A system framework for OKP product planning in a cloud-based design environment

Nowadays, one-of-a-kind (OKP) companies, which generally operate in an 'engineer-to-order' business mode, strive to deliver individualized products with quality to achieve customer satisfaction. Thus, an accurate and prompt analysis of customer requirements (CRs) in the early design stage is critical to its success. However, most OKP companies are small or medium-sized enterprises (SMEs). Due to the limited resources and low product planning budget, they often cannot obtain abundant CR information nor can they afford the expense of complicated planning process. To address these issues, a system framework is proposed in support of OKP product planning process in a cloud-based design (CBD) environment. The challenges and future market niches of OKP companies are presented. The comparison of typical distributed systems shows that CBD, which utilizes advanced information technologies and business model, has advantages in providing sufficient resources, decreasing product development time span for OKP companies in a cost-efficient way. This article describes the proposed system architecture, the business interaction process and the information communication among customers, designers and marketing analysts at the product planning stage. To validate the proposed framework, a prototype system module MyProduct is under development in the CBD environment with an illustrative example.     

Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.     

A knowledge engineering approach to knowledge management

Knowledge management facilitates the capture, storage, and dissemination of knowledge using information technology. Methods for managing knowledge have become an important issue in the past few decades, and the KM community has developed a wide range of technologies and applications for both academic research and practical applications. In this paper, we propose a knowledge engineering approach (KMKE) to knowledge management. First, a knowledge modeling approach is used to organize and express various types of knowledge in a unified knowledge representation. Second, a verification mechanism is used to verify knowledge models based on the formal semantics of the knowledge representation. Third, knowledge models are classified and stored in a hierarchical ontology system. Fourth, a knowledge query language is designed to enhance the dissemination of knowledge. Finally, a knowledge update process is applied to modify the knowledge storage with respect to users’ needs. A knowledge management system for computer repair is used as an illustrative example.     

Recent development of knowledge-based systems,methods and tools for One-of-a-Kind Production

In recent years, product knowledge has played increasingly significant roles in new product development process especially in the development of One-of-a-Kind products. Although knowledge-based systems (KBSs) have been proposed to support product development activities and new knowledge modelling methodologies have been developed, they are still far from complete. This area has become attractive to many researchers and as a result, many new knowledge-based systems, methods and tools have been developed. However, to the best of our knowledge, knowledge-based systems for product development have not been systematically reviewed, compared and summarized. This paper provides a comprehensive review on the recent development of KBS, methods and tools in supporting rapid product development. In the paper, the relevant technologies for modelling, managing and representing knowledge are investigated and reviewed systematically for better understanding their characteristics. The focus is placed on knowledge-based systems that support product development, and how product knowledge is identified, captured, represented and reused during the processes of One-of-a-Kind product development. The limitations and the future trend of KBS are presented in terms of how they can help One-of-a-Kind Production (OKP) companies.     

A knowledge based reliability engineering approach to manage product safety and recalls

PurposeManaging processed food products’ safety and recall is a challenge for industry and governments. Contaminated food items can create a significant public health hazard with potential for acute and chronic food borne illnesses. This industry study examines the challenges companies face while managing a processed food recall situation and devise a responsive and reliable knowledge management framework for product safety and recall supply chain for the focal global manufacturing and distribution enterprise.MethodDrawing upon the knowledge management and product safety and recall literature and reliability engineering theory, this study uses a holistic single case based approach to develop a knowledge management framework with Failure Mode Effects and Criticality Analysis (FMECA) decision model. This knowledge management decision framework facilitates analysis of the root causes for each potential major recall issue and assesses the reliability of the product safety and recall supply chain system and its critical components.ResultsThe main reasons highlighted for a recall and associated failure modes in a knowledge management framework are to devise appropriate deployment of resources, technology and procedures to recall supply chain. This study underscores specific information described by managers of a global processed food manufacturer and their perspectives about the product safety and recall process, and its complexities. Full scale implementation of product safety and recall supply chain in the proposed knowledge management framework after the current pilot study will be carried out eventually through expert systems. This operational system when fully implemented will capture the essence of decision making environments comprising goals and objectives, courses of action, resources, constraints, technology and procedures.ImplicationsThe study recognizes the significance of communication, integration, failsafe knowledge management process design framework, leveraging technology such as Radio Frequency Identification (RFID) within all levels of supply chain for product traceability and the proactive steps to help companies successfully manage a recall process and also reestablish trust among the consumers. The proposed knowledge management framework can also preempt product recall by acting as an early warning system. A formal knowledge management framework will enable a company’s knowledge be cumulative for product safety and recall and serve as an important integrating and coordinating role for the organization.     

