Keywords

1 Introduction

According to the World Bank (2018), the energy sector is a driver of investments, technological development, innovation, and new industries, with an impact on job creation, economic, social growth and the contribution to the Development Goals Sustainable UN 7 - affordable and non-polluting energy, 8 - decent work and economic growth, 9 - industry, innovation and infrastructure, and 11 - Sustainable cities and communities [1]. The energy sector faces new challenges such as a 30% growth in energy demand and, therefore, in the generation, transmission and distribution capabilities of operating companies, the search for new and better sources of energy, the decrease of environmental impacts and occupational health and safety risks for interested parties [2]. Regarding occupational safety and health, it is of high relevance for the competitiveness of the sector due to the complexity in their operations, the regulation, and control of the government, customers, and other stakeholders involved in the energy chain. In this sense, the energy sector is considered as high risk at the Occupational health and safety level, due to the potential risk of accidents and occupational diseases and the seriousness of the damage caused. Given that the circumstances surrounding this type of accident can have an impact on the welfare and health of workers and interested parties, on infrastructure, operational costs and the results of companies in the sector [3].

Following the International Labor Organization, aggregate statistics indicate a general increase in the number of people who died by causes attributable to work from 2.33 million in 2014 to 2.78 million in 2017. In the energy sector, electrical accidents represent a high number of worldwide [4,5,6,7]. This situation increases in countries in Africa and Latin America. In Colombia, for example, the energy sector registered for 2017 the fourth-highest accident rate, with an average of 7.33 accidents. Among the leading causes attributable to accidents in the energy sector, there are insecure behaviors [8], deficiencies in training, training, and awareness in safety and self-help culture and shortcomings in the implementation of comprehensive management systems focused on prevention and in the welfare of workers [9,10,11,12,13].

In the literature review, different studies have been carried out to contributes to the analysis, development, and implementation of methodologies and techniques focused on the prevention of accidents and occupational diseases in the energy sector. In this regard, we found different works oriented to analysis of personal factors and consequences of electrical occupational accidents [3], time series analysis of occupational accidents and the assessment of risks applied to the energy and construction industry [14], the longitudinal descriptive study of occupational accidents and their causes [15] as well as the analysis of occupational safety and health in hydroelectric plants [16], photovoltaic industry [17] and energy supply companies [18]. Although several efforts have been made to address this problem, the evidence base is still scant and with scarce information, especially in the develop of integrated multicriteria decision-making methodologies to evaluate and improve the performance in the prevention of accidents and occupational diseases in energy sector, where previous studies have been found in the logistics industry [19].

This paper bridges this gap by extending the multi-criteria decision-making approach adopted in land cargo transportation [19] to improve the performance in Occupational Health and Safety Management in the energy sector. First, the fuzzy Analytic Hierarchy Process (FAHP) is applied to estimate the initial relative weights of criteria and sub-criteria. The fuzzy set theory is incorporated to represent the uncertainty of decision-makers’ preferences. Then, the Decision-making Trial and Evaluation Laboratory (DEMATEL) is used for evaluating the interrelations and feedback among criteria and sub-criteria. FAHP and DEMATEL are later combined for calculating the final criteria and sub-criteria weights under vagueness and interdependence. Subsequently, we applied the proposed methodology in a company of the energy sector for diagnosis of performance in OHS to establish improvement proposals, the work path, and implementation costs. Finally, we evaluate the impact of the strategies applied in the improvement of the performance of the company.

A real case study considering four criteria, 23 sub-criteria, six decision-makers, and an instrument for the diagnosis of the occupational health and safety management is presented to validate the proposed approach. The results revealed that the criteria planning (58.9%), improvement (15.0%), and application (14.3%) have the most significant weight in the evaluation of occupational health and safety performance in the context of the energy company. Also, we found strong interrelations among criteria and sub-criteria with an impact on the performance evaluation in the adoption of the OHS management. On the other hand, we evaluated the changes and improvement of the company between the initial diagnosis, and the implementation of the action plans one year later. The outcomes of this evaluation evidenced a significant increase in the level of compliance in occupational health and safety management, going from 14% to 88% compliance, with an impact on the reduction in the indicators of accidents and absenteeism in work company results, increasing its sales by 43.8%. Considering the previous results, this integrated methodology facilitates the decision-making process managers in the energy sector for the improvement in the management of occupational health and safety.

2 Approaches of MCDM for Occupational Health and Safety Performance Evaluation in Electric Sector: A Literature Review

Occupational health and safety (OHS) is a multidisciplinary activity that focuses on the analysis of improving the conditions of workers in their workplaces, to reduce the number of accidents and occupational diseases, increase productivity, the commitment of workers and the competitiveness of companies [20, 21]. The previous implies that companies must adopt different standards and methodologies to manage health and safety in their work environments. Concerning the implementation of standards in OHS, for the electricity sector, it is even more relevant, given their complexity and the high risk that this industry poses in the safety and health of workers, users, and other stakeholders.

