Bridging the gap : building environmental, social and governance capabilities in small and medium logistics companies

Nowadays, the popularity of environmental, social, and governance (ESG) performance measurement has dramatically increased, particularly to listed companies, for supporting various investment decisions. Companies with high ESG scores imply that their ongoing business development is recognised to be economically, socially, and environmentally sustainable. From the current ESG measurement practice, the measurement frameworks are built on rating schemes, such as KLD and ASSET4, so as to derive the ESG scores for listed companies. However, such existing measurement frameworks are difficult to be implemented in small and medium enterprises (SMEs) with unstructured and non-standardised business data, especially in logistics and supply chain management (LSCM) practice. In addition, it is inevitable for listed companies to work with SMEs, for example logistics service providers, but they need a systematic framework to source the responsible SMEs to maintain the ESG performance. To address the above industrial pain-points, this study proposes an ESG development prioritisation and performance measurement framework (ESG-DPPMF) by means of the Bayesian best-worst method enabling the group decision-making capability to prioritise the ESG development areas and formulate the performance measurement scheme. Through consolidating the opinions from logistics practitioners, it is found that fair labour practice, reverse logistics and human right in supply chains are the most essential areas to further enhance ESG capabilities in the logistics industry. In addition, the viability of the ESG performance measurement has been validated, and thus the sustainable and human-centric logistics practice can be developed to achieve business sustainability

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

Harnessing IoT Data and Knowledge in Smart Manufacturing

In the modern digitalized era, the use of electronic devices is a necessity in daily life, with most end users requiring high product quality of these devices. During the electronics manufacturing process, environmental control, for monitoring ambient temperature and relative humidity, is one of the critical elements affecting product quality. However, the manufacturing process is complicated and involves numerous sections, such as processing workshops and storage facilities. Each section has its own specific requirements for environmental conditions, which are checked regularly and manually, such that the whole environmental control process becomes time-consuming and inefficient. In addition, the reporting mechanism when conditions are out of specification is done manually at regular intervals, resulting in a certain likelihood of serious quality deviation. There is a substantial need for improving knowledge management under smart manufacturing for full integration of Internet of Things (IoT) data and manufacturing knowledge. In this chapter, an Internet-of-Things Quality Prediction System (IQPS), which is a mission critical system in electronics manufacturing, is proposed in adopting the advanced IoT technologies to develop a real-time environmental monitoring scheme in electronics manufacturing. By deploying IQPS, the total intelligent environmental monitoring is achieved, while product quality is predicted in a systematic manner

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

Experimental study on seismic vibration control of stockers in wafer and LCD panel fabs

Automated stocker system is widely used in semiconductor and liquid crystal display (LCD) industries for handling and storage of valuable wafers or glass panels. Massive front opening unified pods (foups) containing wafers, or cassettes storing glass panels, are placed in shelf stockers during manufacturing. Although several preventative measures have been taken, during the past earthquakes, substantial financial loss from the industries were reported, and one of the main causes was attributed to collision of the foups or cassettes and shake off from the shelfs. This paper proposes a methodology of incorporating viscous fluid dampers into the stokers to mitigate their seismic response. Unlike conventionally been done in buildings where dampers are placed between adjacent stories, it is proposed to install dampers in between the ceiling and top of the stocker. Such configuration utilizes the large velocity at the stocker top under vibration, resulting in smaller damper size, and enables a leverage mechanism that requires smaller damper force to resist the stocker’s vibration. Both shake table tests and simulation of a full-scale stocker under realistic earthquakes have been conducted. Results indicate that both displacement and acceleration responses of the stocker can be significantly reduced, and dynamic response of the stocker under seismic excitations can be well predicted

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system