Fostering continuous innovation in design with an integrated knowledge management approach

In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on knowledge management for innovation. However the strategy of knowledge management is not widely adopted for innovation in industries due to a lack of an effective approach of their integration. This study aims to help the designers to innovate more efficiently based on an integrated approach of knowledge management. Based on this integrated approach, a prototype of distributed knowledge management system for innovation is developed. An industrial application is presented and its initial results indicate the applicability of the approach and the prototype in practice.     

Assessing users'' product-specific knowledge for personalization in electronic commerce

While many electronic commerce (EC) companies are adopting one-to-one marketing approaches using various personalization technologies to make their products and services unique for the purpose of attracting and retaining customers and improving their completion edges in the EC ecosystem, which, nevertheless, has low entrance barriers for new players to join and further intensify the competition, none or few of them consider a fundamental issue—the user's product-specific knowledge. Our research proposed to add this new domain of the customer's knowledge on appropriate target products into the personalization process as a part of the overall EC strategy for businesses. In this paper, we present our initial design for assessing the user's product-specific knowledge using the proposed innovative method for detecting it directly in a non-intrusive way without asking users to answer or fill out any types of questionnaires. Our method is based on customer's on-line navigation behaviors by analyzing their navigation patterns through pre-trained artificial neural networks. An empirical study designed for a case of EC store selling digital cameras was conducted in our research to prove the concept, and a good preliminary result was derived from the study.

For the purpose of comparing the performances between the conventional approach of using questionnaire and the proposed innovative approach of navigation pattern mining, a questionnaire based approach for evaluating the user's product-specific knowledge was designed and incorporated into our knowledge level assessment system (KLAS). Our study result shows that although the pure questionnaire-based KLAS is intrusive and may not be accepted by some users, for those users willing to complete the questionnaire, the proposed navigation pattern approach can be combined with the questionnaire-based approach to create a hybrid KLAS which has a significantly improved accuracy rate in detecting the customer's product knowledge level.     

Application of data mining methods for effort estimation of software projects

Information technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision-making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross-Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision-making process.     

Pattern-based knowledge workflow automation: concepts and issues

As the result of business process automation, more and more knowledge is codified and stored in knowledge repositories and scattered in employees’ computers across functionally and geographically separated business units. While several alternative mechanisms such as chat rooms, search engines, recommender systems exist to retrieve and access knowledge, satisfying a knowledge requirement often involves the coordination of multiple tasks and the use of several technologies. However, there is no existing technology for orchestrating various collaboration, communication and information retrieval components to satisfy the knowledge needs in corporations. In order to address this gap, we propose the approach of pattern-based knowledge workflow that can enable the automation of knowledge flows across an organization. In this paper, we present an overview of the pattern-based knowledge workflow approach and propose extensions to the conventional workflow paradigm. We demonstrate the feasibility of implementing knowledge workflows by means of a Business Process Execution Language (BPEL) specification for executing knowledge workflows in a business setting. We then discuss the engineering challenges and research issues that need to be addressed to further develop this approach.

