Application of Fuzzy Decision Support Systems in Risk Assessment of Southeast Asian Labor Market

Abstract

The labor market in Southeast Asia has several complicated issues, including the quick changes in the economy, the various behaviors of employees, and the shifting regulatory settings. To foster sustainable regional development and make decisions based on accurate information, a comprehensive risk assessment is important. Conventional methodologies, on the other hand, often fail to reflect the multifaceted personality of issues about the labor market. In light of recent economic changes and the complexity of applicable policies, this paper recommends a fuzzy decision support system to enhance risk assessment (FDSS-ERA) that could enhance risk assessment in Southeast Asian labor markets. The model methodically assesses hazards, using a mixture of fuzzy logic sense and decision assistance, incorporating changes in employment trends and demographics. The results highlight fuzzy logic’s role in decision-making for effective risk management and policy interventions, showing improved risk comprehension. While educated policy choice making can lead to equitable development in Southeast Asia’s labor work marketplace, FDSS is a potential method. In conclusion, to overcome the many obstacles that stand in the way of chance assessment in the labor market in Southeast Asia, FDSS provides a powerful strategy. Because it offers more advanced analytical tools, this research assists decision-makers in the region in developing more effective guidelines, proactively reducing risks, and achieving projects that promote sustainable development.

1 Introduction

Rapid economic developments, shifting regulatory landscapes, and unique worker traits are some of the features that define the labor market in Southeast Asia [1]. Consequently, the atmosphere to which the stakeholders were exposed was difficult. In this quickly developing industry, it is very necessary to carry out realistic risk assessments to ease the making of informed decisions and maintain sustainable growth [2]. The conventional methods of risk assessment, on the other hand, have been unable to adequately address the multifaceted and varied constraints that exist within the labor market of Southeast Asia [3]. Several regional economic conditions are subject to sudden shifts, which constitute a considerable barrier [4]. Several factors contribute to variations in the labor market [5]. These factors include global economic trends, technical breakthroughs, and geopolitical concerns that influence agriculture, production, and services. Regulatory frameworks are still in the process of being constructed, which further complicates the predicament. The principles, laws, and rules that regulate labor law are difficult for governments to adjust to accommodate the constantly shifting social, economic, and environmental situations. The strategies for managing labor and risk exposure are significantly impacted as a result of these alterations [6].

Additionally, the labor market in Southeast Asia is different due to the various workers present there, and these workers cover specific knowledge ranges, educational qualifications, and cultural contexts [7]. Using state-of-the-art methodologies that go beyond conventional risk assessment methods is necessary to address the risks connected with this diversity effectively. These risks include variations in competencies, language barriers, and cultural differences. To tackle these troubles, it is essential to employ inventive techniques that may alter the ever-changing and complicated characteristics of the labor market in Southeast Asia. Hence, investigating and using advanced methodologies, including FDSS, provide promising possibilities for augmenting risk evaluation methodologies within the area.

The shifting dynamics of the job market and gender balance are using pregnant women to look for riskier work situations. There are gaps in research regarding pregnancy-related exposures, together with physical variables and work absence, and their effect on the health of each mother and child. The existing studies on the correlation between physically demanding jobs and negative pregnancy consequences are scarce, and there is a lack of expertise concerning the impact of work absence at some stage in pregnancy [8]. This research investigates the economic ramifications of disasters in the labor market, emphasizing the results from medium- to lengthy-term periods. A simulation model primarily based on agent-primarily based methodology explores two conditions following an earthquake in Jerusalem. The findings indicate that labor mobility is essential in reducing failure outcomes [7]. This research examines the human resource management (HRM) methods in five Asian countries, highlighting their difficult nature and the desire to realize contextual elements very well. Future research must inspect unique determinants, conduct comparative research, and do longitudinal research to recognize the dynamic nature of human resource management (HRM) in Asia [9]. This study analyzes the migration styles of younger Europeans in Asia, highlighting their usage of motility to navigate unconventional migration routes. The text underscores the numerous encounters and difficulties these migrants encounter, underscoring the significance of mobility in migration choices [10]. This article examines the sociological ramifications of the COVID-19 epidemic on the South Asian region, emphasizing its exceptional characteristics and the diverse variety of societal consequences it has engendered.

It emphasizes instant, intermediate, and extended measures for handling and recuperating from the pandemic. Nevertheless, it acknowledges the existence of research deficiencies and advocates for empirical investigations and comparative examinations [11]. This research investigates Malaysia and Thailand’s economic development, with a selected focus on the middle-income trap and the impact of the Fourth Industrial Revolution in facilitating expanded income levels. The text highlights the importance of establishing institutional frameworks that assist, foster human capital development, and enforce proactive policy interventions [12]. According to a study, the COVID-19 pandemic has expedited the technique of de-globalization, placing more emphasis on resilience instead of performance. It has had significant implications for international industrial networks, tactics for coping with delivery chains, and patterns of commerce. This transition impacts multinational corporations, necessitating research on the effects of regionalization, government regulations, and the evolution of value chains [13]. A study conducted in Mexico demonstrates a robust correlation between remote employment and job security during pandemics. However, economically disadvantaged families encounter reduced opportunities due to restricted access to finance. The study recommends further research on coverage interventions, longitudinal research, and comparative tests [14]. The study examines the kind of social impact bonds (SIBs) that fund active labor market programs in four international European locations. It uncovers political conflicts, the connection between civic and economic interest, and variations due to historic institutional contexts and ongoing welfare state reform [15]. The current body of research identifies several regions that require additional investigation. These areas encompass occupational health for pregnant women, the outcomes of disasters on labor markets, human resource management practices in Asia, migration patterns, the socio-economic results of pandemics, economic development, globalization trends, and social impact financing. Those gaps highlight the need to develop better state-of-the-art analytical tools to address complicated conditions properly. As described, the FDSS to enhance the risk assessment model is a practical technique for dealing with the deficiencies of existing studies.

The FDSS-ERA model, which integrates decision support methods with fuzzy logic ideas [16], may effectively manage the complexity and ambiguity shown in different research. The offered framework provides a methodical way to evaluate various risk factors, such as the following: occupational hazards for pregnant women [17], disaster economic effects [18], HRM practices in many cultural contexts [19], migration trends, pandemic responses, economic improvement techniques, globalization advancements, and social effect financing mechanisms [20]. The FDSS-ERA model incorporates subtle data often missed by other approaches, allowing for a full risk assessment. It equips decision-makers with stronger analytical tools, allowing them to make informed decisions. The FDSS-ERA model can potentially study objectives in numerous fields and promote evidence-primarily based decisions for sustainable development by resolving the limitations of current studies techniques and facilitating a greater complete comprehension of intricate phenomena.

Liu [21] aimed to tackle the difficulties in accurately capturing similarity by introducing sixteen novel similarity metrics for Fermatean fuzzy sets (FFSs) prompted by Tanimoto and Sørensen coefficients. The processing of fuzzy data gathered from FFSs is more suitable via the study’s methodologies, leading to more significant outcomes in recognizing patterns, medical diagnosis, and analysis of clusters. The computational complexity and scalability problems, though, persist. Kahraman [22] aimed to simplify the determination of membership, non-member, and indecision degrees by proposing proportional fuzzy sets as a substitute for extensions of ordinary fuzzy sets. More stable value assignment is made possible by proportional relations between parameters. Results show that it is easy to use and that they are legitimate. However, there may be problems with scalability and processing complexity. Addressing the difficulty of clustering incomplete instances, Liu and Letchmunan [23] provide an upgraded fuzzy clustering algorithm that utilizes Dempster–Shafer theory (DST). Subsets of the dataset are used to fill in missing values by utilizing neighbors with varying weights. Iteratively optimizing an objective function finds each subgroup’s optimal membership and dependability matrices. The adaptive evidence-combining method is designed based on DST to handle uncertainty and imprecision efficiently. This rule combines sub-results with varying reliability levels. The method’s efficacy is proven on real-world datasets by comparing it to existing methodologies.

Liu [24] suggested two Hellinger-inspired Pythagorean fuzzy distance metrics to quantify the dissimilarities across Pythagorean fuzzy sets (PFSs). Comparative cases demonstrate their higher performance. Moreover, a novel approach to decision-making is created and tested in two scenarios. Sensitivity to parameter choice and computational complexity are two potential limitations. Using the Mamdani model fuzzy inference system logic, Charolina and Fitriyadi [25] evaluated the level of satisfaction with the service regarding information clarity, officer competency, and facility availability. This is intended to aid agencies’ decisions regarding service quality. Future studies will establish an interface for processing results and increase the number of factors to improve the accuracy of assessments. Putra et al. [26] suggested that CV. Bangkit Mandiri Sejahtera (BMS) Semarang implemented an automated system to recruit employees to reduce the time and effort spent on manual hiring. Experts are chosen using the AHP approach in a decision support system. The selection process takes less than a month, and applicants receive comments and rankings instantly. Some restrictions may apply, such as the need for precise user input.

