Jianrong Hou and Xiaofeng Zhao
The purpose of the paper is to develop a methodological framework for supply chain risk management using the hierarchical holographic modeling approach. It analyses supply chain…
Abstract
Purpose
The purpose of the paper is to develop a methodological framework for supply chain risk management using the hierarchical holographic modeling approach. It analyses supply chain risks in a systematic manner and develops a hierarchical methodology for identifying, prioritizing and managing the potential supply chain risks.
Design/methodology/approach
This research reviews supply chain risk management literature and develops a conceptual framework, which outlines general principles and guidelines for managing risks in a systematic manner. Through decomposition, the complexity of supply chain risk can be identified by analyzing smaller subsystems.
Findings
The paper provides a conceptual framework to identify supply chain risks from multiple overlapping perspectives. The structured filtering and ranking procedure enables decision-makers to focus on the most critical risks. The research shows that the supply chain risks associated with the sub-systems within the hierarchical structure contribute to and ultimately determine the risks of the overall supply chain system.
Research limitations/implications
The risks associated with each sub system within the hierarchical structure can contribute to and determine the risks of the overall supply chain system. Further applications in various companies and industry sectors would benefit supply chain managers on a case-by-case basis.
Practical implications
The hierarchical risk identification framework can serve as guidance for applications to specific supply chain systems and processes. The framework from a holistic overlapping perspective can efficiently and effectively help supply chain managers identify supply chain risks and facilitate the evaluation of the subsystem risks.
Originality/value
The paper applies system thinking in supply chain management and presents an efficient and practical framework for supply chain risk identification and evaluation.
Details
Keywords
Xiaofeng Zhao, Hui Zhao and Jianrong Hou
B2B e‐hubs have been studied by IS researchers for close to a decade, and supply chain integration is a critical topic for supply chain management. However, the interface of the…
Abstract
Purpose
B2B e‐hubs have been studied by IS researchers for close to a decade, and supply chain integration is a critical topic for supply chain management. However, the interface of the two topic areas has not received adequate attention from both researchers and practitioners. This paper aims to examine the impact of B2B e‐hubs on supply chain integration, with particular emphasis on information integration, B2B e‐hub architecture, and enabling technologies.
Design/methodology/approach
General system theory (GST) provides the theoretical framework. The main approach is theoretical analysis of information integration and development of e‐hub architecture. The paper discusses how information integration can be achieved through B2B e‐hubs and explores extensible markup language e‐hub architecture and technologies.
Findings
GST could provide the theoretical framework of integration, whereas information integration is the foundation of broader supply chain integration. E‐hubs open up communication and enlarge networking opportunities and thus tremendously affect information integration. By analyzing B2B e‐hubs, this paper explores the mechanism of information integration and points out managerial and technical limitations. Although there are many challenges, e‐hubs create value by aggregating and matching buyers and sellers, creating marketplace liquidity, and reducing transaction costs. E‐hubs could be a crucial solution to supply chain integration.
Originality/value
The paper uses GST as the theoretical foundation to analyze information integration in supply chain operations. The paper explores how e‐hubs can support supply chain integration, examines the design and development of B2B e‐hub architecture, and compares some enabling technologies. The research provides an understanding of how data interchange solutions can be implemented in supply chain operations.
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Xiaofeng Zhao, Jianrong Hou and Kenneth Gilbert
Waiting lines and delays have become commonplace in service operations. As a result, customer waiting time guarantee is a widely used competition strategy in service industries…
Abstract
Purpose
Waiting lines and delays have become commonplace in service operations. As a result, customer waiting time guarantee is a widely used competition strategy in service industries. To implement waiting time guarantee strategy, managers need to not only know the average of waiting time, but also the variance around average waiting time. This paper aims to discuss these issues.
Design/methodology/approach
This research provides a mathematically exact expression for the coefficient of variation of waiting time for Markov queues. It then applies the concept of isomorphism to approximate the variance of customer waiting time in a general queue. Simulation experiments are conducted to verify the accurate approximations.
Findings
A significant feature of the approximation method is that it is mathematically tractable and can be implemented in a spreadsheet format. It provides a practical way to estimate the variance of customer waiting time in practice. The results demonstrate the usefulness of the queuing models in providing guidance on implementing appointment scheduling and waiting time guarantee strategy. Also, the spreadsheet can be used to conduct what-if analysis by inputting different parameters.
Originality/value
This paper develops a simple, easy-to-use spreadsheet model to estimate the standard deviation of waiting time. The approximation requires only the mean and standard deviation or the coefficient of variation of the inter-arrival and service time distributions, and the number of servers. A spreadsheet model is specifically designed to analyze the variance of waiting time.
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Mingyu Gao, Jinghua Xu, Kunqian Liu, Shuyou Zhang and Jianrong Tan
The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced…
Abstract
Purpose
The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced prototypes. Additive manufacturing (AM) costs determine carbon emissions in total life cycle, among which material and energy consumption are major components. Predicting material and energy consumption is fundamental to reducing costs.
Design/methodology/approach
This paper presents a material and energy co-optimization method for AM via multiple layers prediction (MLP). Material and energy consumption are predicted to reduce the AM costs. In particular, scalable, complex curved surface component is used to improve forecasting efficiency. Subsequently, the back pressure distribution is obtained by scale-up specimens, which can lay the foundation for the ergonomic conceptual design.
Findings
Taking evolutionary ergonomic product as an example, the relative gravity direction of backrest is calculated. The material and energy consumption are predicted with low deviation. Physical experiments were carried out to validate information. Digital and physical tests have revealed that material and energy co-optimization improves manufacturing efficiency.
Originality/value
The innovatively proposed MLP method predicts material and energy consumption of scale-up prototypes to reduce the costs. It is propitious to improve the carbon emission efficiency in life cycle of AM. The originality may be widely adopted alongside increasing environmental awareness.