“…El modelo CIM consiste en la integración de computadoras y redes de comunicación, que constituyen un sistema de producción integrado; aunque éste prometía ser una herramienta poderosa para la implementación en la industria, debido a que esto suponía una mejora en los procesos de producción, tuvo dificultades en cuanto a la gestión de una interfaz que habilitara la comunicación de manera eficiente entre diversos puntos en todo el proceso de automatización(Delaram y Fatahi Valilai, 2018); con lo que se dio paso a la evolución de nuevos paradigmas que ayudaran a resolver este problema, como lo fueron en su momento los procesos de manufactura ágil, ingeniería concurrente, sistemas de gestión de calidad(Brandl et al, 2018). Los sistemas de automatización, se representan por la pirámide CIM, la cual permite visualizar la estructura jerárquica los sistemas y dispositivos que integran cada uno de los niveles en automatización industrial, que son: los sistemas de planificación de recursos empresariales-ERP (Enterprise Resource Planning, por sus siglas en inglés)(Pinto et al, 2017), los sistemas de ejecución de fabricación-MES (Manufacturing Execution Systems, por sus siglas en inglés) y piso de planta, donde se encuentran los dispositivos de campo y los sistemas de control(Zernadji et al, 2016).…”
The topic dealt with in the document mainly covers the influence of service-oriented architecture and how services are orchestrated among themselves for the development of more robust and dynamic applications aimed at solving problems presented in the design of industrial automation systems. Currently, industries are compelling to use new systems that support the dynamics that organizations face. However, the current systems of companies lack this dynamism, which often makes it impossible to implement new functionalities to the processes of autonomous production, due to the lack of flexibility and agility to respond to the dynamics of production styles that are experienced today. Software engineering contributes to the dynamics in the area of industrial automation, highlighting significant improvements in the configuration of systems implemented in the field of industrial automation.
“…El modelo CIM consiste en la integración de computadoras y redes de comunicación, que constituyen un sistema de producción integrado; aunque éste prometía ser una herramienta poderosa para la implementación en la industria, debido a que esto suponía una mejora en los procesos de producción, tuvo dificultades en cuanto a la gestión de una interfaz que habilitara la comunicación de manera eficiente entre diversos puntos en todo el proceso de automatización(Delaram y Fatahi Valilai, 2018); con lo que se dio paso a la evolución de nuevos paradigmas que ayudaran a resolver este problema, como lo fueron en su momento los procesos de manufactura ágil, ingeniería concurrente, sistemas de gestión de calidad(Brandl et al, 2018). Los sistemas de automatización, se representan por la pirámide CIM, la cual permite visualizar la estructura jerárquica los sistemas y dispositivos que integran cada uno de los niveles en automatización industrial, que son: los sistemas de planificación de recursos empresariales-ERP (Enterprise Resource Planning, por sus siglas en inglés)(Pinto et al, 2017), los sistemas de ejecución de fabricación-MES (Manufacturing Execution Systems, por sus siglas en inglés) y piso de planta, donde se encuentran los dispositivos de campo y los sistemas de control(Zernadji et al, 2016).…”
The topic dealt with in the document mainly covers the influence of service-oriented architecture and how services are orchestrated among themselves for the development of more robust and dynamic applications aimed at solving problems presented in the design of industrial automation systems. Currently, industries are compelling to use new systems that support the dynamics that organizations face. However, the current systems of companies lack this dynamism, which often makes it impossible to implement new functionalities to the processes of autonomous production, due to the lack of flexibility and agility to respond to the dynamics of production styles that are experienced today. Software engineering contributes to the dynamics in the area of industrial automation, highlighting significant improvements in the configuration of systems implemented in the field of industrial automation.
“…They aim at predicting changes and estimating change impact based on the systems' manufactured products, the technical system itself and the involved stakeholders, such as engineers (Plehn et al 2016). Hybrid approaches, which take into account the complexity and interdisciplinarity of innovation projects in manufacturing, move into focus by adding the concept of agility to the set of models (Brandl et al 2018).…”
Section: Manufacturing Modelmentioning
confidence: 99%
“…Hybrid approaches, which take into account the complexity and interdisciplinarity of innovation projects in manufacturing, move into focus by adding the concept of agility to the set of models (Brandl et al. 2018).…”
Section: State Of the Art In Interdisciplinary Innovation Managementmentioning
In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.
“…For entirely new situations and extensive change types-like sudden modifications of boundary and market conditions e.g. due to a pandemic-, an iterative and agile change management instead of a thorough change analysis-which might not be possible-can be suggested [8]. Furthermore, the reaction to change causes and the implementation of changes can be facilitated by the consideration of changeability [12] and resilience [17] within the design of manufacturing systems.…”
Section: General Definitions and Scope Of Workmentioning
Shorter product innovation cycles, high variant products, and demand fluctuation, as well as equipment life cycles and technology life cycles force manufacturing companies to regularly change their manufacturing system. In order to address this challenge, an efficient and structured change management is required. As change causes and factory elements are connected via a complex network of relations and flows, an essential step in change management is the evaluation of considered adjustments with regard to their effects on the current production system. Depending on the context of the application, change impact analysis must process specific inputs and deliver different results. Current approaches, however, each focus only on selected aspects of the versatility of change effects. To address this challenge, this paper presents a modular approach for the individual design of change impact analysis.
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