A Hybrid Multi-Criteria Approach for Evaluation and Selection of Sustainable Suppliers in the Avionics Industry of Pakistan

: Reliability and quality are the two ultimate objectives in the avionics industry. The risk of counterfeit electronics and the unavailability of screening facilities for 100% components are the most concerning areas in the supply chain of the avionics industry. Unlike most public procurement, the cost is not the only signiﬁcant criterion. Unbiased decision-making criteria to accommodate all the important factors without compromising on quality, reliability, and maintainability are essential for the evaluation and selection of sustainable suppliers. Therefore, this study proposes an unbiased decision methodology based on the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). In the ﬁrst phase, six main and twenty-one sub-criteria are selected from the literature and empirically validated by experts of the avionics industry. FAHP is used to evaluate the weight of the main criteria and sub-criteria. FTOPSIS is used to prioritize eight alternatives (suppliers) concerning their e ﬀ ectiveness and superiority in ﬁnding the best alternatives. The results of the FAHP reveal that traceability (T) is the most important criterion, followed by quality (Q), and cost (C), which rank as the second and third most signiﬁcant criteria. The results of the FTOPSIS rate supplier 8, supplier 2, and supplier 1 as the ﬁrst, second, and third most e ﬀ ective suppliers, respectively.


Introduction
Avionics is mostly used for flight-worthy electronics. All electronic and electrical assemblies or systems of flying objects are termed as avionics. The avionics industry contributes to the field of aviation (Boeing, AirBus, and now Comac by China, etc.), space exploration like National Aeronautics and Space Administration (NASA), China National Space Administration (CNSA), European Space Agency (ESA), Indian Space Research Organization (ISRO), and Space and Upper Atmosphere Research Commission (SUPPARCO), etc., and defense (fighter jets, helicopters, unmanned aerial vehicles (UAVs), missiles, rockets, etc.). Avionics functions as the brain of all such products and is responsible for reliable operations. Due to the application and safety requirements, all avionics products demand To address the study objective, we have developed the following research questions (RQs): RQ1: What are the criteria and sub-criteria for supplier selection reported in the literature? RQ2: What do the experts really think about the criteria and sub-criteria of supplier selection identified from the literature? RQ3: What would be the prioritization-based taxonomy of the identified criteria and sub-criteria? RQ4: How do we investigate the best supplier?
The following is the organization and sequence of this research work: Section 2 contains a research background; Section 3 presents the methodology; Section 4 presents the study findings, results and discussion Section 5 presents the conclusion of the study.

Research Background
This section provides a detailed research background on the supplier selection based on various important criteria and sub-criteria, since supplier selection is a strategic problem in any industry for its sustainable development. This section identifies the research gap for carrying out this research.

Supplier Selection Concept
Sustainable supplier selection is the process of finding suitable suppliers under given constraints and objectives who can provide desired quality products or services at a prescribed time frame without affecting the production cycle, quality, and quantity of products being manufactured. In the literature [18,19], the process consists of the following stages: a.
Initial screening of suppliers from a group consisting of a large number of suppliers to squeeze or shortlist the alternatives to avoid tedious calculations. b.
Finalize selection criteria for supplier selection, based on specific parameters like cost, quality, traceability, delivery, and agility. c.
Continuous evaluation and assessment of suppliers and the identification of sustainable suppliers.
Due to the high reliability of end products, it is necessary to carry out decision making at this point to avoid costly failures. Alikhani et al. [20] studied strategic supplier selection under sustainability and risk assessment. They developed the model for both risk-neutral and risk-averse decision-makers through the classification of suppliers by risk assessment. Memari et al. [21] employed a fuzzy TOPSIS approach to select a suitable supplier that satisfies both main and sub-criteria. Chamodrakas et al. [22] used fuzzy AHP to evaluate available information in the electronic market place to evaluate the suppliers without human biases. Settanni et al. [23] performed their research on the effectiveness of the product-service system (PSS) that is difficult to obtain from the analysis of field reliability data alone in the avionics industry. They further discussed long-term cooperation with suppliers regarding availability based contracts in the avionics industry. Wagner and Friedl [24] suggested supplier development strategies and their performance implication for product development and lifecycle. The idea is to work either on direct or indirect supplier development to facilitate high tech industrial setup like the avionics industry. Lintukangas et al. [25] discussed the role of supply management innovativeness and supplier orientation in firms' sustainability performance. Gören, H. [26] developed a decision framework for sustainable supplier selection and order allocation.

