Visualizing the Alliance Network Structure of Service Industries

A growing body of research focuses on the structure of interfirm value co-creation. Despite this emphasis, little is known about the variation in interfirm collaboration across different service industries. Building on prior work in service value networks and business ecosystems, we analyze the structural characteristics of 11 service industries using a data-driven visualization approach. We first examine the alliance network structure of each service industry individually and differentiate the nature of collaboration using an exploration/coopetition lens. Second, we examine service industries integratively, thereby exploring the extent to which service industries are converging and traditional industry boundaries are blurred. Our results reveal significant structural differences in alliance network structures between service industries as well as diverse value co-creation orientations. Our macro analysis reveals an overall core-periphery structure and different service industry coupling levels, with actors in the ICT industry playing a particularly central role across subclusters. We frame our findings in terms of industry robustness, openness, and embeddedness. We conclude the paper with theoretical and practical implications for understanding and managing service ecosystems and suggest future research opportunities

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

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Leveraging supply network relationships to drive performance

Effective supply chain management requires focal firms to develop capabilities to manage a myriad of multi-tier, interconnected relationships often spanning multiple industries. Conventional assessments of supply chain relationships as linear or dyadic structures, rather than as a network, limit academician and managerial approaches to overcome challenges to effectively manage supply chains. Further, empirical research on innovation and performance implications of supply network structure and its corresponding relationship dynamics is still fairly nascent. My research focuses on leveraging supply network relationships to drive performance. Specifically, in my dissertation I examine how the structural, knowledge, and dependency differences in a firm’s supply network can affect knowledge and information flow, and ultimately the firm’s innovative, operational, and financial performance. My first study (CH. 2) contributes to current research at the interface of supply chain management and innovation. My second (CH. 3) and third paper (CH. 4) incorporate the intensity of each supply network link, reflective of focal firms as customers (suppliers) that may rely heavier on a supplier (customer) based on their percentage of cost (revenue) that goes to (is generated from) that supplier (customer). All three papers extend current research findings by bringing a more holistic assessment of firms that are embedded in a supply network, addressing the need for deeper structural analysis.Ph.D

Strategic knowledge creation through the analysis of the structure of the network formed by the participants of european R&D projects. Case of the emerging sector of renewable energies, at organizatio

321 p.La estrategia del Espacio Europeo de Investigación (ERA) tiene como objetivo aumentar el porcentaje de energías renovables en el mix energético hasta el 20% para el año 2020, impulsando este sector multidisciplinar y emergente. Existe la incertidumbre de crear consorcios de proyectos de I+D incluyendo colaboraciones ineficientes de transmisión de información y conocimiento entre los socios y las regiones locales. Esta tesis doctoral se centra en los proyectos europeos de I + D en los sectores de energía eólica, solar, marina, geotérmica y biomasa, para el período 2000 2013. El objetivo final de la tesis ha sido presentar el potencial de la técnica Análisis de Redes Sociales, para obtener conocimiento estratégico para la toma de decisiones de un sector tecnológico emergente y multidisciplinar. Para ello, se ha tomado como base la aplicabilidad de la teoría de redes sociales y la utilidad de la información que proporcionan los proyectos de I+D. Por un lado, muestra teóricamente el potencial de la información sobre proyectos para crear conocimiento estratégico a través de la aplicación integrada de los enfoques de centralidad y ¿structural hole¿ de Análisis de Redes Sociales. Por otro lado, aporta la creación de conocimiento estratégico en el sector de las energías renovables en Europa, proporcionando conocimiento de valor añadido en base a la eficiencia sobre las organizaciones y regiones locales participantes en estos proyectos. Concluye en cómo influyen estos en el resto de actores de las redes de colaboración, quiénes son eficientes y quiénes tienen un rol facilitador de cohesión de la red de transferencia de información y conocimiento adquirido a través de los proyectos I&D, aplicable a cualquier sector, normalmente subvencionados por organismos públicos cuando son sectores emergentes. Este estudio constituye una novedosa contribución, siendo una herramienta complementaria a los estudios de patentes y publicaciones que los responsables políticos deben considerar al invertir en proyectos públicos de I+D, para construir ERA eficientemente

Understanding the Robot Ecosystem: Don\u27t lose sight of either the trees or the forest

