An end-to-end bidirectional authentication system for pallet pooling management through blockchain internet of things (BIoT)

Pallet pooling is regarded as a sustainable and cost-effective measure for the industry, but it is challenging to advocate due to weak data and pallet authentication. In order to establish trust between end-users and pallet pooling services, the authors propose an end-to-end, bidirectional authentication system for transmitted data and pallets based on blockchain and internet-of-things (IoT) technologies. In addition, secure data authentication fosters the pallet authenticity in the whole supply chain network, which is achieved by considering the tag, location, and object-specific features. To evaluate the object-specific features, the scale invariant feature transform (SIFT) approach is adopted to match key-points and descriptors between two pallet images. According to the case study, it is found that the proposed system provides a low bandwidth blocking rate and a high probability of restoring complete data payloads. Consequently, positive influences on end-user satisfaction, quality of service, operational errors, and pallet traceability are achieved through the deployment of the proposed system

A blockchain-IoT platform for the smart pallet pooling management

Pallet management as a backbone of logistics and supply chain activities is essential to supply chain parties, while a number of regulations, standards and operational constraints are considered in daily operations. In recent years, pallet pooling has been unconventionally advocated to manage pallets in a closed-loop system to enhance the sustainability and operational effectiveness, but pitfalls in terms of service reliability, quality compliance and pallet limitation when using a single service provider may occur. Therefore, this study incorporates a decentralisation mechanism into the pallet management to formulate a technological eco-system for pallet pooling, namely Pallet as a Service (PalletaaS), raised by the foundation of consortium blockchain and Internet of things (IoT). Consortium blockchain is regarded as the blockchain 3.0 to facilitate more industrial applications, except cryptocurrency, and the synergy of integrating a consortium blockchain and IoT is thus investigated. The corresponding layered architecture is proposed to structure the system deployment in the industry, in which the location-inventory-routing problem for pallet pooling is formulated. To demonstrate the values of this study, a case analysis to illustrate the human–computer interaction and pallet pooling operations is conducted. Overall, this study standardises the decentralised pallet management in the closed-loop mechanism, resulting in a constructive impact to sustainable development in the logistics industry

An industrial blockchain-based multi-criteria decision framework for global freight management in agricultural supply chains

In view of increasing supply chain disruption events, for example the China–United States trade war, the COVID-19 pandemic, and the Russia–Ukraine war, the complexity and dynamicity of global freight management keeps increasing. To build a resilient and sustainable supply chain, industrial practitioners are eager to systematically revamp the freight management decision process related to the selection of carriers, shipping lanes, and third-party logistics service providers. Therefore, this study aims at strengthening decision-making capabilities for global freight management, in which an industrial blockchain-based global freight decision framework (IB-GFDF) is proposed to incorporate consortium blockchain technology with the Bayesian best-worst method. Through the blockchain technology, pairwise comparisons can be conducted over the international freight network in a decentralized and immutable manner, and thus, a secure and commonly agreed-on pairwise comparison dataset is acquired. Subsequently, the pairwise comparison dataset with multi-stakeholder opinions is analyzed using the Bayesian best-worst method in order to prioritize the selection decision criteria related to carriers, shipping lanes, and 3PL service providers for global freight management. To verify the methodological feasibility, a case study of an Australian agricultural supply chain firm was conducted to support the development end-to-end (E2E) supply chain solutions originated from Australia. It was found that port infrastructure, ports of call and communication effectiveness were the major criteria for the selection decision, which can be emphasized in future global freight collaboration. In addition, an immutable and append-only record of pairwise comparisons can be established to support the visibility of time-varying stakeholders’ preferences

Blockchain-IoT-driven nursing workforce planning for effective long-term care management in nursing homes

Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today's complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of -13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation

Blockchain-driven IoT for food traceability with an integrated consensus mechanism

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

An intelligent-internet of things (IoT) outbound logistics knowledge management system for handling temperature sensitive products

A comprehensive outbound logistics strategy of environmentally-sensitive products is essential to facilitate effective resource allocation, reliable quality control, and a high customer satisfaction in a supply chain. In this article, an intelligent knowledge management system, namely the Internet-of- Things (IoT) Outbound Logistics Knowledge Management System (IOLMS) is designed to monitor environmentally-sensitive products, and to predict the quality of goods. The system integrates IoT sensors, case-based reasoning (CBR) and fuzzy logic for real-time environmental and product monitoring, outbound logistics strategy formulation and quality change prediction, respectively. By studying the relationship between environmental factors and the quality of goods, different adjustments or strategies of outbound logistics can be developed in order to maintain high quality of goods. Through a pilot study in a high-quality headset manufacturing company, the results show that the IOLMS helps to increase operation efficiency, reduce the planning time, and enhance customer satisfaction

Multi-objective mapping method for 3D environmental sensor network deployment

Effective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided k-means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved

Data analytics and the P2P cloud : an integrated model for strategy formulation based on customer behaviour

For companies to gain competitive advantage, an effective customer relationship management (CRM) approach is necessary. Based on customer purchase behaviour and ordering patterns, companies can be classified into different categories in terms of providing customised sales and promotions for customers. However, companies that lack an effective CRM strategy can only offer the same sales and marketing strategies to all customers. Furthermore, the traditional approach to managing customers is control via a centralised method, in which the information regarding customer segmentation is not shared among the customer network. Consequently, valuable customers may be neglected, resulting in the loss of customer loyalty and sales orders, and the weakening of trust in the customer–company relationship. This paper designs an integrated data analytic model (IDAM) in a peer-to-peer cloud, integrating RFM-based k-means clustering algorithm, analytical hierarchy processing and fuzzy logic to divide customers into different segments and hence formulate a customised sales strategy. A pilot study of IDAM is conducted in a trading company specialised in providing advanced manufacturing technology to demonstrate how IDAM can be applied to formulate an effective sales strategy to attract customers. Overall, this study explores the effective deployment of CRM into the peer-to-peer cloud so as to facilitate sales strategy formulation and trust between customers and companies in the network

Exploring the intellectual cores of the blockchain-Internet of Things (BIoT)

Purpose: Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts. Design/methodology/approach: This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core. Findings: Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem. Originality/value: We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains

An intelligent risk management model for achieving smart manufacturing on Internet of Things

To adapt to the ever-changing environment, Internet of Things (IoT) has emerged for supporting manufacturing plants to better manage the quality of products. Since the application of IoT is relatively new to the manufacturing industry, increasing attention has been paid on how to manage the planning and implementation process so as to achieve smart manufacturing. However, IoT applications in each manufacturing plant are varied due to different specifications, such as the product types, product nature, plant layout, production flow, machine and equipment settings. Hence, it is essential to perform risk analysis to ensure that any possible situation and uncertainty is being considered before the implementation process. Risk management plays an important role since disruption can cause significant financial and reputational loss, especially for electronics products, which are environmental-sensitive. In this study, an electronic manufacturing risk management model (EM-RMM) is designed to assess the risk faced by manufacturing plants for IoT applications. By identifying the risks faced by manufacturing plants for IoT applications, the likelihood and consequences of the risks are analyzed by using fuzzy analytical hierarchy process (FAHP) to calculate the weighting of the risks. Through a case study in a plant which manufactures environmental-sensitive electronics products, the results provide a systematic procedure for risk assessment in IoT implementation, with the aim of achieving smart manufacturing