Toward a decision informatics paradigm: a real-time, information-based approach to decision making

Abstract

The focus of this paper is on decision making; more specifically, on what decision making requirements are needed in the future. We augur for a decision informatics paradigm; it is a real-time, information-based approach to decision making. The paradigm is supported by two sets of technologies (i.e., information and decision technologies) and underpinned by three disciplines (i.e., data fusion/analysis, decision modeling, and systems engineering). We begin by considering the context-and needs-for decision making as the economies of the world change and evolve, especially in regard to emerging services; then our proposed decision informatics paradigm is detailed and illustrated, together with an in-depth review of a critical, underpinning research area (dealing with real-time fusion and analysis of multiple nonhomogeneous data sources), followed by several concluding remarks.Index Terms-Data analysis, data fusion, decision making, mass customization, quantitative modeling, systems engineering. I. CONTEXTUAL CONSIDERATIONST HE focus of this paper is on decision making; more specifically, on what decision making requirements are needed in the future. We augur for a decision informatics paradigm; it is a real-time, information-based approach to decision making. We begin by considering the context-and needs-for decision making as the economies of the world change and evolve (Section I), especially in regard to emerging services (Section II); then our proposed decision informatics paradigm is detailed and illustrated (Section III), together with an in-depth review of a critical, underpinning research area (Section IV), followed by several concluding remarks (Section V).In order to provide a context for considering current and future decision making requirements, it is instructive to review the critical stages in a nation's economic evolution [32]. As summarized in Table I, there have been thus far three stages. The first-mechanical-stage focuses on agriculture and mining (i.e., living off of the land, air, and sea); it seeks to enhance farming productivity, employs mechanical tools that have product life cycles on the order of decades, depends mostly on muscle power, embraces a living standard concerned with subsistence, and is limited in its scope of economic influence-primarily impacting the family or immediate locale. The second-electrical-stage focuses on manufacturing and construction (i.e., creating and producing products and structures); it seeks to enhance factory productivity, employs electromechanical machines that have product life cycles on the order of years, depends on both muscle and brain power, embraces a living standard concerned with the quality of goods, and is broader in its scope of economic influence-impacting the region or nation. The third-information-stage focuses on services (i.e., creating and delivering added value that is essentially intangible); it seeks to enhance information (i.e., processed data from a decision making perspective) productivity, employs information (including communicat...

A Survey of Operations Research Models and Applications in Homeland Security

“…Operations research modeling incorporating the complex interconnections between relief agencies and government entities can clarify these chaotic situations and help relief agencies to coordinate their efforts. Operations researchers have not developed models that support real-time decision making, such as those proposed by Tien (2003Tien ( , 2005 in the decision informatics area. In addition, operations researchers should extend to disaster response such collaborative decision-making frameworks as Raghu et al (2005).…”

Towards real-time customized management of supply and demand chains

“…While such steady state techniques are known to be very useful in optimizing supply chain performance when demand is assumed to be fixed (Krishnamurthy et al 2000), and vice versa, they are of limited use in the presence of flexible supply and demand chains which never reach steady state, especially as product/service life cycles become increasingly shorter. Instead, as speed and flexibility become more critical, the time available for decision analysis and performance evaluation would be reduced and real-time decision support systems that are dynamic and adaptive, like decision informatics (Tien 2003), would become essential. Fourth, it is the quadrant where not only are new technologies and techniques required but also where new products/services will abound, while traditional products and services will fade away.…”

A case for service systems engineering

“…It is in the area of customizationwhere customer involvement is not only at the goods design stage (as indicated in Table 6) but also at the manufacturing or production stagethat services and manufacturing are merging in concept. Another critical distinction between traditional and emerging services is that, although all services require decisions to be made, the former services are based on predetermined decision rules, while the latter require decisions in real-time; that is why Tien (2002) has advanced a decision informatics paradigm that relies on both information and decision technologies. (1989) recognized that the depressed service productivity is itself a key reason why servicesespecially government services (see Table 5) -constitute a large part of the economy; that is, the lagging (until recently) service sector productivity caused it to absorb the labor supply that was no longer needed in the more labor Clearly, the U.S. must significantly improve its service productivity -through the appropriate application of systems engineering.…”

“…Decision Informatics, proposed by Tien (2002), is a paradigm for making real-time, information-based decisions. That is, the nature of the required real-time decision (regarding the production and/or delivery of a service) determines, where appropriate and from a systems engineering perspective, the data to be collected (possibly, from multiple, non-homogeneous sources) and the real-time fusion/analysis to be undertaken to obtain the needed information for input to the modeling effort which, in turn, provides the knowledge to support the required decision in a timely and intelligent manner.…”

“…Today, retailers complain, "We are awash in data but starved for information." Thus, in order to overcome the somewhat embarrassing data rich, information poor (DRIP) problem that Tien (1986) forewarned, it is critical to develop more sophisticated data fusers and data analyzers -as a part of what Tien (2002) calls "decision informatics" -that could yield the information or knowledge for making smart choices (Hammond et al 1999).…”