The relationship between the span of supply chain structure and the “knock-on effect” in the transmission of disruptions

Author(s):  
Artur Swierczek
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mikihisa Nakano ◽  
Kazuki Matsuyama

Purpose The purpose of this paper is to discuss the roles of a supply chain management (SCM) department. To achieve that, this study empirically examines the relationship between internal supply chain structure and operational performance, using survey data collected from 108 Japanese manufacturers. Design/methodology/approach Based on a literature review of not only organizational theory but also other fields such as marketing, logistics management, operations management and SCM, this study focused on two structural properties, formalization and centralization and divided operational performance to firm-centric efficiency and customer-centric responsiveness. To examine the analytical model using these dimensions, this study conducted a structural equation modeling. Findings The correlation between centralization of operational tasks and centralization of strategic tasks, the impacts of centralization of both tasks on formalization and the effect of formalization on responsiveness performance were demonstrated. In addition, the reasons for formalization not positively influencing efficiency performance were explored through follow-up interviews. Practical implications Manufacturers need to formalize, as much as possible, a wide range of SCM tasks to realize operational excellence. To establish such formalized working methods, it is effective to centralize the authorities of both operational and strategic tasks in a particular department. In addition, inefficiency due to strict logistics service levels is a problem that all players involved in the supply chain of various industries should work together to solve. Originality/value The theoretical contribution of this study is that the authors established an empirical process that redefined the constructs of formalization and centralization, developed these measures and examined the impacts of these structural properties on operational performance.


2021 ◽  
Vol 22 (6) ◽  
pp. 1593-1613
Author(s):  
Thi Diem Chau Le ◽  
Judit Oláh ◽  
Miklós Pakurár

Supply chain structure of global enterprises tend to develop dramatically. These lead to more difficulty for enterprises in managing and building information sharing systems. Thus, it is a necessary for enterprises to limit the scope of the information sharing system by selecting essential partners. The aims of this study are to quantify the cooperation of each supply chain member, and evaluate and visualize their effects in information sharing systems in order to support policymakers in making their decisions in supply chain management. The network analytical method in network science is applied to indicate the relationship between supply chain members and present a comprehensive supply chain visually. Moreover, Motor Corporation’s topology in Japan is used as a representation of global enterprise features to analyze the relationships between supply chain members. The data for Motor Corporation is secondary data which includes the number of suppliers, manufacturers, and dealers, and the interaction among them. Data is collected and verified from reputable websites such as www.marklines.com, or www.statista.com. As a result, this study contributes by applying a new method for not only determining the impact levels of supply chain members but also giving visual descriptions of impact levels on the large-scale information sharing system.


2012 ◽  
pp. 1551-1565 ◽  
Author(s):  
Nicholas Ampazis

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.


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