Abstract
As suggested, adaptive strategic planning/positioning technologies are intended to serve as either complements to or substitutes for either the structured or dynamic contingency planning platforms we discussed in the two previous chapters. Both these approaches, recall, rested on planning constructs intelligible as Discrete-Bipartite model-base structures. Therefore, our need here is to be as explicit as possible about the conditions under which discrete-bipartite contingency planning technology may fail to provide an organization with an adequate level of strategic readiness.
As we now advance to consider adaptive strategic planning/positioning, we’ll be looking at strategic management technologies that might be said to sit at the frontier of our contemporary capabilities. For here the focus is on facilities that promise to help organizations cope in contexts edging towards the worst-case condition we’ve identified, state-indeterminacy. For what this holds out is the prospect of organizations being more or less routinely confronted by events/threats that were essentially unanticipated. And this, in its turn, argues for the wisdom of such organizations relying less on —or perhaps abandoning altogether— attempts to operate under a preadaptive approach to readiness via either the structured or dyanmic (intelligence-driven) contingency planning technologies discussed in the two previous chapters. Rather, they would be urged to consider moving towards one of two adaptive strategic management platforms:
The INIERPOLATTVE, intended to serve organizations that can predefine the broad classes of event/threats to which they might be subject, but not the more detailed particulars of the situations that will arise.
The REAL-TIME, most appropriate for organizations that must be prepared to do much of their strategic decision making on an after-the-fact basis because of the high likelihood of their being visited by a stream of essentially novel or unanticipated exogenous events.
Both of the above platforms provide the technical wherewithal to adopt an essentially reactive orientation to organization readiness. It’s just that the former cannot accomodate as high a degree of contextual volatility and complexity as the latter.
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Notes and References
For a general discussion of reactive management concepts and technology, see my paper: “The Case for Reactive Management Systems: Elements of a Real-Time Decision Technology”, in IEEE Transactions on Systems, Man and Cybernetics (Jan./Feb., 1984).
An informative treatise on the socio-cultural aspects of adaptive strategic management is available from Eric Trist’s article “Developing an Adaptive Planning Capability in Public Enterprise and Government Agencies”, in the Management Handbook for Public Administrators (Van Nostrand Reinhold, 1978).
One might look to certain AI-based knowledge engineering conventions to help define categorical referents, as per arguments in J. F. Sowa’s Conceptual Structures (Addison-Wesley).
Interestingly, this subsumation concept corresponds rather well with a distinction that J. K. Friend raised over a decade ago between policy-making and decision-making. For in a fine paper, “The Dynamics of Policy Change” (in Long Range Planning, vol. 10, February 1977), he notes that “... a policy is essentially a stance which, once articulated, constributes to the context within which a succession of future decisions will be made.” The proposition that any generic strategic planning referent will serve to anchor some collectivity of lower-order (less abstract) or actionable scripts corresponds rather well with Friend’s distinction between policy referents and decisions.
In practice, associative network structures are designed to express constructs that result from the superimposition of factors drawn from different populations. Thus, most working associative networks are in fact data base structures predicated on clusters of properties rather than records, as suggested by Miyamoto, et. al. in their “Directed Graph Representations of Associative Structures: A Systematic Approach”, in IEEE Transactions on Systems, Man and Cybernetics (16, 1; 1986). They may also reflect neural-network theory speculations about the mechanics of associative inference and associative memory as a human cognitive characteristics, c.f.: Fukushima, “A Model of Associative Memory in the Brain” in Kybernetik (12, 1973), or T. Kohonen’s volume on Associative Memory (Springer-Verlag, 1978). In contrast, a Constellation network is a model base network where generic prototypes represent planets, discrete event and response alternatives could be moons and arcs comprehensible as difference vectors.
The isolation of similarities and differences among some set of categorical constructs may be thought of as an exercise in qualitative substration, and so perhaps susceptible to formal discipline via fuzzy-set techniques for determining membership in or exclusion from qualitative sets. For more on the mechanics of such operations, see E. Backer’s Cluster Analysis by Optimal Decomposition of Induced Fuzzy Sets (Delft University, 1978). But in the final chapter of this volume, we’ll be introducing some technical bases for the systematic recognition and extraction of qualitative vs. quantitative differences.
These are operations for which there is some technical precedent. First, there are certain Formal system science conventions that might apply, c.f.: Himmelbau (ed.) Decomposition of Large-Scale Systems (American-Elsevier, 1983).
These are also techniques to help determine membership in fuzzy sets, e.g., E. Blacker, Cluster Analysis by Optimal Decomposition of Induced Fuzzy Sets (Delft University, 1978). Finally, some knowledge-engineering initiatives, especially those used by expert system designers, might be used to some advantage.
For more on such, see J. Sowa’s fine book, Conceptual Structures (Addison-Wesley, 1984).
Clearly, at least some of the basic matching/matching tasks that have to be conducted within a primitive/template structure could be computer-aided via modem pattern recognition technology. See, for example, Tou and Gonzales’ volume on Pattern Recognition Principles (Addison-Wesley, 1977).
For some ideas about the critical practical constribution of Template-driven model base technology within modem Command/Control systems, see my article: “Enhancing the Role of Microcomputers in Command/Control Systems” in (Andriole, ed.) Microcomputer Decision Support Systems, op. cit.
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© 1989 Kluwer Academic Publishers
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Sutherland, J.W. (1989). Approaches to Adaptive Strategic Management. In: Towards a Strategic Management and Decision Technology. Theory and Decision Library, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0953-3_5
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DOI: https://doi.org/10.1007/978-94-009-0953-3_5
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