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The application of automated systems is rapidly increasing in different industries and organizations. In this regard, computerized maintenance management systems (CMMS) using information technology play an important role in the automating production systems. The purpose of this paper is to investigate the impacts of CMMSs and relevant supportive organizational factors on the effectiveness of total productive maintenance.

This study is classified as a quantitative survey-based research using structural equation modeling. The scope of the study includes manufacturing companies in Iran. A total of 125 questionnaires from 60 companies were collected from January to March 2014 to help validate the conceptual model and test the hypotheses.

The results support the concept CMMSs positively relates to relevant supportive organizational factors (resource allocation, decision-making structure, senior management support, employees’ involvement and effective instruction) on the effectiveness of total productive maintenance. The relevant supportive organizational factors can also be seen as the predictors of CMMSs.

This study integrates the CMMSs and relevant supportive organizational factors in a robust model to examine the effectiveness of total productive maintenance. This study also examines the impacts of CMMSs and relevant supportive organizational factors on total productive maintenance which seems to not be done previously.

This paper aims to provide a quantitative basis to analytically determine the ranking of the brand personality of Adidas, Asics, Nike, Puma and Saucony brands among Iranian customers via a conventional multi-criteria decision-making (MCDM) method.

Data for determining the importance of evaluation criteria and ranking of brands are gathered by means of distributing questionnaires among a group of Iranian customers of sport shoes, as well as some industrial experts. The fuzzy analytic network process (FANP) was used to rank the brands with regard to dependencies between criteria and alternatives.

The results indicate that FANP is a capable method which provides invaluable insights for strategic marketing decisions in the sport product industry. Results show Adidas has the best performance in the sports shoe market compared to the other four brands. In this study, it was found that expertise sophistication was the most important criterion among Aaker’s five main criteria.

The value of this paper is applying FANP decision-making method for ranking sport shoe brands. This method has not been commonly used in the area of marketing, hence it is added to the pool of techniques used in ranking brands. In addition, evaluation and ranking of brands can be very useful for both academic research and practice. Researchers can benchmark the competences of each brand through evaluating them, and industrialists can extract the competitive advantages of the selected brands.

The aim of this research is to propose a quantitative approach to evaluating the quality of services provided, helping organizations to make strategic decisions by better understanding the characteristics that satisfy consumers.

The approach was based on the integration of the Kano model with SERVQUAL, adapted by the satisfaction equations of Albuquerque et al. (2022) and fuzzy systems theory. Through this, it was possible to infer which attributes influence customer satisfaction, identifying the ranges of satisfaction and, with the help of fuzzy, reducing the imprecision of customer perceptions.

A total of 42% of the attributes were classified as unidimensional, with attribute 11 (Reliability) and attribute 9 (Courtesy) having the highest satisfaction values. Attractive attributes accounted for 38% of the sample, with attribute 29 (Variety of products) and attribute 7 (Location) having the greatest impact on satisfaction. On the other hand, attribute 30 (Order Delay) and attribute 31 (Waiting for payment) caused more dissatisfaction among consumers (ranges −0.6, −0.71, respectively). In addition, Variety of products was the most satisfactory, while Order Delay generated the most dissatisfaction among users.

The originality of this research lies in its contribution to organizations in relation to the services offered by investigating a gap in the studies that use the Kano model, integrated with SERVQUAL, which do not include reverse attributes in their equations and analyses. With the help of fuzzy sets, the subjectivity of the individual can be translated into data for greater clarity of information.

In this article, we identify various foodservice-related attributes that are important for undergraduate students residing in hostels and avail service from specific foodservice providers. We also investigate the performance of attributes to determine areas where the foodservice providers should maintain a high performance or where improvement is required.

We apply the Kano methodology to design the questionnaire for 24 different attributes. For each attribute, we construct three questions; namely, functional type, dysfunctional type, and performance of the hostel foodservice. We collect a total of 317 responses. We use multiple methods to determine the dominant category. Finally, combining the values of these methods, we study relative positions of the attributes in the importance–performance grid.

Based on the Kano categorization, quality-related attributes are most important, followed by hygiene, comfort, availability, variety, and time, in the descending order. The gender of the respondent plays an important role in categorization of some attributes. Using the importance–performance analysis, we identify the attributes where the foodservice provider should maintain a high performance or where improvement is required. Improvements in some attributes are difficult due to foodservice provider's self-assessment of high performance or high difficulty for improvement.

In this study, we examine the importance of various foodservice attributes among undergraduate residential students. We combine multiple methods of Kano categorization to compute importance values of the attributes. We also investigate the reasons behind the gap between student's and foodservice manager's perception of the performance of these attributes.

This 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).

The 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.

The 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.

The 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.

The 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.

The purpose of this paper is to propose a structured procedure for the capture and evaluation of innovative ideas in the early stages of product development. The procedure is designed to take account of internal and external factors affecting the value of innovative ideas.

