Cost estimation is one of the influencial requirements for the current manufacturers when new products are introduced. This paper aims to propose a decision support system (DSS) that retrieves historical cases/products, which have the most similar cost estimates to the current case. This helps users to estimate the costs of new products at early stages of product development. The proposed DSS combines case-based reasoning (CBR), the analytic hierarchy process (AHP) and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. A numerical example is illustrated to show the applicability of the proposed DSS.
Precisely understanding the value and perception of consumers has long been recognized as essential elements of every market-oriented company's core business strategy. For this reason, customers' affection, as the basis for the formation of human values and judgment, should be considered carefully to strengthen the product quality and competitiveness. However, conventional product design places more attention to functional attributes and requires survey process to collect customers' evaluations, neglecting the in-depth study of the underlying associations between design properties and consumers' emotions based on the abundant online consumer response resources. To improve the deficiency, this study was proposed to develop a product affective properties identification approach. Particularly, data mining techniques (e.g. web mining, text mining) are applied to capture online product review resources. Considering the characteristics of user/consumer responses and evaluations, ontology is utilized to assist in the semantic analysis. With the help of product knowledge hierarchy and electronic lexical database, product properties, which can evoke consumers' affect, can be identified. Furthermore, the identified product affective properties are prioritized to provide designers with important reference for future improvement on the product. To illustrate the proposed approach, a pilot study based on iPhone 7 was conducted, in which the influential affective properties have been identified, and a ranking of them has been mapped out.
A.J.C. Trappey, C.V. Trappey, C.Y. Fan, I.J.Y. Lee
985 - 992
The goal of research and development for new product and service development is to satisfy customers. A company that satisfies customers with short life cycle products like smartphones that match or exceed expectations builds brand equity and a global competitive advantage. Companies are challenged with the task of identifying the market demand that evolves with technological innovations. Since many of the short product life cycle communications products are continuously changing, capturing and measuring the satisfaction of customers is increasingly difficult. One approach to accurately identify market demand and customer satisfaction with the functions of products is to listen to what is said among social networks. The Internet has empowered customers to express their opinions, attitudes, beliefs, and purchase intentions about products using community platforms, social media, customer blogs, and other networks. The words, expressed on these platforms, represent the voice of customers and provide collective and dynamic intelligence about the users' purchase intentions as well as experience using the products. Patent documents disclose the technological evolutions of a domain, and contribute to the features and characteristics of products that differentiate between brands and build customer expectations and loyalty. This research proposes a systematic methodology to combine collective intelligence using Internet web crawling and text mining to access the voice of customers. Patent information (retrieved from global patent search engines and analytic function mining of the patent content) provides critical information for planning strategic product repositioning and improvements that match the voice of customers and increase brand loyalty. The systematic extended QFD approach provides intelligent and dynamic demand-compliant strategies for developing new products and services.
Chin-Yuan Fan, Shu-Hao Chang, Hsin-Yuan Chang, Sung-Shun Weng, Shan Lo
993 - 1002
Machine learning has become a key development target globally in recent years. An increasing number of algorithms have been applied to solve practical issues. At the present stage, machine learning technologies have progressed from a pure research topic to tools employed for solving practical issues, becoming a key development direction of practical technologies and a prominent emerging discipline. Furthermore, current machine learning technologies have transformed from tools that supplement decision-making to methods that replace manual decision making when generating optimal decisions. This transformation fundamentally changes the tasks that required relatively long workhours in the past. In addition, this may even facilitate distinctive interpretations to effectively aid researchers and operators in addressing problems from a new perspective. Therefore, this study adopted a machine learning technology, namely artificial neural networks (ANNs), to examine relevant topics in patent quality. To verify the effect and identify the characteristics of machine learning in patent quality analysis, this study focused on the fast-changing internet of vehicles (IoV) industry. tailed analyses of key patents were also performed. Finally, a model of high-quality patents in this industry was developed to serve as a reference for other researchers.
In this paper, we investigate the generation of path-covered test data for automated software testing. Testing plays the critical role in detecting bugs and ensure the quality of the software in the software development lifecycle. As an alternative to the manual testing of high cost, low efficiency and poor reliability, search based approaches have been applied in automated test data generation. We propose a hybrid algorithm to generate the test data by integrating the heuristic approaches of tabu, annealing, and genetic algorithm. We discuss the effects of parameters in the process of genetic operations. Several benchmark source code pieces are used to demonstrate the effectiveness of the proposed approach. The experiment results show that the proposed algorithm has lower time complexity and better performance in convergence compared with other existing algorithms.