Applications of MCDM Used in Supplier Selection
MCDM is an effective approach to address the complex decision making problems regarding supplier selection and offer direction for a sustainable, cost-effective, agile, and reliable methodology for supplier selection in the avionics industry [18,21,27]. Several MCDM approaches exist, such as: AHP, ELimination Et Choice Translating Reality (ELECTRE), the Analytic Network Process (ANP [28], Data Envelopment Analysis (DEA) [29,30], Weighted Aggregated Sum-Product Assessment Sustainability 2020, 12, 4744 4 of 22 (WASPAS) [31], Additive Ratio Assessment (ARAS), the Best Worst Method (BWM), VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR), and TOPSIS method which can be used for supplier selection [32]. The FAHP is widely used in multi-criteria decision-making problems in order to mitigate human errors in dealing with linguistic values. A few examples of widely used FAHP applications include supplier selection in the automotive industry, supplier selection in the Iran steel industry using the AHP and TOPSIS approaches [9], quality of service, and catering evaluation of airlines [33,34], product or service performance evaluations [35], and finalizing alternative ranking in the energy sector [36,37]. Comparison of the FAHP and FTOPSIS methods of sustainable supplier selection shows that both methods are suitable for the problem of supplier selection; the FTOPSIS method is better suited to the problem of supplier selection for changing alternatives and criteria, as it provides more agility, while FAHP reduces the fuzziness in assigning weights to the different main criteria and sub-criteria. In the literature, there are various review studies which have used MCDM methods to evaluate the supplier selection problem.
Although each and every study has a different aim and objective to solve the decision problem, Table 1 presents the recent studies of supplier selection and evaluation problems in the context of various industries.  [56] It has been identified from the previous studies that MCDM methods are beneficial in identifying, analyzing, and prioritizing the supplier selection in the context of different industries. The purpose of Sustainability 2020, 12, 4744 5 of 22 each research or decision problem has been evaluated on multi-criteria analysis. However, this study contributes to the state of the art by proposing a research model based on FAHP and FTOPSIS to assess the supplier selection problem in the context of the avionic industry of Pakistan. This study aims to identify and assess the main criteria and sub-criteria for analyzing the suitable supplier using the FAHP method. Then, these identified criteria and sub-criteria are used in the FTOPSIS methodology to select the supplier for the avionic industry in Pakistan. This study shall assist industry managers and practitioners in adopting critical criteria and sub-criteria for supplier selection.

Research Methodology
The study aims (1) to identify the criteria and sub-criteria from the literature and validate them with experts from industry and academia using the questionnaire survey approach, (2) to prioritize the identified criteria and sub-criteria concerning to their significance for the sustainable selection by applying the step-by-step mechanism of fuzzy AHP, (3) and to prioritize the supplier section using the fuzzy TOPSIS approach. To address the objective of the research, the proposed methodology is presented in Figure 1 and briefly discussed in the subsequent section. suitable supplier using the FAHP method. Then, these identified criteria and sub-criteria are used in the FTOPSIS methodology to select the supplier for the avionic industry in Pakistan. This study shall assist industry managers and practitioners in adopting critical criteria and sub-criteria for supplier selection.

Research Methodology
The study aims (1) to identify the criteria and sub-criteria from the literature and validate them with experts from industry and academia using the questionnaire survey approach, (2) to prioritize the identified criteria and sub-criteria concerning to their significance for the sustainable selection by applying the step-by-step mechanism of fuzzy AHP, (3) and to prioritize the supplier section using the fuzzy TOPSIS approach. To address the objective of the research, the proposed methodology is presented in Figure 1 and briefly discussed in the subsequent section.   The existing literature related to the study objective was collected and reviewed to extract different criteria and sub-criteria for supplier selection in various industries and its relevancy with the objective of this research work. The collection of appropriate and valid literature is significant. Considering the suggestions of Chen et al., and by considering the research experiences of our research team, we reviewed "IEEE Xplore," "Science Direct," "MDPI Digital Library," "Springer Link," "Wiley Inter-Science," and "Google Scholar". The key terms used in the research questions were executed on the selected databases to explore the literature related to the study objective. The authors of this study and industry experts shortlisted the selection criteria and sub-criteria in order to remain concise in criteria selection and avoid irrelevant factors. The experts have 10+ years of experience in procurement and supply chains and are well aware of issues regarding our objective being the front end of problems of the industry. Finally, the selected criteria and sub-criteria were evaluated by industry experts and researchers with direct experience of the production, quality, and reliability of high-tech avionic products. The selected criteria and sub-criteria are briefly explained in Table 2.