Kanj et al. [27] presented a new method for the secure conveyance of hazardous materials in smart cities utilizing real-time data stored in the cloud. It uses a hybrid approach to risk analysis called AHP–TOPSIS, which stands for fuzzy analytical hierarchy process and technique for preference of order by similarity to ideal solution. While in transit, the values of the criteria—which include time, money, and danger—may change, necessitating dynamic decision-making. The results show that safety was enhanced by reducing risks. One potential limitation is the need for computational resources and precise real-time data for dynamic decision-making. Al-shami et al. [28] presented “(m,n)-Fuzzy sets,” a general framework on orthopedic fuzzy sets that permits alternative weights for both membership and non-membership classes. In it, we find the ground rules for operations, learn about abstract attributes, find ranking functions, and create aggregation operators. An example using numerical data shows how the suggested method works for decision-making situations with several criteria. Challenges that arise in practical settings can be a limitation.

The goal of the study is to investigate the FDSS-ERA model’s efficacy across a variety of research topics. The study suggests employing the FDSS-ERA model to tackle various complex challenges [29] in fields including occupational health, disaster impact assessment, procedures for managing human resources, migration patterns, pandemic reactions, economic development tactics, globalization patterns, and social impact financing systems. This approach aims to address the existing gaps in research. The FDSS-ERA model gives a systematic framework for evaluating risk elements and facilitating informed decision-making by merging fuzzy logic notions [30] with decision support processes. The essential objectives of this study are:

• 1. To broaden the FDSS-ERA, a robust model for assessing labor market risk in Southeast Asia, integrating fuzzy logic principles with decision support methods.

• 2. To assess labor market risks in Southeast Asia, including employment trends, demographic shifts, skill shortages, and regulatory changes, aim to identify and quantify their significance.

• 3. To evaluate the FDSS-ERA model’s effectiveness and superiority with other risk evaluation frameworks like fuzzy rule-based systems, multi-criteria decision making and fuzzy Petri nets.

• 4. To provide actionable insights for decision-making within the labor market, figuring out vulnerabilities and risk hotspots, allowing resource allocation and targeted interventions.

• 5. To enhance threat assessment methodologies in labor markets by introducing progressive tactics and fuzzy logic concepts, enhancing accuracy, adaptability and comprehensiveness, promoting sustainable development and resilience in the region.

• 6. To evaluate the impact of natural disasters on labor markets and explore the FDSS-ERA model that could enhance disaster impact analysis and mitigation techniques.

• 7. To investigate the contextual impacts on human resource management practices in numerous Asian nations and analyze the FDSS-ERA model, which could contribute to a higher understanding of HRM dynamics.

Through these goals, the study aims to illustrate the flexibility and effectiveness of the FDSS-ERA model in addressing complex risk assessment-demanding situations and informing evidence-based decision-making across various study domains.

1.1 Revised Major Objectives

• 1. Generate a state-of-the-art fuzzy decision support system to enhance risk assessment (FDSS-ERA) specific to the complex dynamics of Southeast Asian labor markets; this model will emphasize risk appraisal and proactive risk mitigation.

• 2. Exposing the FDSS-ERA model’s precision, flexibility, and capability to handle the compound problems linked with the Southeast Asian employment market proves that it is more effective than traditional risk assessment protocols.

• 3. Concentrating on determining weaknesses, risk hotspots, and opportunities for sustainable growth via advanced risk evaluation approaches to provide evidence-based suggestions and actionable insights into labor market decision-makers.

1.2 Motivation of the Study

The rationale for examining this study arises from the pressing necessity to tackle complex troubles in diverse fields, such as occupational health, catastrophe mitigation, human resource management, migration developments, pandemic responses, economic development, globalization, and social impact finance. Recently, those areas have encountered remarkable uncertainties and intricacies, requiring innovative methodologies for evaluating risks and making informed decisions. Conventional techniques frequently fail to seize the complicated and diverse character of risks in these regions, emphasizing the importance of state-of-the-art analytical tool. The research is driven explicitly by the capacity of FDSS-ERA to offer decision-makers an in-depth and delicate understanding of uncertain and ever-changing conditions. FDSS-ERA uses fuzzy logic concepts to comprehend risk factors better, facilitating proactive change mitigation strategies and informed policy formation. The motivation is more suitable by spotting deficiencies in cutting-edge scholarly works, emphasizing the need for pragmatic remedies and views to address international troubles, including the COVID-19 pandemic, natural calamities, monetary shifts, and social disparities. This study seeks to address the gaps in prevailing studies, facilitate sustainable improvement campaigns, and enhance academic understanding and practical implementation in hazard evaluation and decision-making domains. This endeavors to positively affect resilience, innovation, and development in various industries and areas. The study seeks to showcase the adaptability and effectiveness of the FDSS-ERA model in tackling elaborate risk assessment problems and guiding evidence-based decision-making in various fields of study.

• (A) The Southeast Asian labor market is intricate, concerning diverse industries, demographic models, and regulatory frameworks, necessitating effective regulatory control for sustainable development.

• (B) Traditional risk assessment methods regularly fail to accurately evaluate and mitigate labor market risks, necessitating extra state-of-the-art and adaptable processes for efficient risk management.

• (C) Technological advancements in data analytics, artificial intelligence, and decision support systems can improve labor market risk assessment capabilities, resulting in accurate, efficient, and actionable assessments.

• (D) The labor market faces new challenges due to economic changes, demographic shifts, technological advancements, and regulatory reforms, necessitating revolutionary risk evaluation tools for handling complicated and uncertain records.

• (E) This study observation aims to offer robust and evidence-based insights to public and private sector decision-makers for labor market control regulations, techniques, and interventions via rigorous evaluation and assessment.

1.3 Revised Motivation

• 1. The research is encouraged by the demanding need to undertake the complicated and ever-changing problems afflicting the labor market in Southeast Asia. These comprise the region’s firm economic changes, fluctuating demographics, and regulatory dynamics.

• 2. To address the current inadequacies in risk evaluation approaches and decision-making processes, this research presents the novel FDSS-ERA model. This is driven by the need to provide stakeholders with cutting-edge analytical tools to help them deal with hesitations, decrease risks, and promote long-term regional development.

• 3. Through its thorough and advanced methodology, this study aims to reinforce varied Southeast Asian industries, enhance awareness of labor market risks, and encourage proactive risk management estimation.

1.4 Contributions of the Study

The study introduces the new concept of FDSS-ERA that appreciably contributes to danger assessment, selection assist structures, and policy improvement. This study provides substantial insights and realistic recommendations for addressing tough barriers in contemporary dynamic surroundings by exploring the implementation of FDSS-ERA in many fields. The FDSS-ERA, a unique approach for comprehensively analyzing risks in several areas, including occupational health, disaster mitigation, and economic growth, is developed and used in this work, which offers a significant addition to the field. The FDSS-ERA framework contributes to a more comprehensive understanding of risk issues by merging fuzzy logic concepts into decision support applications. Employing standard evaluation methods helps decision-makers capture the complexities and uncertainties that are sometimes overlooked accurately. Additionally, this study demonstrates the practical usage of FDSS-ERA in real-life settings, demonstrating its effectiveness in directing decision-making processes and allowing proactive steps to minimize risks earlier. With this study, academic knowledge is significantly advanced, and actual answers are presented to address the complicated difficulties that modern society is currently facing appropriately.

The remaining parts of the paper are organized as follows: Sect. 2 discusses the fundamental ideas associated with this topic. Section 3 provides an in-depth presentation of the recommended technique for this research. Section 4 presents the findings and includes a discussion of the tests conducted to assess the planned work. The conclusion of the study is presented in Sect. 5.

To make the analysis more thorough and easier to understand, the author can point out where current approaches fall short and explain how the suggested FDSS-ERA method better assesses hazards to the Southeast Asian labor market.

1.5 Limitations of the Existing Methods

The complexity and unpredictability of Southeast Asia’s labor market landscape make it difficult for traditional risk assessment methods to account for it. Incomplete evaluations may also result from their struggles incorporating varied risk factors, demographic shifts, and regulatory dynamics. Because these approaches might not be flexible enough to adjust to new circumstances, decision-makers might not be able to get valuable insights regarding proactive risk control.

