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

Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers

Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive manual efforts towards the identification of numerous useful reviews from a vast pool of reviews and converting such useful reviews into actionable knowledge by means of prioritisation. By doing so, app developers can resolve the critical bugs and simultaneously address the prominent requests or enhancements in short intervals of apps’ maintenance and evolution cycles. That said, the manual efforts towards the identification and prioritisation of useful reviews have limitations. The most common limitations are: high cognitive load required to perform manual analysis, lack of scalability associated with limited human resources to process voluminous reviews, extensive time requirements and error-proneness related to the manual efforts. While prior work from the app domain have proposed prioritisation approaches to convert reviews pertaining to an app into actionable knowledge, these studies have limitations and lack benchmarking of the prioritisation performance. Thus, the problem to prioritise numerous useful reviews still persists. In this study, initially, we conducted a systematic mapping study of the requirements prioritisation domain to explore the knowledge on prioritisation that exists and seek inspiration from the eminent empirical studies to solve the problem related to the prioritisation of numerous useful reviews. Findings of the systematic mapping study inspired us to develop automated approaches for filtering useful reviews, and then to facilitate their subsequent prioritisation. To filter useful reviews, this work developed six variants of the Multinomial Naïve Bayes method. Next, to prioritise the order in which useful reviews should be addressed, we proposed a group-based prioritisation method which initially classified the useful reviews into specific groups using an automatically generated taxonomy, and later prioritised these reviews using a multi-criteria heuristic function. Subsequently, we developed an individual prioritisation method that directly prioritised the useful reviews after filtering using the same multi-criteria heuristic function. Some of the findings of the conducted systematic mapping study not only provided the necessary inspiration towards the development of automated filtering and prioritisation approaches but also revealed crucial dimensions such as accuracy and time that could be utilised to benchmark the performance of a prioritisation method. With regards to the proposed automated filtering approach, we observed that the performance of the Multinomial Naïve Bayes variants varied based on their algorithmic structure and the nature of labelled reviews (i.e., balanced or imbalanced) that were made available for training purposes. The outcome related to the automated taxonomy generation approach for classifying useful review into specific groups showed a substantial match with the manual taxonomy generated from domain knowledge. Finally, we validated the performance of the group-based prioritisation and individual prioritisation methods, where we found that the performance of the individual prioritisation method was superior to that of the group-based prioritisation method when outcomes were assessed for the accuracy and time dimensions. In addition, we performed a full-scale evaluation of the individual prioritisation method which showed promising results. Given the outcomes, it is anticipated that our individual prioritisation method could assist app developers in filtering and prioritising numerous useful reviews to support app maintenance and evolution cycles. Beyond app reviews, the utility of our proposed prioritisation solution can be evaluated on software repositories tracking bugs and requests such as Jira, GitHub and so on

Using the QFD matrix as a major continuous improvement tool to improve organizational quality

This is a case study of an Australian higher education institution (HEI) using quality function deployment (QFD) to identify areas of improvement in serving and meeting the needs of international students enrolled at this university. The composite institution reflects what is currently happening at the time of this writing as part of a process of determining international student needs and ensuring that these are met while meeting academic and institutional requirements (IR). The use of QFD fills a major gap since most methodologies practiced do not focus on either capturing the international students' voice or align these with IRs to enhance the opportunities for successful completion of a degree and meeting student personal and professional expectations. Results are incomplete at this time and thus cannot be reported, but a discussion of the approach is provided, and initial observations are presented to adequately describe the use of QFD and processes and tools used to complete different parts are the central piece of the process, the house of quality (HoQ)

