Fuzzy Performance Importance Index Research Articles

Environmental Supply Chain Performance Mapping of an Enterprise by Exploring a Novel Vague-Intellectual Approach

The authors notified the two momentous Research Gaps (RGs) via conducting the relevant literature survey. The authors found as first RG that there are still no mathematical models that could address the generalized trapezoidal fuzzy set (GTFs) based green supply chain performance measurement (GSCPM) multi-level hierarchical index for computing the performance of a production enterprise in % except in the forms of GTF set/scale/crisp value. Next, as the second research gap, the authors identified that a few research articles are published in the extent of degree of similarity approaches. Entire approaches are limited to recognize the weak metrics under assessment of two GTFN sets from experts and also not competent to measure the performance gap of metrics from its ideal value. The objective of research work is turned to overcome the identified two RGs. To fulfill the first RG, the authors first of all proposed the two GTFN set-based mathematical models, which are executed to compute the priority weights and appropriateness ratings (PWsaARs) for 1st level measures from 2nd level PWsaARs of metrics (discarded the requirement of PWsaARs data for 1st level measures from experts). Furthermore, the authors developed GTFN set-based novel fuzzy performance index (NFPI) approach (by combining the crisp as well as fuzzy percentage rule over FPI) to compute the performance in %. To address the second RG, the degree of similarity (DoS) approach is modified by introducing idea of negative and positive ideal solution into DoS (eliminate the need for assessment of two GTFN sets from experts). Next, modified DoS is applied over evaluated FPII (fuzzy performance importance index) to identify the weak and strong metrics and also quantify the GSCP gap of metrics from its ideal value. Eventually, the research work is demonstrated with empirical case research of an automobile parts manufacturing industry.

Development of an Assessment Model of Organizational Change Readiness by using Fuzzy Logic

The present research used a fuzzy logic approach to develop a model for assessing organizational change readiness. Based on an in-depth literature review, a conceptual model for change readiness was developed with four enablers, 13 criteria, and 51 attributes. To address the ambiguity in change readiness assessment, the fuzzy logic theory was used to calculate two core indexes: the fuzzy organizational change readiness index (FOCRI) and the fuzzy performance importance index (FPII). These indexes are computed to identify the level of organizational readiness for change and its weak attributes, therefore allowing managers to formulate a plan for the improvement of change readiness within their organizations.The steps to be followed for assessing change readiness are presented in this paper. Also, a case study was used to illustrate the application of this model in the case of a change project that aimed to implement a project management methodology within a Moroccan construction company.

Evaluation of sustainability performance of the rail freight transportation: An index-based analysis

The freight transportation industry must adopt sustainable practices to tackle environmental and social challenges. The existing literature considers sustainable performance measurement of the overall freight transportation industry, which left a research gap in developing a sustainable performance assessment framework comprising sustainability indicators precisely modelled for rail freight transportation. This sets a motivation for the existing research to measure the triple bottom line performance of rail freight business. This study proposes an index-based sustainable performance assessment framework using the spherical fuzzy analytic hierarchy process (SF-AHP) and fuzzy logic-based approach. Thirty-six sustainability indicators are identified using literature analysis and experts’ opinions. These indicators are divided into six sustainability measures and three triple bottom line dimensions. The SF-AHP method is used to evaluate the relative importance of these sustainability indicators, and the fuzzy logic is used to compute the performance of each indicator based on inputs collected from experts in linguistic terms. The proposed research framework is applied to the Indian Railways (IR) for validation. The result of this study indicates a sustainability index of 16.1141 for IR, which is close to “fairly high sustainable” performance. Further, the indicators that hinder sustainable performance are identified by evaluating the fuzzy performance importance index. Government policymakers and IR officials can focus more on these highlighted indicators to further enhance IR’s sustainable rail freight transportation.