The proposed procedure is based on divergent and convergent approaches to innovation. Ideas are generated and captured using targeted questions and subsequently evaluated using modifications of the Kano model and failure mode and effect analysis (FMEA). Idea evaluation considers four criteria – delight, originality, market share potential and simplicity – which are used to compute an “idea priority number”.

A case study looking at innovations in swimming goggles verified the validity of the proposed procedure. The procedure also supports fact-based decision making and applies a structured perspective to the innovation process that makes it easier to manage.

The case study made use of a pilot survey in which 32 customers participated. Although the case study was only intended as a demonstration of the method’s use, such a sample size could lead to unreliable results in certain cases.

Applications of standard or expanded FMEA do not add value to a product because the main purpose of the approach is to prevent failure. The proposed inversion of FMEA logic combined with customer view via the Kano model offers a method for the structured analysis of product innovations. No similar approach to the evaluation of innovations has so far appeared in the literature.

Developing strategic relationships with third-party logistics (3PL) providers has long been one of the key challenges in automotive supply chains. The purpose of this paper is to propose a new approach for evaluation and indexing 3PL providers using the Kano model.

The statistical population used in this research comprises managers from the Iranian automotive industry. The Kano evaluation approach is used to analyze the data collected and to classify the criteria used in selection of the preferred 3PL providers.

The results suggest that the proposed framework, based on the Kano classification, can be a powerful tool for the automotive industry in evaluating 3PL providers. Moreover, the analyses indicate that 3PL providers need to improve their service offering in aspects that are to be found in the Kano model’s requirements, namely, must-be, one-dimensional and attractive.

This study contributes to the supply chain management literature by being the first to classify selection indices of 3PL providers in the automotive industry using the Kano model.

Urbanization and globalization in India have led to the depletion of resources and degradation of the environment to meet the demands. Because of these issues, researchers and practitioners have begun to study various strategies to reduce the level consumption of resources to utilize it for present and future needs. In pursuit of finding solutions to the problems, sustainable building construction is found as the best key to avoid depletion of resources. Sustainable material selection is found as a vital strategy in construction. The paper aims to discuss this issue.

A three-phase methodology is proposed for framing the assessment model for construction industries to select materials for construction. In the first phase, a total of 23 sub-criteria of triple bottom line (TBL) and four brick materials as alternatives were identified. The second phase finds the weights and ranks of criteria and sub-criteria using the best worst methodology (BWM) the third phase involves ranking of materials concerning sub-criteria weights determined in phase II using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The objective of study is fixed to identify the criteria list for the selection of material in construction industries from the literature review especially for Indian construction industries; to rank the criteria for selection of materials with the help of the BWM approach; and to prioritize the identified materials in the view of sustainability with the help of Fuzzy TOPSIS in construction industries perspective. This study analyzed and choosing right sustainable materials by the three pillars of sustainability which are the environment, economic and social, also called TBL, for Indian construction companies by framing a sustainable material assessment model.

The results of this study facilitate to frame an assessment model for evaluating and selecting sustainable building materials.

Supplier segmentation provides companies with suitable policies to control each segment, thereby saving time and resources. Sustainability has become a mandatory requirement in competitive business environments. This study aims to develop a clustering-based approach to sustainable supplier segmentation.

The characteristics of the suppliers and the aspects of the purchased items were considered simultaneously. The weights of the sub-criteria were determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four criteria. The proposed model is applied to a real case study to test the performance of the proposed approach.

The results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are selected for sustainable supplier segmentation and managing supplier relationships.

This study integrates sustainability considerations into the supplier segmentation problem using a hybrid approach. The proposed sustainable supplier segmentation is a practical tool that eliminates complexity and presents the possibility of convenient execution. The proposed method helps business owners to elevate their sustainable insights.

To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of potential suppliers and then selects the best among them. This study aims to present a two-part approach for selecting suppliers consisting of suppliers’ prioritization.

In the first part, the criteria that influence on selecting the suppliers have been identified and extracted using the literature review and experts’ opinion which consists of 19 criteria. Then, these criteria were evaluated by the content validity ratio index and using experts’ opinions, and finally, 16 criteria were selected for selecting green suppliers in a polyethylene’s products producer company in Iran. In the next step, suppliers are selected in a green supply chain using multi-criteria decision-making methods such as Dempster–Shafer theory and grey relationship analysis, which is a strategic decision.

This study attempts to improve the level of reliance on the whole uncertain degree by combining Dempster–Shafer theory and grey relational analysis (GRA), which makes the grey analysis method more robust and its results more reliable. The findings show that Supplier 4 is ranked as first within six suppliers.

Using GRA and Dempster–Shafer theory for green supplier selection problem in polyethylene industry is the novelty of this work.