly suitable for resource-constrained environments. It has been widely validated in various decision-making contexts, demonstrating its robustness in handling subjective assessments and prioritization tasks [30,31].

Before diving into FAHP, it is important to briefly understand the traditional Analytic Hierarchy Process. According to the original structure described by Saaty [22], AHP structures decision-making problems into a hierarchy, typically including the following:

• Goal: The main objective or problem to be solved.
• Criteria: The factors or criteria that will influence the decision.
• Alternatives: The different options or solutions being considered.

In AHP, decision-makers compare each criterion and alternative in a pairwise manner. These comparisons are made using the Saaty Scale (from 1 to 9), which reflects the relative importance of one criterion over another [22,23]. A matrix is constructed to capture these comparisons, and mathematical techniques (often using eigenvalues) are applied to derive a set of priorities or weights for each criterion and alternative. These weights are then used to rank the alternatives and guide the decision-making process [23]. One limitation of traditional AHP is its reliance on precise numerical values for comparisons, which may not always reflect real-world decision-making, especially when human judgment is involved [21,24]. In many situations, decision-makers are uncertain or imprecise in their assessments, and the exact relative importance of one criterion over another may be unclear. This problem can be overcome using fuzzy logic [32], which instead of saying one criterion is definitively more important than another, allows decision-makers to express their preferences using a range of values that reflect uncertainty. For example, instead of stating that one criterion is “3 times more important” than another, FAHP allows them to express this as “around 2 to 4 times more important”.

The steps to use FAHP are as follows [24,25,31,32,33].

• Hierarchical Structuring of the Problem: Like AHP, the first step in FAHP is to structure the decision problem into a hierarchy, such as the one in Figure 1. The top level represents the overall goal, the intermediate levels represent the criteria and sub-criteria, and the bottom level represents the alternatives to be evaluated.
• Fuzzy Pairwise Comparisons: Once the hierarchy is defined, the decision-makers perform pairwise comparisons of the criteria and alternatives in order to evaluate the relative importance of the elements in the hierarchical structure:
  • Criteria: “In order to achieve the goal, how important is the criterion i compared to the criterion j?”
  • Alternatives: “How much the alternative x is better than the alternative i in meeting the criterion j?”

However, instead of using a single numerical value for each comparison, FAHP uses fuzzy numbers—typically in the form of triangular or trapezoidal fuzzy numbers (

Table 1. Fuzzy Saaty scale.

Definition	Triangle Numbers dij
i is extremely more important than j	9	9	9
i is strongly more important than j	6	7	8
i is more important than j	4	5	6
i is slightly more important than j	2	3	4
i is equally important as j	1	1	1
i is slightly less important than j	1/2	1/3	1/4
i is less important than j	1/4	1/5	1/6
i is strongly less important than j	1/6	1/7	1/8
i is extremely less important than j	1/9	1/9	1/9

). These numbers represent ranges that reflect uncertainty or imprecision in the decision-makers’ judgments. This allows for more flexibility in capturing subjective judgments. Triangular numbers can be identified as a triple (d_ij) = [l, m, u], where its membership function is defined as in

$${\mu }_{d_{ij}}\left(x\right)=\left\{\begin{array}{c}{\displaystyle \frac{x}{m-l}}-{\displaystyle \frac{l}{m-l}}, x\in [l,m]\\ {\displaystyle \frac{x}{m-u}}-{\displaystyle \frac{u}{m-u}}, x\in [m,u]\\ 0, otherwise\end{array}\right.$$

(5)

where l, m and u are, respectively, the lower, medium and upper values of d_ij [34].

This scale, illustrated in Table 1, is particularly useful as it allows the decision-makers’ verbal expressions to be translated into numerical values. Therefore, using this scale, it is possible to submit questionnaires to the decision-makers and other experts in disciplines involved in a particular aspect of the problem to gather their judgments, even if they have little experience or familiarity with the FAHP methodology.

3.

Fuzzy Pairwise Comparison Matrix: The pairwise comparisons are compiled into a fuzzy comparison matrix, as illustrated in

Table 2. Example of pairwise comparison matrix.