One of the critical elements in occupational safety and health management is related to performance evaluation, which according to ISO 45001 [22], is performance related to the effectiveness of the prevention of injuries and deterioration of health for workers and the provision of safe and healthy workplaces. In this sense, performance evaluation includes aspects such as compliance with legal requirements, risk and risk assessment, progress in achieving objectives, goals, and indicators in OHS, and the effectiveness of operational controls.

Concerning the evaluation of performance in OHS, it is in itself a complicated process, where different stakeholders intervene with their needs, expectations, and judgments (workers, managers, clients, government, suppliers, among others) multiple criteria to the time to evaluate, and uncertainty environments that make it difficult to establish the most appropriate actions in the prevention of accidents and occupational diseases. In this sense, there are different qualitative and qualitative methodologies that companies can use to identify gaps in performance, their causes and establish improvement actions, such as the cause-effect or Ishikawa diagram, the 5 W and 2H technique, the time series analyses, the risk assessment matrices, among others. However, multi-criteria decision-making (MCDM) based approaches can help companies in the electricity sector to learn about the evaluation of their performance, in real environments, with multiple criteria, and under uncertainty environments in organizations.

Regarding the object of this research, we found in the literature review evidence of studies oriented to apply the Multicriteria Decision Methods (MCDM) in occupational health and safety management (OHS). In this regard, we found a study that develops a critical state-of-the-art review of OHS risk assessment studies using MCDM-based approaches, includes fuzzy versions of MCDM approaches applied to OHS risk assessment [20]. The results of this study, which analyzed a total of 80 papers cited in high-impact journals, demonstrated the growing trend in the use of MCDM in the evaluation of risks, especially in the use of FAHP-based approaches, with application mainly in the industrial sector. On the other hand, the study identifies that methods such as VIKOR, PROMETHEE, ELECTRE, and DEMATEL remain superior methods in risk assessment and management due to their flexibility.

Bibliographic research has shown interesting articles written on the application of support systems for decision-making in occupational safety and health (OHS). Still, little has been published on the evaluation of integral performance in the management of OSH and the complex context of the electrical sector, as seen in Table 1, with the studies found related to an assessment in OHS and the MDCM techniques used.

Table 1. Performance evaluation in OHS based in MDCM approaches
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The articles found focus on the application of MCDM techniques for the evaluation of risks, safety and health conditions, the satisfaction of workers concerning the OHS, but few articles focused on the integral assessment of performance in OHS, that consider the international standard ISO 45001. Besides, the main application sectors of MCDM techniques are mining, and construction, and the literature presents a limited development in the application of the MCDM models applied towards performance evaluation in OHS in the electric sector.

At the level of MDCM techniques, the literature review shows that the most used methods are related to the AHP technique and its extensions, such as Fuzzy AHP, Pythagorean Fuzzy AHP, and Intuitionistic Fuzzy AHP. Besides, the AHP methods can be used in combination with other techniques such as TOPSIS, VIKOR, ELECTRE, and DEMATEL, which gives researchers an open field for the use of hybrid MCDM methodologies according to the reality of the industries.

Concerning to the hybrid MCDM approaches, the combination of different methods allows overcoming the limitations of several techniques to obtain better outcomes [35]. Notably, the AHP method has the disadvantage of the phenomenon known as “reversal rank” related to the change of preference or order after an alternative is added or removed [36]. Concerning other methods such as the Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) do not provide an explicit procedure to allocate the relative importance of criteria and sub-criteria [37]. Due to the considerations mentioned above, it is necessary to apply a hybrid decision-making model that can consider inaccuracy, uncertainty, and lack of consensus in the judgments of the experts regarding the weights of the criteria and sub-criteria and that analyzes the interrelationships between the evaluation factors.

The novelty of the present study is based on the integration of the FAHP, as an extension of the AHP multi-criteria method with the DEMATEL method to evaluate the performance in occupational health and safety in the electric sector to provide a robust framework for evaluation and improvement in OHS. FAHP was chosen due to its capability of calculating the relative importance of criteria and sub-criteria under uncertainly. On the other hand, we proposed the integration of the DEMATEL method to FAHP. DEMATEL is applied to evaluate interrelations be-tween criteria and sub-criteria [38], helping decision-makers identify the interdependencies between decision factors, receiving and dispatching factors, which allows the design of comprehensive improvement plans.

Therefore, this research contributes to the scientific literature and provides a hybrid methodology for overall performance evaluation in occupational health and safety management and provides to managers procedures and techniques to generate a culture of prevention and healthy environments, through strategic alignment, driving the behavior and performance of people towards the achievement of the strategic objectives of electric sector.

