Due to the rapid development of information technology, supply chain network is evolving, which involves a higher level of interdependence between organisations. Conventional production capability evaluation relies on centralised approaches with limited sharing of performance and evaluation data. Besides, traditional evaluation methods are mainly based on subjective manual operation using limited data. In this paper, we propose a production capability evaluation system by incorporating Internet of Things (IoT), machine learning and blockchain technology for supply chain network. It contributes to achieving real-time data collection and automated enterprise production capability evaluation mechanism. Besides, blockchain technology is adopted to enable open and decentralised data storage and sharing, provide fair and automatic trading of data. The proposed system is evaluated through a simulation experiment. It demonstrated how to utilise the proposed system to choose suitable upstream enterprises. The successful development of the system could help to enhance production efficiency, reduce risk and provide a reasonable and more sustainable production management in supply chain network.

With product customisation and emerging business opportunities, small and medium manufacturing enterprises (SMEs) must find ways to collaborate and share competency in a trustable manner to survive a turbulent market. Therefore, service industry turns to the manufacturing industry and SMEs migrate to cloud manufacturing (CM) and ubiquitous manufacturing. However, existing platforms use centralised networking, which suffers from security, scalability and big-data problems. In this paper, we propose a blockchain-based platform as a trustable network to eradicate third-party problems, which can improve the scalability, security and big-data problems for SMEs. Our proposed platform is developed based on a consortium blockchain which provides a peer-to-peer communication network between the end user and the service provider. We improve existing consensus mechanism and communication protocol based on a cyber-physical system (CPS), via an autonomous agent. Firstly, we provide a review of cloud manufacturing, ubiquitous manufacturing and blockchain-based manufacturing approaches by highlighting the main problems. Then, the proposed platform, blockchain ubiquitous manufacturing (BCUM), is explained, based on its architecture, consensus algorithm and CPS, with the help of autonomous agent communication. The proposed platform has been developed for 3D printing companies which are geographically distributed and tested based on network performance and three practical scenarios.

The rapid development of urbanisation and the ever-changing consumers’ demands are constantly changing the urban logistics industry, imposing challenges on logistics service providers to improve customer satisfaction which is one of the indicators for the sustainability of urban logistics. Existing customer satisfaction evaluations are based on a questionnaire survey, which is time-consuming and labour intensive. Moreover, the logistics data are confidential and can only be accessed by the stakeholders in existing logistics models, causing the problem of information non-transparency among logistics enterprises and the third authorities like banks and governments, which may hinder the sustainable development of urban logistics. In this paper, we propose a blockchain-based evaluation approach for customer satisfaction in the context of urban logistics. Four criteria affecting customer satisfaction in urban logistics are identified. A machine learning algorithm Long Short-Term Memory (LSTM) is adopted to predict customer satisfaction in the future period. The implementation is demonstrated to illustrate the proposed approach. A smart contract is designed for compensation and/or refund to customers when their satisfaction with the delivery services is at a low level.

Customer service is transforming from traditional manual service toward automated service, which utilizes different computational informatics to achieve a higher efficient and quality services. Automated customer service requires big data and expertise in data analysis as prerequisites. However, many companies, especially small and medium enterprises, do not have sufficient data and experience due to their limited scale and resources. They need to rely on third parties and result in the lack of development of core customer service competency. In order to overcome these challenges, an open and automated customer service platform based on Internet of things (IoT), blockchain and Automated Machine Learning (AutoML) is proposed. The data is gathered with the use of IoT devices during the customer service. An open but secured environment to achieve data trading is ensured by using blockchain. AutoML is adopted to automate the data analysis processes for reducing the reliance of costly experts. The proposed platform is analyzed through use case evaluation. A prototype system has also been developed and evaluated. The simulation results show that our platform is scalable and efficient.