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This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.

Companies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.

Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.

The procedure proposed here can help companies to identify the best supplier.

The paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

The purpose of this paper is to propose a fuzzy multi‐criteria decision‐making procedure and it is applied to find a set of optimal solution with respect to the performance of each supplier. This method with the use of Monte Carlo simulation produces overall desirability level less imprecise and more realistic than those of the conventional QFD methods for engineering design evaluation.

A few responses obtained from customers are simulated using a triangular fuzzy QFD algorithm, Monte Carlo simulation and a multi‐objective model to optimise the total user preferences.

The proposed approach provides decision‐making with an optimal solution less imprecise in a QFD‐based collaborative product design environment.

The proposed approach depends on the few responses and the random numbers derived from simulation. The random numbers need to be used after passing them through random number testing methods. The responses obtained from the customer are considered to be genuine and original.

The triangular fuzzy, Monte Carlo simulation and multi‐objective optimisation are embedded into QFD environment to make the decisions less imprecise than that of conventional QFD and it is tested for a case study problem. It definitely helps the managers in a collaborative product design environment.

The aim is to determine whether the organisations more strategically committed to their stakeholders present better social and financial performance and, based on this relationship, to determine the state of the art of the Spanish sectors' approach to sustainable development.

This paper analyses the sustainability approach of a sample of 52 Spanish listed firms. This process is based on the study of different indexes generated in order to evaluate the company's commitment through its stakeholders, the social and financial performance of these organisations, and the relationship between them. Previous results showed a positive and not significant relationship between these variables and a positive financial performance. This paper replicates a former research by introducing a fuzzy‐ logic‐based methodology in order to generate the aforementioned indexes.

The current results support the conclusions formerly obtained and simultaneously demonstrate that big Spanish companies are at an incipient stage of development of a clearly sustainability‐oriented management.

The unavailability of a long series of organisations' sustainability information is an obstacle for a broader analysis. This research could motivate the usefulness of the fuzzy logic methodology for analysing the business sustainability approach and to develop studies on the corporate social performance.

The use of fuzzy logic methodology for the generation of indexes related to the organisation's social responsibility and sustainability results.

The purpose of this paper is twofold: to incorporate the symbolic relationships among the attributes of customer requirements (CRs) and engineering characteristics (ECs) as well as to factor in the values numerically to enhance the prioritization process for an improved, comprehensive quality function deployment (QFD) analysis. The aim is to develop the concept of assimilating and factoring in the often-ignored interrelationships among CRs and ECs utilizing the weighted average method for the CR and EC correlations with overall calculations.

After a brief literature review of the methods utilized, the research paper discusses the framework for the correlation triangle challenge and introduces a novel mathematical solution utilizing triangle values in conjunction with computed initial raw weights for CRs and initial priority scores for ECs. The capability and applicability of the proposed model are demonstrated with a real-life example.

Through the proposed technique, the roof and the interrelationship triangle's signs and symbols are translated into numerical values for each permutation of ECs and CRs, and then the prioritization values are processed and finalized. The proposed model successfully modifies and removes vagueness from an otherwise overlooked part of the QFD process.

The illustrated case study aptly proves that the proposed methodology yields more revealing and informative outcomes for engineers and designers, thus adding much-needed reliability to the outcome and its analysis. The validation conducted through the rank comparison endorses the premise, and the results obtained reflect the strength and accuracy of the progressive QFD as a product planning tool.

The research article proposes a fresh and unique QFD approach that solves typical procedural complications encountered in a regular QFD. Whereas the traditional methods neglect the interrelationships among CRs and ECs, this new methodology employs them in an improved, numerical way by incorporating them in quantitative analysis, which leads to judicious and improved decision-making.

The purpose of this paper is to demonstrate a methodology to design a supply chain with a view to achieve a strategic fit between competitive and supply chain strategies.

Quality function deployment (QFD)-based optimization methodology is employed to design a supply chain for a product through aligning the competitive and supply chain strategies. Normal boundary intersection (NBI) method is adopted to obtain optimal weights of the supply chain design objectives. Weighted additive model is developed for multi-objective optimization. Utility-based attribute function, which structure the relationship between the elements of competitive and supply chain strategies is established. The utility functions and the information contained in the House of Quality (HOQ) of QFD are used to define the supply chain performance (SCP).

SCP index is computed using the set of supply chain design objectives obtained by solving the weighted additive model. On the basis of SCP index, the supply chain activities are planned accordingly. An illustrative example is presented in this paper to describe the QFD-based optimization methodology for designing a supply chain.

QFD-based optimization is a novel approach to design a supply chain with a focus on aligning competitive and supply chain strategies.

Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data.

The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations.

First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division.

Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD.