Probability-based Vendor Selection Model for the Hungarian Automotive Supply Network

The aim of this paper is to investigate the structure of the Hungarian automotive supply network and provide a possible solution that mathematically describes the connections between the interested parties. In the study an approximate model is introduced to determine the links between hubs (car manufacturers), nodes (Tier1 suppliers) and edges, combining probability random graph and scale free network theory. During the simulation some main drivers were applied for selection purposes, such as location, turnover, product profile. As a result of the study a potential tool has been designed to support decision-making

Supply Networks as Complex Systems: A Network-Science-Based Characterization

Outsourcing, internationalization, and complexity characterize today's aerospace supply chains, making aircraft manufacturers structurally dependent on each other. Despite several complexity-related supply chain issues reported in the literature, aerospace supply chain structure has not been studied due to a lack of empirical data and suitable analytical toolsets for studying system structure. In this paper, we assemble a large-scale empirical data set on the supply network of Airbus and apply the new science of networks to analyze how the industry is structured. Our results show that the system under study is a network, formed by communities connected by hub firms. Hub firms also tend to connect to each other, providing cohesiveness, yet making the network vulnerable to disruptions in them. We also show how network science can be used to identify firms that are operationally critical and that are key to disseminating information

The moderating impact of supply network topology on the effectiveness of risk management

While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers’ connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience

Supply network science: Emergence of a new perspective on a classical field.

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research

The impact of the supply chain structure on bullwhip effect

The aim of this paper is to study how the structural factors of supply chain networks, (i.e. the number of echelons, the number of nodes and the distribution of links) impact on its dynamics performance (i.e. bullwhip effect). To do so, we systematically model multiple structures according to a robust design of experiments and simulate such structures under two different market demand scenarios. The former emulates a stationary condition of the market, while the latter reproduce the extreme volatility and impetuous alteration of the market produced by the current economic recession. Results contribute to the scientific debate on supply chain dynamics by showing how the advocated number of echelons is not the only structural factor that exacerbates the bullwhip effect. In particular, under a sudden shock in market demand, the number of nodes and the divergence of the supply chain network affect the supply chain performance.Ministerio de Economía y Competitividad DPI2013-44461-P/DP

The impact of supply chain structures on performance.

La Tesis analiza el impacto que tiene la estructura de las redes de suministro sobre su rendimiento, concretamente sobre el “efecto látigo” o efecto bullwhip. Para ello se desarrolla una arquitectura basada en la metodología de los sistemas multi-agente, que permite el modelado de sistemas complejos. Dicha arquitectura es implementada en un software dando lugar a un simulador de redes de suministro llamado SCOPE, que permite el modelado y simulación de una amplia variedad de configuraciones de redes de suministro. SCOPE es utilizado para investigar una de las suposiciones más comunes en el campo del modelado de redes de suministro: el uso de estructuras muy sencillas en forma serial generalmente con muy pocas fases funcionales y pocos nodos. Para determinar el impacto de la estructura de la red sobre el efecto bullwhip se utiliza una estructura más compleja y más acorde con las estructuras de redes de suministro reales: la red divergente. Se realizan tres experimentos: (i) análisis comparativo del efecto bullwhip entre la red divergente y la serial; (ii) análisis comparativo de la eficacia de dos técnicas muy conocidas para la limitación del efecto bullwhip entre la red divergente y la serial; (iii) determinación de los parámetros estructurales de la red de suministro divergente y análisis estadístico para determinar si dichos parámetros estructurales impactan sobre el efecto bullwhip. Los resultados obtenidos revelan que todos los parámetros estructurales analizados impactan significativamente sobre efecto bullwhip. Además, en caso de un impulso inesperado en la demanda, el impacto de la red de suministro en el efecto bullwhip es mayor. Las técnicas para limitación del efecto bullwhip son también efectivas en redes de suministro divergentes, consiguiendo además un aumento de su robustez ante cambios bruscos inesperados en la demanda

Social and engineering perspectives on optimal farm management and reliable grain supply chain networks

The growth in food demand urge the need of increasing agricultural productivity and reducing food losses in a sustainable basis. New opportunities for farm management decision making have been rapidly growing with the proliferation of data and information describing agricultural systems. Farm management performance is affected by complex interactions between factors, such as crop yield, market price, culture task schedule, machinery selections, as well as local weather and environmental conditions. Appropriate farm management practices coupling with abilities to obtain real-time local agricultural information with recently vigorous developed information technologies can improve agricultural productivity, reduce losses, and improve farmers’ profits. Also, a better understanding of strength and weakness of grain supply chains provide opportunities to plan a reliable and robust food networks, thereby assisting farm management and reducing post-harvest losses. Thus, the overall objective is developing a framework to support farming decisions that enhance farm management on a sustainable and profitable basis. To bridge existed information gaps, specialized text mining tools are developed to discover real-time agricultural information by utilizing Twitter, which also provides geolocation data with finer spatial resolution. The results showed that social networks contribute more real-time regional crop planting schedules compared to official NASS reports, which can be ahead of time by five days on average at the early stage of planting. We have also identified influential agricultural stakeholders within social networks, based on social network connections of the communities observed within Twitter. The results showed that the connections of online agricultural communities are exceedingly tight and geo-location-based. This will provide new strategies for the development and deployment of targeted community learning modules for enhanced implementation of best management practices. Qualitative and quantitative analytical tools have been developed to provide decision support on farm management practices. A text mining analysis was performed to identify farming schedules and discover key influential factors behind farmers’ operational decisions from news media. The results showed strong site-specific relationships between harvest, grain price, and moisture for farm management. An optimization model, BioGrain, was developed to maximize farmers’ profits by optimizing critical farm decisions including agricultural machinery selection and harvesting schedules. The optimization modeling showed that crop moisture content is critical for optimal farm management. Farmers should balance the tradeoffs between harvestable yield and drying costs to make appropriate decisions when determining the best management strategy. Large farms outperformed small farms on profits but generated higher grain losses, due to a longer harvesting period. The change of corn price would affect optimal farm decision making when adopting on-farm drying, but not for farmers adopting elevator drying. Grain supply chains are inherently complex due to interactions between farms, grain elevators, and several kinds of grain processing facilities. We have developed an optimization model to reproduce the potential grain supply chain flows within the network based on local crop yields and agricultural infrastructure. Given potential grain transportation flows, we then study the network structure and characteristics of the Illinois grain supply chains from global and local topological perspectives. The result shows that the network has scale-free properties and good network features for supply chains. Using modularity and centrality analyses, important subgroups and facilities were identified. The results revealed two primary subgroups located in western and central Illinois. The most important facilities are identified within those regions and should be well maintained to avoid propagation of system failures