A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning

Customer requirements (CRs) play a significant role in the product development process, especially in the early design stage. Quality function deployment (QFD), as a useful tool in customer-oriented product development, provides a systematic approach towards satisfying CRs. Customers are heterogeneous and their requirements are often vague, therefore, how to determine the relative importance ratings (RIRs) of CRs and eventually evaluate the final importance ratings is a critical step in the QFD product planning process. Aiming to improve the existing approaches by interpreting various CR preferences more objectively and accurately, this paper proposes a weighted interval rough number method. CRs are rated with interval numbers, rather than a crisp number, which is more flexible to adapt in real life; also, the fusion of customer heterogeneity is addressed by assigning different weights to customers based on several factors. The consistency of RIRs is maintained by the proposed procedures with design rules. A comparative study among fuzzy weighted average method, rough number method and the proposed method is conducted at last. The result shows that the proposed method is more suitable in determining the RIRs of CRs with vague information

A comprehensive approach to handle the dynamics of customer’s needs in Quality Function Deployment based on linguistic variables

In the contexture of a customer-driven goods or service design process, a well-timed update of customer’s requirements may not only serve as a necessity indicator to observe how things change over time, but also it incorporates the firms a better ground to interoperate different strategies to meet the future needs of its customer. This paper proposes a systematic methodology to deal with the customer needs’ dynamics, in terms of their relative weights, in the QFD. Compared with previous research, the contribution of this paper is fourfold. First, it applies some linguistic variables to get preferences of customers and experts to determine the relative importance of customer requirements (CRs) and the relationships between customer requirements and engineering characteristics (ECs). Second, it proposes the implementation of a forecasting technique. Third, it describes more comprehensively on how future uncertainty in the weights of customer’s needs could be estimated and transmitted into the design attributes. Fourth, it proposes the implementation of a quantitative approach, which takes into account the decision maker’s attitude towards risk to optimize the QFD decision making analysis. Finally, a real-world application of QFD is provided to demonstrate the practical applicability of the proposed methodology

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

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

A study of an integrated approach for strategy formulation and performance measurement in manufacturing enterprises.

Performance measurement quantifies the efficiency and effectiveness of action that helps organisations translate their strategies into results and fixes accountability to improve performance. This research identifies two problem statements: First, can integrating strategy formulation with measurement initiatives safeguard the performance goals in manufacturing enterprises? And second, how can manufacturing enterprises derive an integrated approach that meet their requirements and needs for strategy formulation (SF) and performance measurement (PM) system implementation? This work proposes an integrated paradigm that aligns the strategy-related performance measures to attain performance improvement in manufacturing enterprises. A two-stage empirical study was conducted, with 232 Hong Kong firms and 85 Shanghai firms participating in the study. The first stage surveys identified the common success factors, problem areas and strategy choices, and examined the relationship amongst corporate, marketing, technology and operational strengths and the `reactive/proactive' strategy choices. The subsequent personal interviews in Hong Kong complemented the survey findings by examining the impact of SF/PM efforts in manufacturing enterprises. There were two series of interviews. The first series acquired the managerial views on the decision criteria on the integration of strategy formulation and performance measures, with the aid of Analytical Hierarchy Process (AHP) methodology. The second interview series derived several design elements and process considerations for aligning strategy formulation with performance measures. The empirical study used in this research provided important inputs and served as a foundation for development of a SF/PM Integration (SPI) model. In an attempt to integrate strategy formulation and performance measurement, the SPI model adopts the guiding principles embodied with the Business Excellence Models and stresses the results-oriented assessments on five categories of SF/PM criteria, namely leadership and constancy of purpose, management by process, people development, continuous improvement, and results orientation. Unlike that of the MBNQA and EQA, the point values for criteria and sub-elements of SPI model were generated collectively from the perspectives of industry practitioners in the manufacturing sectors. These were determined using the normalised weights obtained from the AHP analysis of empirical interview findings. They are taken together to calculate the overall performance index for an organisation. The process framework comprises five stages starting from strategy formulation to implementation and evaluation of an integrated performance measurement system. It encapsulates the requirements, critical processes and activities of strategy formulation and performance measures into the way they are being managed in organisations. The SPI model helps manufacturing enterprises to build a self-assessment platform for amalgamating strategies, plans and actions which can enable performance improvement. It can supplement any Business Excellence Models, and serves three important purposes. Firstly, it is a working tool for integrating SF and PM initiatives and guiding the implementation of performance measurement system in manufacturing enterprises. Secondly, using the model can help improve the effectiveness of management practices in relation to performance measures and self-assessment; and thirdly, using the model can facilitate information sharing of best practices within an organisation and benchmark performance against competitors and other organisations. Results of a post-evaluation survey affirmed that the model and processes could encourage organisational learning and provide a practical means for manufacturing enterprises to devise effective self-assessment and performance improvement. The novel contributions of the research are to identify the key SF/PM attributes, develop the self-assessment scoring method and the process framework accompanying the SPI model. Manufacturing enterprises must evolve a holistic performance measurement system matching their corporate mission, objectives and strategies. The SPI model provides them with a systems approach for building and integrating the capabilities of SF and PM to attain performance improvement goals, irrespective of their business nature and sizes

