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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.

With the rapid growth of perishable food e-commerce businesses, there is a definite need for logistics services providers to manage parcel shipments with multi-temperature requirements. E-commerce ...

Food retailer’s grading and selection is an essential process for determining the consumer priceand guaranteeing the quality before launching to the market. Such a grading process, however, often r...

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

Virtual reality (VR) is rapidly developed and bringing advancement in various related technologies through the virtual world. It has high potential and plays an important role in education and training fields. Mixed reality (MR) is a type of hybrid system that involves both physical and virtual elements. While VR/MR has proved to be an effective way to improve the learning attitude and effectiveness for secondary students, however, not much work has been conducted on university students to compare the MR experience and traditional teaching approaches in learning design subjects. In this project, we investigated the effectiveness of students in learning design subjects with the support of MR. The effectiveness was measured based on their creativity and systematic approaches in design. Pretests and posttests were conducted to measure the learning effects. We also compared the learning effectiveness of a student’s study with the MR and traditional teaching materials. Nonparametric analyses were conducted to investigate whether the improvements were significant. Experimental results showed that after studying with the support of the MR technology, the students’ abilities in geometric analysis (mean difference = 4.36, p

Purpose The purpose of this paper is to develop an Internet of medical things (IoMT)-based geriatric care management system (I-GCMS), integrating IoMT and case-based reasoning (CBR) in order to deal with the global concerns of the increasing demand for elderly care service in nursing homes. Design/methodology/approach The I-GCMS is developed under the IoMT environment to collect real-time biometric data for total health monitoring. When the health of an elderly deteriorates, the CBR is used to revise and generate the customized care plan, and hence support and improve the geriatric care management (GCM) service in nursing homes. Findings A case study is conducted in a nursing home in Taiwan to evaluate the performance of the I-GCMS. Under the IoMT environment, the time saving in executing total health monitoring helps improve the daily operation effectiveness and efficiency. In addition, the proposed system helps leverage a proactive approach in modifying the content of a care plan in response to the change of health status of elderly. Originality/value Considering the needs for demanding and accurate healthcare services, this is the first time that IoMT and CBR technologies have been integrated in the field of GCM. This paper illustrates how to seamlessly connect various sensors to capture real-time biometric data to the I-GCMS platform for responsively supporting decision making in the care plan modification processes. With the aid of I-GCMS, the efficiency in executing the daily routine processes and the quality of healthcare services can be improved.

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.

Quality assurance (QA) of pharmaceutical warehouses is a critical issue for formulating working instructions in accord with many regulations. Currently, such QA planning, relying on human e...

Customer loyalty is one of the core values for business success in beauty industry; however, there is insufficient research on weighing the importance of critical factors contributing to customer l...

Railways provide convenience and efficiency to the travelling public, with passengers’ safety being the top priority in railway transit systems. Nowadays, with the population increase in many cities, railway safety and punctuality is of concern, and an effective surveillance system is needed to minimize the severity of an accident. The fallen object detection (FOD) system is undoubtedly important in a surveillance system, as it is concerned with accidents that are due to objects falling through platform gaps. With the aid of advanced technology, radio frequency identification (RFID) can be used for further enhancement in providing accurate and prompt information. An RFID-based FOD system (RFFODS) is proposed to ensure railway passengers’ safety. A study of the RFFODS in a mass transit system is reported to illustrate the performance of RF technology for FOD in outdoor environments. The location of an antenna is determined by using the analytic hierarchy process. The feasibility and performance of the proposed system are verified with the results of an extensive on-site experiment conducted in a light rail vehicle station. It is expected that the proposed system would play a key role in establishing an intelligent monitoring system for passengers’ safety in future railway developments.