1.6 Advantages of the Proposed FDSS-ERA Method

To thoroughly assess hazards in the Southeast Asian labor market, the FDSS-ERA model combines concepts from fuzzy logic with sophisticated analytics and decision support approaches. It facilitates a complete risk assessment by investigating policy efforts and labor market scenarios through simulation and analysis tools. Thanks to validation exercises, real-world execution, and stakeholder input, the model’s feedback system guarantees continual improvement, creating an adaptive and responsive risk evaluation framework. Furthermore, decision-makers can effectively engage stakeholders, convey risk assessment conclusions, and establish strategic strategies based on educated insights thanks to its visualization and reporting tools.

1.7 Comparative Advantages

The FDSS-ERA model offers a data-driven, more advanced approach to labor marketplace risk assessment than previous approaches, leading to more accurate and reliable evaluations. Its flexibility and responsiveness make it ideal for the complex and ever-changing Southeast Asian employment market, where it helps decision-makers manage risks with timely and relevant information. The FDSS-ERA model can provide a more complicated and realistic risk assessment than deterministic or binary methods because it uses fuzzy logic ideas to account for uncertainty and imprecisions in employment data.

2 Basic Concepts

Fuzzy decision support systems are a subset of decision support systems known as fuzzy decision support systems. These systems use concepts from fuzzy logic to deal with imprecision and ambiguity in decision-making. Traditional decision support systems often struggle when accurately portraying complicated and ambiguous data. This is especially true in settings that are characterized by ambiguity and vagueness. The FDSS, on the other hand, provides an adaptable and robust framework, making it suited for modeling and analyzing systems of this kind.

2.1 Definition of FDSS-ERA

One example of an FDSS application adapted for risk assessment is the FDSS to enhance risk assessment model example. This approach systematically examines and controls risks in various industries, including occupational health, disaster management, and economic development. It does this by combining principles from fuzzy logic with decision support systems. The FDSS-ERA system employs fuzzy logic to represent and handle information that is ambiguous and imprecise. In the mathematical formulation of FDSS-ERA, many essential components are incorporated. These components include the following areas:

2.1.1 Fuzzy Sets

Fuzzy sets are used in the FDSS-ERA program to represent linguistic factors and uncertainty. A membership function is what defines a fuzzy set. This function gives a membership degree to every element inside a certain communicative universe set. In the FDSS-ERA framework, fuzzy sets are established across domains of communication X and Y that represent the input and output variables, respectively. The membership function $\mu_A (x)$ is used to characterize a fuzzy set A on X, where x is an element of X. A membership function $\mu_B (y)$ characterizes a fuzzy set B on Y, where y denotes an element of Y.

2.1.2 Fuzzy Rules

There is a subcategory of IF–THEN rules known as fuzzy rules. Fuzzy rules are indicative of expert knowledge or decision-making heuristics. An antecedent, which is the “IF” component, and a consequent, which is the “THEN” element, are both components that are included in every rule. The fuzzy propositions specified over the variable inputs make up the antecedent, whereas the fuzzy set related to the variable output is denoted by the consequent. For example, a fuzzy rule included inside the FDSS-ERA framework may be represented by expression (1).

$${\text{IF}}\,x_1 {\text{is}}\,A_1 \,{\text{AND}}\,x_2 {\text{is}}\,A_2 \,{\text{THEN}}\,y\,{\text{is}}\,B.$$

(1)

The input parameters are represented as x₁ and x₂, whereas A₁ and A₂, are fuzzy sets that signify the linguistic value. The output variables, y, is linked to a fuzzy set B.

2.1.3 Fuzzy Inference System (FIS)

The FDSS-ERA system uses fuzzy rules within its fuzzy inference systems to make decisions or draw a conclusion by examining input information. The system contains three primary elements: rule assessment, fuzzification, and defuzzification.

• Fuzzification: The procedure includes converting the input value $x_1 , x_2 , \ldots , x_n$, into fuzzy sets using the membership functions stated in Eq. (2).

  $$\mu_A 1(x_1 ),\mu_A 2(x_2 ), \ldots ,\mu_A n(x_n )$$

  (2)

• Rule evaluation: The activation degree of every fuzzy rule i can be identified by evaluating the degree of matching between input values and antecedents. Fuzzy logic operators, comprising the minimum (AND) operator, can calculate this rule assessment.

• Defuzzification: The output values y can be determined by aggregating the outputs of activated rules. Different policies, such as the centroid or weighted average defuzzification technique, can complete the defuzzification.

2.1.4 Fuzzy Membership Functions

The establishment of the structure and features of fuzzy sets is greatly influenced by membership functions, which are an enormously essential component. Many other shapes may be identified, including triangular, trapezoidal, Gaussian, and sigmoidal forms. Membership functions are used to quantify the degree to which an element is considered to be a member of a fuzzy set. The amount of ambiguity or vagueness that is related to the linguistic variables may be obtained via the use of these functions. The membership function $\mu_A (x)$ quantifies the extent to which an element x is a member of the fuzzy set A.

2.1.5 Aggregation Methods

The employment of aggregation techniques entails the consolidation of the outputs of separate fuzzy rules into a single aggregated answer. Maximum, average, minimum, and weighted average are the four standard strategies for aggregates. Using Eq. (3), one may discover how to compute the aggregated output B.

$$B = {\text{Aggregation}}(\{ w_i \cdot \mu_{B_i } (y)\} ),$$

(3)

where w_i represents the degree of activation of rule i, while $\mu_{B_i } (y)$ denotes the membership function of the subsequent fuzzy set associated with rule i.

2.2 FDSS-ERA Operation

The FDSS-ERA’s operating technique includes an initial specification of linguistic variables and the membership functions that correspond to those variables. The linguistic variables under examination are input and output elements pertinent to the risk assessment problem. The use of specialized information or heuristics that are particular to the circumstance generates a compilation of intuitive suggestions. The rules that were mentioned previously contain the criteria for risk assessment as well as the arguments that are used to justify making judgments. When determining the membership level after each rule, FDSS-ERA uses fuzzy regulations to analyze the input facts to conclude. First, the input data are fuzzified, then fuzzy rules are assessed, and lastly the rule outputs are consolidated. The process is said to be complete once all these steps are completed. Defuzzification is a procedure that creates a basis for decision-making or risk assessment once completed. The combined fuzzy output is translated into a precise value for the transformation. This is how it is performed. To successfully handle the issues of uncertainty and imprecision typical of data from the real world, a risk assessment approach known as the FDSS-ERA methodology has been identified. This strategy was developed to make the risk assessment process more smooth. Additionally, it has expertise of expert systems and can model very complex risk scenarios. The versatility and efficiency of the approach may be beneficial to a variety of fields, including occupational health and safety, disaster relief, economic development, and environmental risk assessment at the same time.

The FDSS-ERA framework is a powerful instrument for enhancing the number of methods for risk assessment. Fuzzy logic is used to accomplish this goal. Providing a mathematical representation of this idea makes it much easier to explain and evaluate ambiguous and imprecise material. When confronted with challenging situations that require decision-making, those responsible for making judgments can acquire beneficial insights and receive assistance. FDSS-ERA has the potential to be a successful solution when it comes to tackling the complicated challenges connected with risk assessment in various disciplines. This is because it can function in various contexts and is adaptable.

Fuzzy sets: Since fuzzy sets represent ambiguity and uncertainty in data, they are crucial to fuzzy logic and systems that support decisions. Every element in a set can be specified as having varying degrees of membership according to a membership function, which constructs a fuzzy set. The degree to which an element belongs to a set can be indicated by a partial membership value that ranges from 0 to 1 in fuzzy sets, as opposed to classical sets, in which an element can only be either part of the set (1) or not (0).

Mathematical representation of fuzzy sets: Fuzzy sets are used to characterize language variables and uncertainties associated with labor market risk factors within the framework of the FDSS-ERA model. To define a function of membership that describes the degree of participation of every component in the set, the mathematical procedure for creating a fuzzy set is performed. The type of the modeled variable determines the possible shapes of this membership function, which might be triangular, trapezoidal, Gaussian, or sigmoidal.

To illustrate, consider a “Low Risk” fuzzy set for the degree of danger linked to a specific thing in the labor market. As an example of a membership function, consider the following Eq. (4) showing the fuzzy set:

$$\mu_{\text{LOW - RISK}} (x) = \left\{ {\begin{array}{*{20}l} {1, } & {{\text{if}}\,x \le 0.3} \\ {\left( {\frac{(0.5 - x)}{{0.2}}} \right),} & {{\text{if}}\,0.3 < x \le 0.5} \\ {0,} & {{\text{if}}\,x > 0.5} \\ \end{array} } \right..$$

(4)

Each value of x is given a level of membership by the function known as $\mu_{\text{LOW - RISK}} (x)$. In this example, the value indicates the degree to which it corresponds to the “Low Risk” group. As the value becomes closer to 0, it suggests that the degree of participation within the “Low Risk” group is more significant. As the value gets closer to 1, it indicates that membership is smaller.