A decision support system for the selection of computer-integrated manufacturing technologies

The adoption of computer-integrated manufacturing (CIM) offers manufacturing organizations many tangible and intangible benefits which enable them to produce products of high quality at low costs. However, the selection and evaluation of CIM is a complex process as it involves the consideration of many parameters to ensure that the selected technology meets the requirements of individual companies. This paper describes the development of a quantitative/qualitative decision support system for the evaluation of CIM which takes into consideration the objectives and operating characteristics of a company, thus ensuring that the selected technology matches the individual needs of that company. The methodology used in the decision support system is based on a combination of the analytical hierarchy process (AHP) and database technology. The AHP provides a means to consider both the tangible and the intangible benefits of CIM while databases are used to store the knowledge about the various benefits that CIM may offer. The system has been implemented in EXCEL, which fully automates the evaluation process. A case study is also presented to illustrate the capability of the proposed decision support system.     

Fuzzy temporal reasoning for process supervision

Abstract: Process supervision consists of following the temporal evolution (change) of process behaviours. This task has usually been performed based on the knowledge and experience of domain experts and operators. Actually, these experts and operators almost always express their experience and knowledge about process evolution in an imprecise, fuzzy and vague way. A good supervision system should be capable of dealing at once with two different kinds of knowledge: time and uncertainty.
For many years, time and uncertainty have been two of the most important topics in Artificial Intelligence research and applications. Many approaches have been proposed to deal with either one or the other. Among the various approaches for time, reified logic has been considered as the most influent one. Possibilistic logic, on the other hand, has shown its ability to handle uncertain knowledge and information. This paper describes an approach for managing temporal uncertainty based on fuzzy logic and possibility theory. A fuzzy temporal expert system shell has been developed to perform process supervision tasks.     

An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase

Continuous innovation intended to deliver products with new attributes is an imperative driver for organizations to remain competitive in today’s fast changing market. A successful innovation is often associated with adoption and execution of all SECI (socialization-externalization-combination-internalization) modes of knowledge creation within any Product Development (PD) phase. This article is an attempt to argue with the general notion and to distinguish different PD phases’ affinity corresponding to distinct SECI modes. In this regard, the paper proposes an extended Fuzzy Analytic Hierarchy Process (EFAHP) approach to determine the ranking in which any PD phase is influenced from SECI modes. In the EFAHP approach, the complex problem of knowledge creation is first itemized into a simple hierarchical structure for pairwise comparisons. Next, a triangular fuzzy number (TFN) concept is applied to capture the inherent vagueness in linguistic terms of a decision-maker. This paper recommends mapping TFNs with normal distributions about X-axis. This allows us to develop a mathematical formulation to estimate the degree of possibility (importance value) when two TFNs do not intersect with each other (current techniques in literature calculate this value as zero). In order to demonstrate the applicability and usefulness of the proposed EFAHP in ranking SECI modes, an empirical study of development phase is considered. Five criteria and their 19 sub-criteria for measuring the phase’s performance are identified based on both an extensive discussion with subject matter experts and rigorous literature survey. After stringent analysis, we found that the mode that highly influenced the development phase is ‘combination mode’. The article discusses the application of lean tools that can be employed to improve the knowledge creation process.     

An approach to the development of commonsense knowledge modeling systems for disaster management

Knowledge is the fundamental resource that allows us to function intelligently. Similarly, organizations typically use different types of knowledge to enhance their performance. Commonsense knowledge that is not well formalized modeling is the key to disaster management in the process of information gathering into a formalized way. Modeling commonsense knowledge is crucial for classifying and presenting of unstructured knowledge. This paper suggests an approach to achieving this objective, by proposing a three-phase knowledge modeling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML, and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy.     