3 Proposed Methodology

The proposed approach aims to evaluate the performance in occupational health and safety management in the electric sector by the integrated methodology using FAHP and DEMATEL. In this regard, the methodology is comprised of four phases (refer to Fig. 1):

Fig. 1.
figure 1

The methodological approach for evaluating the performance in occupational health and safety management in the electric sector

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  • Phase 1 (Design of the model for performance evaluation OHS FAHP/DEMATEL): A decision-making group is chosen based on their experience in occupational health and safety in the electric sector. The experts will be invited to be part of the decision-making process through FAHP and DEMATEL techniques. Subsequently, the criteria and sub-criteria are established to set up a decision hierarchy considering the opinion of the expert decision-makers, the literature review, and regulations in occupational health and safety management [19, 35, 38]. Then, the surveys for the application of the FAHP and DEMATEL methods were designed.

  • Phase 2 (FAHP application): In this step, FAHP is used to estimate the global and local weights of criteria and sub-criteria under uncertainty in the ponderation. In this phase, the experts were invited to perform pairwise comparisons, which are subsequently processed following the FAHP method, as detailed in Sect. 3.1.

  • Phase 3 (DEMATEL application): In this phase, DEMATEL is implemented to determine the interdependence and interrelations between criteria and sub-criteria (described in Sect. 3.2) as well as identify the receivers and dispatchers. Additionally, it is used to assess the strength of each influence relation [35]. Then, FAHP and DEMATEL are combined to obtain the criteria and sub-criteria weights with the basis of interdependence.

  • Phase 4 (Validation of the proposed methodology): In this step, GAPs and critical variables were identified to improve the performance in OHS management [10, 19, 39]. Subsequently, were defined the improvement proposals, the schedule, and the costs of implementation of strategies. Finally, was evaluated the impact after the implementation of the strategy in the improvement of the performance in OHS, their statistics or accidents, and the revenue of the company.

3.1 Fuzzy Analytic Hierarchy Process (FAHP)

Fuzzy AHP is a derived method of Analytic Hierarchy Process (AHP) proposed by Thomas L. Saaty as combined technique between AHP and fuzzy logic with the purpose of improve the decision-making process due that the AHP method does not consider vagueness of human judgments, the fuzzy logic theory was introduced due to its capability of representing imprecise data [38, 40].

In FAHP, the paired comparisons are represented in a matrix using fuzzy triangular numbers [41] as described below (Refer to Table 2). Considering the findings from the literature review, a reduced AHP scale has been adopted by the decision-makers when making comparisons [38].

Table 2. Linguistic terms and their fuzzy triangular numbers
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The steps of the FAHP algorithm as follows:

  • Step 1: Perform pairwise comparisons between criteria/sub-criteria by using the linguistic terms and the corresponding fuzzy triangular numbers established in Table 2. With this data, a fuzzy judgment matrix \( \tilde{A}^{k} \left( {a_{ij} } \right) \) is obtained as described below in Eq. 1:

    $$ \tilde{A}^{K} = \left[ {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\tilde{d}_{11}^{k} } & {\tilde{d}_{12}^{k} } & \ldots \\ \end{array} } & {\tilde{d}_{1n}^{k} } \\ {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\tilde{d}_{21}^{k} } \\ \ldots \\ {\tilde{d}_{n1}^{k} } \\ \end{array} } & {\begin{array}{*{20}c} {\tilde{d}_{22}^{k} } \\ \ldots \\ {\tilde{d}_{n2}^{k} } \\ \end{array} } & {\begin{array}{*{20}c} \ldots \\ \ldots \\ \ldots \\ \end{array} } \\ \end{array} } & {\begin{array}{*{20}c} {\tilde{d}_{2n}^{k} } \\ \ldots \\ {\tilde{d}_{nn}^{k} } \\ \end{array} } \\ \end{array} } \right] $$
    (1)

    \( \tilde{d}_{ij}^{k} \) indicates the kth expert’s preference of ith criterion over jth criterion via fuzzy triangular numbers.

  • Step 2: In the case of a focus group, the judgments are averaged according to Eq. 2, where K represents the number of experts involved in the decision-making process. Then, the fuzzy judgment matrix is updated, as shown in Eq. 3.

    $$ \tilde{d}_{ij} = \frac{{\mathop \sum \nolimits_{k = 1}^{K} \tilde{d}_{ij}^{k} }}{K} $$
    (2)
    $$ \tilde{A} = \left[ {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\tilde{d}_{11} } \\ \vdots \\ {\tilde{d}_{n1} } \\ \end{array} } & {\begin{array}{*{20}c} \ldots \\ \ddots \\ \ldots \\ \end{array} } & {\begin{array}{*{20}c} {\tilde{d}_{1n} } \\ \vdots \\ {\tilde{d}_{nn} } \\ \end{array} } \\ \end{array} } \right] $$
    (3)

  • Step 3: Calculate the geometric mean of fuzzy judgment values of each factor by using Eq. 4. Here, \( \tilde{r}_{i} \) denotes triangular numbers.

