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PurposeUnder the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services.Design/methodology/approachThe proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community.FindingsA case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS.Originality/valueFollowing the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

PurposeThis project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.Design/methodology/approachA fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.FindingsThe proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.Research limitations/implicationsThe paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.Originality/valueInventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.