Predicting Hidden Links in Supply Networks

Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP) method to infer supplier interdependencies using the manufacturer’s incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i) number of existing supplier links, (ii) overlaps between supplier product portfolios, (iii) product outsourcing associations, and (iv) likelihood of buyers purchasing from two suppliers together. Naïve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximized by (i) and (iii). The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area

Reconstructing firm-level interactions in the Dutch input–output network from production constraints

Recent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints

How does the position of firms in the supply chain affect their performance? An empirical study

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.</jats:p

Production networks and planetary boundaries: challenges and opportunities for integrated assessment models

Integrated Assessment Models (IAMs) are used to understand the complex interactions between the Earth system and socio-economic processes. These models help us understand possible futures associated with different levels of human impact on the climate system. As such, they wield significant influence on policy-making and society as a whole. At the heart of this thesis is a fundamental inquiry into advancing IAMs. This deep inquiry is rooted in the understanding that the economy is a complex system and in the acknowledgement of the intertwined nature of the climate and ecological crises; both are mostly overlooked in IAMs. This thesis provides insights and advances for IAMs with the goal of strengthening their ability to address the climate and ecological crises together. First, this thesis finds that IAMs are not up to the task of addressing the climate and ecological crises together. Using the Planetary Boundaries (PBs) framework, it assesses the ecological feasibility of Paris-compliant mitigation pathways that were considered in the recent IPCC Sixth Assessment Report. Almost all scenarios transgress PBs. Even “low-demand” or “sustainability” pathways do not meet the ecological feasibility criteria set forth by the PBs framework. These findings highlight the need for a comprehensive and integrated approach. Second, drawing from complexity economics and recent findings in the literature on supply chain networks, this thesis argues that IAMs overlook key micro-level mechanisms that are essential for understanding the evolution, stability and resilience of the economic system – and thus of society as a whole. It explores how we might have a more fine-grained macroeconomic model that takes into account supply chain interactions at the firm level. It finds that serious data limitations must be overcome before we can achieve this level of granularity in IAMs. Using methods from complexity science, it addresses the data limitations using two different approaches and makes two major contributions: (1) it provides the first comprehensive picture of the most fundamental statistics on production networks, thus providing a basis for generating synthetic data or calibrating macroeconomic models; and (2) it provides the first thorough evaluation of a maximum entropy reconstruction method applied to firm-level production networks. This thesis also contributes to an emerging agenda to develop standards for data collection, cleaning and matching for micro-level production network data around the world

Predicting Hidden Links in Supply Networks

Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP) method to infer supplier interdependencies using the manufacturer's incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i) number of existing supplier links, (ii) overlaps between supplier product portfolios, (iii) product outsourcing associations, and (iv) likelihood of buyers purchasing from two suppliers together. Naïve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximised by (i) and (iii). The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area