Collaboration modes and advantages in supply chain

This research aims to address supply chain collaboration with a perspective of broader three-dimensional relationship, not a linear two-dimensional relationship discussed broadly in previous research. Case study was adopted for this research, and data collection was mainly conducted via interview. The research results highlighted that supply chain collaborations are common practice across all levels of the pharmaceutical supply chain. The results also indicated that the different strengthen levels of barging power among collaborative partners will influence the achieved advantages at different supply chain levels, including strategic, operational and political levels

Smart manufacturing and supply chain management

In the fourth industrial revolution, smart manufacturing will be characterized by adaptability, resource efficiency and ergonomics as well as the integration of customers and business partners in business and value processes. Business model, operations management, workforce and manufacturing process all face substantial transformations to reasoning the manufacturing process. This paper explores the impacts of smart manufacturing on supply chain management, and develops several propositions to improve supply chain performance under the context of smart manufacturing

A Reinforcement Learning-based Framework for Proactive Supply Chain Risk Identification

Over the past few decades, global supply chains (GSCs) have seen a significant increase with the widespread adoption of digital technologies and improved trade policies. GSCs are a network of organisations or individuals across the world involved in producing and delivering goods and services to customers. While this globalisation and the use of global technologies have increased the efficiency of supply chain operations, it has also exposed them to various additional uncertainties and risk types that can negatively impact their operations. Thus, for GSCs to function properly, such uncertainties must be managed. Hence, supply chain risk management is critical in the smooth operation of GSCs. The first task in supply chain risk management is risk identification, where risk managers identify the risk events that may negatively impact their operations for further analysis. It is crucial that risk identification is undertaken in a timely manner so that risk managers can be proactive in managing the possible impacts of the identified risks on their operations. This task can be done manually which is tedious and time-consuming, however, with the increased sophistication and capability of artificial intelligence (AI), there is a potential for AI algorithms to be used to enhance the efficacy and efficiency of this task. A review of the existing literature detailed in this thesis highlights that while AI has been widely employed in different disciplines, it has shortcomings which are specific to the area of risk identification in supply chains. In other words, the majority of the existing risk identification techniques in supply chain risk management are either reactive or predictive in their working nature. This means that such techniques either identify the risk events after they occur or predict future occurrences of the known risk events based on their past pattern of occurrences. However, as emphasised in this thesis, for the supply chain risk identification process to be effective and comprehensive, it has to be proactive in its working nature rather than reactive or predictive. By being proactive, the risk identification techniques aim to identify beforehand known or unknown events of risks that have the potential to occur and negatively impact an activity. The analysis obtained assists the risk manager to perform the steps of risk analysis and risk evaluation on the identified risks before developing plans to manage them. Existing literature on supply chain risk identification lacks techniques to achieve this aim. To address this gap in the literature, this thesis develops a framework, namely Reinforcement Learning-based Supply Chain Risk Identification, which assists risk managers in automatedly and accurately identifying the risk events that may have the potential to impact their operations and bring them to his/her attention for further follow up. The proposed framework adopts the science and engineering research approach and four different frameworks are developed that identify the risk events of interest to the risk manager, extract related news articles on these risk events and analyse them, before recommending the most important news articles to the risk manager for follow-up actions. The functionality and viability of these prototypes are validated by experiments and systematised by a supply chain case study to highlight their effectiveness