查看更多>>摘要：Emerging business areas are early indicators of potential business opportunities, which are considered key to formulating new business strategies and envisioning near-future business environments. However, existing methods for analysing business opportunities solely depend on the opinion and knowledge of experts, which are time-consuming and labour-intensive. In academia, recent years have witnessed a significant increase in attempts to identify emerging business areas as near-future business opportunities with data-driven approaches. Although successful innovation requires sources of novelty, how to measure the novelty of business areas has barely been investigated in the literature. As a solution, we propose an approach to identifying emerging business areas with high novelty with a systematic process and quantitative outcomes. At the heart of the proposed approach is the composite use of the language model and local outlier factor (LOF). The meaning of business opportunities become more explicit by identifying emerging business areas composed of novel goods and services, with implications for the business operation stage. Finally, business opportunity maps are developed based on recency and visibility values, thereby investigating the implications as business opportunities. A case study of the trademarks related to scientific apparatus is presented to illustrate the proposed approach. The systematic process and quantitative results are expected to be employed in practice as a complementary tool, serving as a cornerstone for analysing business opportunities using trademarks. (c) 2022 Elsevier B.V.
查看更多>>摘要：Currently, a mega-trend on digitization and servitization using digital technologies and digital twins to support digital (business) transformation can be observed. In literature, emerging digital technologies are considered as an enabler for the creation of additional value, the strengthening of the customer relationship, and as an accelerator of the servitization process in manufacturing. The introduction of such technologies may result in adaptations of the product & service life cycle as well as the business model. A generic product & service life cycle consisting of four phases - design, simulation, manufacturing, and usage of products and services - serves as a foundation for the integration of digital technologies and related data. It can be observed that there is a gap with respect to data integration between the manufacturing and the usage of products and services. A model-based approach including digital twinning is applied to bridge this gap and to show how data can be integrated along the product & service life cycle. The conceptual approach of such a model-based digital twin environment is presented in form of a meta model. To depict how the integration could be eased and guided in manufacturing, findings of the European project Change2Twin, where a digital twin is established for a paint production pilot, are introduced. This industrial manufacturing pilot was supported by production process models. Additionally, a physical experiment was created to raise awareness for the challenges of digitization and servitization. (c) 2022 Published by Elsevier B.V.
查看更多>>摘要：The macro process, as the core process content of the whole machining process for a design part, serves as a guide for the complete machining process planning. It determines the final machining quality and machining cost of the part to a large extent. However, due to the various bottlenecks in machining process knowledge representation, matching, and inference, macro process planning still depends heavily on the knowledge and experience of process designers. In this paper, a macro process decision-making approach combining knowledge graph and deep learning technology is proposed. Firstly, based on deep learning, the macro process reasoning function is learned to model the mapping relationship between the process elements of the design part and macro process analysis rules. Then the process elements of the design part are fed into the reasoning function to activate the applicable process analysis rules of the process knowledge graph. Next, according to the association relationship between the activated process analysis rules and the predefined machining methods of the macro process graph, a series of working steps nodes and the directed edges are activated, which constitutes the feasible solution space for the macro process. Finally, the swarm intelligence algorithm is applied to search for an effective and low-cost macro process scheme from the feasible macro process solution space. In experimental studies, the slot cavity parts are taken as examples to verify the feasibility and effectiveness of the proposed approach.
查看更多>>摘要：Ontologies are increasingly recognised among the key enablers of the digital transformation of knowledge management processes, but still with a low level of adoption in manufacturing companies. Because ontologies and underlying technologies are complex, Ontology Engineering Methodologies (OEMs) provide a set of guidelines to move from an informal to a formal representation of the company's knowledge base. This study evaluates three agile OEMs, i.e. UPONLite, SAMOD and RapidOWL, in terms of their process and outcome features, i.e. the OEM steps and the expected quality of the ontological models produced. The assessment is performed from the viewpoint of developers of ontology-based technologies in real industrial use cases. Results show that the three agile OEMs reflect different features to effectively support the digital transformation of companies' knowledge management; thus, they cannot be interchangeable. UPONLite is more effective in contexts where there is a lack of skills in OE, with the need for a structured approach in involving domain experts and generating documentation. SAMOD requires a more extended development period, but with several cycles that allow to map different types of knowledge and enable a "try-and-learn" approach. Conversely, RapidOWL lacks a structured sequence of modelling activities and encourages developers to be creative, but at the same time requires higher expertise in OE. Thus, companies and personnel dedicated to OE should choose the methodology according to the main aims guiding their digitalisation process, the current development status, and the level of expertise. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Since its emergence a few decades ago, Product Lifecycle Management (PLM) has mainly shaped product development and production engineering and helped achieve a tremendous quickening in processes and operations. On the other hand, Internet of Things (IoT) is currently in very strong emergence and people's interest in it is increasingly growing. Surprisingly, interaction between IoT and PLM systems are very scarce to this day. Industrialists have enabled a few connections when and where return on investment was high and certain. However, nowadays, the struggle for systems integration remains as the culture difference between PLM's engineering background and IoT's computer science background remains. This research work aims to bridge the gap by making explicit all previous research on PLM and IoT through a systematic literature review that explicits IoT's evolving perimeter over the latest decade. It also tackles literature's approach between the PLM & IoT information systems and humans. It finally proposes and discusses a framework supporting the integration of PLM and IoT in manufacturing industry.(c) 2022 Elsevier B.V. All rights reserved.
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