A stochastic fuzzy multi-criteria group decision-making for sustainable vendor selection in Indian petroleum refining sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Akhtar ◽  
Md Tanweer Ahmad
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Petroleum Refining ◽  
Petroleum Industry ◽  
Stochastic Approach ◽  
Spare Parts ◽  
Confidence Limits ◽  
Content Type ◽  
Significance Level ◽  
Petroleum Refineries

PurposeThis paper aims to select key criteria for sustainable vendor assessment and spare-parts supplies in the Indian petroleum refining sector using stochastic fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS).Design/methodology/approachThe criteria for sustainable vendor evaluation and selection are identified from the review of the literature and further; it is finalized using the Delphi method. Eight supply chain (SC) experts from the Indian petro refining sector were identified as having more than five years of experience and agreed to participate in this study (known as decision-makers (DM)). Five vendors supplying spare-parts are shortlisted from the market with the discussion and consent of procurement experts from petroleum refineries. Subsequently, criteria and vendors are rated based on relative importance in linguistic terms from the group of eight DMs. As ratings involve uncertainties in the decision-making, the SFTOPSIS method is applied to determine criteria weight and vendor ranking at a distinct significance level (α). The ranking of the vendors is obtained for sustainable supply of spare-parts in the Indian petro refining sector using the SFTOPSIS method.FindingsThe ranking of sustainable vendors is obtained through the integrated application of the fuzzy and stochastic approach to capture the uncertainties in the ratings of DMs. The sensitivity analysis is carried out at distinct confidence limits of a normal distribution to obtain a robust ranking of the vendors. In this paper, a case application of SFTOPSIS in the Indian petro refining sector is presented in which key criteria and the vendor ranking are found to be changing with confidence limit for sustainable vendor evaluation.Practical implicationsThe fuzziness and randomness in relative ratings collects from a group of DMs are taken in the proposed methodology. The distinct approaches are compared with changing significance-level under stochastic, fuzzy and deterministic TOPSIS to acquire robustness in the ranking. The proposed SFTOPSIS model can be useful to practitioners from the petroleum sector.Originality/valueThe originality of the paper contributes to an application of the SFTOPSIS method that is the extension of FTOPSIS in the petro refining sector of a developing country. The sensitivity analysis with distinct significance-level shows the uncertainties in the collected ratings from the DMs that supports robustness in the ranking. It might be helpful for SC professionals from the petro refining sector, who assess the rank of the vendors at different confidence limits for sustainable supply of spare-parts. Further research in the petroleum industry from emerging economies needs to be undertaken to broaden its scope and applicability.

Dynamic supplier selection approach for mining equipment company

2019 ◽  
Vol 14 (1) ◽  
pp. 77-105 ◽  
Author(s):  
Md. Tanweer Ahmad ◽  
Sandeep Mondal
Keyword(s):  
Sensitivity Analysis ◽  
Supplier Selection ◽  
Business Environment ◽  
Spare Parts ◽  
Mixed Integer ◽  
Optimal Order ◽  
Mining Equipment ◽  
Order Allocation ◽  
Managerial Implication ◽  
Content Type

PurposeThis paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.Design/methodology/approachAn integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.FindingsThe ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.Practical implicationsThis paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.Originality/valueIt provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

A model for assessment of uncertainty in tacit knowledge acquisition

2018 ◽  
Vol 22 (2) ◽  
pp. 413-431 ◽  
Author(s):  
Peyman Akhavan ◽  
Ali Shahabipour ◽  
Reza Hosnavi
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Knowledge Management ◽  
Cognitive Maps ◽  
Bayesian Belief Networks ◽  
Uncertainty Assessment ◽  
Assessment Method ◽  
Fuzzy Cognitive Maps ◽  
Content Type ◽  
Accurate Knowledge

