Ermuntert euch, Aria, No. 5 from Cantata No. 176: Es ist ein trotzig und verzagt Ding (Alto Part)

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Associated with this can occur a loss of trust and willingness to cooperate or work together. What is referred to as directional complexity occurs most frequently at the beginning of a project. If it is not addressed fully at the beginning, lack of clarity breeds loss of trust. Our research in the defense industries indicated that more often than not senior management understood the goals but the project personnel or industry partners, either did not understand, or had a different interpretation, of the goal or goal paths. Although directional complexity is probably the easiest to address given experienced facilitators using a raft of problem structuring and soft systems thinking tools, it is often not addressed adequately because people either do not recognize its presence or tend to ignore it in favor of leaping into what they believe is the meaning of the project.

A number of soft systems thinking tools can be applied with great effect to clarify and share goals and goal-paths Flood and Jackson, ; Midgely, ; Jackson, Dimension Source of Complexity Tools To Be Considered Structural High levels of interconnectedness High level monitoring and control tools, and codependency between including earned value management, activities or organizational procurement via partnerships, flexible complicacy resulting in unclear or procurement options, program management redundant communication and tools, OR tools, complex systems-based risk approval pathways.

Each dimension of complexity requires different tools and in some projects, a vast range of tools must be used in parallel Pollack, b. Even an apparently simple project can go very wrong if the nature of the complexity is not recognized. In addition, the nature of the complexity can change over time, as in the case of the area medical facility discussed above.

It is also important to note the potential impact of one dimension of project complexity on another and the effect that intersection has on choice of tools and approaches. A project that initially presents few technical challenges can become highly problematic with a change of goal path when client requirements change. This kind of situation requires a phased use of tool with approaches, like Jazz, that encourage rich communication, rather than overt control. If directional complexity is also present, enough time must be allowed to achieve understanding and alignment using soft systems thinking tools.

In reality, however, particularly with mega projects, such as large construction and engineering projects, a myriad of projects and interests intersect, each at different stages and exhibiting different dimensions of complexity. In an intercity rail upgrade project, for example, temporal complexity was expected due to the duration of the project over 10 years , the possibility during that time of a change of government which might mean cancellation of the project , and the probability of significant advances in technology during the project life cycle.

This is coupled with high structural complexity due to the size of the project, the number of railway stations involved, limited access to tracks and stations, the complicated approval pathways involving government and commercial entities and the potential for bottlenecks due to shortage of specialist expertise. Technological challenges also abound, associated with how to address the number of bridges, tunnels, and stations that have existing heritage orders when few alternative tracks are available.

However, the most challenging aspects relate to the directional complexity involved in aligning goals, addressing and monitoring conflicting requirements of the many stakeholder groups. Tools are only helpful in these kinds of projects if they form part of a philosophy and methodology that support a systemic and pluralistic approach and if they are able to be used and applied in a timely manner, by people who are competent in their usage.

Conclusion: Tools are just Tools It is important to recognize that managing a complex project is a higher order management activity and should be treated and resourced accordingly. A discussion of tools is not complete without addressing organizational and individual capabilities. Tools in themselves are useless without the appropriate level of capability.

Most important is the capability of the governance team in identifying the nature of the complexity associated with a project, ability to identify the tools or approaches needed, ability to identify the skills and competences to apply the tools, and the willingness to ensure that the right people are engaged to deliver the project. Our data strongly suggests that the project managers who manage complex projects successfully are like artists, selecting the most appropriate tools and approaches from their very large palettes and working with those tools to produce the color, form and texture appropriate to the work in hand.

However, they also behave like scientists in their ability to select, analyze, and synthesize empirical data, and like politicians in their ability to influence and manage a network of relationships. Tools are, in the end, just tools. Bibliography Baccarini, D. The concept of project complexity—A Review. International Journal of Project Management, 14 4 , — Becoming a practice. Management Learning, 40 2 , — Checkland, P.

Systems thinking, systems practice. In Checkland, and Scholes, J. Information, systems and information systems - making sense of the field. Soft systems methodology in action. Crawford, L.

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Fitzgerald, B. Towards dissolution of the IS research debate: From polarization to polarity. Journal of Information Technology, 13 4 , — Flood, R. Creative problem solving: Total systems intervention. Healy, M. Comprehensive criteria to judge the validity and reliability of qualitative research within the realism paradigm. Helm, J.

Project Management Journal, 36 3 , 51— Jackson, M. Towards coherent pluralism in management science. Journal of the Operational Research Society, 50 1 , 12— Kuhn, T.

Systems Thinking

The structure of scientific revolutions. Lendrum, T. The strategic partnering handbook 2nd ed. Coordinating dependencies in complex system development projects. Operationalizing coordination of mega-projects—a workpractice perspective. Midgley, G. Creative methodology design. Systemist, 12, — In Flood, R. Mixing methods: Developing systemic intervention. Gill Eds. Systemic intervention: Philosophy, methodology, and practice. Mingers, J. Multi-paradigm multimethodology. Towards critical pluralism. A classification of the philosophical assumptions of management science methods.