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

A study of an integrated approach for strategy formulation and performance measurement in manufacturing enterprises

Performance measurement quantifies the efficiency and effectiveness of action that helps organisations translate their strategies into results and fixes accountability to improve performance. This research identifies two problem statements: First, can integrating strategy formulation with measurement initiatives safeguard the performance goals in manufacturing enterprises? And second, how can manufacturing enterprises derive an integrated approach that meet their requirements and needs for strategy formulation (SF) and performance measurement (PM) system implementation? This work proposes an integrated paradigm that aligns the strategy-related performance measures to attain performance improvement in manufacturing enterprises. A two-stage empirical study was conducted, with 232 Hong Kong firms and 85 Shanghai firms participating in the study. The first stage surveys identified the common success factors, problem areas and strategy choices, and examined the relationship amongst corporate, marketing, technology and operational strengths and the 'reactive/proactive' strategy choices. The subsequent personal interviews in Hong Kong complemented the survey findings by examining the impact of SF/PM efforts in manufacturing enterprises. There were two series of interviews. The first series acquired the managerial views on the decision criteria on the integration of strategy formulation and performance measures, with the aid of Analytical Hierarchy Process (AHP) methodology. The second interview series derived several design elements and process considerations for aligning strategy formulation with performance measures. The empirical study used in this research provided important inputs and served as a foundation for development of a SF/PM Integration (SPI) model. In an attempt to integrate strategy formulation and performance measurement, the SPI model adopts the guiding principles embodied with the Business Excellence Models and stresses the results-oriented assessments on five categories of SF/PM criteria, namely leadership and constancy of purpose, management by process, people development, continuous improvement, and results orientation. Unlike that of the MBNQA and EQA, the point values for criteria and sub-elements of SPI model were generated collectively from the perspectives of industry practitioners in the manufacturing sectors. These were determined using the normalised weights obtained from the AHP analysis of empirical interview findings. They are taken together to calculate the overall performance index for an organisation. The process framework comprises five stages starting from strategy formulation to implementation and evaluation of an integrated performance measurement system. It encapsulates the requirements, critical processes and activities of strategy formulation and performance measures into the way they are being managed in organisations. The SPI model helps manufacturing enterprises to build a self-assessment platform for amalgamating strategies, plans and actions which can enable performance improvement. It can supplement any Business Excellence Models, and serves three important purposes. Firstly, it is a working tool for integrating SF and PM initiatives and guiding the implementation of performance measurement system in manufacturing enterprises. Secondly, using the model can help improve the effectiveness of management practices in relation to performance measures and self-assessment; and thirdly, using the model can facilitate information sharing of best practices within an organisation and benchmark performance against competitors and other organisations. Results of a post-evaluation survey affirmed that the model and processes could encourage organisational learning and provide a practical means for manufacturing enterprises to devise effective self-assessment and performance improvement. The novel contributions of the research are to identify the key SF/PM attributes, develop the self-assessment scoring method and the process framework accompanying the SPI model. Manufacturing enterprises must evolve a holistic performance measurement system matching their corporate mission, objectives and strategies. The SPI model provides them with a systems approach for building and integrating the capabilities of SF and PM to attain performance improvement goals, irrespective of their business nature and sizes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications

In recent years information and communication technologies (ICT) have played a significant role in all aspects of modern society and have impacted socioeconomic development in sectors such as education, administration, business, medical care and agriculture. The benefits of such technologies in agriculture can be appreciated only if farmers use them. In order to predict and evaluate the adoption of these new technological tools, the technology acceptance model (TAM) can be a valid aid. This paper identifies the most commonly used external variables in e-learning, agriculture and virtual reality applications for further validation in an e-learning tool designed for EU farmers and agricultural entrepreneurs. Starting from a literature review of the technology acceptance model, the analysis based on Quality Function Deployment (QFD) shows that computer self-efficacy, individual innovativeness, computer anxiety, perceived enjoyment, social norm, content and system quality, experience and facilitating conditions are the most common determinants addressing technology acceptance. Furthermore, findings evidenced that the external variables have a different impact on the two main beliefs of the TAM Model, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). This study is expected to bring theoretical support for academics when determining the variables to be included in TAM extensions