Internet of Things (IoT) can be defined as “a world where physical objects are seamlessly integrated into the information network, and where the physical objects can become active participants in business processes. Services are available to interact with these ‘smart objects’ over the Internet, query their state and any information associated with them, taking into account security and privacy issues.” The Internet of Things itself is enabled by a few key technologies which have had extensive progressive in the last few years. As the well world-wide spread of Industry 4.0, IoT-enabled manufacturing plays an important role in supporting smart factory, intelligent automation, and real-time adaptive decision-makings. This paper comprehensively review related technologies and world-wide movements so that insights and lessons could be useful for academia and practitioners when contemplating IoT technologies for upgrading and transforming traditional manufacturing into a Industry 4.0 future.
Most researches on motion control are attempting to control motion from the outside. But as Bernstein pointed out in the case of human motion the number of the degrees of freedom is tremendously large, so it is extremely difficult to control motion from the outside. However, if we note how our bodies contribute to our cognition, there are approaches from the other way , i.e.,from the inside. Gallwey pointed out in his book “The Inner Game of Tennis” what an important role our bodies play in tennis. Our muscles are different from person to person, so to win a game, there is no explicit way, but we must fully utilize our embodied cognition. It is his message. This paper describes a pattern-based approach to motion control, which is based on our capability of embodied cognition.
Industry 4.0 is known as a powerful supportive system that enterprises can enhance their competitiveness. One of critical techniques of industry 4.0 is Cyber-Physical System (CPS). CPS is a mechanism which can control or monitor physical equipment in the front end and utilize the cloud computing in the back end to achieve intelligent production or services. Although the concept of CPS has been understood by industries, how to implement CPS and accomplish the goal of enterprises remains vogue. This study utilizes the framework of CPS to achieve intelligent product design. Based on the data collected from sensors of CPS, Principal Components Analysis (PCA) is firstly employed to figure out key factors. Next, A Artificial Neural Network (ANN) method is developed to build a forecast model to identify parameters which have better yield in the backend. CPS then modify these parameters improve the yield as well as future product design. As a result, the yield issue can be solved not only in the manufacturing but also in the product design stage.
Designing seat belts for modern cars is largely a routine process. Supporting the design process by conventional CAD techniques despite many benefits does not shorten time-consuming design tasks as significantly as we would expect. Only the use of Generative Modeling method can considerably automate routine part of the design process of safety belts. The key to the development of Generative Model is to develop a basis for a seat belt retractor, which determines the whole structure of seat belt assembly. The paper describes in details the design of the Generative Model of the base of a seat belt retractor and the impact of its individual parts on the final form of the belt assembly. In addition, the base is equipped with a Poka-Yoke system, integrated into the Generative Model which ensures the elimination of assembly errors. To build the Generative Model, CATIA system and Knowledgeware tools were used. The paper also shows examples of CAD models of belts assemblies developed using elaborated Generative Models.
Ming Li, Gangyan Xu, Saijun Shao, Peng Lin, G.Q. Huang
1049 - 1056
Logistics resources are of great importance in E-commerce as the essential factors of production, basically including man, machine and material. Optimization for E-commerce logistics always relys on the real-time information of logistics resources. Actually, these resources are exchanging information continuously when they interacting with each other. Since the management granularity for logistics resources still remains at a coarse level, most of the interaction information between resources cannot be well recorded and organized. To achieve fine-grained management of logistics resources that could provide real-time resources visibility, traceability interoperability and availability is urgently needed. The booming of IoT technologies makes it feasible to realize fine-grained management. However, how to make resources smart to interact with other resources and construct a resources-oriented information network to serve enquiries from EISs has not been well studied. This paper presents a ubiquitous cloud object (UCO) framework for logistics resources to achieve fine-grained management. A ubiquitous cloud object model is proposed to abstractly virtualize heterogeneous logistics resources into cloud mappings. The concept of object cluster achieves the flexible resource granularity with a designed object gateway service (OGS) to construct information network. Aiming at facilitating the integration of UCOs with Enterprise Information Systems (EISs), object chain is used to organize UCOs to fulfill the specific workflows.