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

The Functional-Engineered Product-Service System (FEPSS) model

Throughout recent years, environmental perils have increased and awareness regarding such dangers has improved proportionally. In light of the growing concerns, and coupled with fiercer competition and legislation, product based solutions to meet present and future needs have been deemed insufficient to ensure the planet’s survival. Thus, the birth of integrated product-service offerings, where the product is associated to add-on services, enhancing its performance and achieving higher levels of value for the customer, as well as the manufacturer, with embedded ecological advantages. The service-oriented perspective of delivering solutions is known as Product-Service Systems (PSSs). However, despite advances in acknowledging the benefits that lie in adopting a PSS to answer consumer needs, a formal approach to developing PSS solutions is absent. This dissertation investigates the integration of product design and service design strategies into product-service offerings: overall processes for this integration are present, but the intricate steps of each phase are missing. A literature review examines the most dominant design approaches, as well as design frameworks to structure the PSS design process. The outcome of the review led to the absence of a generic design framework as existing design approaches and processes seemed adapted to a specific context and field. From the examination of the respective literature, we present a four-stage design process, entitled the Functional-Engineered Product-Service System (FEPSS) model, built on a design science approach. Ideation and task analysis, conceptual design, embodiment design, and validation and release are thoroughly detailed with the appropriate tools to define the elements of a PSS. The research then concentrates on the first two stages as they represent the core of PSS design and development process. Ideation and task analysis highlight the use of qualitative tools to define customer requirements, as well as quantitative ones, such as the Kano model, Quality Function Deployment (QFD), the fuzzy logic, and the Analytic Hierarchy Process (AHP) to prioritize these requirements and define the value-creating ones as the basis of the PSS design. Conceptual design presents two approaches to define PSS concepts. The first consists of a functional decomposition approach based on adapting morphological matrices (MMs) to a product-service extending traditional MMs to include the service elements and selection of stakeholders in a product-service integrated setting. The choice of the concept is determined according to a life cycle modelling that illustrates the environmental impact of the proposed concept(s) and compares it/them to the existing offering. The second opts for the QFD for PSS tool augmented by fuzzy logic and the AHP to determine the product and service components of the PSS. Then, the use of Axiomatic Design (AD) shows how a functional decomposition and QFD for PSS can be used to develop PSS modules. Four case studies conducted in the agricultural and biomedical field illustrate the use of the FEPSS and, in particular, its first two phases. The results achieved show the potential of such an approach when implementing a PSS approach, especially in the case of a manufacturer that wants to shift from producing products to providing integrated product-service offerings. At the same time, from a more general perspective, the research work highlighted the benefits of PSSs as they allow the achievement of more sustainable solutions without decreasing the customer values

A Decision Support System for Benefits Realisation in Front End Design of Construction Projects in Dynamic Contexts

There is an increasing interest in the performance of construction projects, focussing on measurable value delivery. This research proposes a novel decision support system to support Front End Design (FED) decision making in addressing continuing value constraints in the delivery of project benefits. Stakeholder involvement and interests in projects that impact on project requirements understanding and management often means competing and sometimes conflicting requirements. However, projects now face increasing expectations to cope with emergent needs, which adds to uncertainty in the design process. As a result, there are continuing challenges in understanding and measuring project performance in terms of derived benefits. Increasingly, research points to the need for new understanding of FED processes on account of their vital contribution to value generation throughout the project life cycle. Much of current design practice however relies on qualitative explanatory/rationalistic methods to model uncertainty and predict changes in use cases in projects. The reliability of the approaches in the face of myriad, often conflicting and competing stakeholder interests in AEC design is increasingly under focus. This research adopts a mixed-methods approach in developing, validating and evaluating the proposed system in two case study project contexts for comparative assessment of the modelling results. The research formalises a new decision system (DESIDE), in exploring mathematical modelling based on Bayesian probabilistic models and proposes a new system focussed on the utility of decision making in the realisation of project benefits. The research explores the use of probability theory and appropriate mathematical approaches in the management and modelling of requirements and uncertainty during design decision making. The research also explores the use of complementary requirements forecasting modelling in a holistic integrated modelling approach. The research contributes to knowledge through 1) the new decision system that presents new frontiers in empirical evaluation of FED Benefits Realisation, 2) presenting an integrated analytical modelling approach of project requirements modelling in FED with a focus on the full project lifecycle performance based on analytical utility assessments and cause-effect modelling and 3) presenting a new integrated forecast and uncertainty probabilistic modelling approach of requirements in FED to support benefits realisation in projects

Data Representation Methods For Environmentally Conscious Product Design

The challenge of holistically integrating environmental sustainability considerations with design decision-making requires novel representations for design and sustainability-related data that allow designers to understand correlations among them. Challenges such as (1) lack of suitable data & information models, (2) methods that simultaneously consider environmental sustainability as well as design constraints, and (3) uncertainty models for characterizing subjectivity in environmental sustainability-based decision making, pose serious impediments towards this goal

Estimación del nivel de habilidad en sistemas tutores inteligentes utilizando una metodología multiatributo

Para el funcionamiento ideal de un sistema tutor inteligente es indispensable poder estimar el nivel de habilidad de los estudiantes de acuerdo a objetivos complejos de aprendizaje. En este trabajo se propone una arquitectura para la evaluación del nivel de habilidad del estudiante, basada en la teoría de la utilidad multiatributo, utilizando como operador de agregación a la integral de Choquet. El método toma en cuenta los objetivos de aprendizaje planteados por el tomador de decisiones (académicos, representantes instituciones, etc.) representados por relaciones complejas que se pueden dar entre los criterios considerados para la evaluación.XVI Workshop Tecnología Informática Aplicada en Educación (WTIAE).Red de Universidades con Carreras en Informática (RedUNCI