Assessment framework for workforce agility in higher education institutions

PurposeThe higher education sector has been lately facing new challenges. The ease and speed with which the workforce adapts to these changes determine their agility level. This study attempts to develop a conceptual model to measure workforce agility in higher education.Design/methodology/approachAn extensive literature review revealed limited research on workforce agility assessment. This study addresses the research gap by developing a workforce agility assessment framework and testing the model in a higher education institution (HEI). The study applied the fuzzy logic method for the assessment.FindingsThe computed value of the fuzzy-workforce agility index (3.72, 5.27, 6.86) indicates that the HEI chosen for the assessment is Agile. The fuzzy performance importance index helped identify the six-weak attributes that require immediate attention and improvement.Practical implicationsThe proposed framework would help the institution's management understand the current agility level of their workforce and take corrective actions to improve the identified weak attributes.Originality/valueThe study has contributed to the literature of workforce agility assessment by creating a conceptual model for evaluating workforce agility in higher education. This model can be applied to any HEI.

New Lean Six Sigma readiness assessment model using fuzzy logic: Case study within clothing industry

The implementation of the Lean Six Sigma (LSS) in the textile and clothing industries is a successful strategy to reduce the defects produced along the production line in these industries. Lean Six Sigma readiness knowledge is a pre-requisite for a successful LSS implementation. Yet, the literature on Lean Six Sigma implementation and the readiness evaluation model in small and medium-sized textile and clothing are very limited. The purpose of this study is to develop a model of LSS readiness assessment for the clothing industries (LSRACI) using fuzzy logic. The present model is based largely on both the critical success factors (CSFs) of LSS and the LSS factors (enablers, criteria, and attributes) derived from the literature. The CSFs are identified using a questionnaire distributed to 85 small-medium clothing industries, and integration with the evaluation model. The outcome of this process is the identification of 5 enablers, 6 criteria, and 46 attributes which are finally ready to be used. The use of CSFs from the clothing industries gives a specification to our evaluation model and makes the LSRACI model original and specified to clothing SMEs. This model is developed for assessing the readiness level of the clothing and textile industries and it helps as well as to improve the readiness level for successful implementation of LSS. The present study aims to contribute to the knowledge of readiness for the LSS implementation in clothing industries. The readiness level of this company is average ready with (3.28; 4.9; 6.7). Using a fuzzy performance importance index (FPII), 20 from 46 attributes were identified as weaker attributes and therefore necessary corrective actions were recommended to improve the readiness level. The proposed model helps managers and practitioners to find out the potential of the clothing and textile industries by evaluating the company readiness before implementation in order to overcome problems and achieve a successful LSS implementation.

Development of assessment framework for environmental sustainability in higher education institutions

PurposeUnsustainable actions have resulted in the deterioration of the environment. Higher education institutions are social entities responsible for developing future leaders who have the skills and abilities to solve societal challenges. This study aims to develop a conceptual assessment model of environmental sustainability in higher education and apply the model to test the environmental sustainability level of a higher education institution.Design/methodology/approachThe fuzzy logic method is used to develop the conceptual model, and a higher education institution in India was selected for evaluating the environmental sustainability level.FindingsThe fuzzy environmental sustainability index for the higher education institution selected for the study was computed as (3.58, 4.95, 6.33). The computed value identifies the institution as “environmentally sustainable”. The calculation of the fuzzy performance importance index further helped in identifying weak attributes that need improvement.Research limitations/implicationsThe result of this study would help the management understand their current environmental sustainability level and take corrective measures to improve the weak attributes. It would also assist the institution in reworking its processes and practices.Originality/valueThis study has contributed to the literature of environmental sustainability assessment by creating a conceptual model for evaluating environmental sustainability in higher education. This model can be applied to any higher education institution.

Evaluation quality of service for internet of things based on fuzzy logic: a smart home case study

The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.