	Alternative 1	Alternative 2	…	Alternative n
Alternative 1	1	d12	…	d1n
Alternative 2	1/d12	1	…	d2n
…	…	…	1	…
Alternative n	1/d1n	1/d2n	…	1

, where each element represents the fuzzy number assigned to the comparison between two criteria or alternatives.

4.

Defuzzification: After the fuzzy comparison matrix is constructed, a process called defuzzification is applied to convert the fuzzy numbers into crisp, single values. This is typically performed using methods such as the centroid method, which calculates the center of gravity of the fuzzy number to obtain a single representative value using the following formula [34,35,36]:

$$a_{ij}={\displaystyle \frac{l+4m+u}{6}}$$

(6)

where l, m and u are, respectively, the lower, medium and upper values of the triangular number d_ij, as per its definition.

5.

Calculating the Consistency Ratio (CR): To ensure that the pairwise comparisons are logically consistent, FAHP—like AHP—checks for consistency using the Consistency Index (CI) and Consistency Ratio (CR) indicators. The Consistency Index helps to determine how consistent the judgments are in a pairwise comparison matrix by comparing the eigenvalue of the matrix with the number of criteria being compared. The formula for the Consistency Index is

$$\mathrm{CI}={\displaystyle \frac{{\lambda }_{\max }-n}{n-1}}$$

(7)

where λ_max is the largest eigenvalue of the comparison matrix and n is the number of items being compared. A smaller CI value indicates a higher level of consistency in the judgments. However, this indicator can be of complex interpretation. A more immediate representation of the consistency of a pairwise comparison matrix can be obtained with the use of the Consistency Ratio. The Consistency Ratio compares the Consistency Index (CI) with a Random Index (RI), which is the average CI for a large number of randomly generated matrices of the same size [37]. The formula for the Consistency Ratio is

$$\mathrm{CR}={\displaystyle \frac{\mathrm{CI}}{\mathrm{RI}}}$$

(8)

where RI is a tabulated value (

Table 3. RI tabulated values for n from 1 to 13.

n	1	2	3	4	5	6	7	8	9	10	11	12	13
RI	0	0	0.58	0.89	1.12	1.24	1.33	1.4	1.45	1.49	1.51	1.54	1.56

) that depends on the dimension of the matrix’s dimension n [37].

If the CR is below a certain threshold (usually 0.1), the comparisons are considered consistent; otherwise, if the CR is too high, decision-makers may need to revise their comparisons.

6.

Determining Local and Global Weights: Once the defuzzied comparison matrix is obtained, the eigenvector method is used to calculate the local weights for each criterion or alternative. These weights represent the relative importance of each criterion or alternative within its group. The steps in the eigenvector method to calculate local weights are the following:

• Sum the values in each column of the matrix A:

  $$S_j={\sum }_{i=1}^n{a}_{ij}$$

  (9)

  where S_j is the sum of the j-th column.
• Normalize the matrix by dividing each element by the sum of its corresponding column:

  $${\widehat{a}}_{ij}={\displaystyle \frac{a_{ij}}{S_j}}$$

  (10)
• Average the rows of the normalized matrix: The average value for each row is the local weight w_i for the corresponding criterion or alternative.

  $$w_i={\displaystyle \frac{1}{n}}{\sum }_{j=1}^n{\widehat{a}}_{ij}$$

  (11)

The resulting vector w is the local priority vector (eigenvector) that gives the relative importance (local weight) of each criterion or alternative within that specific group. The global weights are then calculated by combining the local weights at each level of the hierarchy; if a sub-criterion C_i has a local weight w_i, and the parent criterion P has a local weight w_p, the global weight W_i of the sub-criterion is

$$W_i=w_i\times w_p$$

(12)

By continuing multiplying down the hierarchy, it is possible to combine the local weights of criteria and sub-criteria to calculate the global weight for each alternative.

7.

Ranking the Alternatives: After determining the global weights for all alternatives, the final step is to rank the alternatives based on their overall scores (N_i). This ranking provides decision-makers with a clear understanding of which alternatives are most aligned with their goals, considering both qualitative and quantitative factors.