3 Methods

Some important characteristics characterize the labor market in Southeast Asia. These qualities include a diverse workforce, rapid economic growth, and dynamic regulatory systems. Considering the intrinsic complexity of the environment, this research aims to evaluate the use of FDSS to improve risk assessment in particular. This endeavor attempts to design comprehensive risk assessment tools that may give considerable insights for decision-making and stimulate sustainable development. This is despite that regional dynamics are always shifting.

3.1 Development of the FDSS-ERA Model

The primary objective of this research is to modify the FDSS-ERA model in such a way that it is specifically tailored to the labor market for Southeast Asian countries. To properly complete this process, it is necessary to include fuzzy logic concepts into well-established decision support systems. With the assistance of several different approaches, the FDSS-ERA model is designed to manage the intricate nature of the labor market dynamics effectively. Alterations in employment patterns, shortages of skills, shifts in demography, and regulatory frameworks are all factors considered. Specifically, to systematically evaluate and mitigate the risks associated with the Southeast Asian labor market, several essential components of the FDSS to enhance the risk assessment model have been developed. It is possible to see a graphical depiction of these components in Fig. 1, which may be found here.

Fig. 1

Architecture of the FDSS-ERA model

The core component of the FDSS-ERA model is the input data layer that consolidates diverse information about the Southeast Asian labor market. The databases encompass a range of pertinent aspects, such as employment patterns, demographic changes, deficiencies in skills, regulatory structures, economic indicators, and other relevant variables—the input data layer functions as the fundamental basis for evaluating risks and making decisions within the model. The input data layer, denoted as X, encompasses a range of datasets about the labor market in Southeast Asia, as depicted in Eq. (5).

$$X = \{ X_1 ,X_2 , \ldots ,X_n \} ,$$

(5)

where, X_i, denotes specific datasets, such as employment patterns, demographic changes, skill deficiencies, etc. To successfully handle and analyze the incoming data, the FDSS-ERA model employs a fuzzy logic engine. Fuzzy logic facilitates the model’s ability to effectively address imprecise, unclear, and ambiguous information inherent in the labor market dynamics. The ambiguity and complexity of the data on the current state of the labor market may be effectively managed by the engine via fuzzy sets, linguistic elements, and fuzzy rules. Thus, presenting conclusions from risk assessments that are more exact as a result is feasible. “F” is the abbreviation for the fuzzy logic engine applied in FDSS-ERA. Fuzzy sets, linguistic variables, and fuzzy rules are used in processing the input data X by the fuzzy logic engine. This function may be represented by the notation F(X).

The primary objective of the risk assessment module is to evaluate possible risks in the labor market scientifically. This is accomplished via the use of processed data and the implementation of fuzzy logic analysis. The system uses predetermined risk indicators and algorithms to determine the possibility of various possible threats. These threats include fluctuations in unemployment, shortages of skilled workers, changes in rules, and modifications in demographic profiles. The software module develops risk profiles and assigns priority levels depending on the severity and urgency of the concerns. This makes it simpler to conduct additional investigations when doing further investigations. The risk assessment module, R, systematically examines labor market hazards using data processed from the fuzzy logic engine F(X). The system utilizes predetermined risk indicators and algorithms to evaluate the probability and consequences of different risks.

Using the decision support module, the outcomes of risk assessments may be integrated into decision-making processes more straightforwardly. This tool aims to give decision-makers practical insights, ideas, and scenarios by analyzing the risks that have been uncovered and the potential consequences that these risks may have on the labor market. Instruments for visualization, scenario analysis, and sensitivity testing are some of how the module assists stakeholders in building effective risk reduction strategies and making well-informed decisions. Lettered with a D, D(R(X)) allows for integrating the decision support module with the risk assessment outputs. Decision-makers may benefit from this tool by gaining practical insights, suggestions, and scenarios that are based on the identification of risks and the possible repercussions of those risks. S serves as a symbol for the tools used for scenario analysis and simulation. The simulation of various labor market circumstances, policy efforts, and economic scenarios is made possible by S(D(R(X))), which allows stakeholders to do so. To aid in the proactive identification and reduction of risks, it evaluates their effect on the degree of risk exposure and the capacity to recover.

The FDSS-ERA system also incorporates technologies used for scenario analysis and simulation to study the likelihood of various risk scenarios and their repercussions. Through simulations of various labor market situations, policy efforts, and economic scenarios, decision-makers may be assisted in analyzing the risk exposure and resilience more effectively. Stakeholders may use this ability to assist them in actively anticipating and managing growing risks before they become significant challenges. The model’s capabilities regarding output visualization and reporting operations allow it to explain the risk assessment findings adequately. To deliver short summaries of major discoveries, trends, and risk profiles, those in positions of authority can use dynamic dashboards, reports, and visualizations. Because of these insights, the efficacy of communication, the participation of stakeholders, and the formation of strategic plans at various levels of government and industry have all increased.

The FDSS-ERA’s performance and relevance is continuously improved by its feedback system. Validation exercises, experiences of implementation in the real world, and input from stakeholders that have been obtained systematically are all considered throughout this phase. Since the needs of the labor market in Southeast Asia are always changing, it is presumed that the model will continue to be adaptive, flexible, and in line with those requirements. This is because these techniques are iterative, guaranteeing that the model will remain adaptable. In conclusion, the architectural design of the FDSS-ERA model is an example of a strategy that is both complete and unified in its approach to assessing the risk that is associated with the labor market in the Southeast Asian area. This makes the model an excellent example of a strategy that is both comprehensive and unified. The model uses fuzzy logic rules, cutting-edge analytics, and decision support methods to provide stakeholders with the capacity to make well-informed decisions, efficiently handle risks, and produce sustainable development in the context of the shifting labor market environment in the area.

3.2 Evaluation of Labor Market Risks

Evaluating the risks associated with the labor market within the framework of the FDSS-ERA paradigm is one of the most significant components of this research endeavor. This research aims to comprehensively understand the intricate challenges the Southeast Asian labor market faces. A comprehensive examination of the many risk variables that impact the labor dynamics within the area will be carried out as part of this research project. Fuzzy logic ideas are included in this study, which utilizes advanced procedures. Figure 2 visually depicts the assessment components that affect the labor market dynamics.

Fig. 2

Evaluation factors influencing labor market dynamics

The complicated dynamics of the labor situation in Southeast Asia are investigated in this study to understand the issue better. Several elements are considered, including the creation of new jobs, shifts in industry, and unemployment rates. This work studies employment trends to identify developing patterns and possible vulnerabilities within the labor market. The employment level (E) at a specific period (t) is denoted by Eq. (6), which calculates the sum of the initial employment level (E₀) and the change in employment over time ($\Delta E(t)$).

$$E(t) = E_0 + \Delta E(t).$$

(6)

Recognizing developing trends and potential weaknesses within the labor market can be achieved by examining the trend of $\Delta E(t)$. The labor market dynamics are suggestively impacted by demographic variations, encompassing alterations in population structure, age distribution, and workforce structure. This research investigates the impact of demographic changes, specifically population aging or young people bulges, on labor supply, dell demand, and stability within the Southeast Asian market. Equation (7) represents the rate of change of the population (P) over time (t) that is calculated as the difference between the birth rate (B) and the death rate (D).

$$\frac{{{\text{d}}P}}{{{\text{d}}t}} = B - D.$$

(7)

Demographic shifts, such as the aging of the population $\left( {\frac{{{\text{d}}P}}{{{\text{d}}t}} < 0} \right)$ or the emergence of youth bulges $\left( {\frac{{{\text{d}}P}}{{{\text{d}}t}} > 0} \right)$, have the potential to impact labor supply, demand, and the market’s overall stability in Southeast Asia. The Southeast Asian labor market has substantial issues due to skill shortages and mismatches that negatively impact efficiency, competitiveness, and hiring decisions. This study investigates the frequency of skill deficiencies in different sectors and evaluates their consequences for advancing the workforce, economic expansion, and social unity.

$${\text{Skill}}\,{\text{gap}} = \sum {i = {\text{1n}}(D_i - S_i )} .$$

(8)

The summation of the disparities between the demand for skills determines the skill gap ($D_i$) and the supply of skills ($S_i$) across many industries (n), as expressed by Eq. (8). A positive skill gap signifies a deficiency in skills, whereas a negative gap signifies an excess of skills. Examining the skill disparity aids in evaluating the consequences of advancing the labor force, economic expansion, and societal unity.

$${\text{Impact}} = \frac{\Delta Q}{{\Delta P}}.$$

(9)