Delivery (D)
Timely delivery D1 Timely delivery will help in the execution of the production. [11,13,23,64,67] Delayed Delivery D2 Delayed delivery can put production setup on hold.
Fail To Deliver D3 Failure to deliver means the whole process is nullified, and we have to restart from the beginning. High tech production delays are mostly linked with this issue Partial Delivery (D4) A partial order is delivered and a few parts remain pending for too long, which causes delays in production process.

Data Collection and Analysis of Main Criteria and Sub-Criteria
In the above phase, the criteria and sub-criteria selected from the literature review were identified and documented in Table 2. The steps adopted to conduct the empirical study are described here. The empirical evaluation of the criteria and sub-criteria identified during the literature study was conducted by developing a survey questionnaire. The questionnaire consists of both open and close-ended questions. The closed-ended section consists of the criteria and sub-criteria collected via a literature review study. In the open-ended section, we requested the survey participants to add the additional criteria which are not enlisted in the close-ended section. The identified criteria and sub-criteria were assessed against the 5-point Likert survey scale: ("strongly agree, agree, neutral, disagree, and strongly disagree"). It is essential to consider the neutral point in the survey evaluation scale because the absence of neutral options could force the respondents to give a biased answer.
In order to validate the questionnaire survey, we conducted a pilot study with the experts of the avionics industry of Pakistan and academia in the field of reliability (National University of Science and Technology, Islamabad, Pakistan, Institute of Space Technologies, Islamabad, Pakistan, Nanjing University of Aeronautics and Astronautics, Nanjing, China), and supply chain department of avionic and defense industry of Pakistan. They have broad research experience in the avionics and aviation field. The experts suggested some structural changes in the questionnaire. They suggested to add the question in tabular form and to put more queries about respondents' bibliographic data. We carefully revised the questionnaire, and the updated questionnaire was used for the data collection process. A sample of the survey questionnaire is given in Appendix A.

Expert Opinion (Pilot Evaluation of a Survey Questionnaire)
It is challenging to collect data from the potential population working in high tech industries as they usually avoid sharing such information. The population was approached via a personal relation of our research team and using the snowballing approach. The snowballing approach is an effective way to target the population related to the study objective [68,69]. The invitation letter, along with a questionnaire survey, was sent via email, and it requested them to share it with their colleagues. A total of 19 responses were collected. All collected responses were manually reviewed to check the incomplete entries. We found one incomplete response and 18 complete responses, which include: ten from industry, three from the supply chain, and five from academia. The number of respondents was limited, but their experience and first-hand knowledge of the problem helped in getting a positive response. We also assured the respondents that the collected data will just be used for research purpose, and their identity will remain confidential. The demographic details of the survey participants are provided in Appendix B.

Empirical Investigation and Analysis of Criteria and Sub-Criteria
The frequency analysis method was used to analyze the descriptive data. The frequency analysis approach is helpful in analyzing the ordinal and nominal data across variables or groups of variables. Therefore, a total of 6 criteria and 26 sub-criteria were identified, which will be carefully evaluated using a frequency analysis method to identify the most feasible and important criteria for analyzing the sustainable supplier selection of the avionic industry in Pakistan. The criteria vary according to the situation, the location of production setup, and the international obligations of each country. A developing country might be facing different constraints or restrictions in the supply of high-tech components than most developing countries. The selection criteria have become complicated due to the involvement of different factors, such as technology barriers, international political and social bindings, and end-user agreements, along with common factors such as quality, delivery, cost, and service. So, in supplier selection/evaluation, we have to consider the capability of the supplier to handle these issues, and it should be resourceful enough to provide all types of required equipment necessary for continuous improvement in customer satisfaction, which drives the search for new and better ways to evaluate and select suppliers [66]. Thus, the refined or most significant criteria and sub-criteria have been identified after careful assessment of survey respondents. The findings are presented in the results section.

