Futuristic Technologies for Intelligent Manufacturing and Supply Chain Management

Intelligent manufacturing combines the perspective of people, processes, and machines to impact the overall economics of manufacturing. Futuristic technologies such as Internet of Things, blockchain, virtual reality, edge computing, etc. combined with manufacturing principles form a platform that is leading towards many innovations across industries. Artificial intelligence (AI) and machine learning now play a leading role in enhancing the quality of the manufacturing process. From significant cuts in unplanned downtime to better designed products, manufacturers are applying AI powered analytics on data to improve efficiency, product quality and the safety of employees. Furthermore, manufacturers have benefitted from data-driven innovations for demand planning and logistics management (first and last part) of their supply chains. Tracking production across entire processes and managing the supply chain as an integrated platform is now an urgent need. Hence, there is a demand to further explore the futuristic technologies for intelligent manufacturing and supply chain management. The objective of this Special Issue is to collect papers on the latest trends in industry specific to intelligent manufacturing and supply chain management. This Special Issue has discussions on novel, scientific technological insights, principles, algorithms, and experiences in intelligent manufacturing and supply chain management. After a rigorous round of double-blind peer review process, finally eight papers are accepted for publication in this Special Issue.

The first paper is ‘Research on dispersion compensation using avalanche photodiode and pin photodiode’ by Ma et al. Based on the experimental analysis and the comparison results, the performance of the avalanche photodiode is around 11% better than the pin diode.

The second paper is ‘Prediction of energy consumption of numerical control machine tools and analysis of key energy-saving technologies’ by Qiang et al. In this paper, the energy consumption of numerical control machine tools is analysed, and the relevant energy-saving model is established.

The third paper is ‘Design and implementation of construction prediction and management platform based on building information modelling and three-dimensional simulation technology in Industry 4.0’ by Sun et al. In this paper, the virtual simulation technology is applied to solve problems of building design and damage assessment. The influence of this technology on the overall design of the building is discussed, and further, the future developments for industrial automation are also covered.

The fourth research work is ‘Analysis of a building collaborative platform for Industry 4.0 based on Building Information Modelling technology’ by Ding & Kohli. This paper emphasises on the higher degree of data sharing and strengthen the coordination of the work of various agencies in construction engineering.

The fifth paper is ‘Knowledge map visualization of technology hotspots and development trends in China's textile manufacturing industry’ by Huang et al. The authors in this paper suggest the developmental directions of textile manufacturing from traditional to intelligent trends. Furthermore, this paper also provides a reference for the later trends and the dynamic planning of China's textile manufacturing industry technology.

The sixth paper is ‘A flight control method for unmanned aerial vehicles based on vibration suppression’ by Wang et al. In this paper, unmanned aerial vehicles (UAVs) based on vibration suppression are discussed. The system performance is observed in terms of rise time and overshoot and it is observed from the simulation outcomes that the system is stable at all design points in the range of 0–9000 m, providing a rise time of 10 s and zero overshoot. From this study, it is revealed that the designed controller can effectively suppress the vibration of the flexible mode while ensuring the stability and tracking performance of the UAV.

The seventh paper is ‘A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India’ by Gabani et al. This research covers the viability suggesting that the drone technology can solve the current operational challenges in India's urban cities. Furthermore, vital factors are discussed to enable stakeholders to select a model for drone logistics systems planning and experimentations with current challenges.

The eighth research work is ‘A novel method of material demand forecasting for power supply chains in industrial applications’ by Xiao et al. In this paper, two kinds of collaborative transmission process models of supply chain information are established, and simulation analysis is carried out on the two models by the Monte Carlo method to verify the effect of collaborative transmission of information flow in supply chains within big data environments.

The ninth paper is ‘Progress of zinc oxide-based nanocomposites in the textile industry’ by Huang et al. The proposed work identified that the textiles industry can achieve better piezoelectric properties using zinc oxide-based nanocomposites in various sections of the textile industry and it also identified that the slurry type suspensions are also limited.

Finally, the Special Issue discusses ‘Supply chain control towers: Technology push or market pull—An assessment tool’ by Patsavellas et al. The paper examines the market-pull versus technology-push components of the functionalities enabled by digital supply chain control towers and, building on the outcome of an extensive survey and expert interviews, proposes an assessment tool to aid decision making for the consideration of their adoption.