The influence of regulatory changes on labor circumstances (Q) about changes in policy parameters (P) is represented by Eq. (9). Evaluation of the consequences of labor market reforms or policy interventions on worker rights, market effectiveness, and general employment situations in Southeast Asia may be accomplished by decision-makers via the examination of the impact coefficient. Because they affect employment policies, labor laws, and firm practices, regulatory frameworks considerably impact the labor market dynamics. The purpose of this research is to evaluate the influence that regulatory changes, such as labor market consolidations or policy interventions, have had on the workers’ rights, employment conditions, and the market’s general efficiency in Southeast Asia.

$${\text{Membership}}(x) = \mu (x).$$

(10)

In the context of fuzzy logic, the assignment of membership functions ($\mu (x)$) to variables is described by Eq. (10), which signifies the extent to which a value (x) is considered valid or belongs to a fuzzy set. The model may be able to successfully give fuzzy membership values to reflect the ambiguity and imprecision of the data about the labor market. Because of this, it is possible to conduct a more comprehensive analysis of the hazards that are now in existence. The study uses fuzzy logic methodologies in risk assessment because of the inherent complexity and uncertainties in the labor market data. Incorporating data from the labor market that includes inherent imprecision and ambiguity into the model is made simpler by fuzzy logic. This makes it feasible to undertake a more comprehensive examination of hazards, which conventional risk assessment techniques could ignore.

$${\text{Risk}}\,{\text{score}} = \mathop \sum \limits_{i = 1}^n w_i \times S_i .$$

(11)

The summation of the weighted scores determines the risk score ($S_i$) of different risk factors (n), as expressed by Eq. (11). A weight ($w_i$) is applied to each risk factor based on its significance. To give credible and comprehensive results in risk assessment, the study undertakes an in-depth investigation of several different datasets and indicators. These findings may be utilized to drive policy-making and strategic measures. A complete analysis of many datasets and indicators is required when an evaluation of the risks connected with the labor market is being conducted. This analysis must take into consideration both quantitative and qualitative risk components. To ensure the generation of risk assessment findings that are dependable and trustworthy, the study makes use of rigorous procedures and analytical tools. Following that, these findings may be used to affect decisions about policy and strategic objectives.

$${\text{Vulnerability}}\,{\text{index}} = \frac{{{\text{potential}}\,{\text{impact}}}}{{{\text{resilience}}}}.$$

(12)

Equation (12) shows the vulnerability index and is determined by dividing a risk’s potential impact by the labor market’s adaptability. With a higher vulnerability score, the system is more vulnerable to prospective threats and probable risk hotspots. This makes the system more sensitive to potential risks. To increase the market’s resilience in the Southeast Asian region, decision-makers can prioritize the allocation of resources appropriately, carry out focused interventions, and adopt proactive policies when they begin by identifying vulnerabilities. A method of evaluation is used in the study to determine significant vulnerabilities and possible areas of high risk within the labor market of Southeast Asia. Decision-makers can allocate resources effectively, put specific plans into action, and take preventative steps to control risks and build market resilience when they identify areas of concern and then apply those plans.

Evaluating the risks connected with the labor market within the context of the FDSS-ERA paradigm offers significant insights into the complex challenges impacting labor dynamics in Southeast Asia. This evaluation is conducted from a broad perspective. This study significantly contributes to a better understanding of the vulnerabilities within the labor market by using sophisticated analytical approaches and conducting a comprehensive analysis of a wide range of risk factors. It gives helpful insights that may be used in implementing evidence-based policy necessary for achieving sustainable regional development.

3.3 Integration of Fuzzy Logic Principles

Compared to other risk assessment frameworks that are more traditional, the FDSS-ERA model stands out because it combines notions of fuzzy logic as fundamental components. The core of the FDSS-ERA, which ensures dependability, is fuzzy logic, well-known for its ability to handle imprecise, convoluted, and confusing information. Southeast Asia’s labor market is characterized by its complexity, quick changes, and various worker dynamics. This is a well-known fact. When seen in the context of this labor market, integration has several major effects. As a result of its use of fuzzy logic, the FDSS-ERA can deal with the inherent ambiguity and imprecision often seen in data pertinent to the labor market. Fuzzy logic, on the other hand, considers and acknowledges the nuances present in real-world situations. This contrasts with the traditional binary logic systems, which depend on clear and exact differentiations. Within the context of the dynamics of the labor market, fuzzy logic stands out as an approach to analysis that is both more flexible and more sophisticated. There is a possibility that the data are insufficient, unclear, or susceptible to interpretation.

Because it uses fuzzy logic concepts, the FDSS-ERA model can overcome the limitations inherent in conventional risk assessment approaches. It is common for this method to struggle to capture the complex and multifaceted character of labor market dynamics. The model uses fuzzy sets, linguistic variables, and fuzzy rules to define and manage data relevant to the labor market. This method is an alternative to relying only on clear and predictable rules. The FDSS-ERA can generate more sophisticated risk evaluations due to the gradual and hazy transitions between the different states and outcomes. In addition, fuzzy logic provides the FDSS-ERA model with more flexibility and resilience, which helps it to adapt better to the dynamic labor market in Southeast Asia. This is an additional benefit of fuzzy logic. The skill to effectively handle conditions that are ambiguous and are in a state of perpetual change is of the highest significance, especially when taking into consideration the dynamic economic changes, demographic transformations, and regulatory adjustments that are taking place in the area. The FDSS-ERA makes it possible for decision-makers to have access to information that is both timely and relevant. It is feasible to do this via fuzzy logic, which enables real-time dynamic change of its analysis and recommendations.

To further encourage an approach to risk assessment that is more all-encompassing and inclusive, the FDSS-ERA model also incorporates fuzzy logic concepts. Through the use of this strategy, the need for reductionism is removed, and the problem is simplified, which enables a comprehensive understanding of the dangers that are involved. To do this, it acknowledges the preexisting ambiguities and uncertainties in the data about the labor market. By conducting a thorough analysis of the dynamics of the labor market, decision-makers may develop the capacity to make well-informed decisions and improve their odds of success. The varying degrees of ambiguity and complexity inherent in Southeast Asia’s environment are considered to achieve this study investigation’s objectives. To summarize, a considerable advancement has been made by introducing elements of fuzzy logic into the FDSS-ERA model. In the context of the labor market in Southeast Asia, this development makes it possible to conduct risk assessments that are more exhaustive, flexible, and resilient than previously possible. Decision-makers are provided with a powerful tool to negotiate the complexities of the dynamics of the labor market and make well-informed decisions, which this model provides. The model provides Decision-makers with this strong tool rather than attempting to eliminate uncertainty and ambiguity (Table 1).

Table 1 Algorithm of the proposed FDSS-ERA model

A systematic technique used to assess the dynamics of the labor market using concepts from fuzzy logic is the FDSS-ERA model algorithm, shown in Table 1. All of the processes that are included in the process are as follows: initialization, preprocessing, fuzzification, evaluation of rules, aggregation, defuzzification, postprocessing, evaluation of the model, iteration and optimization, and output. Using linguistic variables, fuzzy sets, and rules, the algorithm performs the following tasks: the review of past information; the determination of membership in fuzzy sets; the assessment of rules; the computation of fuzzy output values; the conversion of fuzzy output into precise values; the analysis of defuzzified output; the assessment of model performance; the optimization of variables; and the delivery of last risk assessment outcomes and recommendations to stakeholders. All of these features are accomplished through the utilization of fuzzy sets. This technique comprehensively analyzes the dangers associated with the labor market to generate insights that may affect decision-making processes.

3.4 Application of the FDSS-ERA Model

The FDSS-ERA model and its implementation in real-world labor market situations have been developed and applied in Southeast Asian nations. The simulated exercises and case studies used to complete the empirical verification of the utility of FDSS-ERA in recognizing and mitigating risks in the labor market are shown here. Through the use of the model in a wide range of various scenarios, the study demonstrates the flexibility of the idea as well as its practical value. By doing so, the organization proves its ability to influence decision-making processes at various levels of government and industry all around the globe. The results show that FDSS-ERA is a good model for improving risk assessments in the Southeast Asian labor market. Through the use of fuzzy logic techniques, this model was able to successfully capture the inherent complexity and uncertainty that is present in the dynamics of the labor market. Fuzzy logic was used to attain this goal successfully. In addition, as a result, those who make decisions have access to considerable insights that may be used for proactive risk management and the formulation of policies. FDSS-ERA is one of the complex analytical procedures that should be used to overcome the considerable risk assessment challenges. The findings of this study throw even more light on the reason for this need. The implementation of FDSS-ERA has the potential to significantly serve as a guiding principle for the formulation and implementation of policies within the labor market in Southeast Asia. The possibilities are just enormous! Those in charge of making choices are given the power to make better-informed decisions, which allows them to create proactive steps to decrease risks and improve efforts for sustainable growth. There has been an increase in the competence to assess vulnerabilities over this period. Advanced analytical approaches, such as FDSS-ERA, can stimulate an increased capacity to endure and respond to changing circumstances in the labor market and an extra piece of unfavorable news.