In the context of the proposed model, Fuzzy Analytic Hierarchy Process (FAHP) was used to integrate qualitative factors into the decision-making process. As discussed, FAHP allows for the incorporation of stakeholder judgments and preferences in a structured manner, providing a qualitative ranking of KPIs based on managerial priorities such as strategic relevance, operational impact, and sustainability.

3.3. Quali-Quantitative Model Combining CBA and FAHP

The integration of Cost–Benefit Analysis (CBA) and Fuzzy Analytic Hierarchy Process (FAHP) can provide a robust and well-rounded approach to evaluating Key Performance Indicators (KPIs). This combined method first uses CBA to establish a financial baseline for KPI assessment. As discussed, CBA systematically evaluates the anticipated costs and benefits of each KPI, assigning each a Benefit–Cost Ratio (BCR) that enables direct comparison of the financial return. By monetizing both positive and negative impacts, CBA translates each KPI’s effect into a unified economic measure. However, CBA lacks the nuance to account for intangible factors like strategic alignment or stakeholder preferences, which are crucial where managerial insight and long-term vision are paramount. However, while CBA is highly effective in quantifying direct financial implications, it does not inherently capture aspects that are difficult to monetize, such as operational feasibility, strategic fit, or organizational impact. These qualitative dimensions play a crucial role in KPI selection, particularly in dynamic and resource-constrained industrial environments. To address this, the FAHP methodology complements CBA by incorporating subjective, qualitative criteria [12]. FAHP enables decision-makers to systematically structure complex decisions and assess the relative importance of KPIs based on expert knowledge and strategic considerations. Unlike traditional multi-criteria decision-making (MCDM) approaches, FAHP allows for uncertainty in expert judgments, making it particularly suitable for industrial environments where data availability is limited or expert opinions vary. This flexibility accommodates the subjective aspects of decision-making, resulting in weighted scores that reflect not only the perceived effectiveness of each KPI but also its alignment with organizational goals. By integrating FAHP, the proposed model overcomes the rigidity of purely financial assessments, ensuring that KPIs critical for long-term strategic success are not undervalued due to short-term cost–benefit considerations.

Combining the results of CBA and FAHP involves merging the quantitative BCR values with the qualitative weights derived from FAHP. This fusion is achieved by normalizing the FAHP weights and then scaling them to match the BCRs, enabling the creation of a single comprehensive ranking metric. The proposed computation of the combined ranking is illustrated by the following formula:

$$V_i={\mathrm{BCR}}_i\times {\displaystyle \frac{N_i}{(1-N_i)}}$$

(13)

The equation used for the final ranking therefore converts the FAHP weights (N_i), which range within [0, 1], to values that range within [0, +∞]. This allows for more direct comparison to the BCR values, which range within [1, +∞]. Unlike a simple weighted sum approach, this formulation ensures that FAHP scores closer to 1 (indicating strong expert consensus on the importance of a KPI) have a greater impact on the final ranking, while those closer to 0 contribute less significantly. This non-linear transformation prevents scenarios where KPIs with high financial returns but low strategic value dominate the ranking, thereby preserving the balance between economic feasibility and organizational relevance. The outcome of this process is a prioritization of KPIs that is both financially grounded and strategically aligned. This integrated approach not only provides a more holistic decision-making framework but also offers a novel contribution to the field by bridging the gap between financial evaluation models and expert-driven prioritization techniques. By ensuring that high-impact KPIs are identified through a combination of quantitative and qualitative insights, the proposed model represents an optimized and adaptable solution for performance measurement in diverse industrial contexts.

4. Case Study Overview

The case study focuses on a Cambodian charcoal manufacturing company, which produces eco-friendly charcoal briquettes from sustainable biomass sources. Operating in a developing economy, the company faces unique challenges, including limited access to advanced technologies, constrained budgets, and the need for environmentally sustainable practices. As the company seeks to improve operational efficiency and reduce waste, selecting the right KPIs becomes critical for supporting both economic growth and social responsibility. This company was chosen for the study due to an academic partnership between the authors and the company, where the proposed model provided a solution to an actual business need. The study and the entire process—from methodology application to result analysis—were conducted in June 2022. Before this study, the company lacked a structured performance measurement system, relying solely on basic financial indicators (e.g., EBITDA) and production metrics (e.g., production, inventory levels). The company’s management welcomed the opportunity to define and implement performance indicators that were both useful and feasible with the available technology and data infrastructure.

