Supply Network Structure Research Articles

Прогноз выработки электроэнергии инвариантных ветроэнергетических установок методом нечеткой логики

The current state of development of autonomous electric power supply systems, including objects of gas transmission systems is considered. Use in the combined generating complexes of alternative sources of the electric power on the basis of wind power installations is proved. Are offered options of structures of power supply networks for pipeline service consumers with use of wind power installations. Are analyses energy-efficient invariant systems on the basis of the combined installations according to the scheme "the synchronous generator-the frequency converter". The predicted power generation volumes are received by power sources at stochastic character of a wind stream.

Risky Suppliers or Risky Supply Chains? An Empirical Analysis of Sub-Tier Supply Network Structure on Firm Performance in the High-Tech Sector

Past research in supply chain risk management has focused on the interactions between buyers and their immediate suppliers and/or assumed independence of risks imposed by these suppliers. However, supply network structure may induce inter-dependency of risks due, for example, to overlapping sub-tier suppliers. This paper empirically studies the prevalence of overlapping sub-tier suppliers and their impact on financial performance for firms in the high-tech sector. Using firm-level supplier-customer relationship data, we find that on average 20 (2.3) percent of tier-2 suppliers are shared by at least two (five) tier-1 suppliers. We also find that the risk, measured as stock return volatility, of the focal tier-0 firm is positively associated with common tier-2 supplier risk, and the association is stronger for suppliers with a higher degree of tier-2 commonality. To disentangle the impact of risky supply network structure from risky tier-2 suppliers, we define two network metrics, viz., diamond ratio and cosine commonality score. We find that a one standard deviation increase in each of these metric leads to an increase in standard deviation of 0.58 and 0.41 respectively in tier-0 firm's risk. Our results reveal substantial unmanaged supply chain risks due to overlapping sub-tier suppliers, and highlight the need for firms to increase visibility into their extended supply network.

Preface to the special feature

The world economy is changing at an increasing pace in the wake of the ongoing digital revolution. Modern information and communication technologies (ICT) strengthen forces of global flows of goods, people, information, and finance. We live in an increasingly interconnected world in which infrastructures composed of different techno-social system layers. Examples include transportation and mobility infrastructures, global supply chain networks, and financial networks. Many studies analyze the socioeconomic systems such as cascading failures and financial stability issues using network theory and agent-based modeling. The financial crises, for instance, triggered many studies about systemic risk caused by contagion effects via the interconnections in the global financial networks. Network analysis provides powerful mathematical tools for analyzing the global circulation of goods, resources, people, money, and information. The accessibility of new data and the advances in theory and modeling are providing an integrated framework that brings us closer to analyzing the behavior of socioeconomic systems from a new perspective. An agent-based approach has also proven useful in many applications. Agent-based modeling provides answers to questions where traditional analysis methods are weak, and it is leading to improved models across wide types of financial risks. This special issue is being launched at a time of ever-increasing connectivity and complexity in socioeconomic networks. The first article in the issue, ‘‘Towards capturing heterogeneity of supply network structures and their temporal transitions: an investigation of supply relationships in the Japanese automobile industry’’ by Tomomi Kito and Steve New, consider connectivity among car manufacturers (OEMs) and auto-parts suppliers in the

Towards capturing heterogeneity of supply network structures and their temporal transitions: an investigation of supply relationships in the Japanese automobile industry

Empirical longitudinal data on supply relationships between car manufacturers (OEMs) and auto-parts suppliers in the Japanese automobile industry are studied systematically. Such supply relationship formations, which will have emerged over time, are likely to reflect various contingent factors such as industrial conditions (which affect OEMs’ procurement strategies and resulting product demands). There are other important factors such as the level of standardization of the product, and the skills required for manufacturing the product. Furthermore, the effects of such factors may vary from product to product, and year to year. Heterogeneity between the supply networks of various products is elucidated via characterization with several measures, and also via a visualization of the transitions of the network properties. Our systematic analysis of the data provides profound insight into how product markets may have become stabilized or more competitive, and how the market selection process may affect the evolution of the supply network characteristics. The proposed approach and findings of the study can contribute to supply network modeling, and lead to the prediction of future changes in product markets, as well as suppliers’ production strategy planning.