知识图谱构建技术:分类、调查和未来方向

知识图谱的概念由谷歌于2012年提出,随后逐渐成为人工智能领域的一个研究热点,已在信息搜索、自动问答、决策分析等应用中发挥作用。虽然知识图谱在各领域展现出了巨大的潜力,但不难发现目前缺乏成熟的知识图谱构建平台,需要对知识图谱的构建体系进行研究,以满足不同的行业应用需求。文中以知识图谱构建为主线,首先介绍目前主流的通用知识图谱和领域知识图谱,描述两者在构建过程中的区别;然后,分类讨论图谱构建过程中存在的问题和挑战,并针对这些问题和挑战,分类描述目前图谱构建过程中的知识抽取、知识表示、知识融合、知识推理、知识存储5个层面的解决方法和策略;最后,展望未来可能的研究方向。     

On technology for functional requirement-based reference design retrieval in engineering knowledge management

Engineering design is a knowledge-intensive process, and includes conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each of these tasks involves various aspects of technical knowledge and experience. Whether this technical knowledge and experience can be effectively shared is key to increasing product development capability and quality, and also to reducing the duration and cost of the development cycle. Consequently, providing engineering designers various query methods for retrieving engineering knowledge is one of the most important tasks in engineering knowledge management.The study develops a technology for functional requirement-based reference design retrieval as a decision support mechanism, which can assist engineering designers to retrieve relevant design and associated knowledge for reference in conducting functional requirements of a product. This study involves the following tasks: (i) designing a functional requirement-based reference design retrieval process, (ii) developing techniques related to the technology for functional requirement-based reference design retrieval, and (iii) implementing a functional requirement-based reference design retrieval mechanism. The retrieval process includes the steps of functional requirement-based query, case searching and matching, and case ranking. The technology involves (i) a structured query model for functional requirements, (ii) an index structures for historical design cases, (iii) functional requirement-based case searching and matching mechanisms, (iv) a functional requirement-based case ranking mechanism, and (v) a case-based representation of designed entities. Finally, the experimental example with indexing and retrieving similar designed entities is conducted to demonstrate the proposed techniques worked efficiently.     

Systematic modeling and reusing of process knowledge for rapid process configuration

Rapid product development relies heavily on quick and reliable process planning and knowledge reuse. Process configuration borrows the principles of product configuration and applies them to process planning. It allows the reuse of hierarchical, historical and case-based manufacturing process knowledge to generate process plans efficiently and effectively. This paper presents a systematic knowledge model for manufacturing process cases. The model represents process knowledge at different levels of granularity and to facilitate process configuration. With this knowledge model the process knowledge is categorized into 6 levels: (i) core process skeletons, (ii) process networks, (iii) process routes, (iv) process segments, (v) processes/workplan, and (vi) operations/working step. Based on the systematic knowledge model, this paper also proposes an approach to achieve rapid process configuration by reusing the process knowledge and by performing the involved configuration rules so as to create an appropriate, reliable and effective process plan. The prototype for a graphical process planner based on this knowledge model is developed and an aerospace example is used to validate the proposed process knowledge model and process configuration.     

A system framework for gamified Cost Engineering

This study develops a system framework and an enterprise IT solution for integrating gamification elements to efficiently implement and continuously perform Cost Engineering. Cost Engineering is a systematic approach to manage knowledge and competencies regarding costs reduction measures throughout the life cycle of products and is technology, resource and time intensive. Gamification as a non-monetary multidimensional incentive system holds great potential to implement and establish Cost Engineering in a novel and less resource demanding manner and stipulate knowledge sharing and dissemination. Building on a review of the relevant literature we conducted 20 interviews with experts from eight companies of the German and Austrian high-tech manufacturing industry to examine the system requirements from an organizational perspective. Analyzing the interviews, we found that companies need a flexible platform where the game elements help to align management objectives with concrete tasks, meet legislative regulations and have different evaluation methods. Our proposed system framework combines the organizational and IT requirements with gamification elements to efficiently steer Cost Engineering methods and best manage knowledge and competencies.     

A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing

The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision-making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision-making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing.     

知识编译研究

知识编译是处理命题逻辑知识库常用的一种新技术,知识编译的过程就是把知识转换成易于推理的表示形式.作为一种有效的推理机制,这种技术已经被广泛地应用到各种各样的人工智能领域中.对目前知识编译的研究与应用进行了综述性的介绍,并且从简洁性、多项式时间内支持的查询操作以及多项式时间内支持的转换操作3个方面对各种知识编译目标语言做了系统的阐述.