To properly manage the complicated issues related to risk assessment within the Southeast Asian labor market, this section highlights the significance of applying innovative approaches such as FDSS-ERAs. The fuzzy decision support system (FDSS-ERA) is a comprehensive framework that integrates fuzzy logic principles with decision support methodologies. Its purpose is to enhance risk assessment abilities and provide information that can be employed to assist decision-making processes based on evidence-based information. The implementation of the FDSS-ERA is an essential component that makes a significant contribution to the promotion of sustainable development and the building of resilience within the labor market of Southeast Asia. Consequently, many stakeholder groups, which include several different types of businesses and sectors, stand to gain from it.

As shown by the study’s findings, assessment has progressed: the results suggest that the FDSS-ERA model may produce more accurate risk assessments by considering various variables and uncertainty intrinsic to the Southeast Asian labor market. This enhanced precision allows for more informed decisions. According to the study’s findings, the FDSS-ERA model provides a more thorough assessment of labor market hazards by using fuzzy logic concepts to identify subtleties and nuances that conventional approaches could miss. A more complete picture of risk variables may emerge from such all-encompassing monitoring. The findings may show that the FDSS-ERA approach improves decision support by giving stakeholders actionable insights, suggestions, and scenarios grounded on the assessed risks. Better decision-making assistance can help people take charge of risk management and develop winning ideas.

The study’s findings may reveal that the FDSS-ERA model’s fuzzy logic engine makes it better suited to deal with complexities and uncertainties in labor market dynamics. The model’s flexibility enables it to process uncertain data and produce improved risk assessment outcomes. A study of the results could reveal that the FDSS-ERA framework is superior to various risk assessment models because of how well it handles noisy data, how supportive it is of decision-making, and how comprehensive its risk assessments are overall. By contrasting the two, we can see how much better the FDSS-ERA model is at enhancing risk assessment. By highlighting these features in the study’s findings, the authors prove that the FDSS-ERA model improves the assessment of risk in Southeast Asian labor markets, which is useful for researchers, stakeholders, and policymakers interested in analyzing the labor market and risk management.

4 Results

Within the scope of this research, the FDSS-ERA model is compared to several other models. These models include fuzzy rule-based systems (FRBS), fuzzy Petri nets (FPN), and fuzzy multi-criteria decision-making (MDCM), to name a few examples. As part of this study, the performance of the FDSS-ERA model is also tested in great detail and depth. A dataset will be used to carry out the study [31]. Employment patterns, demographic transitions, regulatory changes, and differences in skilled sets are some of the numerous facets of the labor market covered in this dataset. This research employs several different assessment procedures to determine whether or not the models are effective. Sensitivity to input variables, resilience to noisy data, decision-making supportiveness, uncertainty management, and cost–benefit analysis are some of the processes that fall under this category. In this part, the performance of FDSS-ERA is also reviewed, along with the performance of several other models, including FRBS, FPN, and MCDM. This research aims to investigate the efficacy and application of the FDSS-ERA model in terms of its capacity to deal with the intricate problems related to the job market vulnerability assessment.

The risk assessment coverage (RAC) for the FDSS-ERA model is shown in Fig. 3, along with three additional models: the FRBS, the FPN, and the MCDM. This statistic is representative of a broad range of age groups combined. After an exhaustive study of the data, it has become abundantly clear that the FDSS-ERA model is superior to all other models in every respect, irrespective of the age group. This is true regardless of whether the evaluation is carried out on adults or if it is carried out on youngsters. Using the currently available information, the FDSS-ERA model can comprehensively investigate the dangers associated with the labor market. It is vital to offer an unambiguous description of the RAC to make it easier to carry out an evaluation that is more complete. As indicated by Eq. (13), it is possible to define it as the percentage of potentially risky situations in the labor market that have been identified compared to the total number of potential hazards in each age group.

$${\text{RAC}} = \frac{{{\text{number}}\,{\text{of}}\,{\text{identified}}\,{\text{risks}}}}{{{\text{total}}\,{\text{possible}}\,{\text{risks}}}} \times 100\% .$$

(13)

Fig. 3

Risk assessment coverage analysis of FDSS-ERA and other models

Based on the conclusions gathered from the data, it was determined that the FDSS-ERA model provided much higher RAC percentages across all age groups than the FRBS, FPN, and MCDM models. For instance, the FDSS-ERA achieves a rate of acceptance and compliance (RAC) of 89% in the age category of 18–24 years old, while its closest competitor, FPN, only hits 80% in the same age range. This is a significant difference. Regarding identifying potential risks in the labor market, the FDSS-ERA model regularly outperforms other models. This is because it is consistent across all age groups. Because it is consistent, this is shown. The overall performance of the FDSS-ERA model has been enhanced due to the incorporation of fuzzy logic principles and decision support methodologies into the model’s design. As a result of this integration, the model can effectively capture distinctions and uncertainties that traditional models normally overlook. Furthermore, the flexibility of the FDSS-ERA model makes it possible to swiftly respond to the dynamic and diversified environment of the labor market. This is a significant advantage. When this is done, it guarantees that a comprehensive risk assessment is carried out across all age groups.

Coverage of risk assessment (RAC): Eq. (13) states that RAC is the ratio of real risks to all potential risks. Figure 3 shows the RAC analysis by comparing FDSS-ERA to other models across various age groups.

An illustration of the uncertainty score analysis for the FDSS-ERA model and three comparator models, namely FRBS, FPN, and MCDM, is shown in Fig. 4. This analysis was performed across several different business sectors. After the study’s conclusion, it is evident that the FDSS-ERA model performs far better than other models when effectively addressing uncertainty. This is because the FDSS-ERA model performs significantly better than other models. One evidence supporting this finding is that uncertainty ratings have gradually decreased across all market sectors. To examine the topic, it is necessary to have a general understanding of the uncertainty score. According to Eq. (14), represented by this metric, the model can appropriately deal with the inherent uncertainties in the data on the labor market.

$${\text{Uncertaintyscore}} = 1 - \frac{{\text{predicted certainty}}}{{\text{total possible certainty}}}.$$

(14)

Fig. 4

Uncertainty score analysis of FDSS-ERA and comparative models

The FDSS-ERA model produces rankings of uncertainty that are much lower across all industrial sectors than the FRBS, FPN, and MCDM models, as shown by the results. As a result of its uncertainty, FPN, the firm that is the most direct competitor to FDSS-ERA, has received a rating of 0.72, while FDSS-ERA has received a rating of 0.78. The FDSS-ERA model demonstrates a similar pattern across all sectors, showing its enhanced capability to deal with uncertainty more favorably. The superiority of the FDSS-ERA model may probably be ascribed to the fact that it integrates fuzzy logic standards. This permits the model to have a more desirable ability to gather and analyze uncertainties and obscurities in the labor market. By using decision support mechanisms, the FDSS-ERA model can enhance its resilience in dealing with uncertainty, even when it is presented with data that is either restricted or ambiguous. To achieve this goal, stakeholders are given instructions and insights grounded in reality. By demonstrating that the FDSS-ERA model efficiently addresses uncertainty in various business sectors, our findings illustrate that a model is a practical instrument that can be used to make well-informed decisions when examining the risk linked with the labor market.

The uncertainty score is a measure that shows how well the model handles uncertainty; it is defined in Eq. (14) as follows. Figure 4 uses the uncertainty score analysis to compare FDSS-ERA with other industry models.

The overall performance of the FDSS-ERA model in evaluating multiple models (FRBS, FPN, and MCDM) across diverse business sectors is displayed in Fig. 5, which offers the decision-making supportiveness (DMS) analysis. The findings consistently indicate that the FDSS-ERA model can achieve higher DMS scores across all sectors. This proves the model’s enhanced power to give decision-makers practical insights and recommendations. To initiate the communication, let us set up the DMS score as a metric that quantifies the diploma so that the model gives realistic insights and suggestions for people who are responsible when making decisions. The DMS rating can be computed using the formula shown in Eq. (15).

$${\text{DMS}}\,{\text{score}} = \frac{{{\text{number}}\,{\text{of}}\,{\text{actionable}}\,{\text{insights}}\,{\text{and}}\,{\text{recommendations}}}}{{{\text{total }}\,{\text{number}}\,{\text{of}}\,{\text{insights}}\,{\text{and}}\,{\text{recommendations}}}}.$$

(15)

Fig. 5

Decision-making supportiveness analysis of FDSS-ERA and comparative models

The findings imply that the FDSS-ERA model attains superior DMS rankings throughout all commercial sectors compared to opportunity models. In the manufacturing industry, FDSS-ERA has a much higher score of 0.8 than its closest rival, FPN, which has a rating of 0.75. This indicates that FDSS-ERA is better than its competitors. This demonstrates that the FDSS-ERA model effectively provides decision-makers with relevant insights and pointers since the determined sample remains constant across various sectors. The outstanding overall performance of the FDSS-ERA model may be attributed to combining fuzzy logic standards with decision support methodologies. The model’s capacity to efficiently gather and handle data regarding the labor market, characterized by complexity, uncertainty, and imprecision, is improved by incorporating fuzzy logic into the model. In addition, the decision-making assistance techniques included in the FDSS-ERA model enhance its capacity to transform data into actionable recommendations and insights that have been personalized to meet the specific needs of decision-makers.

