6. Relational model-base enabled real time operationsIf the increased reliance on decision models and computer-based decision agents is one of the keyattributes of the contemporary administrative arena, another is the burgeoning interest in moving awayfrom traditional planning-based (anticipatory) managerial postures towards more real-time (adaptive-reactive) regimes. This movement owes much to the realization that it’s improvident to opt for aprobabilistic approach to a decision situation for which a non-inferior deterministic solution is available,and to the companion realization that transitions towards real-time open up opportunities to transformpreviously probabilistic into deterministic decisions. More particularly, what real-time transits offer are opportunities for administrative decision-makers to ease their dependency on projections or expectations in favor of more definite data of morerecent vintage. To exploit these opportunities, administrative authorities can turn to an ever expanding arrayof real-time data-capturing devices. These devices, along with regular improvements in digital datacommunications capabilities, have shortened the intervals between the time of the first emergence ofinformational items and the point at which they become available as decision predicates. What’s alsoemerged are new-age conceptual referents, like the “dashboard” model of business administration(Eckerson, 2005), that suggest how managers should put accelerated predicates to proper use. So it is, also,that the main message of one of the most influential of modern management treatises, The VirtualCorporation (Davidow and Malone 1993), is how manufacturers might make their production decisions —and retailers their restocking decisions— less reliant on demand forecasts and more responsive to revealeddemand (per purchases recorded by point-of-sale terminals). Decision choices that wind up owing less to expectations and more to actualities have acorrespondingly better chance of proving more favorable in terms of whatever criterion applies. This is theraison d’etre for essays in the direction of dynamic resource allocation. Hence the modern military’sembrace of remote sensor platforms and other facilities that can deliver real-time reconnaissance data,which allows force disposition and other combat management decisions to be based on authenticated vs.merely anticipated battlefield conditions. Applications in the public sector are also not uncommon, one ofthe most impressive being the United States Forest Service's elicitation of real-time fire location data fromthe MODIS Rapid Response system in an effort to make the best possible use of high-impact, low-availability assets like smoke jumpers and airplanes (Kaufman, et. al., 1998; Giglio, et. al., 2003). Anyway, the focus for the remainder of this section is on how relational model-base structures cansupport attempts at adaptive-reactive management, and so enable more extensive essays in dynamicresource allocation. 6.1 Centripetal Data Acquisition What’s proposed by way of a real-time information handling system is a centripetal scheme likethat illustrated in Figure 1. Figure 1: A real-time information handling protocolCentripetal processing protocols acquire contributions from a multiplicity of monitoring/reportingreporting sources and channel them into a single data stream directed at a single relational model-basesubstructure. A relational model-base structure may be the province of an Administrative Agent rather thatmerely a disembodied mathematical function (Davidsson, et. al., 2003). It’s also possible, probable even,that the decision function/agent to which a relational model-base substructure pertains will be incorporatedin a manifold network model, and so be counted as aspect of an administrative task. An important implicit provision of the protocol shown in Figure 1 is that any data items that arereceived will have their significance assessed more or less immediately, and can thereafter be discarded.This pretty much obviates the need for archival databases, much less the huge data warehouses that serve asthe spinal substance for the management systems now being produced under the ERP banner. Forenterprises operating under a real-time regime, historical data is of substantive significance only in so far asit’s necessary to satisfy externally-imposed reporting or internal auditing requirements. Thus, enormouslypopular though they are these days among both administrators and academics, data mining devices wouldbe seen as having no practical role to play in the context of real-time management systems.1 Retrospectivepattern-recognition operations would simply be deemed unnecessary, as whatever patterns might bepractically pertinent to a decision situation would presumably already have been routinely recognized in thenormal course of coefficient estimation exercises. This view of the fate of real-time information iscertainly not unprecedented; it’s not really all that different from the approach incorporated in ComplexEvent Processing protocols that engine event-driven information systems (Luckham, 2002).As for information acquisition requirements, these would expectedly first be established withrespect to the factors (determinants, state-variables) over which a decision model is defined. Thereafter,items may be assigned different acquisition priorities depending on their apparent importance to the qualityof decision choices, or what remains to be achieved in terms of increases in levels of precision or probityfor a particular component (parametric or coefficient entry). The informational requirements specific to arelational substructure/decision model might then be denoted as {I}dx <, which sets a maximally-acceptable time interval ( ) between the emergence of potentially interesting informational items and theirpresentation as decision inputs. This phrasing is consistent with the concept of effective vs. strict real-time.Real-time, in its strict interpretation, requires that information always
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