4.1. KPI Selection Process

The study aimed to evaluate KPIs crucial to the company’s operations. As the company seeks to improve operational efficiency and reduce waste, selecting the right KPIs becomes critical for supporting both economic growth and social responsibility. The selection process was therefore conducted in close collaboration with company experts and top management, ensuring the chosen indicators were physically measurable or implementable with existing technology. The KPIs also reflected the company’s strategic priorities and areas for improvement in its operations.

Initially, the researcher proposed a set of possible KPIs, from which the CEO and CFO selected five key indicators that best aligned with the company’s needs and focus:

• Effectiveness;
• Quality Rate;
• Availability;
• Carbon Footprint;
• Reworks.

These KPIs were selected to address both economic and environmental factors—key priorities for the company in balancing profitability with its social mission. Ultimately, the selection was the result of discussions between the authors and company management, where both the KPIs and the FAHP evaluation criteria were defined.

4.2. Data Collection and Interviews

To ensure an accurate evaluation, structured interviews were conducted using a questionnaire-based approach. The CEO and CFO were interviewed separately to prevent response contamination, and their consistency was assessed first qualitatively and then quantitatively through a consistency check. Regarding the pairwise comparisons in the FAHP, both the CEO and CFO participated, making a total of two expert evaluators. Their insights were essential in defining both the KPI priorities and the weightings of the evaluation criteria. Beyond the FAHP evaluation, top management was also actively involved in defining the parameters for the CBA calculations. This included:

• Estimating direct and indirect costs associated with the implementation of each KPI, including technical costs, personnel costs (e.g., recruiting, training, overtime), and data collection expenses.
• Determining projected benefits, expressed as potential cost savings, productivity improvements, or waste reduction, quantified based on historical company data and expert judgment.
• Setting key financial assumptions, such as discount rates, expected time horizons for cost–benefit realization, and baseline operational metrics, to evaluate improvements over time.

By involving both financial and operational decision-makers, the model ensured that the CBA results were realistic, context-specific, and actionable, providing the company with practical guidance for implementation.

5. Results

5.1. Results of Cost-Benefit Analysis

In the Cost–Benefit Analysis (CBA) phase, each KPI’s economic impact is evaluated over 3 years. For this purpose, both direct and indirect costs are estimated for each KPI. As for the costs, three classes are identified and quantified for each KPI: technical costs for the implementation of the KPI, personnel costs (e.g., recruiting, training, overtime) and data collection costs (e.g., data acquisition, analysis and reporting). For the benefits, they are quantified by projecting potential gains, such as reduced waste, increased productivity and cost savings; in this particular case study, these quantities are defined by the company’s top managers as a percentage of the total costs of goods sold that has been estimated to be potentially saved by implementing the specific KPI under analysis. Then, based on these assumptions, all costs and benefits are converted into monetary terms. Finally, the Net Present Values (NPVs) and Benefit–Cost Ratios (BCRs) are calculated. The NPV is computed using a discount rate to account for the time value of money (based on the country’s average inflation rate over the past 4 years). The BCR is then derived by dividing the total monetized benefits by the total costs.

In the proposed model, the KPIs are then prioritized to obtain a first ranking of the proposed alternatives, and this is performed using the CBR value as a ranking metric due to its efficiency in comparing the relative financial returns of different KPI implementations, particularly in resource-constrained environments such as developing industries. In fact, unlike Net Present Value (NPV), which provides an absolute measure of profitability over time, CBR evaluates the proportional return per unit of investment, making it highly effective for comparing multiple initiatives. This is especially valuable in settings like the one of the companies in the analysis, where the optimization of performance must occur within stringent budgetary constraints. By calculating the ratio of expected benefits to incurred costs, the CBR offers a streamlined method for identifying KPIs that deliver the highest return on investment. After performing all due calculations for the Cost Benefit Analysis, the results were summarized in

Table 4. Cost benefit analysis final results.