Journal of the Operational Research Society, 54, — Multimethodology: Towards a framework for mixing methodologies. Omega, International Journal of Management Science, 25 5 , — Paton, G. On the contrary, as will be seen below, awareness and reflection are required for problem-solving in complex systems. Often, it is not possible to establish direct causal relations between events, especially in complex systems.

This multi-disciplinary knowledge base was able to identify some possible causes of the observed problem. This is exactly what Rosenberg stresses in the following passage:. It overlaps with development.

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The learning involved requires participation in the production process Rosenberg, , pp. It is worth reminding that models are used to describe what the system ought to be. By definition, models represent reality in a simplified way. Every model has simplifications of reality precisely because not everything that is necessary to describe a situation is known. An efficient model reproduces data in a satisfactory way for the established purposes. That is to say, there are many factors that may be considered irrelevant to these established purposes.

Models are interchange of language games , built specifically to deal with our bounded knowledge of the natural and artificial world. Reflective confrontation between stock and flow of knowledge. The origin of the analyzed events depends on the teams that designed, developed, integrated, launched, and operated the satellites. Figure 3 presents the iterative learning between the previous stock of knowledge and the flow of knowledge which is concrete, dynamic and relational, derived from praxis and action.

The stock of previous knowledge is represented by organizational processes and by project procedural knowledge.

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Space engineering begins the establishment of project procedural knowledge, based on the previous body of organization procedural assets. Integration of subsystems is done through JPO supervision. The operation occurred through teams that controlled orbit and attitude and teams that converted data into images, which is an Earth observation attribution. The divergence between the qualities of the generated image from that predicted by subsystems design started intensive interaction between these teams. Available procedural knowledge is confronted by concrete, dynamic and relational reality.

This process created the conditions for the discovery of the root-cause of the divergence between the expected and observed behaviors. Feedback looping increases and refines both. Although traditional knowledge management focuses on a type of knowledge that people have, i. This occurred because the subsystem had already been produced. In fact, when the problem was discovered, the CBERS-2 project was in its final phase of integration and testing. Research efforts that allow the establishment of the learning cycle are accumulated over a certain innovation path and follow rules that are determined by the systems architecture.

The possibilities of incremental and modular improvements to the system are severely constrained by forms of perception that are shared by the project teams Rechtin, This occurs because the number of interactions between system components exponentially increases with the number of components, allowing many new and subtle types of emergent behaviors Eisner, This coupling was because the vibration frequency of the scanner was approximately equal to the resonant frequency of the spring supporting the mirror of the CCD.

In a hindsight perspective, after the learning cycle is established, this problem may seem obvious. But it is not. Intuition about new problems often fails. The complex nature of problems leads to causal ambiguity, which is a characteristic of systems with nonlinear behavior. Many tests were introduced in integration phase to try to avoid similar problems. These new tests cost money and take time to perform.

Budget and schedule pressures play against the introduction of these tests. Multifunctional teams committed to systems evolution are those who define what should and what should not be done. The flow of relevant knowledge, that allows the integration of knowledge, enhances the crystallization of a common knowledge base - tacit and explicit. A project-base organization provides the integrative environment to link operational and learning processes.

Learning by using, considered as the reflective confrontation of a previous stock of knowledge and that derived from praxis, creates a cumulative effect that is associated with systems architecture, as represented in Figure 3. The more innovative the architecture is, the more it is subject to unpredicted emergent properties in complex systems industries. Extension of traditional project life cycle and iterative learning. In terms of economic value creation, these incremental innovations, which are derived from problem-solving, are extremely significant.

Complex systems projects require an extension of the traditional life cycle to include operations activities, aiming at economic value creation. The learning cycle proposed by Chesbrough is very similar to that proposed by Brady, Davies, and Gann and Davies and Hobday , all of whom emphasize the growing importance of learning by using, linking operational and learning processes, through the provision of services to the final user. Systems integration capabilities must be seen from a dynamic point of view. This dynamic perspective is necessary to identify innovation paths based on systems architecture.

When systems architecture is well-known and complexities are well understood, an organization developing a next generation of a product can predict many aspects of systems performance and accurately estimate costs and schedule in a manageable exposure of risk. In the words of Nelson :. Such interdependence militates against trying to redesign a number of components at once, unless there is a strong knowledge that enables viable designs for each of these to be well predicted ex-ante, or there exist reliable tests of cheap models of new systems Nelson, , p.

Contrary to theoretical propositions to link modular organization to modular products, in mix and match fashion , evolution of complex systems have to be carefully coordinated, especially when choosing innovative components, parts and subsystems that reduce the predictive capacity of systems when in operation environment. When ripple effects are easily mapped into manageable configurations spaces, confidence in projects deliverables grows.

Derivative and retrofitting projects can be exploited by the organization and many economies can be withdrawn from systems architecture. The ultimate test of systems architecture in its co-evolutionary change is represented by longevity, allowing technical improvements that positively respond to stakeholders needs. Systems integration capabilities associated with this architecture allow the transformation of systemic uncertainty into manageable risks, within established confidence limits by meaningful assignments of objective probabilities.

These capabilities bring many opportunities to the expansion of international business involving CBERS platform. It must be considered that any system architecture has performance limits. The form of subsystems integration that defines the systems architecture will sooner or later constrain technical advance.