FUZZY-LOGIC-BASED ERGONOMIC ASSESSMENT IN AN AUTOMOTIVE INDUSTRY

The objective of this study was to develop and evaluate a tool for measuring an organisation’s ergonomic level. The study was carried out to fill a research gap in which no attempt had been made to design an index for evaluating a company’s ergonomics. An ergonomic index measuring model with 15 criteria using a fuzzy logic technique was developed during this study. The fuzzy ergonomic index (FEI), which represents the ergonomic level of the company, and the fuzzy performance importance index, which assists in resolving ergonomics barriers, were then calculated. The findings show that the model is capable of analysing ergonomics adequately and has practical application. Application of the methodology presented in this paper will reveal the measures necessary to improve an organisation’s ergonomic level. Keywords Fuzzy Ergonomic Index; Manufacturing; Fuzzy Logic; Ergonomic Assessment;

Assessment of healthcare organizational readiness for change: A fuzzy logic approach

The primary purpose of the paper is to identify the level of organisation's readiness for change in order to improve change in health organizations. The current research is focused to establish an evaluation model for organizational readiness. Fuzzy methods offer a streamlined and analytical environment, which enables to design, analyse or test models in a reasonable shorter time compared to other approaches. Development of conceptual framework through defining enablers, criteria and attributes based on literature review and expert opinion. Four enablers are defined for the development of a conceptual model along with 12 criteria and 38 attributes. Fuzzy logic approach is performed by two levels of analysis. In order to determine the organizational readiness level of the hospital, the Fuzzy Organizational Readiness Index (FORI) and Fuzzy Performance Importance Index (FPII) were calculated. The FORI can be estimated as the “Averaged Ready” (3.48, 5.21, 6.95) and FPII is calculated to identify eighteen weaker attributes that necessities management attention to prepare a hospital for the process of change. The suggested structure strengthens the ability of administrators to effectively incorporate improvements in their organizations.

Assessment of readiness level for implementing lean six sigma in healthcare organization using fuzzy logic approach

PurposeLean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting time by combing the principles of lean thinking with Six Sigma methodologies. To implement LSS successfully in healthcare organizations it is necessary to know the readiness level before starting the change process. Thus, the purpose of this paper is to assess the readiness level for the implementation of LSS in healthcare using a fuzzy logic approach.Design/methodology/approachThe current study uses a fuzzy logic approach to develop an assessment model for readiness to implement LSS. The conceptual model for readiness is developed with 5 enablers, 16 criteria and 48 attributes identified from the literature review. The current study does the study in a medium-size hospital from India.FindingsThe fuzzy readiness for implementation of LSS index (FRLSSI) and fuzzy performance importance index (FPII) are calculated to identify the readiness level for the implementation of LSS in the case hospital. The FRLSSI is computed as average ready with (3.30, 5.06 and 6.83) and the FPII computed helps to identify 15 weaker attributes from 48 attributes.Research limitations/implicationsThe current study uses only one hospital for study. In the future, the model can be tested in many hospitals.Practical implicationsThe current study would be used by the managers of a healthcare organization to identify the readiness level of their organization to implement LSS. The proposed model is based on the identification of enablers, criteria and attributes to assess the readiness level of a healthcare organization and it helps to improve the readiness level to implement LSS effectively.Originality/valueThe present study contributes to the knowledge of readiness for the implementation of LSS in a healthcare organization. The conceptual model is developed for assessing the readiness level of a healthcare organization and it helps to improve the readiness level for successful implementation of LSS. Weaker attributes are identified and necessary corrective actions should be taken by the management to improve the readiness. The continuation of the assessment readiness model over a period of time would help to improve the readiness level of healthcare for the implementation of LSS.

Assessing the Readiness Level of Healthcare for Implementing Agility Using Fuzzy Logic Approach

An agile strategy in healthcare would authorize organizations to provide efficient, customized and standard service on time at an optimized cost. To implement agility, the management of the healthcare organization needs to know the mechanisms and how to make the whole organization ready for change process. Thus, the current study assesses the readiness level of healthcare utilizing fuzzy logic approach to implement agility. The conceptual readiness model is framed with five enablers, twenty criteria and fifty-six attributes. The proposed model is applied in to a case hospital to assess the readiness level. The Fuzzy readiness for the implementation of agility index (FRAI) and fuzzy performance importance index (FPII) are computed to determine the readiness level of the case hospital and its weaker attributes in the implementation of agility. FRAI is computed as average ready with (3.00, 4.78, 6.56), and FPII identifies 15 weaker attributes from 56 attributes. It constructs the conceptual model which analyses the current readiness of healthcare and weaker attributes are denoted and required corrective actions should be carried by the management of healthcare organization to improve the readiness. The continuation of assessment readiness model over a period of time would help to improve the readiness level of healthcare for agile implementation.