Purpose This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment. Design/methodology/approach A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system. Findings The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity. Practical implications Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society. Originality/value This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

The e-commerce internal order processing flow is streamlined and re-designed.A GA-rule-based system for efficient e-commerce order fulfilment is proposed.An optimal order processing plan is generated by genetic algorithm technique.A system implementation shows a significant order processing time reduction. In today's world of digitization, the rise of the e-commerce business around the globe has brought a tremendous change not only in our purchasing habits, but also to the entire retail and logistics industry. Given the irregular e-commerce order arrival patterns, limited time for order processing in e-fulfilment centres, and the guaranteed delivery schedules offered by e-retailers, such as same-day or next-day delivery upon placing an order, logistics service providers (LSPs) must be extremely efficient in handling outsourced e-commerce logistics orders. Without re-engineering the order fulfilment processes, the LSPs are found to have difficulties in executing the order fulfilment process due to the tight handling requirements. This, in turn, delays the subsequent processes in the supply chain, such as last-mile delivery operations, consequently affecting customer satisfaction towards both the retailer and the LSP. In view of the need to improve the efficiency in handling e-commerce orders, this study aims at re-engineering the fulfilment process of e-commerce orders in distribution centres. The concept of warehouse postponement is embedded into a new cloud-based e-order fulfilment pre-processing system (CEPS), by incorporating the genetic algorithm (GA) approach for e-commerce order grouping decision support and a rule-based inference engine for generating operating guidelines and suggesting the use of appropriate handling equipment. Through a case study conducted in a logistics company, the CEPS provides order handling solutions for processing e-commerce logistics orders very efficiently, with a significant reduction in order processing time and traveling distance. In turn, improved operating efficiency in e-commerce order handling allows LSPs to better align strategically with online retailers, who provide customers with aggressive, guaranteed delivery dates.

Purpose In view of the elderly caregiving service being in high demand nowadays, the purpose of this paper is to develop an intelligent e-healthcare system for the domestic care industry by using the Internet of Things (IoTs) and Fuzzy Association Rule Mining (FARM) approach. Design/methodology/approach The IoTs connected with the e-healthcare system collect real-time vital sign monitoring data for the e-healthcare system. The FARM approach helps to identify the hidden relationships between the data records in the e-healthcare system to support the elderly care management tasks. Findings To evaluate the proposed system and approach, a case study was carried out to identify the association between the specific collected demographic data, behavior data and the health measurements data in the e-healthcare system. It is found that the discovered rules are useful for the care management tasks in the elderly healthcare service. Originality/value Knowledge discovery in databases uses various data mining techniques and rule-based artificial intelligence algorithms. This paper demonstrates complete processes on how an e-healthcare system connected with IoTs can support the elderly care services via a data collection phase, data analysis phase and data reporting phase by using the FARM to evaluate the fuzzy sets of the data attributes. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness.

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.

Amid the coronavirus outbreak, many countries are facing a dramatic situation in terms of the global economy and human social activities, including education. The shutdown of schools is affecting many students around the world, with face-to-face classes suspended. Many countries facing the disastrous situation imposed class suspension at an early stage of the coronavirus outbreak, and Asia was one of the earliest regions to implement live online learning. Despite previous research on online teaching and learning, students' readiness to participate in the real-time online learning implemented during the coronavirus outbreak is not yet well understood. This study explored several key factors in the research framework related to learning motivation, learning readiness and student's self-efficacy in participating in live online learning during the coronavirus outbreak, taking into account gender differences and differences among sub-degree (SD), undergraduate (UG) and postgraduate (PG) students. Technology readiness was used instead of conventional online/internet self-efficacy to determine students' live online learning readiness. The hypothetical model was validated using confirmatory factor analysis (CFA). The results revealed no statistically significant differences between males and females. On the other hand, the mean scores for PG students were higher than for UG and SD students based on the post hoc test. We argue that during the coronavirus outbreak, gender differences were reduced because students are forced to learn more initiatively. We also suggest that students studying at a higher education degree level may have higher expectations of their academic achievement and were significantly different in their online learning readiness. This study has important implications for educators in implementing live online learning, particularly for the design of teaching contexts for students from different educational levels. More virtual activities should be considered to enhance the motivation for students undertaking lower-level degrees, and encouragement of student-to-student interactions can be considered.

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.