    $$ \tilde{r}_{i} = \left( {\mathop \prod \limits_{j = 1}^{n} \tilde{d}_{ij} } \right)^{1/n} , i = 1,2, \ldots ,n $$
    (4)

  • Step 4: Determine the fuzzy weights of each factor \( (\tilde{w}_{i} ) \) by applying Eq. 5.

    $$ \tilde{w}_{i} = \tilde{r}_{i} \otimes \left( {\tilde{r}_{1} \oplus \tilde{r}_{2} \oplus \ldots \oplus \tilde{r}_{n} } \right)^{ - 1} = \left( {lw_{i} ,mw_{i} ,uw_{i} } \right) $$
    (5)

  • Step 5: Defuzzify \( (\tilde{w}_{i} ) \) by performing the Centre of Area method [42] via using Eq. 6. \( M_{i} \) is a non-fuzzy number. Then, normalize \( M_{i} \) via applying Eq. 7.

    $$ M_{i} = \frac{{lw_{i} + mw_{i} + uw_{i} }}{3} $$
    (6)
    $$ N_{i} = \frac{{M_{i} }}{{\mathop \sum \nolimits_{i = 1}^{n} M_{i} }} $$
    (7)

3.2 Decision Making Trial and Evaluation Laboratory (DEMATEL)

DEMATEL is an MCDM technique applied to identify the complex causal relationships between criteria and sub-criteria involved in a multicriteria decision model [43]. DEMATEL it´s based on the graph theory, and the outcome is a visual representation called impact-digraph map that categorizes the criteria into two groups: dispatchers and receivers [44]. Dispatchers are the criteria or sub-criteria that highly influence other criteria or sub-criteria, while the receivers are the affected criteria or sub-criteria [38]. Additionally, the DEMATEL method indicates the influence degree of each element so that significant interdependencies can be identified [46].

The procedure of DEMATEL method is given as follows [38]:

  • Step 1: Make the matrix of direct influence: The decision-makers are asked to make comparisons between criteria/sub-criteria to measure their causal relationship. For this, the experts, based on their personal experience, point out the direct impact that each element i exerts on each of the other elements j using this four-level comparison scale: nonexistent impact (0), low impact (1), medium impact (2), high impact (3) and very high impact (4). With these comparisons, an average n x n matrix called the direct relationship matrix is generated. In this matrix, each element bij represents the average degree to which the criterion/sub-criterion i affect the criterion/sub-criterion j.

  • Step 2: Normalize the direct influence matrix: The normalized direct relation matrix N is calculated using Eq. 8–9:

    $$ N = k \cdot B $$
    (8)
    $$ k = min\left( {\frac{1}{{\mathop {\rm {\max}_{1 \le i < n} }\limits_{{}} \mathop \sum \nolimits_{j = 1}^{n} \left| {b_{ij} } \right|}} , \frac{1}{{\mathop {\rm {\max}_{1 \le j < n} }\limits_{{}} \mathop \sum \nolimits_{i = 1}^{n} \left| {b_{ij} } \right|}}} \right)i,j \in \left\{ {1,2,3, \ldots ,n} \right\} $$
    (9)

  • Step 3: Obtain the total relation matrix: After normalizing the direct relation matrix N, the total relation matrix S is obtained by implementing Eq. 10, where I is the identity matrix:

    $$ S = N + N^{2} + N^{3} + \ldots = \sum\nolimits_{i = 1}^{\infty } {N^{i} } = N\left( {I - N} \right)^{ - 1} $$
    (10)

  • Step 4: Develop a causal diagram: Using the \( D + R \) and \( D - R \) values, where \( R_{i} \) represents the sum of the \( j - th \) column of the matrix S (see Eq. 11–12) and \( D_{i} \) represents the sum of the \( i - th \) row of the matrix S (see Eq. 11 and Eq. 13), dispatchers and receivers can be identified. Criteria/Sub-criteria with positive values of D-R, have a strong influence on the other criteria/sub-criteria, and are called dispatchers. The negative values of D − R indicate that the criteria/sub-criteria are very influenced by others (receivers). Besides, the D + R values indicate the degree to which the criteria/sub-criteria i affect or are affected by others.

    $$ S = \left[ {s_{ij} } \right]_{nxn} , i,j \in \left\{ {1,2,3, \ldots ,n} \right\} $$
    (11)
    $$ R = \sum\nolimits_{j = 1}^{n} {s_{ij } } $$
    (12)
    $$ D = \sum\nolimits_{i = 1}^{n} {s_{ij} } $$
    (13)

  • Step 5: Establish the threshold value and obtain impact-digraph map: The threshold value is calculated to identify the significant interrelationships between criteria or sub-criteria (see Eq. 14). If the influence degree of a criterion/sub-criterion in the matrix S is bigger than the threshold value (p), then this criterion/sub-criterion is included in the map of impact digraphs. This graph is done by assigning the data set (\( D + R \), \( D - R \)).

