A hybrid Kano-fuzzy AHP method for measuring customer satisfaction: A case study of transportation system

An increase competition on today's economy has created motivation for many organizations to look for different alternatives on better serving the customers. There are always some budget limitations on any customer relationship method, which leads us to prioritize different alternatives. In this paper, we present an empirical method based on an integrated Kano and fuzzy analytical hierarchy procedure to rank suitable alternatives. The proposed model of this paper uses a questionnaire survey to gather customer's opinions and implements the method for a real-world case study of transportation planning. The questionnaire includes 37 questions distributed among 976 passengers for two trips in Iran. The results indicate that driver's physical and mental health, buss equipments with GPS functionality and familiarity of drivers with road and road's conditions play important role on choosing a transportation company

A comparative Kano analysis on customer satisfaction based on customer and employment perspectives

These days, there is a tight competition among business units and all production units or service businesses attempt to use different techniques to increase their market shares. In such environment, customer normally says the last word, in fact, customer plays an important role and in many organizations, it is considered as part of their assets. The purpose of this paper is to propose a hybrid method to detect important criteria using Kano three-dimensional method and prioritize them using analytical hierarchy process. We implement the proposed model of this paper for one of Iranian banks called Bank Melli Iran. The study determines 25 different attributes, categorizes them in three different groups based on Kano model, and ranks them in terms of customers and employees' perspective. The results of the survey indicate that customer and employees mostly have similar views since there are 21 common attributes between them. However, the priorities of these 21 items are often different in terms of two groups of employees and customers

Appliance to Predict the Quality of Hypothetically Modified Products

Customizing the quality of the product to change customer expectations is a necessary action in good, prospering organizations. In enterprises, the most beneficial solutions consider the future satisfaction of customer with the product. This issue is not easy and is not resolved; therefore, integration of different techniques was proposed as part of a single, coherent appliance. Therefore, the aim is to propose the appliance to predict the quality of hypothetically modified products. The appliance was developed by adequately selected and combined techniques, i.e., survey research with the Likert scale, AHP method (Analytic Hierarchy Process), Pareto rule (20/80), WSM method (Weighted Sum Model) and Naive Classifier Bayes. The concept of the proposed appliance concerns the possibility of determining important product attributes and possible combinations of feature states. Based on this, the quality levels were estimated, and then satisfaction with the hypothetical modifications of the product was predicted. The test was carried out on the vacuum cleaner. As a result, four combinations of product modifications were determined, which have been created based on hypothetical and actual attributes. Each modification was satisfying for the customer. Therefore, the proposed apparatus turned out to be effective in predicting customer satisfaction for the modified quality levels. Originality is to propose a new integration of different techniques to predict levels of quality product modification based on current product quality

Manufacturing Quality Function Deployment: Literature Review and Future Trends

A comprehensive review of the Quality Function Deployment (QFD) literature is made using extensive survey as a methodology. The most important results of the study are: (i) QFD modelling and applications are one-sided; prioritisation of technical attributes only maximise customer satisfaction without considering cost incurred (ii) we are still missing considerable knowledge about neural networks for predicting improvement measures in customer satisfaction (iii) further exploration of the subsequent phases (process planning and production planning) of QFD is needed (iv) more decision support systems are needed to automate QFD (v) feedbacks from customers are not accounted for in current studies

Prioritization of engineering Characteristics in QFD in the case of customer requirements orderings

Quality Function Deployment (QFD) is an effective tool to orient the design of a product and related production processes towards the real exigencies of the end-user. Its first phase – the House of Quality – is aimed at translating Customer Requirements (CRs) into Engineering Characteristics (ECs) of the product of interest, also determining an ECs’ prioritization. All of the techniques proposed for tackling this problem are based on the assumption that the importance of each CR is expressed on interval or ratio scales (i.e. cardinal scales). To this end, customer evaluations – naturally expressed on ordinal scales – are artfully turned into numbers. This paper introduces a novel technique – denominated as Ordinal Prioritization Method – that can be applied to prioritize ECs. The method addresses the problem of the prioritization of ECs when the importance of CRs is given on an ordinal scale. The description of the method is supported by a some application examples

Balancing Global Customer Needs and Profitability Using a Novel Business Model for New Model Programmes in the Automotive Industry

AbstractBusiness models need to evolve and respond to changing customer requirements and this is only further exaggerated when considered in the context of a ‘Global Market’ which has shifted in the last 60 years from ‘Manufacturer’ led to ‘Customer’ led ‘fashion’ based industry. The automotive industry is one example of an emerging fashion based industry.The objective of most viable businesses is to make a profit for their shareholders but, given the typical gestation period between concept establishment and the start of the production volume build, it is a challenge to establish a structured method to ensure programme and business profitability against the backdrop of a fashion based market. In this paper, a data driven methodology is proposed which focuses on data, structure, and the customer to maximise the probability of profitability. To achieve this goal, joins between Multi Criteria Decision Analysis, Parametric Cost Estimating and ‘Should’ Cost Estimating are explored. ‘Margin engineering’ is thus proposed as a new foundation for a future business model to guide medium term (one to six years) development projects towards a profitable outcome