Provide knowledge support for quality assurance in the garment industry.Extract relationships among parameters and quality by fuzzy association rule mining.Design novel nature-inspired genetic algorithms with variable lengths.Optimize fuzzy association rules with the use of the slippery genetic algorithm. Due to the error-prone nature of garment manufacturing operations, it is challenging to guarantee the quality of garments. Previous research has been done to apply fuzzy association rule mining to determine process settings for improving the garment quality. The relationship between process parameters and the finished quality is represented in terms of rules. This paper enhances the application by encoding the rules into variable-length chromosomes for optimization with the use of a novel genetic algorithm (GA), namely the slippery genetic algorithm (sGA). Inspired by the biological slippage phenomenon in DNA replication, sGA allows changes to the chromosome lengths by insertion and deletion. During rule optimization, different parameters can be inserted to or removed from a rule, increasing the diversity of the solutions. In this paper, a slippery genetic algorithm-based process mining system (sGAPMS) is developed to optimize fuzzy rules with the aim of facilitating a comprehensive quality assurance scheme in the garment industry. The significance of this paper includes the development of a novel variable-length GA mechanism and the hybridization of fuzzy association rule mining and variable-length GAs. Though the capability of conventional GA in rule optimization has been proven, the diversity in the population is inherently limited by the fixed chromosome length. Motivated by this phenomenon, the sGA suggested in this paper allows various parameters to be considered in a rule, improving the diversity of the solutions. A case study is conducted in a garment manufacturing company to evaluate the sGAPMS. The results illustrate that better quality assurance can be achieved after rule optimization.

The digitalisation in healthcare opens opportunities for more effective chronic disease management. Digitalised medical records are valuable data sources for identifying high-risk patients and facilitating early clinical intervention. However, the liberation of data has plagued adoption amongst physicians as massive data mean more difficult to identify important knowledge from the data. In the cervical cancer context, many patients are adherence to prescription medications only when symptoms appear, beyond the earlier point-in-time of the disease progression. Regular screening is the only way to detect abnormal cells that may develop into cancer if left untreated. Yet, without a comprehensive understanding of the relationship between risk factors and healthcare outcomes, inappropriate screening procedures may be conducted, lengthening the treatment process. Delay in the treatment process may have an irreversible influence on patients’ conditions as chronic diseases progress. This study demonstrates a data-mining framework which extracts knowledge that can advance cervical cancer screening processes in the form of association rules and improves the generalisation potential of the rules for deployment. The knowledge discovered serves as an additional supplement for physicians’ experience and uncovers appropriate screening strategies based on patients’ risk factors, increasing the chances of high-risk patients getting treated for cervical pre-cancers.

In today's volatile markets, increasingly unpredictable customer demand is exerting great challenges to responsive replenishment. The complexity of responsive replenishment is higher when the business is global in which demand in both domestic and overseas markets has to be catered for. The emergence of cloud computing has eased the difficulties as it allows nearly real-time access to a universal platform for information sharing between franchisors and franchisees, creating huge opportunities for understanding global market needs for responsive replenishment. Considering the existence of uncertainties due to the fluctuating demands, fuzzy logic is useful in providing decision support for replenishment in uncertain environments. This paper presents a cloud-based responsive replenishment system to manage operation data of a franchise business using cloud computing, and for analysis using fuzzy logic in order to provide franchisors with the required inventory levels. To the best of our knowledge, this is the first study that applies cloud computing and artificial intelligence techniques in franchising. A pilot run of the system is conducted in an education company, which is considered to be a good representation of an industry operating with a franchise model. The results show that the system allows franchisors to formulate effective responsive replenishment strategies.

Purpose– The purpose of this paper is to propose a franchising decision support system (FDSS) for future development planning by center positioning strategy formulation under a franchising business model.Design/methodology/approach– The system makes use of data collected from the franchising business and external environment analysis for decision making in center positioning problems. The fuzzy logic approach is integrated into the system for analyzing the geographical market dynamics including profitability and competitiveness in the district concerned. To demonstrate the application of the proposed FDSS, a case study is conducted in a Hong Kong-based franchising private education center, i.e. Dr I-Kids Education Center.Findings– The tailor made FDSS helps to facilitate the business operations of the franchising education center and develops a district positioning model for the centers located in the 18 districts of Hong Kong. The findings provide a solid foundation for marketing strategy and expansion direction formulation.Originality/value– Due to the globalization of business, managing a growing franchising business model becomes challenging to the franchisor. In order to fully leverage the merits of the franchising system, an intelligent decision support model, focusing on making strategic development plans, is needed so as to expand the scale of business efficiently.