    $$ p = \frac{{\mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} s_{ij} }}{{n^{2} }} $$
    (14)

3.3 The FAHP-DEMATEL Method

A combined FAHP-DEMATEL method is suggested to offer more robust results [45]. The mixed technique tackles the drawbacks of FAHP, which is not capable of assessing the feedback and interdependence among decision elements. It is, therefore, necessary to complement it with DEMATEL, which can help occupational health and safety professionals to design short, medium- and long-term plans that improve the performance evaluation in OHS management. The relative weights of factors and sub-factors (\( w_{j} ) \) based on interdependence are obtained by multiplying the weights derived from FAHP and the normalized direct relation matrix N (refer to Eq. 15).

$$ wi_{j} = \begin{array}{*{20}l} {SC_{1} } \hfill \\ {SC_{2} } \hfill \\ {SC_{3} } \hfill \\ \begin{aligned} . \hfill \\ . \hfill \\ \end{aligned} \hfill \\ {SC_{z} } \hfill \\ \end{array} \left[ {\begin{array}{*{20}l} {SC_{1} } \hfill & {SC_{2} } \hfill & { \ldots .} \hfill & {SC_{z} } \hfill \\ {n_{11} } \hfill & {n_{12} } \hfill & { \ldots .} \hfill & {n_{1z} } \hfill \\ {n_{21} } \hfill & {n_{22} } \hfill & { \ldots .} \hfill & {n_{2z} } \hfill \\ {n_{31} } \hfill & {n_{32} } \hfill & { \ldots .} \hfill & {n_{3z} } \hfill \\ \begin{aligned} . \hfill \\ . \hfill \\ \end{aligned} \hfill & \begin{aligned} . \hfill \\ . \hfill \\ \end{aligned} \hfill & \begin{aligned} \ldots . \hfill \\ \ldots . \hfill \\ \end{aligned} \hfill & \begin{aligned} . \hfill \\ . \hfill \\ \end{aligned} \hfill \\ {n_{z1} } \hfill & {n_{z2} } \hfill & { \ldots .} \hfill & {n_{zz} } \hfill \\ \end{array} } \right]*\left[ {\begin{array}{*{20}l} {w_{1} } \hfill \\ {w_{2} } \hfill \\ {w_{3} } \hfill \\ . \hfill \\ . \hfill \\ {w_{z} } \hfill \\ \end{array} } \right] $$
(15)

4 Application of the Integrating Proposed Methodology

4.1 Design of the Model for Performance Evaluation FAHP/DEMATEL

In this section, an empirical example is presented to validate the proposed methodology. The case study is illustrated in a medium-sized electric company located in Colombia.

One of the objectives of the company is the improve in the levels of customer satisfaction considering their requirements and needs (e.g., quality, delivery times, price, service), through the active development of services and products, the competence and commitment of its human talent, also fulfilling with the legal regulation in occupational safety and health. The company under study supplies electrical products and performs outsourcing services to improve electrical and telecommunications infrastructure. To support the quality and safety in its operations, the company must adequately identify the regulatory requirements in occupational safety and health, through the design of a decision model that involves different criteria and sub-criteria to evaluate and improve their performance in OHS.

Subsequently, a decision-making team was selected to validate the criteria and sub-criteria through the application of FAHP and DEMATEL techniques for the performance evaluation in occupational health and safety management, given their expertise in these topics and the electric sector. In this regard, four types of experts were found to be meaningful for the decision-making process: three leaders of the company under study, two experts consultors in health and safety management with expertise in the electric sector, and two representatives of academic sector linked to the occupational health and safety in companies. The team of experts for develop of integrated methodology is described below:

  • Expert 1 is the General Manager of the company, with more than 10 years of experience in the electric sector.

  • Expert 2 is the Leader in OHS department of the company, with more than 5 years of the experience.

  • Expert 3 is the Head of the legal department of the company, with more than 5 years of experience in government regulations in OHS.

  • Expert 4 is a professional in occupational health and safety with a master’s degree in Management Systems and 10 years of experience as a consultant in both private and public organizations in the diagnostic, design implementation and improve health and safety programs in different companies.

  • Expert 5 is an Electrical Engineer with a specialization in Automatic Control Systems and more than five years of experience and safety standards in the electric sector.

  • Expert 6 is industrial engineering, specialist in occupational health with knowledge and 10 years of experience in health and safety in work, regulations and standards in occupational health and safety management (OHS), risk assessment, and industrial hygiene.

  • Expert 7 is an Industrial Engineer and specialist in the application of multivariate methods and multi-criteria models for performance evaluation. The industrial engineer acted as a facilitator to take over the judgment process.