Purpose Expert systems have come to the forefront in the modeling of problems. One of the major problems facing the expert system designers is to develop an accurate knowledge base and a meaningful model of uncertainty associated with complex models. Decision-making is based on knowledge, and decision system support needs a knowledge base as well. An adequate knowledge acquisition (KA) process leads to accurate knowledge and improves the decision-making process. To manage the risk of a medical service (twin pregnancy in this case) a knowledge management system was created. The captured knowledge may be associated with an uncertainty. This study aims to introduce a method for evaluating the reliability of a tacit KA model. It assisted engineering managers in assessing and prioritizing risks. The study tried to use this method in risk management and new case in the health domain. Design/methodology/approach In this study, relevant variables were identified in the knowledge management literature reviews and the domain of expertise management. They are validated by a group of domain experts. Kendall’s W indicator was used to assess the degree of consensus. On the basis of combined cognitive maps, a cognitive network was constructed. Using Bayesian belief networks and fuzzy cognitive maps, an uncertainty assessment method of tacit KA was introduced. To help managers focus on major variables, a sensitivity analysis was conducted. Reliability of model was calculated for optimistic and pessimistic values. The applicability and efficacy of the proposed method were verified and validated with data from a medical university. Findings Results show that tacit KA uncertainty can be defined by independent variables, including environmental factors, personality and acquisition process factors. The reliability value shows the accuracy of the captured knowledge and the effectiveness of the acquisition process. The proposed uncertainty assessment method provides the reliability value of the acquisition model for knowledge engineers, so it can be used to implement the project and prevent failures in vital factors through necessary actions. If there is not a satisficed level of reliability, the KA project reliability can be improved by risk factors. The sensitivity analysis can help to select proper factors based on the resources. This approach mitigated some of the disadvantages of other risk evaluation methods. Originality/value The contribution of this study is to combine the uncertainty assessment with tacit KA based on fuzzy cognitive maps and the Bayesian belief networks approach. This approach used the capabilities of both narrative and computational approaches.

Multi-criteria decision-making toward supplier selection: exploration of PROMETHEE II method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nishant Agrawal
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
Case Studies ◽  
Supplier Selection ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Promethee Ii ◽  
Wide Range ◽  
Managerial Implications

PurposeSupplier Selection (SS) is one of the vital decisions frequently executed by numerous industries. In recent times, the number of suppliers has increased enormously depending on a wide range of criteria. A selection of suppliers is a sensitive process that may impact various supply chain activities. The purpose of this research is to explore an underutilized technique called PROMETHEE II method for SS.Design/methodology/approachVarious tools and techniques are available under multi-criteria decision-making tools, which sometimes creates confusion in researchers' minds regarding reliability. PROMETHEE II was the most prominent method for ranking all available alternatives that ultimately avoid decision-making errors. To execute this equal and unequal weights approach has been used with three case studies.FindingsIn this research, three case studies have been used and soved with the help of the PROMETHEE II approach. The study also provides fundamental insights into the supplier's ranking on different criteria using sensitivity analysis. Further, criteria were divided as per benefits and non-beneficial to get a robust result. The pros and cons of PROMETHEE II approaches are also highlighted compared to other MCDM tools in this study.Originality/valueMost of the SS research uses either AHP or TOPSIS as per existing literature. There are very few attempts highlighted in the literature that use PROMETHEE II for the SS problem with sensitivity analysis. The proposed method is probable to motivate decision-makers to consider using a more sophisticated method like PROMETHEE II in supplier evaluation processes. This study opens a new direction for the ranking of suppliers in the field of the supply chain. The study also bears significant practical as well as managerial implications.

Evaluation of product development partners using an integrated AHP-VIKOR model

Kybernetes ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 220-237 ◽  
Author(s):  
Gülçin Büyüközkan ◽  
Ali Görener
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Product Development ◽  
Evaluation Criteria ◽  
Partner Selection ◽  
Evaluation Framework ◽  
Selection Problem ◽  
Evaluation Procedure ◽  
Content Type ◽  
Criteria Weights

Purpose – Today, customers are generally perceived to be demanding higher quality and better performing products, in shorter and more predictable development cycle-times and at a lower cost. These market pressures drive firms to collaborate with possible partners in product development (PD) processes. However, the selection of a suitable partner for an effective PD is not an easy decision and is associated with complexity. The purpose of this paper is to propose an integrated multi-criteria decision-making (MCDM) approach to effectively evaluate PD partners. Design/methodology/approach – The proposed evaluation procedure consists of several steps. First, based on a literature review and expert validation, the strategic main and sub-criteria of the PD partner selection process that companies consider the most important are identified. After constructing the evaluation criteria hierarchy, the criteria weights are calculated by applying the Analytic Hierarchy Process (AHP) method. The VIKOR (a compromise ranking) method is used to obtain the final partner ranking results. A case study is given to demonstrate the potential of the methodology. In the last part of the study, a sensitivity analysis is performed to determine the influence of criteria weights on the decision making process. Findings – The PD partner evaluation model contains three main criteria, namely, partner, collaboration and PD-oriented criteria, with 13 sub-criteria. The market position, competency of the partner, compatibility, technical expertise and complementarity are found as the most considerable evaluation criteria for the ABC case company. Results of the sensitivity analysis from different cases demonstrate that the integrated AHP-VIKOR model is quite sensitive to the weights assigned to the evaluation criteria. This finding underlines the importance of forming a capable, qualified group of experts for the decision-making procedure. The results of the empirical study show that the proposed evaluation framework is practical for solving partner selection problems. Originality/value – Partner selection is critical to the success of a collaborative PD process. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers for suitable PD partner selection. In our knowledge, there exists no study in the literature that combines the established AHP VIKOR model for PD partner selection problem. This study can be useful to researchers to better understand PD partner selection problem theoretically, as well as to organizations in designing better satisfying PD partner evaluation systems.