Purpose – The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion. Design/methodology/approach – The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which are not only fashionable, but are also saleable in the market. Findings – To evaluate the proposed approach, a case study is conducted in a Hong Kong-based fashion company in which a real-set of data are tested to generate fuzzy association rules. The results reveal that the FARM approach can provide knowledge support to the fashion industry during NPD, shorten the NPD cycle time, and increase customer satisfaction. Originality/value – Compared with traditional association rule mining, the proposed FARM approach takes the fuzziness of data into consideration and the knowledge represented in the fuzzy rules is in a more human-understandable structure. It captures the voice of the customer into fashion product development and provides a specific solution to deal with the challenges brought by fast fashion. In addition, it helps increase the innovation and technological capability of the fashion industry.

In today's time-sensitive markets, effective storage policies are widely accepted as a means for improving the efficiency of order picking. As a result of customization, the variety of products handled by a warehouse has increased, making storage location assignment problems more complicated. Different approaches have been proposed by researchers for improving storage assignment and order picking. However, many industrial practitioners find it difficult to adopt such approaches due to complexity and high associated costs. In particular, small and medium enterprises (SMEs), that generally, lack resources and who have staff members with weak artificial intelligence backgrounds, still rely on experience when assigning storage locations for diverse products. In these circumstances, the quality of decision making cannot be guaranteed. In view of this, an intelligent system which can be easily adopted by SMEs is designed to improve storage location assignment problems. The proposed system, an RFID-based storage assignment system (RFID-SAS), is a rule-based system incorporating radio frequency identification (RFID) provides decision support for storage assignment in a warehouse. Unlike many existing situations, RFID tags are attached to products at the item level instead of at the pallet level. As the knowledge embedded in the system is represented in the form of rules, evaluation is important and is outlined in this paper. The effectiveness of the system is verified by means of a case study in which the system is implemented in a typical SME specializing in machinery manufacturing. The results illustrate that RFID-SAS can enhance the efficiency of order picking in a warehouse.

With the increasing concern about product quality, attention has shifted to the monitoring of production processes to be assured of good quality. Achieving good quality is a challenging task in the garment industry due to the great complexity of garment products. This paper presents an intelligent system, using fuzzy association rule mining with a recursive process mining algorithm, to find the relationships between production process parameters and product quality. The goal is to derive a set of decision rules for fuzzy logic that will determine the quantitative values of the process parameters. Learnt process parameters used in production form new inputs of the initial step of the mining algorithm so that new sets of rules can be obtained recursively. Radio frequency identification technology is deployed to increase the efficiency of the system. With the recursive characteristics of the system, process parameters can be continually refined for the purpose of achieving quality assurance. A case study is ...

Product aesthetics plays an important role in new product design and development. Product form can deliver product images and affect customer's impression to a product. However, it is usually difficult to apply conventional approaches to represent the product form precisely and effectively for modeling the relationship between product image and customer perception. The objective of this work is to develop a computational technique for product aesthetics design so that customer perception can be taken into product form design in a more systematic and intelligent manner. To achieve this aim, a novel parametric approach is proposed to introduce design parameters such as line, size, and ratio into product design model and the technique of generalized superellipse fitting is adopted to describe the outline pattern of a product. Since customer perception on a product is highly non-linear and very difficult to be described by any traditional mathematical approaches, an artificial neural network (ANN) model is therefore established to relate the design parameters and the perceptual values for the design of a new product. A case study of mobile phone design, in which twelve numerical parameters are defined for the conceptual model, has been conducted to explain the implementation of the proposed approach. A three-layered perceptron ANN model is developed to predict the perceptual values of stylishness based on a survey using 32 mobile phone samples. The results of the case study illustrate that the proposed approach can successfully generate an optimum design of a mobile phone by applying a genetic algorithm (GA) on the trained ANN model.

In the competitive and low entrance barrier beauty industry, customer loyalty is a critical factor for business success. Research literature of customer relationship management recommends various factors contributing to customer loyalty in the general setting, however, there are insufficient studies empirically weigh the importance of each critical factor for the beauty industry. This study investigates and ranks empirically the critical factors, which contributes to customer loyalty of Online-to-Offline (O2O) marketing in the beauty industry. Our result shows that customer satisfaction, customer switching costs, customer trust, corporate image and customer value positively influence customer loyalty of O2O marketing and in the order of decreasing importance. Attributes contributing to the five critical factors have also been studied and ranked. Findings of this study can help the beauty industry to develop an effective O2O marketing plan and hence customer loyalty can be enhanced through the process of implementing targeted marketing activities.