Fuzzy AHP Method
The AHP was introduced by Thomas L. Saaty [70], and since then, it has been a useful and widely used tool of MCDM. It can solve MCDM problems by creating a functional hierarchy and quantifying decision-makers' priorities for a set of available alternatives or solutions [71]. AHP is used to develop preference weights for each alternative by assigning a numerical value, a language statement, or weights on a ratio scale from decision-makers' opinion or inclination towards a problem. We employ a consistency check to verify the weights allocated among different options or alternatives. We combine fuzzy set theory with AHP because traditional AHP has some limitations, like dealing with an unbalanced judgmental scale, handling ambiguity, and uncertainty associated with human biases and anonymous rankings. FAHP is a more powerful tool to deal with all limitations regarding uncertainties and human factors affecting decision-makers' feedback. Here we used FAHP by introducing fuzziness; the pairwise numeric operates in a matrix using triangular fuzzy number (TFN) to rank suppliers and finalize selection criteria in the avionics industry of Pakistan. FAHP is applied in the following steps [72]; Step 1: Develop the hierarchical structure of the supplier selection problem.
Step 2: Define the scale for the pairwise comparison matrix, as shown in Table 3.
Step 3: Construct the fuzzy performance/decision matrix and choose the appropriate linguistic variables for the alternatives concerning criteria.

Fuzzy TOPSIS Method
TOPSIS is the methodology of moving closer to a positive ideal solution (i.e., minimizing the gap between criteria) and moving away from a negative ideal solution (maximizing the gap in each criterion) [75]. This method is particularly suitable for solving the group decision-making, problem under fuzzy environment [37]. A combination of fuzzy mathematic with TOPSIS gives us FTOPSIS used to solve decision criteria problems having uncertainty, immeasurable, and incomplete information problem under a fuzzy environment [76]. The following are the main steps of the multi-person, multi-criteria decision-making process with fuzzy TOPSIS for dealing with the supplier selection [35]: Step 1: Choose the appropriate linguistic variables for the importance weight of selection criteria and the linguistic ratings for suppliers. Table 5 lists the linguistic variable scale. Very High (VH) (7,8,9) Step 2: Construct the fuzzy-decision matrix. Let X i = (x i1 , x i2 , x i3 ) be a TFN for i ∈ I. Subsequently, normalized the fuzzy number of each X i is indicated as: where i = 1, 2, 3, . . . , m and j = 1, 2, 3, n.
Step 3: Normalize the fuzzy-decision matrix. Normalization will consider both cost and benefit criteria separately as given below; Normalization for benefit (Positive) criteria: where x * 3 j = maxx 3ij (Benefit criteria). Normalization for cost (Negative) criteria: x − 1j = min x 1ij (Cost criteria).
Step 5: Calculate the distance of each supplier from FPIS (d + i ) and FNIS (d − i ), respectively.

Findings of the Main Criteria and Sub-Criteria
Supplier selection criteria vary from product to product and industry to industry. Key performance indicators for different industries are different depending upon their core business, nature of products, customers, market competition, and end-user requirements. Unlike the consumer goods and services industry, the requirements for the end-users are entirely different in the case of the avionics industry. In particular, the electronic part is considered as the brain of flying objects, and its failure can lead to the failure of the product or mission. In most industrial procurements, the main criteria include the cost, delivery, and continuous supply [74]. In contrast, for high-reliability end products the cost is less significant. Delivery is linked with the capability of the supplier. Traceability and quality are also the main criteria for performance evaluation and ranking of suppliers. From the literature survey and industrial experience, we have selected six criteria (cost, traceability, quality, after sales, risk, and delivery). These criteria are further divided into sub-criteria in order to profoundly investigate the impact of the criteria and sub-criteria of the objective study. A total of 26 sub-criteria have been selected from the literature that can affect our main criteria and objective function.

Empirical Investigation and Analysis (Survey Respondents/expert Opinion)
The questionnaire survey was developed based on the main criteria and sub-criteria identified through the literature review. The collected opinions from the survey participants were categorized as positive (strongly agree and agree), negative (strongly disagree and disagree), and neutral. Table 6 shows the analysis of the participant's opinion. The results presented in Table 6 indicate that the majority of the survey respondents agreed with the investigated main criteria and sub-criteria of supplier selection. We noted that the majority of criteria and sub-criteria contain ≥50% results. For a further analysis process, we consider the critical criteria and sub-criteria. According to Akbar et al. [77], a factor is said to be critical in the empirical study if it is cited by ≥50% of the experts. Only the criteria and sub-criteria with a frequency ≥50% were considered for further analysis. In the final analysis, the main criteria and sub-criteria are squeezed to 6 and 21, respectively. In the next section, these main criteria and sub-criteria are analyzed and ranked using the FAHP method.