The robot sector in many countries has thrived recently thanks to government supports and innovations in various industries. This study, using the patent database to define the robot sector, reconfigures IO (Input-Output) data to analyze the relationships among various sectors. In particular, we consider the internal description of the robot sector (mesoscopic view—the trees) as well as the relationship between the robot and the non-robot sectors (macroscopic view—the forest), so that we can not only understand robot ecosystems in various dimensions but also develop policy insights. For the sake of systematic analysis of the intra- and inter-sector relations as well as the meso-macro links, this study constructs network models and employs several network measures. Our model and analysis present a good case study to understand the nature of the robot sector in terms of the business ecosystem. This novel approach also contributes to finding out a promising path that leverages the strengths of intra-sector relations and spreads the impact of the robot sector across the macro relations

Understanding the Robot Ecosystem: Don't lose sight of either the trees or the forest

The robot sector in many countries has thrived recently thanks to government supports and innovations in various industries. This study, using the patent database to define the robot sector, reconfigures IO (Input-Output) data to analyze the relationships among various sectors. In particular, we consider the internal description of the robot sector (mesoscopic view—the trees) as well as the relationship between the robot and the non-robot sectors (macroscopic view—the forest), so that we can not only understand robot ecosystems in various dimensions but also develop policy insights. For the sake of systematic analysis of the intra- and inter-sector relations as well as the meso-macro links, this study constructs network models and employs several network measures. Our model and analysis present a good case study to understand the nature of the robot sector in terms of the business ecosystem. This novel approach also contributes to finding out a promising path that leverages the strengths of intra-sector relations and spreads the impact of the robot sector across the macro relations

Framework For Effective Resilience Managmenet Of Complex Supply Networks

In today\u27s environment with high global and complex supply chains for engineered products, the ability to assess and manage the resilience of supply chains is not a luxury but a fundamental prerequisite for business continuity and success. This is particularly true for firms with deep-tier supply chains, such as the automotive original equipment manufacturers (OEMs) and their suppliers. Automotive supply networks are particularly facing growing challenges due to their complexity, globalization, economic volatility, rapidly changing technologies, regulations, and environmental/political shocks. These risks and challenges can disrupt and halt operations in any section of the supply network. Given that supply chains have become quite lean in the 21st century with relatively little slack, the COVID-19 pandemic has fully exposed these vulnerabilities. According to Allianz\u27s Business Risk Report from 2014, half of all supply chain disruptions stemming from tier-2 and tier-3 suppliers. However, the industry\u27s supply network assessment practice is primarily limited to immediate (i.e., tier-1 ) suppliers with no real consideration for the deep-tiers. The added complication due to poor supplier relations is that there is no visibility to the upstream deeper-tiers of the supply network, which could lead to severe vulnerabilities and impose massive disruption costs. Our research goal is to enhance the resilience of deep-tier automotive supply networks through improved resilience assessment and management mechanisms. In this collaborative study with a global automotive OEM (Ford Motor Company), we seek to develop methods to assess and manage the resilience of deep-tier supply networks. This research considers the multi-dimensional nature of resilience management, focusing on metrics around cost efficiency, effective inventory management, demand fulfillment, capacity management, and delivery performance. We develop and evaluate our proposed resilience assessment and management framework with a real case study supply network for an automotive climate control system. The supply network contains 20 firms (nodes) located in various global regions and 21 connections (edges) between firms. The network includes three tiers of suppliers with different transportation modes, making the network a rich illustrative example for proposed resilience assessment and management methods and analysis. All inventory and shipping policies with related parameters have been defined and set for each supplier and their connections. The proposed resilience assessment framework relies on discrete-event simulation for effectiveness; computational efficiency is maintained by relying on modern open-source packages for modeling, optimization, and analysis. The framework starts by generating a digital supply network model that includes the focal firm and its suppliers and deeper-tiers based on the available visibility. Disruption scenarios, including disruption sources, frequency, and severity, are then efficiently generated using private and public regional risk sources. For illustrative purposes, we primarily relied on public secondary data sources. The secondary regional risk indices that we relied upon aggregate political, economic, legal, operational, and security risks for the given region. Finally, the digital supply network is simulated with an adequate number of replications for reliable assessment. In this research, discrete-event simulation is implemented using NetworkX and SimPy Python packages. We employ the network analysis techniques combined with discrete-event simulation informed by secondary data sources for improving the assessment framework. Our resilience assessment results confirm that visibility into the deeper-tiers of the supply network (through primary or secondary data sources) leads to a more accurate network resilience assessment. Finally, we offer a global sensitivity analysis procedure to determine the supply network players, parameters, and policies that most influence the network performance. We also propose an effective resilience management framework that efficiently leverages simulation-based optimization. For illustrative purposes, we considered the mitigation strategies typical in the automotive industry, such as dual sourcing, reserve capacities (at primary or secondary suppliers), and contracts with backup suppliers besides carrying safety stock. Sourcing and transportation mode decisions can be easily incorporated into the framework. The method seeks to minimize the cost of risk mitigation strategies while attaining the target resilience. The framework is flexible and can entertain other objectives and constraints. Given that simulation-based optimization methods can be computationally expensive, we employ surrogate models that relate supply network resilience performance to network design parameters within our mathematical programming formulation. Without loss of generality, the surrogate models are based on linear regression models that define the relationship between the focal firm and tier-1 suppliers\u27 resilience levels and network design decision variables. The imperfections of the regression models are accounted for in the formulation through constraints with slack (function of the RMSE of the regression model). We demonstrate that optimal resilience management would stem from jointly allocating safety buffers (e.g., capacity, inventory levels) across the network and not by independently applying a simplistic/static set of rules for all nodes/arcs. Our validation experiments with a real-world case study informed by secondary data from public data sources confirm the effectiveness and efficiency of the proposed supply network resilience management method

