Prof. Alain Yee Loong Chong
Graduate School, University of Nottingham (Ningbo China)
Research Area：Information systems and operations management; Electronic word of mouth and social media;
Digital transformation and business models innovation; e-SCM integration
Speech Title: Tracing the Evolution of Blockchain-Based Banknote Supply Chain: A Path Dependency Perspective
Abstract: The banknote supply chain is an indispensable and integral part of modern-day financial system by ensuring an adequate supply of bills in circulation to meet market demands. This is accomplished through the detection of counterfeit bills, the destruction of damaged currency and the issue of new notes to replace those which have been destroyed. Yet, conventional models of banknote supply chain, which are governed centrally by a dominant player in the form of the central banking authority of a country, suffers from problems of operational efficiency. The emergence of blockchain as a distributed technological infrastructure has the potential of disrupting how banknote supply chains are configured. Through an in-depth case study of how the banknote supply chain ecosystem in mainland China has evolved from a centralized governance architecture to one that is founded on peer-to-peer collaborations among banking institutions, this study draws on path dependency perspective to shed light on the inter-organization mechanisms underpinning the reinforcement and cessation of pre-existing path pursued by entities within the ecosystem as well as the creation of a new path for the collective body.
Prof. Lei Yang
South China University of Technology
Research Area：Network Economics and Enterprise Management, Logistics Management,
Supply Chain Management, Operations Research, Stochastic Processes
Speech Title: Optimal timing of big data application in a two-period decision model with new product sales
bstract: We study a firm's strategy in adopting big data technology to motivate customer demand over two periods. In the first period, the firm designs a product to sell to the market and determines whether to apply big data to attract more customers. In the second period, the firm designs a new product and determines whether to sell the old product and the new product simultaneously, where big data can also be applied in this period to stimulate more demands. We formulate this problem into four models considering whether the firm adopts big data in the first period and/or the second period, and whether the firm only sells the new product or sells both the old and new products in the second period. We find that the firm prefers to apply big data over both periods when the cost is low, only over the second period when the cost is median and will not apply big data when the cost is high. Interestingly, only applying big data in the first period is never the best choice. Moreover, applying big data over both periods brings the most social welfare.
A. Prof. Xiaogang Liu
Wuhan University of Technology
Research Area：Intelligent manufacturing, Blockchain, Mechanical Systems
Speech Title: The Tracing of Vehicle Data Based on Blockchain Technology
Abstract: With the development of intelligent manufacturing and intelligent logistics, the security and authenticity of data are attracting more concern. The security and authenticity of data can be guaranteed by the characteristics of blockchain technology, such as immutability, traceability, security and credibility. Although the blockchain technology has been applied in the financial industry in recent years, its application in the field of intelligent manufacturing and intelligent logistics is still under investigation. In this research, the blockchain technology is applied to establish reliable digital transactions of data for vehicle inspection, certification and vehicle maintenance, guaranteeing the authenticity and security of these kinds of data for the whole lifecycle of vehicles. Therefore, it seems that the application of blockchain technology in the tracing of vehicle data in this research can also provide a reference for practical applications of blockchain technology in the field of intelligent manufacturing and intelligent logistics.
A. Prof. Chaoan Lai
South China University of Technology
Research Area：Intelligent Manufacturing, Industry 4.0, Innovative methods, Operation management, Logistics Management
Speech Title: Transformation and upgrading of manufacturing industry based on machine learning and data driven
Abstract: At present, China's manufacturing industry needs to transform and upgrade. We need to deeply integrate the Internet, big data and artificial intelligence with the manufacturing industry, and promote high-quality development of manufacturing enterprises through emerging industries such as service-oriented manufacturing, personalized customization and mixed-flow production. Machine learning and machine vision have a high degree of universality. Using the data fusion of camera to achieve automatic driving is proof of the universality of machine vision. For a service-oriented manufacturing, Hitachi elevator company, for example, the company through the acquisition of the lift the main board signal, vibration, noise and environmental data gathered by sensors, real-time video data gathered by machine vision, based on the static data and dynamic data combination of big data analysis, digging out the elevator fault rules, giving real-time detection of abnormal elevator operation and warning, achieve remote elevator anomaly monitoring, fault diagnosis and prediction, transforming from selling elevator products to selling service transformation. For personalization and mixed flow production, taking Haier interconnected factory as an example, through more than 12000 sensors, it produced more than 40 million large data per day, through the data-driven layout simulation, line balance simulation, making the optimal layout with high flexible modular; through logistics simulation, value stream simulation, construct efficient self-optimization of product manufacturing. It supports mixed production of personalized customization and mass production to achieve seamless, transparent and visual user experience. Through the above specific cases, it shows that machine learning, machine vision and data-driven technology have broad application prospects in the transformation and upgrading of manufacturing industry.