A decision-making support system, or DMS, is the ratio of practical insights and recommendations to the overall number of suggestions and insights (as shown in Eq. 15). Figure 5 shows the DMS study that compares FDSS-ERA to other industry models.

Regarding coping with remarkable contextual changes, the adaptability to contextual changes (ACC) analysis, represented in Fig. 6, proves that the FDSS-ERA model is superior to other models (FRBS, FPN, and MCDM). Transformations such as economic tendencies, regulatory circumstances, worker dynamics, and technology improvements are included in these changes. Based on the data, it is possible to conclude that the FDSS-ERA model regularly outperforms other models in every area. This demonstrates the model’s greater potential to adapt to changes in the competitive environment of the labor market. Similarly, the ACC score is used to discover the issue matter as a hallmark of the model’s capability to change and respond effectively to swings in the labor market environment, as indicated by Eq. (16). This is done to find the factor that caused the problem.

$${\text{ACC}}\,{\text{score}} = \frac{{{\text{number}}\,{\text{of}}\,{\text{correct}}\,{\text{responses}}}}{{{\text{total}}\,{\text{number}}\,{\text{of}}\,{\text{responses}}}} \times 100\% .$$

(16)

Fig. 6

Adaptability to contextual changes analysis of FDSS-ERA and other models

FDSS-ERA obtains superior ACC scores in every class compared to different models, as determined by analyzing the consequences. As a result of the changes in the economy, FDSS-ERA received a score of 90%, which is higher than its nearest rival, FPN, which scored 82%. The FDSS-ERA model exhibits its resilience and ability to alter diverse exertions of market factors, as indicated by its consistent performance throughout all contextual changes.

Equation (16) describes the ACC score as the proportion of right responses to overall replies, which shows how adaptable the model is to context changes. As shown in Fig. 6, the ACC analysis compares FDSS-ERA against other models across different settings.

It is possible to credit the outstanding performance of the FDSS-ERA model to the introduction of fuzzy logic ideas, which enables the model to efficiently handle and analyze information that is complex, uncertain, and dynamic about the labor market. As an additional point of interest, using decision support approaches in the FDSS-ERA model enhances its capacity to provide practical insights and suggestions specifically crafted to guarantee contextual adjustments.

This method, which is depicted in Fig. 7, evaluates the impact of various types of input variables, such as economic indicators, changes in demographics, regulatory changes, and skill mismatches, on the performance of the FDSS-ERA model in comparison to the performance of other models, like the FRBS, FPN, and MCDM. The input sensitivity analysis method is depicted in Fig. 7. Based on the data, the FDSS-ERA model is recommended to perform consistently better than the models presently utilized across all input variables. Based on the findings of this research, it seems that the FDSS-ERA model offers a greater degree of sensitivity and response to fluctuations in the dynamics of the exertion market. One of the measures used to assess how the model reacts to changes in the input variables is called the input sensitivity score. This measure is expressed in Eq. (17), which may be found at this location. This definition will make examining the more complete issue simpler.

$${\text{Input}}\,{\text{sensitivity}}\,{\text{score}} = \frac{{{\text{change}}\,{\text{in}}\,{\text{output}}}}{{{\text{change}}\,{\text{in}}\,{\text{input}}}}.$$

(17)

Fig. 7

Input sensitivity analysis of FDSS-ERA and comparative models

After analyzing the data, it has become evident that the FDSS-ERA version has greater input sensitivity scores than the alternative model across various input variables. This is the case regardless of the type of input variable being considered. For example, when assessing the responsiveness to economic indicators, FDSS-ERA scores 92%, surpassing its closest competitor, FPN, which receives a score of 85%. Even though the FDSS-ERA model exhibits a consistent pattern across all input variables, it may be susceptible to swings in economic indicators, demographic shifts, regulatory adjustments, and skill mismatches. There is a possibility that fuzzy logic standards are responsible for the increased sensitivity of the FDSS-ERA model. These standards make it easier to analyze problematic fluctuations and refinements within the information about the occupation market. Through the use of decision support techniques inside the framework of the FDSS-ERA model, the capacity of the model to analyze the impact of various input variables on the outcomes of the labor market is effectively expanded.

The input sensitivity rating measures the model’s responsiveness to input variables. It is defined by the ratio of trade in output to change in enter, as shown in Eq. (17). Figure 7 indicates the consequences of the Input Sensitivity analysis, which compares FDSS-ERA to different models regarding numerous input variables.

The performance evaluation of the FDSS-ERA model in terms of its ability to handle noisy information across a wide range of risk categories, such as financial, environmental, social, and political aspects, is shown in Fig. 8. The FDSS-ERA model is compared to similar models, including the FRBS, FPN, and MCDM for this assessment. When the data are considered, it is possible to conclude that the FDSS-ERA model performs better than the others across all risk classes. This emphasizes the model’s better power to cope with instances of noisy data, which enables us to develop the robustness score as a measure that analyzes the model’s potential to retain its performance when presented with noisy data, as indicated in Eq. (18). In addition, this measure ensures that the model can handle instances of noisy data. As a result, we shall gain more comprehensive knowledge of these findings.

$${\text{Robustness}}\,{\text{score}} = \frac{{{\text{model}}\,{\text{performance}}\,{\text{with}}\,{\text{noisy}}\,{\text{data}}}}{{{\text{model}}\,{\text{performance}}\,{\text{with}}\,{\text{clean}}\,{\text{data}}}} \times 100\% .$$

(18)

Fig. 8

Robustness to noisy data analysis of FDSS-ERA and comparative models

Equation (18) describes a model’s robustness to noisy information as its performance relative to clean information when dealing with noisy information. The robustness analysis compares FDSS-ERA with other models across various risk types (Fig. 8).

The data indicate that the FDSS-ERA model generates greater robustness scores across the board for all risk categories. Upon analyzing the results, this was discovered. Regarding economic risk, the FDSS-ERA has a competency level of 90%, greater than the scores of FRBS, FPN, and MCDM, respectively, 85%, 80%, and 78%. As a result of the fact that the trend described above is consistent across all risk categories, it can be deduced that the FDSS-ERA model has a stronger resilience in noisy data across the board for all aspects of risk assessment. The enhanced robustness of the FDSS-ERA model might be attributed to the incorporation of fuzzy logic notions. Because of this inclusion, the model can handle the uncertainties and imprecisions associated with the data about the labor market. Furthermore, decision support mechanisms inside the FDSS-ERA model assist the model in becoming more robust by including strategies to eliminate unneeded data, gain excellent insights from noisy datasets, and help the model become more resilient.

Stakeholder satisfaction: Evaluating the efficacy of the FDSS-ERA method in enhancing labor market chance assessment is based closely on stakeholder satisfaction. It entails talking with stakeholders to decide what they think about the model and how it may help decision-making. Several strategies exist for quantifying this feedback, together with polls, interviews, or ratings according to installed standards. Using the Likert scale is a standard method of measuring stakeholder pleasure. Using Likert scales, stakeholders can indicate how much they agree or are satisfied with particular claims or queries concerning the model’s performance.

Higher values on the Likert scale, generally from 1 to 5 or 1 to 7, imply more agreement or satisfaction. Collecting and analyzing all stakeholder replies is first-rate to get an experience of how satisfied anybody is. Qualitative techniques, awareness businesses, or interviews are other ways to gauge stakeholder satisfaction. These methodologies allow stakeholders to give particular, in-depth feedback on the model’s deserves, shortcomings, and regions for improvement. Thematic and content material analysis are two techniques for studying qualitative statistics that can help display recurring thoughts and styles in stakeholders’ replies.

Figure 9 is a visible depiction of satisfaction profiles that can be used to enhance the presentation with user-friendly images. Net promoter score (NPS) is one metric that can quantify stakeholder satisfaction; others consist of Likert scales and qualitative methods. By aggregating stakeholder satisfaction and advocacy into a single score, NPS determines whether stakeholders will recommend the model to others. Measuring stakeholder satisfaction is essential to information on the FDSS-ERA model’s realistic results and locating methods to improve its capability for manual labor market decisions.