Assessment of environmental and social sustainability performance of the freight transportation industry: An index-based approach

The freight transportation activities having an impact on the freight logistics process. The environmental and social sustainability-related processes are poorly managed compared to the economic process. The quality of environmental and social sustainability (EnSoS) practices ought to be enhanced by measuring current sustainability practices, and at the same time, obstacles to sustainability have to be identified. Some of the previous work try to develop a sustainability framework, but less attention is paid to develop the EnSoS assessment framework for the freight transport industry. To bridge this research gap, this study develops an EnSoS framework based on an integrated multi-criteria decision-making (MCDM) method, i.e., fuzzy best-worst method (FBWM) and fuzzy logic approach. The proposed framework evaluates the current sustainability performance with an index and highlights the obstacles to the sustainable freight transportation system. The proposed framework presents two sustainability enablers, nine dimensions, and sixty-three sustainability attributes, including ten new attributes. The FBWM method is used to compute the fuzzy importance weight of attributes, whereas the fuzzy logic is used to assess the performance of each attribute. The proposed framework is validated with the case of the Indian freight industry. The fuzzy transportation EnSoS index (FTEnSoSI) is computed as (3.511, 6.366, 8.418), and close to ‘very environmental and social sustainable (VESS)’ label. The fuzzy performance importance index (FPII) is computed to assess the performance of each attribute. Based on FPII value, thirty attributes are identified as obstacles to transport sustainability. The result analysis presented proposed policy measures for improving EnSoS obstacles.

A multi-criteria sustainability assessment approach for energy systems using sustainability triple bottom line attributes and linguistic preferences

Regarding the high level of pollution in energy industries, evaluating the current sustainability level in energy systems in developed/developing countries remains a challenge. A sustainable energy system has to proceed simultaneously social, economic and environmental practices with the goal of enhancing the sustainability level in associated systems. The evaluation of sustainability plays an active role in implementation of short- and long-term sustainability practices. This study proposes a novel framework as a benchmark for evaluations of sustainability in energy systems by introducing sixty-five key attributes and thirteen criteria for three pillars of sustainability. Due to the existence of incomplete information and uncertainty in evaluations of sustainability, the linguistic terms are used for expressing the condition of weight and performance rate for every single attribute based on expert judgment in accordance with fuzzy logic. Triangular fuzzy membership functions are also deployed to assign those linguistic judgments with appropriate fuzzy sets. The outputs of the proposed methodology involve a three-level fuzzy computational approach for obtaining the overall energy system’s sustainability index, using Euclidean distance approach for determination of sustainability level, a sieve algorithm for identifying the obstacles of sustainability using fuzzy performance importance index and finally enhancing the overall sustainability level with optimizing the identified weak points. An illustrative case is also resolved using proposed approach to elucidate the wide application of proposed framework in diverse energy systems/companies. The validation of approach is clarified by crisp calculations. The findings of this study will contribute policymakers and practitioners to take appropriate steps toward improvement of energy corporations.