The influence of supply network structure on firm’s multiple innovation capabilities

Purpose – The purpose of this paper is to propose a theoretical framework grounded in the fashion industry that investigates the relational capabilities of the lead firms to sustain synchronously multiple types of innovation by leveraging their supply network attributes. Both network functional diversity (NFD) and density are considered as impacting on the lead firm’s stylistic and process innovative capabilities. Design/methodology/approach – A fixed-effect regression model was adopted for testing the theoretical model and hypotheses. The data have a cross-sectional time series structure with each of the 21 lead firms as a panel that spans a ten-year period. Findings – The empirical results mostly confirm the theoretical assumptions and suggest an inverted U-shaped relationship between NFD and both stylistic and process innovation as well as a positive linear linkage between key-component suppliers network density and both types of innovations. The study also confirms key-component suppliers network density mediates the relationships of network diversity with both stylistic and process innovative capabilities. Originality/value – This study departs from the current dominant logic of analysing each innovation type separately and explores the network configurations enhancing the joint generation or adoption of multiple innovation types. The study also proposes an empirical test of the theoretical assumptions in a specific industrial setting: the fashion industry.

A Study of the Effect of Supply Network Structures on Supplier Performance: A Case of the Korean Automotive Parts Industry

A Study of the Effect of Supply Network Structures on Supplier Performance: A Case of the Korean Automotive Parts Industry

Water supply networks as cyber-physical systems and controllability analysis

Cyber-physical systems (CPS) is a system of systems which consists of many subsystems that can stand alone in an individual manner and can be taken as a typical complex network. CPS can be applied in the critical infrastructures such as water supply networks, energy supply systems, and so on. In this paper, we analyze the structure of modern city water supply networks from the view of CPS theory. we use complex network theory to build an undirected and unweighted complex network model for the water supply networks to investigate the structural properties, and present the structure of the water supply networks and detect communities by a spectral analysis of the Laplacian matrix. Then, we analyze the structure and controllability of water supply networks by the structural controllability method. The results show the feasibility and effectiveness of the proposed complex network model.

Analyzing the Impact of Firm’s Embeddedness in a Centralized Supply Network Structure on Relational Capital Outcomes

Abstract This research looks at the different effects of firms’ network structural positions in an upstream supply network upon the firms’ level of relational capital outcomes. Previous research largely focused on the context of decentralized network structure. However, the supply network is a centralized network because of the existence of the focal firm. The existence of the focal firm may influence the impact of relational capital outcomes. Hence, the objective of this research is to determine the type of network structural positions required to obtain a reasonable relational capital outcome in upstream supply network. This study found that network structural positions, i.e. degree centrality contributed to firms’ level of relational capital trust. Hence, a firm embedded in upstream supply network benefits differently in terms of relational capital through different degree of embeddedness. The firm resources should be re-aligned to match the benefits of the different network structural positions.

How “smart cities” will change supply chain management

Purpose – The purpose of this paper is to analyze the impact of smart city initiatives and big data on supply chain management (SCM). More specifically, the connections between smart cities, big data and supply network characteristics (supply network structure and governance mechanisms) are investigated. Design/methodology/approach – An integrative framework is proposed, grounded on a literature review on smart cities, big data and supply networks. Then, the relationships between these constructs are analyzed, using the proposed integrative framework. Findings – Smart cities have different implications to network structure (complexity, density and centralization) and governance mechanisms (formal vs informal). Moreover, this work highlights and discusses the future research directions relating to smart cities and SCM. Research limitations/implications – The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM has been proposed. Practical implications – Smart cities and big data alone have limited capacity of improving SCM processes, but combined they can support improvement initiatives. Nevertheless, smart cities and big data can also suppose some novel obstacles to effective SCM. Originality/value – Several studies have analyzed information technology innovation adoption in supply chains, but, to the best of our knowledge, no study has focused on smart cities.

Monitoring of water distribution system effectiveness using fractal geometry

Abstract The paper discusses issues related to monitoring quality and pressure of water transmitted using water supply networks. Special attention was paid to methods of determining location of measuring points, which to a large extent influence effectiveness of the monitoring system. The purpose of the paper is to present authors’ own method of determining location of points of measuring quality and pressure of transmitted water. The basis for considerations was a real water supply network in a city of about 10.000 residents. The presented method is based on existence of self-similarity properties of the set of fractals formed by the geometrical structure of the water supply network. It is a rank-ordered method involving 3 basic stages - reduction of the number of potential measuring points, providing more details of a target location and checking usefulness of selected points for monitoring purposes. At the preparatory stage, existence of fractal properties of the examined network structure is required to be demonstrated as well as the construction of its numerical model. The ranking is based on two indicators referring by analogy to human circulatory system monitoring and elements of the risk theory. This theory was also used to evaluate usefulness of selected measuring points for monitoring purposes.