Fig. 9

Stakeholder satisfaction with the suggested FDSS-ERA model

4.1 Comparative Study

This section compares the obtained outcomes, inspecting the performance of the FDSS-ERA model versus other comparative models. The objective is to determine the effectiveness of the FDSS-ERA model in dealing with the complicated difficulties associated with labor market risk evaluation. Choosing appropriate assessment metrics and similar models is vital to comprehensively assessing the strengths and boundaries of the FDSS-ERA model.

To assess the risks associated with the labor market, the FDSS-ERA model is an all-encompassing tool that provides a complete view of capacity hazards. To provide decision-makers with a holistic perspective, the risk assessment coverage of the device assigns a numerical value to the proportion of identified hazards compared to the total number of potential risks. Because of this, the reliability of risk assessments is improved by the uncertainty management measure of the model, which evaluates the model’s capacity to effectively deal with the uncertainty inherent in the data about the labor market. By evaluating the model’s capability to provide decision-makers with reasonable insights and suggestions, the decision-making supportiveness measure determines whether the model can allow stakeholders to make well-informed decisions. It is guaranteed that the model will continue to be relevant and effective in changing circumstances because of its flexibility to changes in the setting and adjustments in economic characteristics and dynamics within the group of workers. To make plans and technique systems that are effective over the long term, it is of the utmost necessity to have accurate long-term period preparations. Given that it allows for selecting factors that have a major influence on the outcome, it considers the sensitivity of the components, which helps prioritize interventions and allocate resources. It is ensured that the model’s performance remains constant even when the statistics are not perfect by evaluating its ability to deal with disorganized data.

Because it employs fuzzy logic procedures comparable to those used in FDSS-ERA, FRBS emerges as the favored alternative. The purpose of this model is to serve as a trend for measuring the efficacy of FDSS-ERA when it comes to dealing with data on the labor market that are fuzzy and unclear. FPN offers an approach that is mostly based on fuzzy logic as an opportunity method. When determining the level of risk associated with fuzzy models, this method can provide significant insights into the overall performance of various fuzzy models. In addition to demonstrating that there is potential for enhancement in terms of capabilities, the comparison to FPN demonstrates that the efficacy of FDSS-ERA has been verified. Multi-criteria decision-making (MCDM) models show an opportunity point of view. These models emphasize decision-making techniques and the compromises that may be made between a couple of principles. A comparison of the two frameworks may also be used to evaluate the applicability of FDSS-ERA and MCDM to help decision-making in complicated labor market scenarios. This can be done by comparing the two frameworks. To guarantee that the FDSS-ERA model follows the various requirements of stakeholders in the labor market and contributes to well-informed decision-making and sustainable development, it is possible to evaluate the model’s performance comprehensively. This is made possible by the widespread utilization of these comparative models and assessment metrics.

This section on effects and evaluation presents a complete examination of the usefulness of the FDSS-ERA model in measuring the risk of the labor market. There is also a discussion of the implications of this analysis. Regarding its insurance of chance evaluation, handling of uncertainty, stage of assistance for decision-making, capability to evolve to contextual shifts, accuracy in long-term forecasts, sensitivity to enter variables, and resilience to noisy information, it is abundantly clear that FDSS-ERA outperforms other models. This is the case in several different ways. This result was arrived at after an in-depth discussion and analysis of several different assessment measures and comparing models. When tackling the problematic elements of labor market dynamics and facilitating well-informed decision-making for sustainable growth, the outcomes described above give insight into the efficacy and flexibility of the FDSS-ERA model.

The following benchmark models are added to assess the efficacy and excellence of the FDSS-ERA model in handling labor market risk evaluation problems. The FDSS-ERA paradigm is comparable to the fuzzy rule-based systems (FRBS), a famous model that uses fuzzy logic ideas. It is a benchmark for measuring how well FDSS-ERA handles indistinct and unpredictable labor market data. Petri nets and fuzzy logic are combined inside the modeling technique called fuzzy Petri nets, which allows for the representation and evaluation of complex systems. It can be included if pertinent to the observer’s comparative framework, although it is not always referenced in the text. Models for multi-criteria decision-making (MCDM) consider a couple of criteria or goals while deciding. These models are regularly used in risk evaluation and decision assistance.

4.2 Limitations and Discussion

Making a significant addition to the field, this study examines the FDSS-ERA model to assess labor market hazards. Still, it is critical to note some caveats; further research is needed. The model’s performance may depend on the accuracy and thoroughness of the input data. Misleading or missing data could compromise the model’s predictive and risk assessment capabilities. This is why strengthening the model’s dependability requires a focus on improving data collection techniques and ensuring data integrity. Furthermore, the FDSS-ERA model may not be appropriate in all cases due to the dynamic nature of regulatory landscapes and labor markets. Economic changes, new technologies, and government policies might render the model’s predictions irrelevant or outdated as time passes. Monitoring and updating the version to keep it functional and ensure it represents the conversion circumstances is critical.

Similarly, those without specialized knowledge or technological scalability may find it challenging to adhere to one of the restrictions associated with the difficulty of implementing fuzzy logic principles. The model’s interface may be made more user-friendly and accessible to a wider audience to increase the likelihood of its adoption. While much of the research focuses on the Southeast Asian labor market, it is crucial to remember that different areas have different socio-economic conditions and labor market dynamics; thus, the results may not be applicable everywhere. It is suggested that more studies be conducted to examine the FDSS-ERA model’s flexibility in different settings and to confirm its effectiveness via cross-regional comparisons. It is critical to remove these constraints to optimize the usability and efficacy of the FDSS-ERA model, even if it shows the capacity to compare labor market risks. To make informed decisions in labor markets and advance the risk control area, it is vital to improve, verify, and modify the model continuously.

It is vital to point out how fuzzy logic principles can handle the uncertainty and complexity of the Southeast Asian labor market to justify the usage of FDSS for labor market risk assessment. Data and risk assessment in the Southeast Asian labor market are inherently uncertain because of the market’s diversified and ever-changing characteristics. Because of their strength in dealing with nebulous, obscure, or missing data, fuzzy logic ideas are ideal for simulating unknown and complex systems. Conventional binary decision-making models may oversimplify the complex structure of labor market risks. Because fuzzy logic can describe gradations of actuality, decisions may be made with greater delicacy and context awareness. Because the Southeast Asian labor market is risky, risk assessment methodologies are on the way to adjust to new circumstances quickly. Because of their malleability and flexibility, fuzzy decision structures are well suited for modeling and analyzing fluid systems. The FDSS-ERA mode and other fuzzy decision support structures use a multifaceted technique to assess risk by simultaneously considering various variables, scenarios, and uncertainties. This thorough analysis helps make well-informed decisions about the country of the labor market. According to historical studies, fuzzy logic-based total models are beneficial in several fields, which include risk assessment. The FDSS-ERA modelhas been used for  assessing risks in the labor market by demonstrating its performance, validation, and aggressive advantages.

5 Conclusion

To better understand the risks connected with the labor market in Southeast Asian nations, this study looked at how FDSS was developed and used to enhance the risk assessment model. The FDSS-ERA model provides a well-established framework for assessing many risk factors, including changes in demographics, skill shortages, regulatory frameworks, and employment trends. Fuzzy logic and decision support techniques are combined to form this framework. The main conclusions show that the FDSS-ERA model outperforms comparative models in many assessment metrics, including risk evaluation coverage, uncertainty management, decision-making assistance, adaptability to contextual modifications, sensitivity to input variables, and robustness to noisy data. The findings have shown that the FDSS-ERA model is flexible and effective and can provide detailed and useful information for making smart decisions in the job market. More research could be needed to improve the model’s data quality and integrity, better predict future outcomes by considering the dynamic nature of the labor market, and make it applicable to a wider range of geographic situations. Also, more people, including lawmakers, experts in the labor market, and industry participants, will be able to identify the model if we can make its interface easier to use and more accessible. We may enhance its predictive power and decision-making skills by exploring the potential integration of technologies like AI and device development into the FDSS-ERA model.

The article presents the novel FDSS-ERA model for assessing labor market risk in Southeast Asia and demonstrates its effectiveness compared to other models using various criteria. Contributing to sustainable development, its organized framework assesses different risk variables and helps with well-informed decision-making. Better analytical tools are available to decision-makers due to the model’s use of fuzzy logic concepts. Data quality enhancement, regional applicability, user interface simplification, and the incorporation of new technologies, including machine learning, should be the primary goals of future studies. Academic knowledge will be advanced, labor market difficulties will be addressed, and risk evaluation and decision-making will undergo additional developments due to these endeavors.