Using supply chain databases in academic research: A methodological critique

This article outlines the main methodological implications of using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain for academic purposes. These databases provide secondary data on buyer–supplier relationships that have been publicly disclosed. Despite the growing use of these databases in supply chain management (SCM) research, several potential validity and reliability issues have not been systematically and openly addressed. This article thus expounds on challenges of using these databases that are caused by (1) inconsistency between data, SCM constructs, and research questions (data fit); (2) errors caused by the databases' classifications and assumptions (data accuracy); and (3) limitations due to the inclusion of only publicly disclosed buyer–supplier relationships involving specific focal firms (data representativeness). The analysis is based on a review of previous studies using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain, publicly available materials, interviews with information service providers, and the direct experience of the authors. Some solutions draw upon established methodological literature on the use of secondary data. The article concludes by providing summary guidelines and urging SCM researchers toward greater methodological transparency when using these databases

Coopetition in an open-source way : lessons from mobile and cloud computing infrastructures

An increasing amount of technology is no longer developed in-house. Instead, we are in a new age where technology is developed by a networked community of individuals and organizations, who base their relations to each other on mutual interest. Advances arising from research in platforms, ecosystems, and infrastructures can provide valuable knowledge for better understanding and explaining technology development among a network of firms. More surprisingly, recent research suggests that technology can be jointly developed by rival competing firms in an open-source way. For instance, it is known that the mobile device makers Apple and Samsung continued collaborating in open-source projects while running expensive patent wars in the courts. On top of multidisciplinary theory in open-source software, cooperation among competitors (aka coopetition) and digital infrastructures, I (and my coauthors) explored how rival firms cooperate in the joint development of open-source infrastructures. While assimilating a wide variety of paradigms and analytical approaches, this doctoral research combined the qualitative analysis of naturally occurring data (QA) with the mining of software repositories (MSR) and social network analysis (SNA) within a set of case studies. By turning to the mobile and cloud computing industries in general, and the WebKit and OpenStack opensource infrastructures in particular, we found out that qualitative ethnographic materials, combined with social network visualizations, provide a rich medium that enables a better understanding of competitive and cooperative issues that are simultaneously present and interconnected in open-source infrastructures. Our research contributes back to managerial literature in coopetition strategy, but more importantly to Information Systems by addressing both cooperation and competition within the development of high-networked open-source infrastructures.Yhä suurempaa osaa teknologiasta ei enää kehitetä organisaatioiden omasta toimesta. Sen sijaan, olemme uudella aikakaudella jossa teknologiaa kehitetään verkostoituneessa yksilöiden ja organisaatioiden yhteisössä, missä toimitaan perustuen yhteiseen tavoitteeseen. Alustojen, ekosysteemien ja infrastruktuurien tutkimuksen tulokset voivat tuottaa arvokasta tietämystä teknologian kehittämisestä yritysten verkostossa. Erityisesti tuore