scholarly journals AHP-based support tools for initial screening of manufacturing reshoring decisions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Movin Sequeira ◽  
Per Hilletofth ◽  
Anders Adlemo
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Support ◽  
Fuzzy Ahp ◽  
Decision Support Tools ◽  
Initial Screening ◽  
Content Type ◽  
Priority Weights ◽  
Support Tools ◽  
High Level

Purpose The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions. Design/methodology/approach Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools. Findings The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria. Research limitations/implications The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular. Practical implications This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions. Originality/value This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Selection of environmental-conscious sourcing: an empirical investigation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudipta Ghosh ◽  
Madhab Chandra Mandal ◽  
Amitava Ray
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Selection Process ◽  
Mathematical Tool ◽  
Decision Matrix ◽  
Chain Management ◽  
Content Type ◽  
Automobile Manufacturing

PurposeThe prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.Design/methodology/approachThe literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.FindingsThe result shows that “Total CO2 emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.Research limitations/implicationsThis research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.Practical implicationsThe methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.Originality/valueThe design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.

Extension of PROMETHEE for robot selection decision making

2016 ◽  
Vol 23 (4) ◽  
pp. 983-1014 ◽  
Author(s):  
Dilip Kumar Sen ◽  
Saurav Datta ◽  
S.S. Mahapatra
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Support ◽  
Selection Process ◽  
Subjective Evaluation ◽  
Preference Ranking ◽  
Data Set ◽  
Subjective Data ◽  
Content Type ◽  
Robot Selection

Purpose – Robot selection is basically a task of choosing appropriate robot among available alternatives with respect to some evaluation criteria. The task becomes much more complicated since apart from objective criteria a number of subjective criteria need to be evaluated simultaneously. Plenty of decision support systems have been well documented in existing literature which considers either objective or subjective data set; however, decision support module with simultaneous consideration of objective as well as subjective data has rarely been attempted before. The paper aims to discuss these issues. Design/methodology/approach – Motivated by this, present work exhibits application potential of preference ranking organization method for enrichment evaluations (extended to operate under fuzzy environment) to solve decision-making problems which encounter both objective as well as subjective evaluation data. Findings – An empirical case study has been demonstrated in the context of robot selection problem. Finally, a sensitivity analysis has been performed to make the robot selection process more robust. A trade-off between objective criteria measure and subjective criteria measure has been shown using sensitivity analysis. Originality/value – Robot selection has long been viewed as an important decision-making scenario in the industrial context. Appropriate robot selection helps in enhancing value of the product and thereby, results in increased profitability for the manufacturing industries. The proposed decision support system considering simultaneous exploration of subjective as well as objective database is rarely attempted before.

A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider

2020 ◽  
Vol 120 (9) ◽  
pp. 1635-1657
Author(s):  
Yichen Qin ◽  
Hoi-Lam Ma ◽  
Felix T.S. Chan ◽  
Waqar Ahmed Khan
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Inventory Management ◽  
Benders Decomposition ◽  
Spare Parts ◽  
Service Planning ◽  
Content Type ◽  
Benders Decomposition Algorithm ◽  
Problem Instances ◽  
The Mathematical Model

PurposeThis paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.Design/methodology/approachA stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.FindingsCompared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.Research limitations/implicationsThe results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.Practical implicationsExtending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.Originality/valueA novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

Selection of Khadi fabrics for optimal comfort properties using multi-criteria decision-making technique