Fuzzy AHP Results
Now, we apply the developed framework to solve this complex decision problem, which is a sustainable supplier selection criterion for a reliable supplier in the avionics industry of Pakistan. It has been an unsolved issue with no clear solution. However, experts from different departments (both technical and procurement) have gone through long sessions of brainstorming to finalize criteria that provide a fair chance and competition to every alternative without compromising the key features of the quality and reliability of components purchased.

Develop the Hierarchy Structure of Criteria and Sub-Criteria
The identified criteria and sub-criteria in Section 4.1 were categorized by experts into 6 main criteria and 21 sub-criteria. Based on the categorization and the results of the analytical survey in Table 6, we develop the hierarchy structure, as shown in Figure 2. The aim of the study is presented at the first level, the criteria and sub-criteria are presented at the second and third level, respectively. Level 4 is for the objective of research, which is the selection of sustainable suppliers using the fuzzy TOPSIS technique.

Fuzzy AHP Results
Now, we apply the developed framework to solve this complex decision problem, which is a sustainable supplier selection criterion for a reliable supplier in the avionics industry of Pakistan. It has been an unsolved issue with no clear solution. However, experts from different departments (both technical and procurement) have gone through long sessions of brainstorming to finalize criteria that provide a fair chance and competition to every alternative without compromising the key features of the quality and reliability of components purchased.

Develop the Hierarchy Structure of Criteria and Sub-Criteria
The identified criteria and sub-criteria in Section 4.1 were categorized by experts into 6 main criteria and 21 sub-criteria. Based on the categorization and the results of the analytical survey in Table 6, we develop the hierarchy structure, as shown in Figure 2. The aim of the study is presented at the first level, the criteria and sub-criteria are presented at the second and third level, respectively. Level 4 is for the objective of research, which is the selection of sustainable suppliers using the fuzzy TOPSIS technique.

Developing the Pairwise Comparison and Calculation of Priority Weights
The evaluation and ranking of a suitable supplier is a complicated problem from the decision-making perspective. However, this attempt has been made to address the complicated decisions in the context of Pakistani industries. Therefore, this section consists of the results and analysis of this study. The key aim of this study is to explore and prioritize the criteria, sub-criteria, and their alternative using FAHP and then rank suppliers using the FTOPSIS. Experts from industry and academia calculate FAHP weights. The same 18 aexperts were again requested to provide priority weights to different criteria and sub-criteria based on their experience. Using these priority weights, we perform a pairwise comparison. The collected responses from the FAHP survey participants been transformed in the form of a geometric mean, aiming to determine the pairwise comparison of the criteria and sub-criteria. To convert the feedback of survey respondents into TFNs, the geometric mean methods were adopted. To calculate the geometric mean, the following formula was considered: Geometric mean= [(x1) (x2) (x3) . . . . . . (xn)] 1/n x = "Individual weight of each judgement" n = "Number of feedbacks." The triangular fuzzy scales and their related linguistics variables are given in Table 4. To determine the priority weights of criteria and their corresponding sub-criteria, the pairwise comparison was performed and their evaluation matrices were developed [78].
The study will select the most reliable supplier and will rank it according to established criteria. It will encourage suppliers (alternatives) to focus more on services, quality, and reliability rather than just being cheaper. Here, we have established different criteria and sub-criteria for supplier selections. These criteria and sub-criteria were analyzed using the FAHP method. Then, FTOPSIS was used to assess and prioritize the suppliers based on assessed criteria and sub-criteria. The Fuzzy TOPSIS results are provided in Appendix D, and the fuzzy pairwise comparison matrix of the main criteria and further detailed stepwise analysis are provided in Appendix E.