Concerning to hierarchy of the decision-making model is composed of four criteria (C1, C2, C3, and C4) and 23 sub-criteria (S1, S2,…, S23) according to the model developed by Jimenez et al. [19]. These criteria and sub-criteria were determined based on the regulations applicable to the electric sector such as the Decree No 1072 of 2015 (establish the rules in occupational health and safety management for companies) [47], Resolution No 0302 of 2019 (minimum standards in occupational health and safety in organizations) [48], and requirements of the international standard in OHS ISO 45001 [22]. Then, the experts validate these criteria and sub-criteria according to the health and safety regulations, and the literature review presented in order to provide an MCDM model responding to the current needs of the electric sector. Subsequently, the multi-criteria hierarchy was then verified and discussed through different sessions with the expert decision-making team to establish the comprehension of the model and the hierarchy. Finally, the decision model is shown in Fig. 2.

Fig. 2.
figure 2

Multi-criteria decision-making model to evaluate the overall performance in occupational health and safety management in the electric sector

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Particularly, the criteria and sub-criteria were labeled and described in Table 3 [19].

Table 3. Description of criteria
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4.2 Design of Data Collection Tools for FAHP and DEMATEL

In this step, a data collection instrument was designed for the paired comparisons process performed by the experts (refer to Fig. 3). In this regard, for each pairwise evaluation, the participants answered the following question: According to the goal/criteria, ¿how important is each element on the leftover the item on the right? The experts used Table 1 to represent their responses until finalizing all the factors and sub-factors. Then, via Eqs. 1–7, the weights of criteria and sub-criteria were determined.

Fig. 3.
figure 3

Data-collection instrument implemented for FAHP judgments

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On the other hand, we design a survey for the application of the DEMATEL technique (refer to Fig. 4) with the purpose of analyzing the interdependence between factors and sub-factors. Subsequently, it’s applying the Eqs. 8–14, to identify the dispatchers and receivers. For each comparison, it was asked: With respect to goal/factor, ¿how much influence each element on the left has over the element on the right? The experts responded by using the 5‐point scale shown in Sect. 3.2. The decision process was also repeated to finally calculate D + R and D − R values.

Fig. 4.
figure 4

Data-collection instrument implemented for DEMATEL comparisons

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4.3 Calculating the Relative Weights of Criteria and Sub-criteria Using FAHP

In this phase, by the application of the FAHP method, the local and global weights of criteria and sub-criteria were determined considering the uncertainty and vagueness in the judgments of experts. In this sense, first, the fuzzy matrixes were calculated, taking into account the paired comparisons made by the selected experts. In Tables 4, 5 and 6, the results of the FAHP process for the criteria comparison matrix can be seen as an example, applying Eqs. 1–7 of the methodology detailed in Sect. 3.1. Finally, Table 7 presents the local and global weights of all the criteria and sub-criteria that make up the multi-criteria decision model.

Table 4. Fuzzy reciprocal comparison matrix for criteria
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Table 5. Geometric means of fuzzy comparisons for criteria
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Table 6. Normalized fuzzy priorities for criteria
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Table 7. Local and global weights of criteria and sub-criteria
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The Fig. 5 shows the results of the FAHP technique show. Firstly, the weights of the criteria associated with the evaluation of performance in OHS applied to the electricity sector. Ac-cording these outcomes, “Planning” (GW = 58.9%) is the most relevant factor in this evaluation is “Planning” (GW = 58.9%). This factor presents a difference greater than 40% with respect to the other criteria of the model. On the other hand, the sub-criteria “Application” (GW = 14.3%), “Verification” (GW = 11.8%), and “Improvement” (15.0%), present minor differences in their weights or importance, to evaluate the performance in OHS. These results demonstrate the importance of planning to obtain a satisfactory performance in the assessment of OHS by the design of strategies and plans focused on generating a culture of prevention and safety in work environments. On the other hand, evaluation can allow decision-makers to establish the most appropriate strategies to improve OHS performance, considering the elements of the P-D-C-A (Plan-Do-Check-Act) cycle.

Fig. 5.
figure 5

Global weights of criteria in the performance evaluation in occupational health and safety in companies of electric sector

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Regarding the sub-criteria, Figs. 6a, 6b, 7a, and 7b show the distribution by the level of importance of the sub-factors for each cluster in the evaluation of performance in OHS. Regarding these results, in the “Planning” cluster, an essential sub-criterion is “Leadership” (23%) and “Strategic Planning” (19.36%). In the “Application” cluster, the sub-criteria “Diagnosis in OHS” and “Operational Control” were identified as the most relevant (20%). For the “Verification” cluster, “Management review” (59.30%) was identified as the most critical sub-criterion, followed by the “Internal Audits” sub-factor (23.06%). In the “Improvement” cluster, the sub-criterion with the highest weight corresponds to “Improvement Actions” (71.68%). All these factors and sub-factors are part of the legal requirements in OHS in correspondence with the ISO 45001 standard, intending to identify opportunities for improvement in OHS performance, define action plans, resources for its execution, and evaluating their impact on the organization in the prevention of accidents and occupational diseases.