Highlights? We study Hong Kong Stock Exchange which was the second largest stock market in Asia up to 2010. ? We examine association of Hang Seng Index with other economics indices. ? Increasing RMB-HK exchange rate and export value will highly increase the variation of Hang Seng Index. ? Increasing RMB-HK exchange rate, export value and GDP will also highly increase the variation of Hang Seng Index. ? The minded rules reveal interesting patterns between different economical indices and the individual stock market price. In the rapidly changing financial market, investors always have difficulty in deciding the right time to trade. In order to enhance investment profitability, investors desire a decision support system. The proposed artificial intelligence methodology provides investors with the ability to learn the association among different parameters. After the associations are extracted, investors can apply the rules in their decision support systems. In this work, the model is built with the ultimate goal of predicting the level of the Hang Seng Index in Hong Kong. The movement of Hang Seng Index, which is associated with other economics indices including the gross domestic product (GDP) index, the consumer price index (CPI), the interest rate, and the export value of goods from Hong Kong, is learnt by the proposed method. The case study shows that the proposed method is a feasible way to provide decision support for investors who may not be able to identify the hidden rules between the Hang Seng Index and other economics indices.

This paper presents an innovative electronic medical records (EMR) system, RF-MediSys, which can perform medical information sharing and retrieval effectively and which is accessible via a ‘smart’ medical card. With such a system, medical diagnoses and treatment decisions can be significantly improved when compared with the conventional practice of using paper medical records systems. Furthermore, the entire healthcare delivery process, from registration to the dispensing or administration of medicines, can be visualised holistically to facilitate performance review. To examine the feasibility of implementing RF-MediSys and to determine its usefulness to users of the system, a survey was conducted within a multi-disciplinary medical service organisation that operates a network of medical clinics and paramedical service centres throughout Hong Kong Island, the Kowloon Peninsula and the New Territories. Questionnaires were distributed to 300 system users, including nurses, physicians and patients, to collect feedback on the operation and performance of RF-MediSys in comparison with conventional paper-based medical record systems. The response rate to the survey was 67%. Results showed a medium to high level of user satisfaction with the radiofrequency identification (RFID)-based EMR system. In particular, respondents provided high ratings on both ‘user-friendliness’ and ‘system performance’. Findings of the survey highlight the potential of RF-MediSys as a tool to enhance quality of medical services and patient safety.

In today's competitive environment, business organizations are forced to maintain their competitive advantage by their ability to cut costs, increase revenue and uncover hidden issues. In order to enhance the visibility and transparency of value added information in a supply chain network, a process mining system is proposed for discovering a set of fuzzy association rules based on the daily captured logistics operation data, within the network. The proposed methodology provides all levels of employees with the ability to enhance their knowledge and understanding of the current business environment. Once interesting association rules have been extracted, organizations can identify the root-causes of quality problems in a supply chain and improve performance by fine-tuning the configuration of process parameters in specified processes. The application of the proposed methodology in a case company has also been studied. The prototype system has been developed and evaluated after performing a spatial analysis. The results obtained indicate that the system is capable of extracting high-quality and actionable information in the case company.

In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization's success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ''distributed process mining'' feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

PurposeThis paper aims to develop a genetic algorithm (GA)‐based process knowledge integration system (GA‐PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network.Design/methodology/approachThe proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items.FindingsThe application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality.Research limitations/implicationsIn order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes.Originality/valueBecause of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA‐based approach can help to optimize the process parameters along the supply chain network.

