Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.     

Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection

Decision-making techniques are used to help evaluate the current suppliers’ aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.     

Systematical decision-making approach for quality function deployment based on uncertain linguistic term sets

A systematical decision-making approach is constructed for quality function deployment (QFD) in uncertain linguistic situations. The mathematical expression and operation of linguistic terms play important roles in the proposed approach in terms of customer requirements (CRs) and design requirements (DRs) in QFD. First, hesitant fuzzy linguistic term sets are designed to conveniently express uncertain linguistic terms and compute with words after the data derived from customers are pretreated and integrated in the decision-making process. Second, the tolerance deviation is defined to restrict innovatively the deviation range of fuzzy linguistic terms in the assessment stage of relative importance for CRs. Third, information entropy is originally designed to determine the final importance of DRs. Moreover, an empirical study on the research project called vortex recoil hydraulic retarder is conducted to demonstrate the performance of the systematical decision-making approach. The proposed approach can be applied to a wide variety of new product development problems in uncertainty settings.     

An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information

Nowadays selecting the most suitable robot is a difficult task for manufacturing firms due to increase in production demands and availability of various robot models. Robot evaluation and selection can be regarded as a multiple criteria decision-making (MCDM) problem and three key issues are the assessment of robots, the determination of criteria weights and the prioritisation of alternatives. This paper aims to propose an integrated model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach for handling robot selection problems with incomplete weight information. The new model can not only manage uncertain and imprecise assessment information of decision-makers with the aid of hesitant 2-tuple linguistic term sets, but also derive the important weights of criteria objectively when the weight information is incompletely known. Moreover, based on the extended QUALIFLEX algorithm, the priority orders of robots can be clearly determined and a more reasonable and credible solution can be yielded in a particular industrial application. Finally, a robot selection case study is carried out, and comparative experiments indicate the practicality and effectiveness of the proposed integrated linguistic MCDM approach.     

An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D

The selection of the outsourcing manufacturing partners (OMPs) is an important issue for research and development (R&D) in the pharmaceutical industry. The selection process considers several main criteria, as well as a few sub-criteria for each main criterion. Such problems can be formulated as hierarchical structures and can usually be resolved with multi-criteria decision-making approaches. This paper presents an integrated fuzzy approach for selecting a suitable OMP in pharmaceutical R&D. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and fuzzy TOPSIS are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria's weights in a hierarchical structure. Fuzzy TOPSIS aims to find the OMP with the best performance with respect to the sub-criteria. We exemplify the integrated approach using a numerical application to demonstrate its feasibility.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).     

Normalisation models for prioritising design requirements for quality function deployment processes

The prioritisation of design requirements is critical for determining resource allocation in the new product design stage through the quality function deployment (QFD) planning processes. To prioritise design requirements, normalisation models are usually used to perform aggregation and normalisation functions. Recognising the weakness of the Wasserman normalisation model from both theoretical and practical viewpoints, this paper proposes an improved normalisation model for the aggregation and normalisation functions. It is verified that the proposed model satisfies Lyman’s normalisation requirement and avoids the problem of Wasserman’s normalisation model. In addition, a normalisation model that takes into account the correlation among customer requirements is also developed to complete a generalised normalisation model for QFD planning processes. A product design case is presented to demonstrate the advantages of the proposed normalisation models.     

Prioritizing quality characteristics in dynamic quality function deployment

Due to the combination of rapid influx of new technology, high pressure on time-to-market and increasing globalization, the number of products that have highly uncertain and dynamic specifications or customer requirements might significantly increase. In order to deal with these inherently volatile products or services, we need to adopt a more pro-active approach in order not to produce an unwanted product or service. Thus, based on the idea of the quality loss function and the zero-one goal programming, an intuitively simple mathematical model is developed to prioritize the quality characteristics (QCs) in the dynamic quality function deployment (QFD). It incorporates a pro-active approach towards providing products and services that meet the future voice of the customer (FVOC). The aim is to determine and prioritize only the ‘important’ QCs with a greater confidence in meeting the FVOC. It is particularly useful when the number of the potentially dominant QCs is very large so that, by using the prioritization, the size of the QFD can be effectively reduced. Some constraints, such as minimum customer satisfaction level and limitation on budget are also taken into consideration. A sensitivity analysis is suggested to give an insight to the QFD users in the change of parameters of the proposed model.     

An integrated quality function deployment and capital budgeting methodology for occupational safety and health as a systems thinking approach: The case of the construction industry

In this paper, an integrated methodology for Quality Function Deployment (QFD) and a 0–1 knapsack model is proposed for occupational safety and health as a systems thinking approach. The House of Quality (HoQ) in QFD methodology is a systematic tool to consider the inter-relationships between two factors. In this paper, three HoQs are used to consider the interrelationships between tasks and hazards, hazards and events, and events and preventive/protective measures. The final priority weights of events are defined by considering their project-specific preliminary weights, probability of occurrence, and effects on the victim and the company. The priority weights of the preventive/protective measures obtained in the last HoQ are fed into a 0–1 knapsack model for the investment decision. Then, the selected preventive/protective measures can be adapted to the task design. The proposed step-by-step methodology can be applied to any stage of a project to design the workplace for occupational safety and health, and continuous improvement for safety is endorsed by the closed loop characteristic of the integrated methodology.     