The Main Criteria Results
We developed the decision support framework for ranking suppliers based on different criteria and sub-criteria. A geometric mean approach has been employed in group decision-making [79]. Therefore, the experts were asked to perform a pairwise comparison matrix of the criteria, and sub-criteria based on the importance scale and analysis results are shown in Table 7. The fuzzy pairwise comparison matrix of main criteria and further detailed stepwise analysis is presented in Appendix A. The determined priority weights indicated that traceability (T) is the highest-ranked criterion, with a weight of 0.3449 for the supplier selection process. The quality (Q) and cost (C) are declared as the second and third highest priority criteria, with a weight of 0.2473 and 0.1527, respectively. The risk (R) criterion are considered as the fourth most significant criteria with a weight of 0.1221. We also noted that the delivery (D) and after sales (AS), with a weight of 0.0882 and 0.0447, are declared as the least significant criteria for the effective supplier selection process.

Sub-Criteria Results
By applying the FAHP approach, the local and global weights of each sub-criterion are determined. The pairwise comparison matrix of sub-criteria are given in Appendix C. The local rank renders the priority order of a sub-criteria within their corresponding main criteria. For example, the main criterion, 'Cost (C)', has three sub-criteria, i.e., C1, C2, and C3. The results show that Price Variation (C1), with a weight of 0.4150, is ranked as the most significant sub-criterion in the cost (C) main criterion (Table 8, Figure 3). Similarly, the local ranking of each sub-criterion was determined with the aim to check the priority order of each sub-criterion within their main criteria. Consequently, the final ranking or global ranking was determined with the aim to check the impact of each sub-criterion for the overall study objective. The global rank was determined by multiplying the weight of sub-criteria with their respective criteria ( Figure 3, Table 8). For example, the global rank of C1 = 0.4150 × 0.1527 = 0.0634, and based on the ranking order, C1 is ranked as the seventh-highest priority sub-criterion (Table 8). By considering the same method, the global ranks of each sub-criterion were determined. Using the calculated local and global ranking, the prioritization-based taxonomy of the criteria and sub-criteria is developed, which will assist the practitioners and researcher in considering the most significant criteria and their respective sub-criteria with respect to their interest and working area. Overall weights, local and global ranks of criteria, and sub-criteria are shown in Figure 3.

Fuzzy TOPSIS Results
After the FAHP methodology for sustainable supplier selection using the criteria and sub-criteria, this section discusses the prioritization of eight suppliers using the fuzzy TOPSIS approach. The names of the supplier have not been mentioned in order to keep the secrecy of both supplier and avionics setup of Pakistan. The analysis by the group of experts in this study helped in constructing a fuzzy evaluation matrix into TFNs using linguistic variables. Thus, this study determined the evaluation matrix with respect to the alternatives. This followed the development of a fuzzy decision matrix, fuzzy normalized decision matrix, and weighted, normalized fuzzy decision matrix with regard to each factor in this study (see Appendix E). The ranking criteria and sub-criteria were subsequently assessed. Finally, the prioritized order of the eight suppliers has been obtained, as provided in Table 9.
Researchers have used ANP, AHP, and FAHP methodologies to evaluate and rank suppliers [22,77]. However, here we employed both FAHP and FTOPSIS for sustainable supplier selection in the avionics industry of Pakistan.

Fuzzy TOPSIS Results
After the FAHP methodology for sustainable supplier selection using the criteria and subcriteria, this section discusses the prioritization of eight suppliers using the fuzzy TOPSIS approach. The names of the supplier have not been mentioned in order to keep the secrecy of both supplier and avionics setup of Pakistan. The analysis by the group of experts in this study helped in constructing a fuzzy evaluation matrix into TFNs using linguistic variables. Thus, this study determined the evaluation matrix with respect to the alternatives. This followed the development of a fuzzy decision matrix, fuzzy normalized decision matrix, and weighted, normalized fuzzy decision matrix with regard to each factor in this study (see Appendix E). The ranking criteria and sub-criteria were subsequently assessed. Finally, the prioritized order of the eight suppliers has been obtained, as provided in Table 9. Researchers have used ANP, AHP, and FAHP methodologies to evaluate and rank suppliers [22,77]. However, here we employed both FAHP and FTOPSIS for sustainable supplier selection in the avionics industry of Pakistan.   From Table 9, we conclude that the final ranking of supplier is Supplier 8 > Supplier 2 > Supplier 1 > Supplier 5 > Supplier 3 > Supplier 4 > Supplier 6 > Supplier 7. All experts' opinions and criteria finally provide us with this ranking. The ranking is not based on a single criterion, but it has considered all criteria and sub-criteria. For any further investigation of supplier performance evaluation or selection, we can jointly study these rankings with our required criteria and sub-criteria. We may select different suppliers relative to the requirement of supplies. This is an overall ranking for a list of suppliers evaluated under the required main and sub-criteria. For some particular cases, a supplier may be selected by ignoring the overall ranking. For example, if a supplier is an authorized dealer of some particular brand, even it falls low in the criteria, in that particular case, he is the optimum choice.