The hard disk drive is a reliable and relatively cheap mass storage device used in every computer nowadays. In this study, one major issue affecting the product quality of the fixture inside a hard disk drive is the surface contamination of the arm finger of actuator (AFA). For economical exploitation, a primary concern is to generate a model for optimizing the process parameter settings necessary to sustain the desired cleanliness level in an ultrasonic cleaning process. Two approaches were employed to identify critical process parameters, followed by the determination of the optimal parameter settings. The former approach was a statistical design of experiments (DOE) for developing regression equations for predicting the cleanliness level and finding out the dependence of each parameter and outcome. The latter approach was in using an artificial neural network (ANN) for building prediction models. A comparative study showed that both approaches have advantages over other methods. The results obtained show a reduction in contamination of the AFA; hence it provides an aid in the improvement of product quality.

Network topology management is one of the critical concerns when designing a Wireless Sensor Network (WSN). In this research, four basic factors including the total production cost, sensing coverage, network connectivity and fault tolerance are considered. A mathematical model is proposed to optimize four optimization metrics corresponding to the four design factors. This approach attaches a weighting coefficient to each optimization metric to adjust their importance in the optimization model. To solve the proposed model, an Ant Colony Optimization (ACO) based metaheuristics method, called MAX–MIN Ant System (MMAS) is used. In the experiment, Greedy algorithm (Greedy) and Genetic Algorithm (GA) are also adopted to solve the proposed model. The results indicate that MMAS shows a satisfactory performance on solving the proposed model, which there is an improvement on the number of sensor nodes comparing to the result of Greedy, and a better fitness value than the result of GA.

Biological and pharmaceutical (B&P) products are essential in modern life. Currently, the demand and requirements of these products are dramatically growing; product quality and integrity then become crucial. As a key factor for ensuring quality and integrity, the current product status monitoring system needs to be enhanced in a more sophisticated and comprehensive way. This research describes the design of a wireless sensor network (WSN) monitoring system for B&P products supply chain management. In this study, the system architecture is presented based on the proposed framework. The framework consists of three processes: the first one is to generate a WSN with full coverage, the second one is to determine the optimal position of the sink, and the last process is to add additional relay nodes for providing fault tolerance to the network. Finally, a feasibility test on B&P product storage in a hospital is conducted. The proposed system provides a systematic approach for the deployment of sensor nodes and relay nodes. It also provides a relatively simple and rapid method for industries in general to follow and implement when designing a WSN system.

The globalization of competition and changing customer requirements are the emerging phenomenon of today's manufacturing environment. Nowadays, manufacturers need useful information prior to making important decisions based on their experience and knowledge. However, the lack of seamless data transformation between database and knowledge based system hinders design practitioners from responding rapidly to the changes in the complex and dynamic product development environment. This paper proposes a dynamic product information schema which is characterized by its ability to provide designers with a product data exchange standard, thus, enhancing the responsiveness of product development. The approach is based on the modification of the XML standard which is assimilated with object technology to form a cross-platform protocol that enables enterprises to update their data constantly. In particular, data from the traditional relational database model can be transformed to a format which is acceptable in a knowledge base system. To validate the feasibility of the proposed schema, a trial run has been conducted in a local company based on the suggested approach.

PurposeThis paper proposes an infrastructure of a responsive supply chain network, focusing on the deployment of the m‐commerce technology which transforms a traditional supply chain network to be more effective in coping with market changes.Design/methodology/approachThe proposed supply chain infrastructure embraces the concepts of distributed object technology, wireless markup language (WML), and extensible markup language (XML) schema to enable efficient data exchange among various data objects which reside in distributed platforms over geographically‐isolated regions, thereby leveraging the responsiveness of the entire supply chain network. A case study is conducted to evaluate the feasibility of the proposed model.FindingsRecent studies have found that wireless technology, mobile computing and internet programming techniques drive the development of mobile solution in various industries. Apart from location tracking of goods as well as relevant services, m‐commerce is able to play an important role to enhance the performance of a supply chain network, which is concerned with the proper monitoring of suppliers and production circles, encompassing a wide spectrum of value chain activities ranging from product design to after‐sales services.Originality/valueThe significance of this research is the demonstration of the synergy of using a combination of emerging technologies to form an integrated system that helps achieve flexibility and agility in supply chain network.

