Design for manufacturing and assembly (DfMA) has been widely applied to support the decision-making process in offsite construction. With a DfMA approach, cost estimation requires taking product design and production processes into consideration. Current studies conduct cost estimation built upon quantity take-offs. However, they do not provide a vocabulary to relate cost estimates to offsite construction processes. This paper presents a new domain ontology, Offsite Housing Ontology (OHO) using the NeOn methodology framework to support cost estimation considering products, resources, and production processes. OHO semantically defines offsite construction domain terminology and relationships. This supports a unified model, required for efficient collaborative design management. The efficiency and effectiveness of the OHO approach are demonstrated in a real-world DfMA scenario through the development of a Knowledge-Based Engineering tool to automate cost estimation. The approach can be adapted and extended to accommodate a very wide range of offsite housing, delivering important optimization and automation benefit from DfMA solutions.
Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer recommendations for overcoming them, ultimately expediting the pace of contact centre automation.
Syntactic and semantic interoperability is a fundamental requirement for the success of the Internet of Things (IoT)-enabled Smart Water Networks (SWNs). Still, whilst consuming publicly accessible IoT data, the syntactic and semantic representation of the collected data poses challenges for the success of pervasive and ubiquitous sensing in the water domain.Challenges include the heterogeneity of data representation formats, semantic models, and the adoption of domain-specific standards and ontologies. These challenges emphasise the requirement for enhanced interoperability in SWNs. To address this, we propose a Data and Information Interoperability Model (DIIM) by combining the Semantic Web technologies, widely known for overcoming interoperability issues, and Model-driven architecture (MDA) approach. DIIM facilitates syntactic interoperability by serialization conversion and adoption of domainspecific standards as well as semantic interoperability of metadata by aligning the semantic models of IoT and Smart Water Network (SWN) applications. Furthermore, it automatically creates an ontology as a semantic model if it is missing and adds references to existing domain-specific ontologies as annotation in their models. We evaluate DIIM methodology by applying it to a real-world use case of IoT-enabled applications for water quality monitoring.
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