Decision support system toward evaluation of resilient supplier

PurposeSupplier evaluation is a part of logistic management. In the present era, resilient supply chain performance (RSCP) assessment of the vendor enterprise is respected as a hot topic. The purpose of this paper is to enable the managers to map the performance in percentage system and also enabling managers for identifying the weak indices-metrics, which need to be improved up to ideal or standard level and strong indices-metrics.Design/methodology/approachThe authors found two research gaps via a literature survey. The first research gap revealed that the performance of a resilient supplier is computed solely in terms of a fuzzy mathematical scale. The articles are not yet published, which could measure the RSCP in percentage. The second research gap argued about the mitigation of the multi-level hierarchical resilient vendor/supplier evaluation framework for materializing RSCP and identifying weak and strong performing indices-metrics. To compensate the both research gaps, the authors developed a novel fuzzy gain-loss evolutionary computational approach to assess the performance of a firm in percentage. Next, a revised ranking technique coupled with trapezoidal fuzzy set based fuzzy performance importance index is implemented on the framework to seek weak and strong indices-metrics. The performance loss of each metric using the ideal solution concept considering the attitude of decision makers is also revealed.FindingsThe authors found the RSC performance of supplier firm 74 per cent, whereas performance loss 26 per cent, while actual performance is compared with standard fuzzy performance index (SFPI). Performance loss 26 per cent can be compensated by improving the performance of weak indices-metrics.Originality/valueThe novelty of the paper is that the authors used the ideal solution concept to compute the SFPI and compare it with actual FPI for evaluating the gain and loss of resilient supplier firm in percentage and identify weak and strong indices so that managers can improve the performance of weak indices. The work possesses the significant for all organizations, as research work enables the managers to map and improve the RSC performance of any vendor firm in future. The presented work considers the case of an automobile parts supplier industry to validate the developed approach.

Developing an integrated index to assess social sustainability in construction industry using fuzzy logic

A sustainable construction ought to enhance the quality of social, economic and environmental practices by determining the current sustainability level and identifying the weak points and consequently improving them. Even though so many studies have been carried out the context of economic and environments in construction projects, less attention was always paid to the social aspect of the construction projects. In this article, a novel construction social sustainability performance evaluation is presented based on fuzzy logic to evaluate the current social sustainability status in associated construction. This study originally adds to the state-of-the-art literature on sustainable construction by articulating a proposal and applications of a new as fuzzy construction social sustainability index. Critical social sustainability attributes are collected from the literature review and expert's judgments to address main dimensions and enablers of construction projects. The proposed index is validated by both triangular fuzzy and crisp approaches, and the results showed that all approaches lead to the same conclusions. Fuzzy performance importance index is then obtained and ranked for all attributes to identify obstacles and challenges behind the social sustainable construction concept. The scientific value of the proposed model is determined by its proper contribution in computing the new index with trapezoidal fuzzy principles and identifying the obstacles of associated construction.

Assessment of Lean Six Sigma Readiness (LESIRE) for manufacturing industries using fuzzy logic

Purpose Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue. Design/methodology/approach This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review. Findings To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE. Research limitations/implications The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process. Practical implications The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment. Originality/value The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.

Fuzzy multi-objective economic-statistical design of X-bar control chart

Nowadays manufacturing organisations are aiming towards producing higher quality outputs at lower cost of production to keep stride with the competition. The control charts are the most popular and widely used statistical process control tools that can be used for monitoring the unevenness in quality characteristics of a production process. The economic-statistical design of control chart selects three parameters, namely sample size n, sampling interval h and control limit width k to achieve the lowest cost with desirable statistical properties. However, the cost function and statistical properties are interrelated. Hence, minimising the cost as the only objective is not an efficient method of designing control charts. In this paper, a multi-objective model for the economic-statistical design of X-bar control chart has been proposed with three objectives, i.e., to minimise mean hourly cost, maximise in-control average run length and maximise the power of detecting process shift. A multi-objective evolutionary genetic algorithm is used to obtain the Pareto-optimal solution for this design which is then ranked by fuzzy performance importance index. The performance of the proposed method is compared through a numerical example and observed to be superior to that reported in the literature.

Fuzzy multi-objective economic-statistical design of X-bar control chart

Nowadays manufacturing organisations are aiming towards producing higher quality outputs at lower cost of production to keep stride with the competition. The control charts are the most popular and widely used statistical process control tools that can be used for monitoring the unevenness in quality characteristics of a production process. The economic-statistical design of control chart selects three parameters, namely sample size n, sampling interval h and control limit width k to achieve the lowest cost with desirable statistical properties. However, the cost function and statistical properties are interrelated. Hence, minimising the cost as the only objective is not an efficient method of designing control charts. In this paper, a multi-objective model for the economic-statistical design of X-bar control chart has been proposed with three objectives, i.e., to minimise mean hourly cost, maximise in-control average run length and maximise the power of detecting process shift. A multi-objective evolutionary genetic algorithm is used to obtain the Pareto-optimal solution for this design which is then ranked by fuzzy performance importance index. The performance of the proposed method is compared through a numerical example and observed to be superior to that reported in the literature.