In today's competitive environment, rapid advances in globalization and information technology have brought change at an unprecedented rate. Manufacturing, merchandising, and service companies are finding that their current approach for existing logistics operation no longer suffices. It is desirable to have a seamless and bi-directional interchange of information, coordination of decisions, and enhancement of assimilation of subsystems within and outside the relevant organization. In this paper, the proposed logistics workflow optimizer embraces the combination of On-Line Analytical Processing and Genetic Algorithm to provide decision support in different logistics activities within the supply chain. In addition, extensible Markup Language is incorporated to support the overall infrastructure, thereby providing essential support in enterprise integration. To validate the feasibility of the Internet-based logistics workflow optimizer, the implementation of the prototype in electronic industry is described...

In today’s globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper.

PurposeThis paper proposes an infrastructural framework, involving various emerging technologies, for the development of an agent‐based quality enhancement system with the distinct feature of the ability to cope with the potential quality problems from various customers' requirements.Design/methodology/approachThe proposed system is equipped with the “distributed problem solving” feature which automatically identifies and divides the potential quality problems into sub‐problems and directs straight to the associated agents that are capable of interacting and negotiating with each other to support customer needs. The new feature of this agile quality enhancement system is characterized with the incorporation of the quality mining engine through the capabilities of the online analytical processing, case‐based reasoning, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations.FindingsIn this paper, the detailed architecture and the components included in the proposed system are described with a case study to validate its feasibility of application in actual environment.Originality/valueThe proposed system is to enhance customer satisfaction by automating problem solving procedures in all business processes.

In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two-step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise.

Facing the problem of variation and chaotic behavior of customers, the lack of sufficient information is a challenge to many business organizations. Human analysts lacking an understanding of the hidden patterns in business data, thus, can miss corporate business opportunities. In order to embrace all business opportunities, enhance the competitiveness, discovery of hidden knowledge, unexpected patterns and useful rules from large databases have provided a feasible solution for several decades. While there is a wide range of financial analysis products existing in the financial market, how to customize the investment portfolio for the customer is still a challenge to many financial institutions. This paper aims at developing an intelligent Financial Data Mining Model (FDMM) for extracting customer behavior in the financial industry, so as to increase the availability of decision support data and hence increase customer satisfaction. The proposed financial model first clusters the customers into several sectors, and then finds the correlation among these sectors. It is noted that better customer segmentation can increase the ability to identify targeted customers, therefore extracting useful rules for specific clusters can provide an insight into customers' buying behavior and marketing implications. To validate the feasibility of the proposed model, a simple dataset is collected from a financial company in Hong Kong. The simulation experiments show that the proposed method not only can improve the workflow of a financial company, but also deepen understanding of investment behavior. Thus, a corporation is able to customize the most suitable products and services for customers on the basis of the rules extracted.

The fluctuating and competitive economy today is affecting the production industry worldwide. Under the stress of various forceful challenges, customer satisfaction and product loyalties form the necessary key for enterprises to survive or even thrive in this decade. The concept of Quality Management System (QMS) offers a chance for enterprises to win customer satisfaction by producing consistently high-quality products. With the use of Data Mining (DM) and Artificial Intelligence (AI) techniques, enterprises are able to discover previously hidden yet useful knowledge from large and related databases which assists to support the high-valued continuous quality improvement. Continuous quality improvement is of utmost importance to enterprises as it helps turn them potent to compete in today’s rivalrous global business market. In this chapter, Intelligent Quality Management System with the use of Fuzzy Association Rules is the main focus. Fuzzy Association Rule is a useful data mining technique which has received tremendous attention. Through integrating the fuzzy set concept, enterprises or users are able to decode the discovered rules and turn them into more meaningful and easily understandable knowledge, for instance, they can extract interesting and meaningful customer behavior pattern from a pile of retail data. In order to better illustrate how this technique is used to deal with the quantitative process data and relate process parameters with the quality of finished products, an example is provided as well to help explain the concept.

Aviation industry is highly dynamic and demanding in nature that time and safety are the two most important factors while one of the major sources of delay is aircraft on ground because of it complexity, a lot of machinery like vehicles are involved and lots of communication are involved. As one of the aircraft ground services providers in Hong Kong International Airport, China Aircraft Services Limited (CASL) aims to increase competitiveness by better its service provided while minimizing cost is also needed. One of the ways is to optimize the number of maintenance vehicles allocated in order to minimize chance of delay and also operating costs. In the paper, an agent-based model is proposed for support decision making in vehicle allocation. The overview of the aircrafts ground services procedures is firstly mentioned with different optimization methods suggested by researchers. Then, the agent-based approach is introduced and in the latter part of report and a multi-agent system is built and proposed which is decision supportive for CASL in optimizing the maintenance vehicles' allocation. The application provides flexibility for inputting number of different kinds of vehicles, simulation duration and aircraft arrival rate in order to simulation different scenarios which occurs in HKIA.