A product design framework for one-of-a-kind production using integrated quality function deployment and operational research techniques

The process of product design as an early stage of new product development provides systematic approaches that can lead to the success of a company’s competitive strategy in the current turbulent market. By launching an efficient product design procedure can result in the reduction of engineering modifications, cost and production time. One-of-a-Kind Product (OKP) is known as a particular manufacturing system of new product design and development with emphasis on the special order concept. Quality Function Deployment (QFD) is a comprehensive design framework with cross-functional team members that leads to the development of new or improved products. QFD starts with the House of Quality (HOQ) as an organizing matrix to identify the customers’ requirements (CRs) and translate them into the technical attributes (TAs) of the product and followed by determining the target values for the sets of technical attributes. An evaluation approach to determine the relative importance of CRs and TAs should be considered. In previous researches, the traditional methods such as simple scoring method and application of operational research techniques such as Analytic Hierarchy Process (AHP) were reported to weigh the requirements and attributes. Despite the obvious inner-relationships among the elements, considering the HOQ as a hierarchical system may be inefficient. In addition, the contradictory effects of a TA on two or more CRs, is the problem that has been neglected. Here, a mathematical model was developed for calculating the TAs target values. A case study (dry gas filter, Namdaran Petro-Gas Industries (NPI™)) is presented to exhibit and verify the procedure of OKP product design. Initially, the framework was developed by integrating QFD-operational research (Analytic Network Process (ANP)) as a systematic method for improvement of dry gas filter design. Interview and study of documents were used to identify the CRs. A robust evaluation on customers’ priority and attributes’ importance with respect to inner-relationships among criteria/sub-criteria was performed. Furthermore, the effects of TAs on CRs with regard to their direction (positive/negative) were considered as the fundamental for developing a Multi-Objective Decision Model (MODM) to be used for determining the TAs target values. For this purpose, the fuzzy conversion scaling technique followed by formulating the partial satisfaction separately was applied. Modified TOPSIS was used to select the basic design among the available designs for further modification. Later, the process continues with the second phase, translating the TAs into the key parts. The available options (retailers) to supply the key parts were identified. As the normal procedure of QFD the relative importance’s of key parts and the options were determined. Finally, a zero-one goal programming was presented to select the optimum options for each key part subject to the budget constraint. Overall, the developed QFD-ANP framework provides a systematic approach that has the potential to be used for designing OKP product

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Flexible aggregation operators to support hierarchization of Engineering Characteristics in QFD

Quality Function Deployment (QFD) is a management tool for organizing and conducting design activities of new products and/or services together with their relevant production and/or supply processes, starting from the requirements directly expressed by the end-users. It is organized in a series of operative steps which drive from the collection of the customer needs to the definition of the technical characteristics of the production/supply processes. The first step entails the construction of the House of Quality (HoQ), a planning matrix translating the Customer Requirements (CRs) into measurable product/service technical characteristics (Engineering Characteristics – ECs). One of the main goals of this step is to transform CR importances into an EC prioritization. A robust evaluation method should consider the relationships between CRs and ECs while determining the importance levels of ECs in the HoQ. In traditional approaches, such as for example Independent Scoring Method, ordinal information is arbitrarily converted in cardinal information introducing a series of controversial assumptions. Actually, the current scientific literature presents a number of possible solutions to this problem, but the question of attributing scalar properties to information collected on ordinal scales is far from being settled. This paper proposes a method based on ME-MCDM techniques (Multi Expert / Multiple Criteria Decision Making), which is able to compute EC prioritization without operating an artificial numerical codification of the information contained in the HoQ. After a general description of the theoretical principles of the method, a series of application examples are presented and discussed

Engineering characteristics prioritisation in QFD using ordinal scales: a robustness analysis

Quality function deployment (QFD) is a management tool used for the design of new products/services and the related production/supply processes. One of the goals of the method is to translate the customer requirements (CRs) into measurable engineering characteristics (ECs) of the new product/service and prioritise them, basing on their relationships with CRs and the related importances. To this purpose, the current scientific literature encompasses several alternative approaches (the most used is the independent scoring method – ISM), in most of which cardinal properties are arbitrarily attributed to data collected on ordinal scales. This paper describes and discusses a new approach based on ME-MCDM (multi expert/multiple criteria decision making) techniques, which do not require any debatable ordinal to cardinal conversion. The theoretical principles and the robustness of the method are presented and tested through some application examples related to a well-known case study reported in the scientific literature