Then, a new architecture must be found. That is a much more demanding effort than innovating within a given architecture Chesbrough, , since organizational processes must be integrated in different ways, and a new forms of learning have to be found to define a new cycle. Moreover, systems architecture may cause an exclusion effect, limiting and guiding what has to be done downstream Rechtin, In the absence of a dominant design the difference between these forms of learning is well framed by Kim , p.

New systems architecture requires the establishment of a different "interchange of language-games" to be embedded in project procedural knowledge. New systems architecture imposes different constraints and commitments to researchers who will be following different rules, in each of the three levels of systems integration herein considered. The analysis of the satellite program CBRES demonstrates the adequacy of considering learning by using as a result of the reflective confrontation between the previous stock of knowledge, accumulated through organizational processes, and knowing, derived from concrete, dynamic and relational experience.

This confrontation creates the conditions to better understand complex systems in the operational environment. The stylized cognitive model herein presented shows that learning by using may be conceived within Dewey's pragmatic view of productive inquiry. We have been arguing that systems integration capabilities building depends on learning by using and is associated with specific architecture. A practice-oriented epistemology overcomes the shortcomings of possession epistemology as considered in the traditional forms of knowledge conversion.

Learning by using creates conditions to identify and to measure unpredicted and sometimes unwanted emergent properties that threaten systems performance. The process of productive inquiry represent an important means of subsystems design and interfaces refinement that must be integrated in a specific architecture. This article draws attention to the need to extend the traditional project life cycle into the operational phase in complex systems industries.

The procedural knowledge that arises from learning by using indicates possible paths of innovation. Learning by using crystallizes a common knowledge base of specialized technicians and tends to have increasing relevance in the designs and interfaces refinement of complex systems. Systems integration capabilities, associated with specific systems architecture, are required to transform systemic uncertainty into manageable risks.

In light of these challenges, one should not consider that the modularity of products implies organizational modularity in a mix and match fashion. On the contrary, in the face of complexity, a common knowledge base that permits multifunctional teams to work effectively takes time to form. It is this common base that will allow the integration of aspects of knowledge that are relevant to that kind of interaction among specialist teams, which may be tacit or explicit in nature. In the absence of a dominant design, systems integration capabilities are even more strategic to the coordination of complex systems industries than in mass-production industries.

In addition, it is concluded that capabilities in systems integration are an effective strategy for bridging technological gaps. As organizations compete in an increasingly connected world, systems integration capabilities become more valuable to the success of technological innovations.

What is a Complex System?

This new fact, which took place after this article was approved, confirms what the authors have been arguing: that learning by using must consider knowing - the flow of knowledge derived from praxis and action - as confronted with a previous stock of knowledge, in order to be effective. This is the amount of time required to assemble, integrate and test AIT activities. To produce two identical satellites in each generation represents, among other things, a means to deal with high-risk projects. Brady, T. Creating value by delivering integrated solutions.

International Journal of Project Management, 23 5 , Brusoni, S. Knowledge specialization, organizational coupling, and the boundaries of the firm: why do firms know more than they make?

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Administrative Science Quarterly, 46 4 , Chagas, M. Inventing the eletronic century: the epic story of the computer eletronics and computer industries. New York: Free Press. Chesbrough, H. Towards a dynamics of modularity. Prencipe, A. Hobday Eds. Oxford: Oxford University Press. Bringing open innovation to services.

Cook, S. Bridging epistemologies: the generative dance between organizational knowledge and organizational knowing. Organization Science, 10 4 , Davies, A. The business of projects: managing innovation in complex products and systems 2nd ed. Cambridge: Cambridge University Press.

Dewey, J. The quest for certainty: a study of the relation of knowledge and action. New York: Putnam. Eisner, H. Managing complex systems: thinking outside the box. Essentials of project and systems engineering management. Gholz, E. Systems integration in the US defence industry: who does it and why is it important? Gourlay, S. Conceptualizing knowledge creation: a critique of Nonaka's theory.

Journal of Management Studies, 43 7 , Risk modeling, assessment and management. Henderson, R. Architectural innovation: the reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35 1 , Hobday, M.

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Product complexity, innovation and industrial organisation. Research Policy, 26 6 , The project-based organization: an ideal form for managing complex products and systems? Research Policy, 29 7 , Systems integration: a core capabilities of the modern corporation. Industrial and Corporate Change, 14 6 , Johnson, S. Three approaches to big technology: operations research, systems engineering and project management. Technology and Culture, 38 4 , Systems integration and social solution of technical problems in complex systems.

Kim, D. The link between individual and organizational learning. March, J. Organizations 2nd ed. Cambridge: Blackwell Publishers. Nelson, R. The sources of economic growth. Cambridge: Harvard University Press. Nonaka, I. The knowledge-creating company. New York: Oxford University Press. Block Allow. Hardback Seiten. John Wiley and Sons Ltd. Howard Eisner. Business mathematics systems. This book presents nine innovative methods to think outside the box and solve complex system problems. Managing Complex Systems provides specific tools and guidance needed to be a more creative and innovative thinker.