Radio frequency identification (RFID) is the technology that put an RFID tag on objects or people, so that they can be identified, tracked, and managed automatically. With its wide application in the automobile assembly industry, warehouse management and the supply chain network, RFID has been recognized as the next promising technology in serving the positioning purpose. Existing positioning technologies such as GPS are not available indoors as the terminal cannot get the signal from satellites. To enhance the availability of the positioning systems for indoors, the development of RFID positioning system for locating objects or people have became a hot topic in recent research. Compared with conventional active and high-cost solutions, this paper studied the feasibility of using passive RFID tags for indoor positioning and object location detection to provide real time information for tracking movement. Results of experiment show that readability of the passive RFID positioning system is satisfactory, and it is a more cost effective solution when compared with other positioning technologies.

The purpose of this article is to propose a Supply Chain Knowledge Discovery System including Radio Frequency Identification (RFID) technology together with temporal rule induction that the retailers and distributors can have a better Customer Relationship Management by realising the change of customer purchasing behaviour with latest information. The aim of this research is to identify how information system manipulates the Point-Of-Sales data and customer historical data so as to formulate the cross-selling strategy. With the advert development and application of RFID, the data collected in the linear time interval reveal the characteristics of temporal features of daily transactional data. The proposed system discovers the knowledge by querying data in temporal database interactively and recursively with Temporal Query and Mining Language. Data cleaning process is carried out prior to applying temporal association rule for basket market analysis. The significance of this research is the demonstration of the synergy of using a combination of latest technologies to form an integrated system to improve direct marketing campaign.

Reliability and safety in flight is extremely necessary and that depend on the adoption of proper maintenance system. Therefore, it is essential for aircraft maintenance companies to perform the manpower scheduling efficiently. One of the objectives of this paper is to provide an Integer Programming approach to determine the optimal solutions to aircraft maintenance planning and scheduling and hence the planning and scheduling processes can become more efficient and effective. Another objective is to develop a set of computational schedules for maintenance manpower to cover all scheduled flights. In this paper, a sequential methodology consisting of 3 stages is proposed. They are initial maintenance demand schedule, the maintenance pairing and the maintenance group(s) assignment. Since scheduling would split up into different stages, different mathematical techniques have been adopted to cater for their own problem characteristics. Microsoft Excel would be used. Results from the first stage and second stage would be inputted into integer programming model using Microsoft Excel Solver to find the optimal solution. Also, Microsoft Excel VBA is used for devising a scheduling system in order to reduce the manual process and provide a user friendly interface. For the results, all can be obtained optimal solution and the computation time is reasonable and acceptable. Besides, the comparison of the peak time and non-peak time is discussed.

Today, enterprises are under pressure to improve process performance while still remaining customer oriented. The problem of workflow and process quality is a very important issue in supply chain management. Successful improvement of the logistics process has to be viewed as one way of making improvements to the integrated supply chain network. This article describes the application of an Online Analytical Processing (OLAP) based neural ensembles strategy to the acquisition of process knowledge during the supply chain operations. It demonstrates the capabilities of the proposed approach to analyse and predict the quality of the finished product under different process operation parameters. The simulation results indicate that the proposed model is generally superior to the traditional approach by providing real-time prediction and better decision support functionality.

In a dispersed manufacturing environment where the production value chain is being dissected and rationalised to take advantage of dissimilar core competencies of geographically isolated firms, manufacturing strategy is required to modify from time to time in accordance with the turbulent market. A 'smart' Decision-Support System (DSS) is proposed with the distinct feature of artificial intelligence capabilities to achieve progressive knowledge acquisition and creation to the entity it is associated with. In particular, this proposed system is characterised by the inclusion of a Neural Object Agent (NOA), which is built upon a newly developed technology coined as neural object technology.