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System integration

About: System integration is a(n) research topic. Over the lifetime, 8771 publication(s) have been published within this topic receiving 117914 citation(s). The topic is also known as: systems integration.


Papers
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Journal ArticleDOI
TL;DR: The author discusses the pros and cons of implementing an enterprise system, showing how a system can produce unintended and highly disruptive consequences and cautions against shifting responsibility for its adoption to technologists.
Abstract: Enterprise systems present a new model of corporate computing. They allow companies to replace their existing information systems, which are often incompatible with one another, with a single, integrated system. By streamlining data flows throughout an organization, these commercial software packages, offered by vendors like SAP, promise dramatic gains in a company's efficiency and bottom line. It's no wonder that businesses are rushing to jump on the ES bandwagon. But while these systems offer tremendous rewards, the risks they carry are equally great. Not only are the systems expensive and difficult to implement, they can also tie the hands of managers. Unlike computer systems of the past, which were typically developed in-house with a company's specific requirements in mind, enterprise systems are off-the-shelf solutions. They impose their own logic on a company's strategy, culture, and organization, often forcing companies to change the way they do business. Managers would do well to heed the horror stories of failed implementations. FoxMeyer Drug, for example, claims that its system helped drive it into bankruptcy. Drawing on examples of both successful and unsuccessful ES projects, the author discusses the pros and cons of implementing an enterprise system, showing how a system can produce unintended and highly disruptive consequences. Because of an ES's profound business implications, he cautions against shifting responsibility for its adoption to technologists. Only a general manager will be able to mediate between the imperatives of the system and the imperatives of the business.

3,636 citations

Book
01 Jan 1978

2,974 citations

Journal Article
TL;DR: In the laboratory, the laboratory investigates several areas, including protein-ligand docking, protein-protein docking, and complex molecular assemblies, as well as developing a number of computational tools such as molecular surfaces, phenomenological potentials, various docking and visualization programs which are used in conjunction with programs developed by others.
Abstract: One of the challenges in bio-computing is to enable the efficient use and inter-operation of a wide variety of rapidly-evolving computational methods to simulate, analyze, and understand the complex properties and interactions of molecular systems. In our laboratory we investigates several areas, including protein-ligand docking, protein-protein docking, and complex molecular assemblies. Over the years we have developed a number of computational tools such as molecular surfaces, phenomenological potentials, various docking and visualization programs which we use in conjunction with programs developed by others. The number of programs available to compute molecular properties and/or simulate molecular interactions (e.g., molecular dynamics, conformational analysis, quantum mechanics, distance geometry, docking methods, ab-initio methods) is large and growing rapidly. Moreover, these programs come in many flavors and variations, using different force fields, search techniques, algorithmic details (e.g., continuous space vs. discrete, Cartesian vs. torsional). Each variation presents its own characteristic set of advantages and limitations. These programs also tend to evolve rapidly and are usually not written as components, making it hard to get them to work together.

2,353 citations

Journal ArticleDOI
TL;DR: This paper reviews two types of DSM, static and time-based DSMs, and four DSM applications, effective for integrating low-level design processes based on physical design parameter relationships and leads to conclusions regarding the benefits of DSMs in practice and barriers to their use.
Abstract: Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMs vis-a-vis alternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development; project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: (1) component-based or architecture DSM, useful for modeling system component relationships and facilitating appropriate architectural decomposition strategies; (2) team-based or organization DSM, beneficial for designing integrated organization structures that account for team interactions; (3) activity-based or schedule DSM, advantageous for modeling the information flow among process activities; and (4) parameter-based (or low-level schedule) DSM, effective for integrating low-level design processes based on physical design parameter relationships. A discussion of each application is accompanied by an industrial example. The review leads to conclusions regarding the benefits of DSMs in practice and barriers to their use. The paper also discusses research directions and new DSM applications, both of which may be approached with a perspective on the four types of DSMs and their relationships.

1,489 citations

Proceedings Article
01 Jan 1995
TL;DR: The goal of the TOVE (TOronto Virtual Enterprise) Enterprise Modelling project is to create the next generation Enterprise Model, a Common Sense Enterprise Model that has the ability to deduce answers to queries that require relatively shallow knowledge of the domain.
Abstract: As information systems play a more active role in the management and operations of an enterprise, the demands on these systems have also increased. Departing from their traditional role as simple repositories of data, information systems must now provide more sophisticated support to manual and automated decision making; they must not only answer queries with what is explicitly represented in their Enterprise Model, but must be able to answer queries with what is implied by the model. The goal of the TOVE (TOronto Virtual Enterprise) Enterprise Modelling project is to create the next generation Enterprise Model, a Common Sense Enterprise Model. By common sense we mean that an Enterprise Model has the ability to deduce answers to queries that require relatively shallow knowledge of the domain. We are taking what can be viewed as a `second generation knowledge engineering' approach to constructing our Common Sense Enterprise Model. Rather than extracting rules from experts, we are `engineering ontologies.' An ontology is a formal description of entities and their properties, relationships, constraints, behaviours. Through interaction with our industrial partners, we encounter problems that arise in their particular enterprises. Our approach to engineering ontologies begins with using these problems to de ne an ontology's requirements in the form of questions that an ontology must be able to answer. We call this the competency of the ontology. The second step is to de ne the terminology of the ontology its objects, attributes, and relations. In this way the ontology provides the language that will be used to express the de nitions in the terminology and the constraints required by the application. The third step is to specify the de nitions and constraints on the terminology, where possible. The speci cations are represented in First Order Logic and implemented in Prolog. Lastly, we test the competency of the ontology by proving completeness theorems with respect to the competency questions. Our initial e orts have focused on ontologies to support reasoning in industrial environments. The tasks that we have targeted to support are in `supply chain management' which extends MRP (Manufacturing Requirements Planning) to include logistics/distribution [Fox

1,476 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20225
2021157
2020224
2019236
2018266
2017323