	Total Costs	Total Benefits	NPV	BCR
Effectiveness	USD 15,662.60	USD 68,349.90	USD 52,687.30	4.36
Availability	USD 15,721.70	USD 13,669.98	USD −2051.72	0.87
Quality Rate	USD 17,307.51	USD 47,844.93	USD 30,537.42	2.76
Reworks	USD 15,975.59	USD 20,504.97	USD 4529,18	1.28
Carbon Footprint	USD 2378.72	USD 13,669.98	USD 11,291.26	5.75

, as shown below.

Through CBA, a clear ranking based on financial viability was established (shown in

Table 5. CBA ranking.

Ranking	KPI	BCR
1	Carbon Footprint	5.75
2	Effectiveness	4.36
3	Quality Rate	2.76
4	Reworks	1.28
5	Availability	0.87

), indicating which KPIs offered the highest short-term returns on investment. However, while financially favorable, this ranking did not incorporate non-monetary factors crucial to the company’s sustainable goals. For these reasons, once the quantitative analysis is completed and satisfactory, the proposed model proceeds with the prioritization of the alternatives using FAHP, as previously explained.

5.2. Fuzzy Analytic Hierarchy Process

Fuzzy Analytic Hierarchy Process (FAHP) is employed to prioritize KPIs by incorporating qualitative factors often overlooked in purely quantitative analyses. This method is particularly suited for the analyzed company, where decisions needed to align with the company’s dual focus on economic efficiency and environmental sustainability. By systematically capturing and quantifying stakeholder preferences, FAHP provides a structured framework to assess KPIs based on their strategic relevance, ease of implementation and stakeholder importance. This approach ensures that the selected KPIs not only support operational efficiency but also align with the company’s broader mission and long-term goals.

The FAHP begins with the structuring of the problem into a hierarchical framework, with the goal of creating a ranking of the KPIs at the top of the hierarchy, the evaluation criteria at the intermediate level and the KPIs themselves as the alternatives to be ranked at the bottom. For this specific case of study, the goal is formalized as follows: ”prioritize KPIs in order to provide the decision-makers with a ranking of the best KPIs to implement in the company”. In order to achieve such a goal, then, a set of criteria are determined in accordance with the company’s management team; specifically, five criteria are used:

•

Strategic: How much the KPI is oriented towards the organization’s goals;

•

Guiding: How much the KPI supports decision-making;

•

Actionable: How much the KPI can be acted on to drive performance improvement;

•

Expressive: How much the KPI immediately reveals the performance;

•

Implementable: How easily the KPI can be implemented.

The FAHP process can therefore be carried out by following the steps explained in the methodology section. To evaluate each criterion and each KPI, stakeholders—including the CEO and CFO of the company—participated in pairwise comparisons (both of the criteria in Figure 2 and of the KPIs relative to each criterion Figure 3) by means of questionnaires and interviews, as illustrated in Figure 4. To account for the inherent uncertainty and subjectivity in their judgments, fuzzy triangular numbers are used to express the relative importance of one KPI over another on a range rather than a fixed value, enabling a more nuanced evaluation of the alternative KPIs.

After gathering the pairwise comparison data, the fuzzy comparison matrices are constructed, representing the comparisons for each criterion. These matrices are then defuzzied to generate crisp weights for each KPI under each criterion. The local and global weight can then be represented in the hierarchical tree, as shown in Figure 5.

To verify the quality of the data collected, a consistency ratio is calculated to ensure the logical consistency of the comparisons, with any inconsistent judgments revisited and revised until the data can be considered satisfactory. The calculated consistency indices are shown in

Table 6. Consistency indices.

	Criteria	KPIs Strategic	KPIs Guiding	KPIs Actionable	KPIs Expressive	KPIs Implementable
CEO’s CI	0.0400	0.0398	0.0444	0.0115	0.0364	0.0090
CFO’s CI	0.1721	0.2602	0.0442	0.0344	0.0364	0.3556
Overall CI	0.0896	0.1317	0.0159	0.0139	0.0220	0.0956

below.