The Performance of Green Supply Chain Management Governance Mechanisms: A Supply Network and Complexity Perspective

Global supply networks are becoming increasingly fragmented and complex. At the same time, societal pressures surrounding the environmental accountability of firms are growing, with an increasing number of environmental scandals related to supply networks. Traditional governance frameworks of green supply chain management (GSCM) may be insufficient to deal with this new reality, because they do not consider supply network structure/complexity, and how these factors interact with different governance mechanisms. Our study takes into account the complex interplay between GSCM governance mechanisms, supply network structure/complexity, and environmental performance. We introduce a series of theoretical propositions grounded in an extensive review of the GSCM, supply networks, network complexity, and organizational design literatures. In particular, we argue that supply network structure and complexity directly affect GSCM effectiveness, but that these effects will be contingent upon the type of governance mechanism applied (formal or informal).

The Impact of Supply Network Structure on the Financial Performance of the Firm

Supply chain finance (SCF) as a topic of academic interest and professional practice is rather nebulous, in the sense that the financial aspects of the supply chain have far reaching and complex effects on the entire enterprise. Firms that properly manage their SCF initiatives experience significant benefits to market capitalization and cash flow performance throughout the extended enterprise. Additionally, recent research has emerged that conceptualizes supply chains under the guise of networks. However, even with the important role that SCF plays throughout the supply network, there has been little research that specifically examines the characteristics of a firm’s supply network structure and its impact on financial performance. Consequently there is a gap in the literature dealing with this relationship and thus the primary undertaking in this research is to answer the following research question: what role does a firm’s supply network structure have in explaining its financial performance? We estimate several random effects GLS regressions to test several theoretically driven hypotheses. Our results suggest that firms that can understand and optimize their network structures can use them to their advantage to generate significant improvements in ROA, ROI, ROE, and ROS.

How automobile parts supply network structures may reflect the diversity of product characteristics and suppliers’ production strategies

This study quantitatively investigates the heterogeneous nature of the structures and characteristics of real-life auto-parts supply networks, through complex network analysis of large-scale empirical data. Comprehensive data containing “who-supplies-what-to-whom” information successfully highlights the wide diversity of product characteristics, companies’ production capabilities, and also their resulting strategies. Furthermore, by using an advanced complex network analysis technique, the supply networks are projected into a different information space, generating another network that elucidates the proximities between product types and between companies’ production strategies. Analysis of this network provides profound insights into how supply network structures may emerge reflecting various factors.

"Exploring triadic dynamics between government, OEMs and suppliers"

We use triadic dynamics and agency theory to explore organizational behaviours in a longitudinal supply network setting involving a complex warship programme. We argue that discussions around UK defense supply networks must include the effect of triadic relationships during performance-based contracting (PBC). The investigation explores the implications of triadic relationships in PBC to understand the supply network structures. Principal-agent-customer triads analysed over the 30 year span of the warship’s development reveal the dynamics of evolving players in seemingly steady triadic relations in defence contracts. Using a historical narrative method we highlight the specific strategy of one supplier and its unique dual approach to ‘link and bridge’ towards other participating stakeholders. We argue existing literature which emphasises the importance of dyadic risk sharing between partners is insufficient when addressing complex dynamics of public-private extended programmes involving multiple agents. Our study not only reflects the efficacy or lack thereof of PBC in managing government defence contracts, but also the strategies deployed by agents to secure their future. As a result of extensive outsourcing in the UK, one supplier has assumed such considerable responsibility in running defence infrastructure that it now assumes the responsibilities and influence of government.

Supply network disruption and resilience: A network structural perspective

Increasingly, scholars recognize the importance of understanding supply network disruptions. However, the literature still lacks a clear conceptualization of a network-level understanding of supply disruptions. Not having a network level understanding of supply disruptions prevents firms from fully mitigating the negative effects of a supply disruption. Graph theory helps to conceptualize a supply network and differentiate between disruptions at the node/arc level vs. network level. The structure of a supply network consists of a collection of nodes (facilities) and the connecting arcs (transportation). From this perspective, small events that disrupt a node or arc in the network can have major consequences for the network. A failure in a node or arc can potentially stop the flow of material across network. This study conceptualizes supply network disruption and resilience by examining the structural relationships among entities in the network. We compare four fundamental supply network structures to help understand supply network disruption and resilience. The analysis shows that node/arc-level disruptions do not necessarily lead to network-level disruptions, and demonstrates the importance of differentiating a node/arc disruption vs. a network disruption. The results also indicate that network structure significantly determines the likelihood of disruption. In general, different structural relationships among network entities have different levels of resilience. More specifically, resilience improves when the structural relationships in a network follow the power-law. This paper not only offers a new perspective of supply network disruption, but also suggests a useful analytical approach to assessing supply network structures for resilience.