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashis Mitra
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Multi Criteria Decision Making ◽  
Phase Sensitivity ◽  
Two Phase ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Hierarchy Process ◽  
Selection Of

Purpose Khadi fabrics are known for their unique comfort properties which are attributed to their unique structural and functional properties. For getting optimal comfort from a collection of available Khadi fabrics, further exploration is needed. Ranking the Khadi fabrics from a competitive lot for optimal comfort is a challenging job, which has not been addressed so far by any researcher. The purpose of this study is to present one such selection problem using the multi-criteria decision-making (MCDM) technique, a popular branch of operations research, which can handle almost any decision problem involving a finite number of alternatives and multiple decision criteria. Design/methodology/approach Two widely popular methods/exponents of MCDM, namely, analytic hierarchy process (AHP) and multiplicative analytic hierarchy process (MAHP) have been deployed in this study for ranking a competitive lot of 15 Khadi fabrics and selecting the best alternative for optimal summer comfort based on three comfort attributes, namely, drape coefficient, thermal insulation value and air permeability. Findings Both the approaches yield a similar ranking pattern with Spearman’s rank correlation coefficient of 0.9857, Khadi fabric K1 achieving Rank 1 (best in terms of optimal comfort) and sample K6 acquiring Rank 15 (worst choice). Two-phase sensitivity analyses were performed subsequently to demonstrate the stability of the two approaches: sensitivity analysis by changing weightage levels of the criteria and sensitivity analysis in dynamic decision conditions by changing the elements of the initial decision matrix. During sensitivity analyses, no occurrence of rank reversal is observed for the best and worst alternatives in either of the two approaches. This corroborates the robustness of the two models. Practical implications Khadi fabrics are widely acclaimed for their intrinsic comfort properties for both summer and winter. Although the popularity of Khadi fabrics is increasing day by day, this domain is under-researched, and hence, needs to be explored further. The present approach demonstrates how the MCDM technique can serve as a useful tool for ranking the available Khadi fabrics in terms of optimal comfort in summer. The same approach can be extended to other domains of the textile industry, in general, as well. Originality/value This study is the first-ever theoretical approach/research on the selection of Khadi fabrics for optimal summer comfort using the MCDM tool. Another novelty of the present study is that the efficacy of AHP and MAHP approaches, in this study, has been validated through a two-phase sensitivity analysis. This validation part has been ignored in most of the hitherto published applications of AHP and MAHP in other domains.

Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework

2019 ◽  
Vol 26 (6) ◽  
pp. 1823-1844 ◽  
Author(s):  
Sanjay Kumar ◽  
Abid Haleem ◽  
Sushil
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Assessment Tool ◽  
Practical Experience ◽  
Manufacturing Firms ◽  
Neutrosophic Sets ◽  
Firm Level ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Noise Factors

Purpose The purpose of this paper is to provide a framework for assessing the overall innovativeness of manufacturing firms using a multi-attribute group decision-making methodology. Design/methodology/approach This study identifies the indicators of firms’ innovativeness from the literature. The concept of neutrosophic numbers has been used to assign different importance weights to individual decision makers to account for the differences in their educational backgrounds and practical experience. An intuitionistic fuzzy based TOPSIS procedure is adapted for ranking the candidate firms based on their performance on identified criteria. The implementation of the proposed methodology is demonstrated through an explanatory example. Sensitivity analysis is carried out to judge the robustness of the proposed framework. Findings The proposed framework provides an efficient and reliable tool to subjectively evaluate and compare the innovativeness of manufacturing firms. The sensitivity analysis shows that the methodology is robust enough to absorb the noise factors/errors/variations, etc. Research limitations/implications Motivated by this work, future studies can consider developing an integrated innovativeness index for evaluation of innovativeness of manufacturing firms. The concept of interval valued intuitionistic fuzzy and neutrosophic sets can be utilized to reduce the margin of perceptual errors even further. Practical implications The study will provide the firms with a framework for benchmarking their innovative performance. The firms can analyze their current performance and reconfigure their resources and capabilities suitably to improve their competitive position. Originality/value This study is one of the few attempts that have been made to articulate a firm level innovativeness assessment tool for manufacturing firms operating in an industry sector. Advanced concepts of fuzzy and neutrosophic sets have been utilized to eliminate the chances of bias/perceptual errors that most often affect the quality of decisions in today’s dynamic and uncertain decision-making environment.

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