Systems architecture, procedural knowledge and learning by using: implications on systems integration capabilities.
| Autor | Chagas Junior, Milton de Freitas |
| Cargo | Report |
Introduction
Innovation in complex systems shows many specific and important characteristics that differ from traditional innovation in mass-production systems. Rosenberg (1976) was the first to recognize the need to establish research streams aimed at modeling dynamics of innovation in complex systems, stressing the importance of balancing advances in technological disciplines incorporated in complex systems. Systemic uncertainty and the relevance of learning by using to successful innovation in complex systems are framed in Rosenberg (1982). During the past fifteen years, since the formalization of an innovation model ideally developed to deal with dynamics of innovation in complex systems (Hobday, 1998), many researchers are finding creative ways to strengthen this theory (Brady, Davies, & Gann, 2005; Brusoni, Prencipe, & Pavitt, 2001; Chagas, Cabral, & Campanario, 2011; Chesbrough, 2003, 2011; Davies & Hobday, 2011).
In this research two different perspectives have been purposefully melded to further analyze distinctive characteristics of innovation in complex systems. The first perspective focuses on the system as a whole, taking into account all components, subsystems and their interdependencies, which make the system more than the sum of its parts. Moreover, system properties do not depend on individually considered subsystems but, instead, emerge from the way the parts interact. Emergent properties are crucial to the concept of a system (Rechtin, 2000; Sillitto, 2005). However, systems can be decomposable from a structural, but not functional, standpoint (Zandi, 2007). This perspective imposes many difficulties in predicting the behavior of a system in its operational environment. Emergent properties may turn out to represent the main functionalities of a system and also compromise the whole system's functionalities. For this reason, a great deal of attention and effort is dedicated to these phenomena.
The second perspective focuses on capabilities required to organizations that deal with complex systems. A combination of systems engineering and project management processes enables organizations to get systems integration capabilities (Eisner, 2008; Prencipe, Davies, & Hob-day, 2003). Organizations must deal with diverse and varied specializations so that these processes facilitate the integration of dispersed expertise in various departments. Forms of interaction between different specialized disciplines are influenced by systems architecture (Eisner, 2005, 2008; Sage & Lynch, 1998). To organizations that deal with complex systems, systems integration capabilities are a kind of procedural knowledge (Johnson, 1997, 2003), exercised in the attainment of real tasks that could not be derived simply by declarative knowledge, expressed explicitly by propositions.
Taken together, these two theoretical perspectives have created conceptual insights to explore the linkage between a significant phenomenon and the creation of organizationally relevant knowledge. These insights show that the traditional knowledge creation model proposed by Nonaka and Takeuchi (1995) limits and distorts our understanding about rates and directions of technical change in complex systems. Investment to convert tacit knowledge into codified knowledge as in the four modes proposed by Nonaka and Takeuchi (1995) are much more related to configuration and information management than to creation of new knowledge. Relying on Dewey (1960) and related works (Cook & Brown, 1999; Gourlay, 2006; Orlikowski, 2002) we explore how a complex system evolves in practice.
Learning by using indicates many possible ways to systems evolution, through the linkages established by the previous stock of knowledge and the flow derived from action, which is concrete, dynamic and relational. Concrete because it considers reality and results from a techno-social construction. Dynamic because it considers elements of actual interaction confronted with the previous knowledge base. And relational because it is rooted in context where praxis and action take place. In order to solve problems and refine solutions this paper argues that these linkages come out of the confrontation between what systems behavior in its operational environment actually is and what it was designed to be. A stylized cognitive model, based on stock-and-flow logic, is proposed to represent this reflective confrontation and its implications to systems integration capabilities building. The development of systems of great complexity will require project-based organization (Hobday, 2000), aiming at capitalizing the knowledge creation that results from the merging of learning and operating processes associated with system-specific architectures. Hence, from the competitiveness perspective, systems integration capabilities bring important implications for organization design and innovation policy.
The research is based on a case study of the China-Brazil Earth Resources Satellite (CBERS) program, established through a project-based organization responsible for definition of systems architecture and the procedural knowledge required for the development of satellite platforms. The most important objective of this international joint project is to capture precise images of Earth. The first most evident failure of the project was an unexpected decrease in the sharpness of the image that came from the main satellite imager. This subsystem was technically well designed, but it turns out to be quite a frustrating experience to learn that well-designed subsystems don' t necessarily lead to a good end product. Rather, something else is needed. In this project, learning by using is analyzed from the perspective of practice-oriented epistemology and is considered an important element of systems integrations capability building, allowing systemic uncertainty to be transformed into manageable risks, through successive generations of satellites within the same systems architecture.
This study is organized in six sections, besides this introduction. Second section presents the theoretical framework of the article, identifies organizations as integrators of knowledge and emphasizes the peculiarities of innovation dynamics in complex systems. This section also shows the shortcomings of the traditional approach to knowledge creation and argues that Dewey's concepts of knowing and productive inquiry fit well with the peculiarities of innovation in complex systems. Third section explains the choice of case study as a research method. Fourth section analyses the CBERS program, presenting the project-based organization responsible for the development of platform satellites. It also presents the modularity of subsystems: division of labor between the Chinese Academy of Space Technology (CAST) and National Institute for Space Research (INPE), highlighting the phenomenon of emergent properties. Fifth section discusses the organizational conditions necessary for effective learning by using and the challenges of systems integrators. A stylized cognitive model is proposed that represents learning by using as the result of the reflective confrontation between the flow of knowledge that comes from operational practice and the stock of previous knowledge embedded in organizational procedural knowledge. Final section presents article conclusions.
Theoretical Framework
The central problem of innovation in complex systems is how the activities of cooperating specialists are organized to get productivity gains. Although a body of individual specialized knowledge is effectively achieved in purposeful environments, its practical use requires cooperation with other specialists. Cooperation of specialists demands a common knowledge base to take place. Core firms--or systems integrators--are in charge of industry coordination efforts. To do it effectively they must maintain and enhance their integrated learning bases (Chandler, 2001).
Systems integration capabilities and emergent properties
A distinctive role in industry coordination is fulfilled by systems integrators. Each project has its own coordination rules defined by systems architecture. Systems architecture have profound implications on subsequent project phases: development, manufacturing, final integration and operation. "Many systems are highly nonlinear in their behavior, and such behavior may be said to be more complex than the well-known alternative of a (mostly) linear system ... Further, our intuition often fails, due to their complex nature" (Eisner, 2005, p. 21).
The basic purpose of system architecture is to define subsystems (or modules) in terms of what they will do and what their interfaces are to other subsystems. Different principles of modular partitioning may follow, aiming at reducing complexity by encapsulating functionalities in specific parts of the system (e.g. structured analysis and object-oriented analysis for software systems). A modular architecture allows a subsystem to implement one or few functionalities. There are few well-defined interactions among subsystems named fundamental interactions. Once these interactions are mapped and well-defined it is possible to encapsulate functionalities in some specific part of the system, reducing its complexity. Systems architecture represents important means to mange complexity. In fact complexity is a matter of degree (Simon, 1997). In the very same way, modularity is, as well, a matter of degree (Schilling, 2003).
The extreme of a modular architecture is a configuration where it is possible to isolate functionalities in parts, on a one-to-one basis, with well-defined or standardized interfaces and interdependences. Modular architecture allows a design change to be made in one specific subsystem without requiring change in other subsystems. If a modular architecture is one extreme of a variation spectrum, integral architecture will be the other extreme. Modularity is a relative property of...
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