Modelling inter-supply chain competition with resource limitation and demand disruption

This paper proposes a comprehensive model for studying supply chain versus supply chain competition with resource limitation and demand disruption. We assume that there are supply chains with heterogeneous supply network structures that compete at multiple demand markets. Each supply chain is comprised of internal and external firms. The internal firms are coordinated in production and distribution and share some common but limited resources within the supply chain, whereas the external firms are independent and do not share the internal resources. The supply chain managers strive to develop optimal strategies in terms of production level and resource allocation in maximising their profit while facing competition at the end market. The Cournot–Nash equilibrium of this inter-supply chain competition is formulated as a variational inequality problem. We further study the case when there is demand disruption in the plan-execution phase. In such a case, the managers need to revise their planned strategy in order to maximise their profit with the new demand under disruption and minimise the cost of change. We present a bi-criteria decision-making model for supply chain managers and develop the optimal conditions in equilibrium, which again can be formulated by another variational inequality problem. Numerical examples are presented for illustrative purpose.

Supply Network Structure, Visibility, and Risk Diffusion: A Computational Approach

ABSTRACTUnderstanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.

The influence of supply network structure on firm innovation

In this study, we examine the structural characteristics of supply networks and investigate the relationship between a firm's supply network accessibility and interconnectedness and its innovation output. We also examine potential moderating effects of absorptive capacity and supply network partner innovativeness on innovation output. We hypothesize that firms will experience greater innovation output from (1) higher levels of supply network accessibility and supply network interconnectedness, (2) the interaction between the levels of these two structural characteristics, (3) the moderating role of absorptive capacity on supply network accessibility and the moderating role of supply network partner innovativeness on supply network interconnectedness. Supply network partner relationships are drawn in the context of the electronics industry using data from multiple sources. We use social network analysis to create measures for each supply network structural characteristic. Using regression techniques to test the relationship between these structural characteristics and firm innovation for a sample of 390 firms, our findings suggest that supply network accessibility has a significant association with a firm's innovation output. The results also indicate that interconnected supply networks strengthen the association between supply network accessibility and innovation output. Moreover, the influence of the two structural characteristics on innovation output can be enhanced by a firm's absorptive capacity and level of supply network partner innovativeness. By addressing the need for deeper structural analysis, this study contributes to supply chain research by accounting for the embedded nature of ties in supply networks, and showing how these structural characteristics influence the knowledge and information flows residing within a firm's supply network.

Measuring and evaluating of the network type impact on time uncertainty in the supply networks with three nodes

Nowadays, business competition turns from inter-company competition into competition between supply networks (Rice and Hoppe, 2001) [1]. Winning customer satisfaction is one of the primary elements of survival in the market. Organizations are no longer committed to long-term cooperation with suppliers. Furthermore, choosing suppliers is only based on their qualifications with regard to providing service and their compatibility with the type of customers’ demands. Thus, each supply network needs to be designed according to a specific market opportunity with regard to structure and members. As a result, the structure of supply networks must be more flexible and move toward dynamics (Humphries and Mena, 2012) [2]. Delivery time is one of the main criteria for evaluating the performance of a supply network. Delivery speed and accuracy in dynamic supply networks are the main challenges ahead of network managers due to the short-time nature of such networks (da Silveira and Arkader, 2007) [3]. Therefore, from a different viewpoint, uncertainty and its sources, which directly affect delivery time, could not be ignored easily. Therefore, this study essentially focuses on the impact of uncertainty on delivery time in three nodes supply networks. The aim of this paper is to identify the impact of the accumulation of the individual delivery time uncertainties on overall delivery time uncertainty. The idea is the type of network and their structures have a crucial impact on the delivery time uncertainty. To prove this idea a probabilistic method is created to measure and evaluate this influence by implementing the Markov theorem. This research is an important step toward the better understanding of more complex networks and the impact of network type in the delivery time uncertainty of these networks.

On bullwhip-limiting strategies in divergent supply chain networks

The amplification of demand variation in a supply chain network (SCN) is a well-known phenomenon called the bullwhip effect. This effect generates a large volume of inefficiencies as it moves a greater number of units than necessary, increases stock and generates stock-outs. There are two different approaches for avoiding and/or limiting this detrimental phenomenon that have received attention in the literature: Collaboration and information sharing in SCNs on one hand, and the adoption of smoothing replenishment rules on the other. The effectiveness of both approaches have been often analyzed only for “serial linked” SCNs, which is a supply network structure rarely found in real-life. In order to give an insight of how these techniques would perform in more generic SCNs, a divergent SCN has been benchmarked against the classical serial SCN. The computational experience carried out show that the bullwhip effect can be considerably reduced by collaboration or the smoothing replenishment rules in divergent SCNs, but it always performs worse than the serial SCN due to its inherent complexity.