In this case, it is clear that the CEO’s answers were significantly more consistent than the CFO’s. This, however, does not come as a surprise, and actually it highlights how fuzzy the decisional context can be, and in particular the context of this case study. Looking at the Consistency Indices, it is possible to see how, although the CFO’s indices present many anomalies, most of them are below the set threshold of 0.10 indicatively proposed in the literature and, in the end, the overall C.Is. are all far below the limit. Moreover, the only overall C.I. above the limit is only slightly above it. Therefore, it was decided to take into account both decision-makers’ answers as input for the algorithm’s calculations.

Finally, the global weights were calculated and are reported in

Table 7. FAHP global weight and ranking of the KPIs.

Ranking	KPI	FAHP Global Weight
1	Quality Rate	37.40%
2	Effectiveness	29.29%
3	Carbon Footprint	13.63%
4	Availability	10.88%
5	Reworks	8.80%

, to represent the relative qualitative importance of each KPI to the goal, capturing both subjective managerial preferences and strategic priorities based on the five criteria identified.

6. Discussion of the Results

As previously discussed in the methodology section relative to CBA, in order to be considered a viable alternative, a KPI must have either a BCR value greater than 1.0 or an NPV value greater than 0; these conditions, in fact, indicate that the considered KPI offers positive economic returns for the company, and is therefore a valid implementable option. From the reported summary (Table 7), it is immediately noticeable how all but one KPI had a positive NPV. The one and only KPI with a negative NPV, which is in fact also the only one with a BCR lower than 1, is the KPI Availability; therefore, the KPI Availability is not an economically viable alternative to be implemented and should be discarded from further consideration. The KPIs Effectiveness and Carbon Footprint, on the other hand, emerged as the highest in BCR values, showing strong potential to improve the company’s operational profitability. By contrast, Availability and Reworks Rate had lower BCR values, reflecting more gradual financial benefits, which, while strategically important, presented less immediate economic gain.

To fully evaluate the quality of these KPIs, however, the results provided by FAHP analysis should be taken into consideration. From the summary in Table 7, it is possible to notice that the highest ranking KPI, according to the qualitative analysis, is the KPI Quality Rate, followed by Effectiveness and Carbon Footprint. The FAHP analysis thus enabled the company to identify KPIs that resonated with its broader mission. These results served as a qualitative complement to the Cost–Benefit Analysis (CBA), forming the foundation for a combined evaluation that balanced economic and strategic considerations.

Finally, to complete the evaluation, the two rankings must be combined as explained in the methodology section, using the equation illustrated at [14]. All due calculations were therefore performed in order to obtain, for each KPI, the final score needed to give an overall ranking of the alternatives. The results are presented in

Table 8. KPI final ranking.

	KPI	CBA’s BRCi	FAHP’s Ni	Final Values Vi
1	Effectiveness	2.76	0.2929	1.15
2	Carbon Footprint	5.75	0.1363	0.91
3	Quality Rate	1.28	0.3740	0.77
4	Reworks	4.36	0.0880	0.42
5	Availability	0.87	0.1088	0.11

, below.

The analysis underscored Effectiveness as the top choice for the company, balancing cost-effectiveness with strategic and implementable attributes. Carbon Footprint ranked second due to its resource efficiency, despite being less impactful in absolute economic terms. Quality Rate’s position reflected its perceived qualitative importance but moderate economic viability. Reworks, though profitable, was deprioritized due to low interest from stakeholders. Availability, the lowest-ranked KPI, failed to meet the economic criteria for implementation and should therefore be discarded as an option entirely.

Beyond the ranking itself, the study provided significant value to the company. The structured integration of quantitative financial considerations (CBA) and qualitative managerial insights (FAHP) resulted in a more holistic approach to KPI selection, aligning the company’s financial constraints with its strategic ambitions. By the end of the study, the company had a clearer understanding of which KPIs could drive the most significant improvements, and the management team expressed interest in refining and expanding their performance measurement framework further. Ultimately, the results provide a clear roadmap for the company to focus on KPIs that not only deliver measurable economic benefits but also align closely with strategic objectives and ease of implementation.