Sensitivity Analysis
The sensitivity analysis is undertaken to evaluate the robustness of the obtained results in this study; for instance, to analyze how the new ranking of alternatives evolves while the criteria weights are varied. In this context, 14 cases were developed and evaluated by varying the weights to measure the final result/priority of the suppliers. The varying weights of criteria under these 14 cases are presented in Table 10. The actual weights of the criteria remained the same in case-1, while 13 other cases were analyzed by changing the weights under the sensitivity analysis. It is evident that in the majority of the cases, the criteria weights remained the same. Finally, the ranking of the suppliers based on 14 cases of sensitivity analysis is given in Table 11 and Figure 4. Supplier-8, supplier-2, and supplier-1 remained the important suppliers in the avionic industry of Pakistan. It is found in these analyses that the ranking of the priority order of the suppliers remained the same in most cases. Table 10. Sensitivity analysis.

Results Summary
The results give a complete methodology for decision making by removing human error and biases for high-tech applications. The above study can be used in the military and avionics industries

Results Summary
The results give a complete methodology for decision making by removing human error and biases for high-tech applications. The above study can be used in the military and avionics industries of developing countries. The study contributed by providing the state-of-the-art criteria and sub-criteria reported in the literature and the insight of the experts. These in-depth reviews and empirical investigations provide a vast knowledge of the criteria and sub-criteria that need to be focused on by the industry experts and researchers. In addition, the FAHP analysis provides the rank order of the criteria and their respective sub-criteria that will assist the practitioners and researchers in considering the most significant criteria concern to their working area and interest. The fuzzy TOPSIS analysis provides a rational bias-free scientific approach for supplier selection, which can further be refined by data logging about supplier performance for the next few years. The results provide a new criterion which incorporates more important factors for product reliability along with the cost. The continuous learning process and availability of traceable record will further refine the alternatives as a system of continuous performance monitoring is established.

Conclusions
The avionics industry of Pakistan entirely relies on the sustainable supply of electronic components from different suppliers. As the reliability of the product primarily depends upon the authenticity of the source, the supplier and product lifecycle support are linked to the sustainability of the supply chain and supplier. Thus, concerns about the effectiveness and rationality of the critical task of sustainable supplier selection for the purchase of reliable and original electronic components have become central to the decision-makers of the avionics industry of Pakistan. The study originated from the inevitable failures of products that lead to carrying out root cause analysis. Finally, it was revealed that failure occurred due to low quality and counterfeit electronic components. This raised questions on procurement procedures for the industry, which were relying mostly on cost and pre-qualification of registered suppliers. The unavailability of parts for lifecycle support exposed the sustainability of the supply chain and the presence of opportunist suppliers.
This study proposes a novel method for addressing supplier selection issues based on sustainability and reliability rather than cost. Due to human bias and links of suppliers, it was decided to build decision criteria based on feedback from technical experts. To make it unbiased, handle inconsistencies, and make it error-prone, we used the FAHP and FTOPSIS techniques. From the analysis of initial inputs from experts and decision-makers, it was concluded with results and discussion that the newly developed criteria give more weight to reliability and quality instead of cost and deliveries. The methodology is flexible and can be used for continuously upgrading and evaluating the alternatives (suppliers); it can also adopt any additional criteria and sub-criteria in the real-time environment. This study about the Pakistan avionics industry is carried out for the first time; the study can accommodate more parameters for selection criteria.
The study has covered almost all types of criteria explored from the literature or by experts. Nevertheless, it can identify more criteria, or it can be modified to address other similar issues. For future work, other MCDM methods can also be used, such as VIKOR, ANP, ELECTRE, and DEMATEL, to compare the results of the current study and determine the feasibility of other MCDM methods. Since every MCDM method has its own functions and steps to perform and solve the decision-problem. Further, for uncertainties and missing data, we can combine our methodology with gray prediction theory to eliminate the uncertainty and obtain more reliable results. Due to the limited number of suppliers, we can work on sustainable supplier development tools and criteria in the future for the better support of the avionics industry.