A fuzzy expected value-based goal programing model for product planning using quality function deployment

Product planning is one of four important processes in new product development using quality function deployment (QFD), which is a widely used customer-driven approach. In this article, a hierarchical framework for product planning using QFD is developed. To tackle the fuzziness in functional relationships between customer requirements and engineering characteristics (ECs) in product planning, the least squares method is incorporated into fuzzy regression to investigate those functional relationships, by which a more central tendency can be obtained. Furthermore, a fuzzy expected value-based goal programing model is proposed to specify target values of ECs. Different from some fuzzy product planning approaches for QFD, the proposed programing model has unambiguous interpretations. An illustrated example of a quality improvement problem of emulsification dynamite-packing machine design is given to demonstrate the application and performance of the proposed approach.     

A membership function approach for aggregate production planning problems in fuzzy environments

This paper proposes a novel approach for the aggregate production planning (APP) problem with fuzzy parameters. Different from the results of previous studies, in this paper the membership function of the fuzzy minimal total cost is constructed based on Zadeh's extension principle and fuzzy solutions are provided. A pair of mathematical programs parameterised by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy total cost at α. By enumerating different values of α, the membership function of the fuzzy total cost is constructed. To illustrate the validity of the proposed approach, the example studied by Lai and Hwang (1992 Lai, YJ and Hwang, CL. 1992. Fuzzy mathematical programming: methods and applications, Berlin: Springer.  [Google Scholar]) using Chanas's approach is investigated. Since the objective value is expressed by a membership function rather than by a crisp value, the proposed approach can represent APP systems more accurately, thus obtained solutions which contain more information can offer more chance to achieve the feasible disaggregate plan, and it is beneficial to the decision-maker in practical applications. The proposed approach can also be applied to APP problems with other characteristics.     

Modified QFD and problem‐solving techniques integrated approach implementing corrective actions: a case study in an Italian manufacturing plant

The ISO 9001:2000 and Total Quality Management models impose that organizations adopt structured procedures to implement corrective and preventive actions. In this paper, a structured framework to solve a non‐conformity in a product or in a process, selecting the best corrective actions, is proposed. Some problem‐solving techniques are applied in a structural method (i) to find the problem (product or process non‐conform), (ii) setting the problem (using data‐collecting instruments), (iii) analyzing the problem (with techniques such as Pareto charts, histograms, stratification analysis) and (iv) searching possible alternative solutions. Then, a modified quality function deployment technique is applied to evaluate and to classify the alternative solutions with respect to three different parameters (benefit, cost and impact). The framework has been developed to select and implement corrective actions in an Italian manufacturing plant. The case study is presented in this paper. However, the proposed approach is applicable in many other contexts. Copyright © 2008 John Wiley & Sons, Ltd.     

Application of fuzzy quality function deployment for sustainable design of consumer electronics products: a case study

With a steep increase in the demand for consumer electronics products, the contemporary manufacturers are committed toward sustainable development of such products. There exists a scope for developing a methodology for enabling sustainable development of consumer electronics products. In this context, fuzzy quality function deployment (QFD) approach has been presented in this article in order to prioritize relevant customer requirements, sustainability parameters and sustainability initiatives. Key influential parameters for sustainable development of consumer electronics products have been identified from the literature. In the first phase of fuzzy QFD, parameters influencing sustainable development have been prioritized in accordance with customer requirements. In the second phase, environmental design initiatives have been prioritized based on critical sustainability parameters. From phase I of fuzzy QFD, ‘reduction in environmental release’ has been found as the most significant sustainability parameter with a crisp value of 22.83, and from phase II, environmental impact assessment is proved to be the significant design method with a crisp value of 20.40. The methodology would provide a comprehensive understanding to practitioners on the interrelationships among customer requirements, sustainability parameters and environmentally benign initiatives for development of consumer electronic products. The generic model developed can be applied to most of the consumer electronics product     

A membership function approach to lot size re-order point inventory problems in fuzzy environments

Inventory models play an important role in logistics and supply chain management for reducing cost and increasing customer satisfaction. This paper develops an approach to derive the fuzzy objective value and decision variables of the fuzzy lot size re-order point inventory problem with parameters being fuzzy numbers and the shortages are backordered with extra cost incurred. Different from the existing studies, the idea is based on Zadeh's extension principle. A pair of mixed integer nonlinear programs (MINLP) parameterised by the possibility level α is formulated to calculate the lower and upper bounds of the minimal total cost per unit time at α, through which the membership function of the minimal total cost per unit time is constructed. At the same time the membership functions of the optimal order quantity and the optimal re-order point are also provided. A numerical example studied by previous studies is solved successfully to demonstrate the validity of the proposed method. Compared with previous studies, the obtained results which precisely and completely conserve the fuzziness of the input information are more informative for finding the best inventory policy since they are expressed by membership functions rather than by crisp ones. Moreover, to provide representative crisp solutions for designing inventory systems, the Yager's ranking index method is adopted to defuzzify the obtained membership functions. The successful extension of inventory models to fuzzy environments permits inventory models to have wider applications in practice.     

Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system

In product development, the identification of critical design requirements (DRs) is key to satisfying customer needs because it helps produce more successful products in a shorter time. Quality function deployment (QFD) is a tool used in product development to systematically determine the DRs so as to attain higher customer satisfaction. In the QFD process, the simultaneous optimisation of more than one conflicting objective is generally required. However, it is very difficult for decision makers to determine the goal value of each objective in imprecise and uncertain environments. In order to overcome this problem, the present study proposes a fuzzy mixed-integer goal programming model that determines a combination of optimal DR values. Different from the existing fuzzy goal programming models, the values of the DRs in the proposed model are taken as discrete. Finally, a new Decision Support System is developed. The new system integrates QFD and mathematical programming, enabling the design team to effectively compare product design alternatives and make product development easier and faster. The proposed methodology is illustrated using a real-world application in the Turkish white goods industry.     

A novel fuzzy linguistic model for prioritising engineering design requirements in quality function deployment under uncertainties

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer requirements (CRs) into the design requirements (DRs) of a product. Deriving the priority order of DRs from input variables is a crucial step in applying QFD. Due to the inherent vagueness or impreciseness in QFD, the use of fuzzy linguistic variables for prioritising DRs has become more and more important in QFD applications. Existing approaches make use of the associated fuzzy membership functions of linguistic labels based on the fuzzy extension principle. However, an inherent limitation of such fuzzy linguistic approaches is the information loss caused by approximation processes, which eventually implies a lack of precision in the final results. This paper proposes an alternative approach to prioritising engineering DRs in QFD based on the order-based semantics of linguistic information and fuzzy preference relations of linguistic profiles, under random interpretations of customers, design team and CRs. Ultimately, this approach enhances the fuzzy-computation-based models proposed in the previous studies by overcoming the mentioned limitations. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous techniques based on fuzzy computation.     

Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach

This study aims at improving the effectiveness of Quality function deployment (QFD) in handling the vague, subjective and limited information. QFD has long been recognised as an efficient planning and problem-solving tool which can translate customer requirements (CRs) into the technical attributes of product or service. However, in the traditional QFD analysis, the vague and subjective information often lead to inaccurate priority. In order to solve this problem, a novel group decision approach for prioritising more rationally the technical attributes is proposed. Basically, two stages of analysis are described: the computation of CR importance and the prioritising the technical attributes with a hybrid approach based on a rough set theory (RST) and grey relational analysis (GRA). The approach integrates the strength of RST in handling vagueness with less priori information and the merit of GRA in structuring analytical framework and discovering necessary information of the data interactions. Finally, an application in industrial service design for compressor rotor is presented to demonstrate the potential of the approach.     

Understanding the effect of deployment on the risk of fatal motor vehicle crashes: a nested case-control study of fatalities in Gulf War era veterans, 1991-1995

Motor vehicle crashes (MVCs) are an important cause of morbidity and premature loss of life among military personnel during peacetime and particularly following combat. A nested case-control study of fatal MVC occurring between 1991 and 1995 was conducted in a cohort of Gulf War era veterans. Cases were validated MVC deaths in the Fatality Analysis Reporting System. Controls were selected using risk set sampling by gender and year of case ascertainment in a 10:1 ratio. Preliminary results, consistent with previous reports of increased fatal MVC risk among returning combat veterans, showed a crude odds ratio of 1.45 (95% confidence interval 1.27-1.65). Multivariable logistic regression modeling was used to identify important independent predictors, as well as to quantify the influence of deployment on a risk profile for fatal MVC. Because of significant interaction between deployment and inpatient diagnosis of substance abuse, the final model was stratified by deployment status. Results suggest that demographic, military, and behavioral characteristics of deployed healthy warriors are similar to the risk profile for fatal MVC. In addition to young, single, high school-educated, enlisted male personnel, those who served during times of ground combat, particularly in infantry, gun crews, or seamanship occupations, should be targeted for preventive interventions.     

Why do quality and reliability feedback loops not always work in practice: a case study

The increasing competition in the market of consumer electronics forces industry to simultaneously improve the functionability, reliability and costs of their products. Due to the strong dynamics of this field an important measure in improving product quality is the feedback of information on actual field behaviour of a product which is extremely relevant for product development. This information, usually gathered by the service department, can help development to learn from the failures of earlier products and to improve the next product generations. This feedback process, however, does not always bring the expected benefits. This paper presents a model, based on an actual case study, of the structure of the feedback process in terms of information flows. The information flow model describes the chain or network of activities in the feedback process as a function of (different) drivers such as cost, time or quality. Using this information flow model it is possible to explain how quality and reliability related information propagates through a business process and where and why communication problems lead to disruptions in the feedback of this information. With this model, it is easier to understand the function of different parties in the service or after sales process, so that management of the information in this process becomes more transparent and can be improved.