Metrology and the Study of Complex Organizations(1999/06)
by Frank Voehl (Article for Hanchung's Website)
Frank Voehl is the CEO and President of Strategy Associates and former Corporate Vice President and General Manager of FPL Qualtec Quality Services. He is an author, consultant, and life-long learner, specializing in Quality Management, Strategic Planning, Logistics, Continuous Improvement, Teambuilding, and Productivity Management. His book *Deming: The Way We Knew Him* was a National Book Award Finalist.
He has consulted with hundreds of organizations around the world, including Fortune 500 companies, city and state governments. His work at FPL led to the formation of the Malcolm Baldrige National Quality Award in 1987. He is a Visiting Professor at the University of Miami and Florida International University, is married and is the father of three sons and a daughter.
The study of "Complex organizations" has been an important arena in Internet postings and organizational studies for decades. Historically, organizational scholars have examined vertical complexity (the number of levels in a hierarchy), horizontal complexity (the number of differentiated departments), and spatial complexity (the geographic dispersion of organizational subunits). Organizational environments have also been characterized as more or less complex depending on how heterogeneous and dispersed resources are within them.
However, a different view of complexity is emerging that may have important implications for Metrology scholarship. Within the past decade, interest in the "sciences of complexity" has increased dramatically. The study of complex system dynamics has perhaps progressed furthest in the natural sciences, but it is also beginning to penetrate the measurement sciences. This interdisciplinary field of study is still pre-paradigmatic, and it embraces a wide variety of approaches. Although it is not yet clear whether a genuine science of complexity will emerge, it does seem clear that scholars in a variety of fields are viewing complexity in a different way than organizational scholars traditionally have.
A number of findings now seem fairly well-established, including the following:
* Many dynamic systems do not reach an equilibrium (either a fixed point or a cyclical equilibrium).
* Processes that appear to be random may actually be chaotic, in other words may revolve around identifiable types of "strange attractors." Tests exist that can detect whether apparently random processes are in fact chaotic.
* Two entities with very similar initial states can follow radically divergent paths over time. The behavior of complex processes can be quite sensitive to small differences in initial conditions. This can lead to highly path-dependent behavior, and historical accidents may "tip" outcomes strongly in a particular direction.
* Very complex patterns can arise from the interaction of agents following relatively simple rules. These patterns are "emergent" in the sense that new properties appear at each level in a hierarchy.
* Complex systems may resist reductionist analyses. In other words, it may not be possible to describe some systems simply by holding some of their subsystems constant in order to study other subsystems. * Time series that appear to be random walks may actually be fractals with self-reinforcing trends. In such cases we may observe a "hand of the past" in operation.
* Complex systems may tend to exhibit "self-organizing" behavior. Starting in a random state, they may naturally evolve toward order instead of disorder.
The most interesting research into complex systems sheds fresh light on nonlinear dynamics, which usually evolve from interactions among agents. Measurement scholars seldom come to grips with nonlinear phenomena. Instead, we tend to model phenomena as if they were linear in order to make them tractable, and we tend to model aggregate behavior as if it is produced by individual entities which all exhibit average behavior.
Understanding complex processes may also require a shift from measuring sequential processes to studying simultaneous or parallel processes. At this juncture, organizational researchers have few templates that suggest to them how to hypothesize about or model such behavior. Researchers have suggested that it is difficult to know how to draw a conceptual model and how to report the results of empirical inquiries into complex organizational phenomena. The special issue aims to provide scholars with useful templates to follow when analyzing complex processes that involve organizations.
Although studies of complex systems in other disciplines are often very sophisticated technically, The Standard has not made much of an effort to bridge the teachings of metrologists with Complexity Theory. Examples of appropriate topics might include, but certainly are not limited to, the following:
* Research that specifies plausible sources of hidden order in apparently random processes that occur within or among organizations. Can we illuminate how that which appears random is actually ordered but in complex ways? Such insights are particularly interesting if they generate testable and measurable implications.
* Research that explains how simple organizational processes become complex ones. At what point do behaviors that are individually well-understood interact in ways that create difficult-to-understand aggregate outcomes? What are the measurement consequences of rising complexity in this sense?
* Research that compares several plausible rule sets for a group of interacting agents, and shows that behavior we observe in organiza- tions can be produced by one model of interactions but not by others.
* Research that simultaneously explores processes unfolding across multiple layers of context (e.g., economies, industries, and firms). What dynamics distinguish such processes, and how do they interrelate?
* Research addressing such topics as innovation and change, power and conflict, or crisis and reorientation may be particularly appropriate for the special issue.
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