Green supply chain management assessment under chains of uncertain indices

PurposeAround the world, protecting environment and purchasing green products by the manufacturing firms progressively becomes a popular and important issue. Manufacturers are realizing the importance of producing green products under green practices. This study aims to propose an appraisement platform to evaluate the overall performance index of a firm under green practices. Furthermore, the study also helps in identifying ill-performing areas, which necessarily require future attention to augment green supply chain (GSC) of a firm. A case research is conducted to assess the real-life application by the proposed approach.Design/methodology/approachThe authors used fuzzy performance index to measure the overall performance index of a firm. Beside this, they proposed a degree of similarity approach amalgamated with fuzzy performance importance index to classify the ills and strong indices in GSC extent.FindingThe intermittent assessment of green practices and their metrics in the organizational supply chain management (SCM) is indeed necessary. The present study provides an appraisement module to assess overall GSC fuzzy performance index and also helps in identifying the ill-performing areas which require future augmentation toward successful green implementation.Originality/valueThe exposed research work dealt with chains of subjective indices (measure and their interrelated metrics), which are induced into hierarchical appraisement module. To tackle the uncertainty of indices, the subjective indices are transposed into interval-valued fuzzy number set (IVFNS), as IVFNs are preferred to undertake the uncertainty of GSC indices. The proposed approach is demonstrated with a case research to justify its validity and originality.

On evaluation of supply chain’s ecosilient (g-resilient) performance index

PurposeRecently, in turbulent and highly competitive marketplace, organizational sustainability in the long run necessitates the adaptation of appropriate supply chain (SC) strategies. Hence, traditional SC philosophies are being restructured nowadays to fulfill different business goals. Articulation of lean, agile, green and resilient SC strategies could amply be found in the literature; however, integration of those in various modes may definitely improve overall SC’s performance. Past researchers have focused on the integration of lean, agile and green paradigms together to ensure an efficient SC construct. But the integration of green and resilient paradigm has been rarely reported in the literature. To deal with the unexpected situations/disturbances in the SC management along with embedded environmental consciousness, the purpose of this paper is to integrate the resilient SC and green SC philosophies; thereof to evaluate of an overall SC “g-resilient”/“ecosilient” index for a case automotive company.Design/methodology/approachA consolidated list consisting of supply chain practices (combining green and resilient performance indices) have been articulated in this study. A decision-making group has been assumed; where, the role of the decision makers is to provide individuals’ judgment (subjective opinion) toward determining the weight and the rating (performance extent) of various performance indices. The overall g-resilient SC performance has been determined by computing a unique ecosilient (g-resilient) index. The concepts of fuzzy performance importance index along with Degree of Similarity (DOS) adapted from fuzzy set theory (FST) have been applied to rank various performance indicators. In addition to that, the interrelationships amongst various g-resilient indices (performance indicators) have also been established through interpretive structural modeling.FindingsBy exploring the concept of fuzzy DOS, outlined in the trapezoidal fuzzy numbers set theory, various SC performance indicators have been classified into three distinct performance categories/levels (namely regretful, tolerable, and satisfactory). Such categorization has been found helpful in order to determine ill (poor) performing SC areas, which need future improvement toward boosting up the overall g-resilient index of the company’s SC.Originality/valueThe study bears significant managerial implications. The decision support framework suggested in this paper is found capable enough to determine a unique index known as “ecosilient (g-resilient) index” toward exploring “greenness” as well as “resiliency” for the case automotive company. Application potential of the proposed ecosilient (g-resilient) index evaluation system has been explored in this reporting. The recommended framework enables the managers to cope up with unexpected disruptions and found helpful in order to reduce the environmental impacts.