7. Conclusions

This study introduced a comprehensive quali-quantitative analysis model for optimizing Key Performance Indicator (KPI) implementation, integrating Cost–Benefit Analysis (CBA) and the Fuzzy Analytic Hierarchy Process (FAHP). This dual approach bridges the gap between financial feasibility and qualitative strategic alignment, providing a structured decision-making framework for selecting KPIs that that align with both short-term economic objectives and long-term organizational goals.

The case study of the Cambodian charcoal factory demonstrated the model’s effectiveness in addressing the challenges of KPI selection in resource-constrained environments. By applying CBA, the study identified KPIs that delivered the highest financial returns, offering clear insights into economic viability. The FAHP component added depth by capturing qualitative aspects such as sustainability and strategic relevance, which are critical to the company’s mission as a socially responsible enterprise. The combined analysis revealed that KPIs like Effectiveness and Carbon Footprint emerged as top priorities, balancing cost-effectiveness with strategic and environmental considerations. Meanwhile, KPIs such as Reworks and Availability were deprioritized due to their lower alignment with financial or qualitative goals. These results highlight the importance of integrating both quantitative and qualitative methodologies to provide a balanced perspective on performance measurement.

This dual approach offers significant implications for industries operating both in developed and developing nations, where resource constraints and sustainability goals often coexist, although the challenges faced in these contexts differ. In developing nations, where financial and technological resources are often limited, the model provides a structured approach to prioritizing cost-effective KPIs, helping industries balance financial constraints with strategic objectives. Conversely, in developed economies, where businesses have greater access to data-driven decision-making tools, the model serves as a way to integrate managerial insights with financial considerations, ensuring that KPI selection aligns with broader goals such as sustainability, digital transformation and long-term strategic planning. By systematically combining financial and managerial insights, the model empowers organizations to make informed, holistic decisions that support both profitability and long-term strategic success.

While the case study focused on a single company in a developing economy, the proposed model is not limited to this specific context. The methodology is scalable and can be applied across various industries, including manufacturing, logistics and service sectors, where decision-makers must balance financial constraints with strategic objectives. The combination of CBA and FAHP ensures adaptability, making the approach applicable in both developed and emerging economies. To demonstrate scalability, future research should validate the model in different industrial settings and explore modifications that enhance applicability to SMEs and large enterprises. Additionally, integrating AI-driven decision-support tools could further improve the model’s automation and applicability across dynamic business environments.

Ultimately, RQ1 is addressed by demonstrating that quantitative (CBA) and qualitative (FAHP) components can be effectively integrated to overcome the limitations of single-method approaches—particularly in constrained industrial settings—by simultaneously evaluating financial viability and strategic or sustainability-oriented criteria. RQ2, on the other hand, is answered by showing that the combined approach not only identifies high-return KPIs but also accounts for essential yet subjective considerations (e.g., managerial insights, alignment with social responsibility), thereby offering a more balanced and robust prioritization framework than traditional, single-method analyses.

Furthermore, while the current research focuses on a single-case study, the underlying concepts and processes are sufficiently flexible for application in organizations with similar or complementary operational profiles. The acknowledgment of varying organizational structures, regulatory environments and stakeholder priorities forms a natural boundary to the approach, highlighting inherent limitations in any single-case demonstration. Nonetheless, the methodology’s core principles remain adaptable and allow different industries to leverage both financial and qualitative insights to support KPI selection in a wide range of operational contexts, thus underlining the practical utility of the approach. Future research could enhance the proposed model by exploring its application to diverse industries and incorporating dynamic decision-making tools to adapt to changing environments. Moreover, simplifying the approach for SMEs, integrating sustainability metrics like SDGs or ESG, and automating the framework through AI-driven decision-support systems could increase its accessibility and impact. Additionally, further studies could examine how variations in expert judgments or cost–benefit parameters influence the ranking outcomes and assess the performance of FAHP independently from CBA in similar decision-making contexts.