Fig. 6.
figure 6

Local contributions for factors a) Planning b) Application

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Fig. 7.
figure 7

Local contributions for factors a) Verification b) Improvement

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Finally, we calculated the consistency (refer to Table 8) to guarantee the reliability of the judgments contributed by the expert team. The outcomes evidence that all criteria present adequate consistency values (CR ≤ 0.1). Therefore, the factors and sub-factors can be then applied consistently to evaluate the performance in occupational health and safety management.

Table 8. Consistency values for FAHP matrices
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4.4 Evaluating the Interdependence Between Criteria and Sub-criteria via DEMATEL

In this step, we applied a survey to collect the paired evaluation of the experts for the DEMATEL technique. To do this, the experts answered the following question: “how much influence has each criterion/sub-criterion over the criterion/sub-criterion on the left?” In this regard, the experts responded, taking into account the following scale: Nonexistent impact (0), Low impact (1), Medium impact (2), High impact (3), and Very high impact (4). The experts repeat this procedure until finishing all the evaluations.

Then, applying the Eq. 8–13 of the DEMATEL technique, the prominence (D + R) and relation (D-R) values are calculated (refer to Table 9). As a result of this process, the dispatchers and receivers were identified, as shown in Table 9. According to these results, “Planning” (C1) and “Improvement” (C4) were classified as dispatchers, while “Application” (C2) and “Verification” was identified as receivers. Besides, the outcomes show that “Planning” (C4) has the highest D + R value (3.686), establishing that this criterion is the principal generator of impacts and the most determining factor when evaluating the performance in occupational health and safety management.

Table 9. Dispatchers and receivers in the decision-making model
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Besides, impact-digraph maps were diagrammed for analyzing the interdependencies between each cluster, both criteria, and sub-criteria. “Criteria” (refer to Fig. 8a) and “Verification” (see to Fig. 8b) groups are presented as examples. Concerning the outcomes of the impact digraph, in Fig. 8a shows the unidirectional interrelations (red arrows) between dispatchers and receivers. In particular, the “Planning” and “Improvement” are the criteria that most influence the other factors. Therefore it´s evidenced that the performance in the OHS is controlled by the capability of the companies of the electric sector for planning and establishing improvement actions with impact in the application and verification of the OHS management.

Fig. 8.
figure 8

Impact digraph maps for a) Criteria and b) Improvement (Color figure online)

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On the other hand, in Fig. 8b, it is observed the presence of one unidirectional interrelation between dispatchers and receivers “Management review” and “Indicators in OHS” (red arrow) and two feedback interdependencies (green arrows) between “Management review” and “Internal Audits,” and between “Internal Audits” and “Evaluation of OHS.” For example, the “Management review” influences the “Internal audits” through the guidelines and necessary resources for the development of these audits. Moreover, “Internal audits” provide valuable outcomes for “Management review” as an input element for this process.

4.5 Integration of FAHP and DEMATEL Methods

In this section, FAHP and DEMATEL methods are integrated using Eq. 15 to calculate the global and local weights of criteria and sub-criteria in the performance evaluation of occupational health and safety management taking into account factors such as the uncertainty in the pair comparisons of the judgments and the interdependence between criteria and sub-criteria. The final global and local contributions of criteria and sub-criteria were presented in Table 10. Figure 9 shows the ranking of criteria according to their global contributions.

Table 10. Local and global contributions of criteria and sub-criteria resulting from FAHP-DEMATEL integration
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Fig. 9.
figure 9

Ranking of criteria (GW) of FAHP/DEMATEL integration

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Concerning the results, “Improvement” (0.292) and “Verification” (0.270) were identified as the most relevant criteria in the performance evaluation of OHS management. However, there is not a big difference (9.7%) between the essential criterion “Improvement” and the last in the ranking “Planning.” These outcomes can be explained by the fact that occupational health and safety management standards regulate all these criteria to guarantee the culture of prevention in the electric sector through adequate performance evaluation in OHS. In this regard, it is crucial to design multi-criteria plans that consider the principles, requirements, and criteria involved in the evaluation process. Thereby, the companies of the electric sector can improve their performance continuously with an impact on the wellness of their workers and stakeholders.

On the other hand, the sub-criteria “Strategic management” (0.025), “Contingency plans” (0.035), “Evaluation of OHS” (0.109) and “Nonconformities and corrective plans” (0.144) were identified as the sub-criteria with the highest global priorities taking into account the integration of FAHP and DEMATEL techniques.