tutkimustieto osoittaa että kilpailevat yritykset voivat yhdessä kehittää teknologiaa avoimeen lähdekoodiin perustuvilla käytännöillä. Esimerkiksi tiedetään että mobiililaitteiden valmistajat Apple ja Samsung tekivät yhteistyötä avoimen lähdekoodin projekteissa ja kävivät samaan aikaan kalliita patenttitaistoja eri oikeusfoorumeissa. Perustuen monitieteiseen teoriaan avoimen lähdekoodin ohjelmistoista, yhteistyöstä kilpailijoiden kesken (coopetition) sekä digitaalisista infrastruktuureista, minä (ja kanssakirjoittajani) tutkimme miten kilpailevat yritykset tekevät yhteistyötä avoimen lähdekoodin infrastruktuurien kehityksessä. Sulauttaessaan runsaan joukon paradigmoja ja analyyttisiä lähestymistapoja case-joukon puitteissa, tämä väitöskirjatutkimus yhdisti luonnollisesti esiintyvän datan kvantitatiivisen analyysin ohjelmapakettivarastojen louhintaan ja sosiaalisten verkostojen analyysiin. Tutkiessamme mobiili- ja pilvipalveluiden teollisuudenaloja yleisesti, ja WebKit ja OpenStack avoimen lähdekoodin infrastruktuureja erityisesti, havaitsimme että kvalitatiiviset etnografiset materiaalit yhdistettyinä sosiaalisten verkostojen visualisointiin tuottavat rikkaan aineiston joka mahdollistaa avoimen lähdekoodin infrastruktuuriin samanaikaisesti liittyvien kilpailullisten ja yhteistyökuvioiden hyvän ymmärtämisen. Tutkimuksemme antaa oman panoksensa johdon kirjallisuuteen coopetition strategy -alueella, mutta sitäkin enemmän tietojärjestelmätieteeseen, läpikäymällä sekä yhteistyötä että kilpailua tiiviisti verkostoituneessa avoimen lähdekoodin infrastruktuurien kehitystoiminnassaUma crescente quantidade de tecnologia não é desenvolvida internamente por uma só organização. Em vez disso, estamos em uma nova era em que a tecnologia é desenvolvida por uma comunidade de indivíduos e organizações que baseiam suas relações umas com as outras numa rede de interesse mútuo. Os avanços teórico decorrentes da pesquisa em plataformas computacionais, ecossistemas e infraestruturas digitais fornecem conhecimentos valiosos para uma melhor compreensão e explicação do desenvolvimento tecnológico por uma rede de multiplas empresas. Mais surpreendentemente, pesquisas recentes sugerem que tecnologia pode ser desenvolvida conjuntamente por empresas rivais concorrentes e de uma forma aberta (em código aberto). Por exemplo, sabe-se que os fabricantes de dispositivos móveis Apple e Samsung continuam a colaborar em projetos de código aberto ao mesmo tempo que se confrontam em caras guerras de patentes nos tribunais. Baseados no conhecimento científico de software de código aberto, de cooperação entre concorrentes (também conhecida como coopetição) e de infraestruturas digitais, eu e os meus co-autores exploramos como empresas concorrentes cooperam no desenvolvimento conjunto de infraestruturas de código aberto. Ao utilizar uma variedade de paradigmas e abordagens analíticas, esta pesquisa de doutoramento combinou a análise qualitativa de dados de ocorrência natural (QA) com a análise de repositórios de softwares (MSR) e a análise de redes sociais (SNA) dentro de um conjunto de estudos de casos. Ao investigar as industrias de technologias móveis e de computação em nuvem em geral, e as infraestruturas em código aberto WebKit e OpenStack, em particular, descobrimos que o material etnográfico qualitativo, combinado com visualizações de redes sociais, fornece um meio rico que permite uma melhor compreensão das problemas competitivos e cooperativos que estão simultaneamente presentes e interligados em infraestruturas de código aberto. A nossa pesquisa contribui para a literatura em gestão estratégica e coompetição, mas mais importante para literatura em Sistemas de Informação, abordando a cooperação e concorrência no desenvolvimento de infraestruturas de código aberto por uma rede the indivíduos e organizações em interesse mútuo