4.6 Validate the Proposed Methodology in a Company of Electric Sector

In this phase, the multi-criteria decision model was applied for the evaluation of the performance in OHS in the electrical sector company described in Sect. 4.1. Table 11 shows the performance obtained by the company, in the first diagnostic evaluation, as in the second evaluation, after implementing the improvement plans. For this, the GAPS or deviations from the maximum value were calculated using the Eq. 16 [39].

Table 11. Identification of GAPS and critical variables for improvement of MCDM FAHP-DEMATEL in OHS performance evaluation
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$$ GAP \left( \% \right) = \frac{Score\,obtanied - Score\,max}{Score\,max} \times 100 $$
(16)

In the first evaluation, it’s evidenced that the company has a compliance index of 14% concerning the minimum compliance required by national regulations that are of 86%. According to this evaluation, all evaluation criteria were identified as critical, with GAPS or deviations more significant than 70%.

The results were shared with the company’s manager, with whom improvement plans were established according to the company’s context and resources. Table 12 shows examples of the improvement plans adopted, and Fig. 10 presents the PERT diagram drawn up with the critical path for the project, which was estimated with an execution time of 42 weeks and an investment of USD 18,000.

Table 12. Improvement plans in OHS performance evaluation
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Fig. 10.
figure 10

Scheduling and Critical Route PERT-CPM

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On the other hand, we evaluate the effect of the improvement plans both in the evaluation of performance in OHS and in critical indicators for the company. Figure 11 shows the positive evolution in the company’s OHS performance, growing from 14.2% to 88.62%, meeting the minimum percentage required established by national regulations in Colombia. The growth mentioned above can be seen in Table 11, where the company meets 100% of the “Verification” and “Improvement” criteria and only presents GAPS of 18% in the “Planning” criterion and 12.5% in the “Application” criterion, which demonstrates the effectiveness of the plans adopted in the performance in OHS.

Fig. 11.
figure 11

Improvement in Performance Evaluation in OHS Management

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Finally, in Figs. 12a and 12b, the influence of the improvement plans implemented in OHS on critical indicators for the company is observed. In this sense, in Fig. 12a, the 80% reduction in absenteeism from work is evident, as well as the decrease in accidents by 100%, taking into account the data collected between the first and second performance evaluations at OHS. Figure 12b shows the increase in sales derived from improvement in the performance of OHS management, with growth between the first and the second evaluation of 43.83%. The aforementioned outcomes illustrate the positive impact of the assessment of performance in OHS on the well-being of workers, on the productivity of companies, and the competitiveness of the electricity sector.

Fig. 12.
figure 12

Improvements in indicators a) Absenteeism and accidentality in OHS b) Sales (in USD) derived from OHS management

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5 Conclusions and Future Work

This research presents an integrated fuzzy multicriteria approach using FAHP and DEMATEL to evaluate and improve the performance in occupational health and safety management with application in the electric sector. The proposed methodology includes four phases since the design of the MCDM model, the application of FAHP technique to calculate the importance of the criteria and sub-criteria in OHS performance evaluation, the use of DEMATEL method to identify the interrelations between criteria, the integration of FAHP/DEMATEL to establish the final weights of the criteria, and the validation of the proposed approach in a real context in the electric sector.

Concerning the results of this study, it was obtained two critical conclusions. The first conclusion was the identification of the criteria and sub-criteria with most importance and impact in the performance evaluation in OHS for the companies of the electric sector and the second issue is the evaluation of the effects of the proposed methodology considering the importance and interrelations of different criteria involved in performance in OHS that include the elements of P-D-C-A cycle: planning, application, verification, and improvement.

In this sense, the FAHP-DEMATEL outcomes evidence “Improvement” and “Verification” were identified as the most important criteria with global contributions of 0.292 and 0.270, respectively. However, it is necessary to consider the other criteria that composed the performance evaluation due to the little differences in their contributions. Besides, the criteria “Planning” and “Improvement” were identified as the essential criteria in the performance evaluation of OHS management with D + R of 3.686 and 3.308. The managers and leaders should consider these criteria in OHS to design and implement adequate strategies for improving the performance in OHS with impacts in the companies and their stakeholders. Concerning the effects and benefits of the proposed methodology, its evidence that the company obtained an improvement in their OHS performance of 14.2% until 88.62% exceeds the minimum percentage of compliance requirements by national regulations.

In addition, the company decreased critical indicators in OHS, such as absenteeism and the accidents with reductions of 80% and 100%, respectively. On the other hand, the organization under study obtained an increase of 43.83% in their sales that consequence of their improvement in the performance of OHS management. For the considerations mentioned above, the integrated methodology can be helping the companies to generate a culture of prevention in occupational health and safety, taking into account the complexity of the sector, the multiple criteria involved in the evaluation, and the interdependencies between these criteria.

Finally, as future work, the integrated methodology proposed will be extended in other industries. In addition, we propose continue the develop of the approach proposed using different hybrid methods as Fuzzy DEMATEL, TOPSIS, and VIKOR in order to validate and improve the performance of the method.