Quality Factors for DMSS Assessment

The objective of this chapter is to demonstrate the use of a decision support systems research (DSSR) framework to improve decision making support systems (DMSS) quality. The DSSR Framework, which was developed to integrate theoretical constructs from various information systems areas into a coherent theme, can serve as a tool for DMSS developers. Developed to provide a unified reference to theoretical constructs used in theory building and testing, the DSSR framework can also be used as the basis for the identification and selection of a hierarchy of factors potentially affecting the quality of DMSS development. The chapter proposes that a unified set of quality factors derived from the DSSR framework be used in tandem with the generic software quality metrics framework specified in IEEE Standard 1061-1992. The integration of these two frameworks has the potential to improve the process of developing high-quality decision making support systems and system components. The usage of these frameworks to identify system quality factors is demonstrated in the context of a military research and development project.

Customer Relationship Management at Harrah's Entertainment

Data warehousing has significantly changed how decision making is supported in organizations. A leading application of data warehousing is customer relationship management (CRM). The power of CRM is illustrated by the experiences at Harrah’s Entertainment, which has assumed a leadership role in the gaming industry through a business strategy that focuses on knowing their customers well, giving them great service, and rewarding their loyalty so that they seek out a Harrah’s casino whenever and wherever they play. In 1993, changing gaming laws allowed Harrah’s to expand into new markets through the building of new properties and the acquisition of other casinos. As management thought about how it could create the greatest value for its shareholders, it was decided that a brand approach should be taken. With this approach, the various casinos would operate in an integrated manner rather than as separate properties. Critical to their strategy was the need to understand and manage relationships with their customers. Harrah’s had to understand where their customers gamed, how often and what games they played, how much they gambled, their profitability, and what offers would entice them to visit a Harrah’s casino. Armed with this information, Harrah’s could better identify specific target customer segments, respond to customers’ preferences, and maximize profitability across the various casinos.

On the Ontology of a Decision Support System in Health Informatics

A decision support system can be approached from two major disciplinary perspectives, those of information systems science (ISS) and artificial intelligence (AI). We present in this chapter an extended ontology for a decision support system in health informatics. The extended ontology is founded on related research in ISS and AI and on performed case studies in health informatics. The ontology explicates relevant constructs and presents a vocabulary for a decision support system, and emphasises the need to cover environmental and contextual variables as an integral part of decision support system development and evaluation methodologies. These results help the system developers to take the system’s context into account through the set of defined variables that are linked to the application domain. This implies that domain and application characteristics, as well as knowledge creation and sharing aspects, are considered at every phase of development. With these extensions the focus in decision support systems development shifts from a task ontology towards a domain ontology. This extended ontology gives better support for development because from it follows that a more thorough problem analysis will be performed.

Procedural Cuing Using an Expert Support System

This chapter describes the development of a system to assist teams in determining which problems to address and what data to collect in order to incrementally improve business processes. While prototyping is commonly advocated for expert system development, this project used a structured development methodology comprising requirements analysis, knowledge acquisition, and system development. The knowledge acquisition phase resulted in a logical model, which specified the decision task and suggested a system structure. The field prototype developed from this model uses procedural cuing to guide decision makers through a decision making process. The system provides decision support, interactive training, and expert advice.

Decision Making Support Systems

Previous chapters have described the state of the art in decision making support systems (DMSS). This chapter synthesizes the views of leading scientists concerning the achievements of DMSS and the future challenges and opportunities. According to the experts, DMSS will be technologically more integrated, offer broader and deeper support for decision making, and provide a much wider array of applications. In the process, new information and computer technologies will be necessitated, the decision makers’ jobs will change, and new organizational structures will emerge to meet the changes. The changes will not occur without displacements of old technologies and old work paradigms. In particular, there will be an evolution toward team-based decision making paradigms. Although the evolution can require significant investments, the organizational benefits from successful DMSS deployments can be significant and substantial. Researchers and practitioners are encouraged to collaborate in their effort to further enhance the theoretical and pragmatic developments of DMSS.

How Synthetic Characters Can Help Decision-Making

Synthetic Characters are intelligent agents able to show typical human-like cognitive behavior and an artificially-made perceived personality by means of complex natural language interaction and artificial reasoning and emotional skills. They are mainly spreading on the web as highly interactive digital assistants and tutoring agents on online database systems, e-commerce sites, web-based communities, online psychotherapy, and in several consulting situations where humans need assistance from intelligent software. Until now, synthetic characters, equipped with data, models, and simulation skills, have never been thought as the building blocks for natural language interaction-based intelligent DMSS. This chapter illustrates the first research and development attempt in this sense by an Open Source project in progress centred on the design of a synthetic character-based DMSS.

Innovative Features in a Distributed Decision-Support System Based on Intelligent Agent Technology

The author and his students were engaged in a multi-year project, SENTINEL, aimed at computerizing the strategic and tactical planning processes of the U.S. Coast Guard (USCG). In the course of this activity, we were also creating a decision support system for the human participants acting at the different levels of the USCG hierarchy. The chapter will describe the objectives, the problems and constraints of the task environment, as well as the solution to some problems that are fundamental and ubiquitous in many real-time, spatially and temporally distributed multi-agent systems. The fundamental and overall task of a Decision Support System (DDS) implemented was to allocate moving resources to moving tasks in an optimum manner over space and time while considering numerous constraints. We have introduced three significant innovations necessary to accomplish our goals. 1. Dynamic Scoping refers to a need-driven change in the size of the domain from which moving resources are called upon to accomplish moving tasks. The size of the domain has a limitation prescribed by the dynamic task environment, the technical capabilities of the resources, and the relationship between the expected gains and expenses. 2. The second innovation concerns “resource scheduling under time constraints.” We have introduced a method for the proper ordering of operating attributes and constraints in terms of a utility function. PRIORITY = IMPORTANCE*URGENCY Here, Importance is a measure of the relative static importance of an attribute in the decision making process. Urgency characterizes its gradually changing (usually increasing) relative importance over time. The constraints are arranged according to the priorities. More and more details are taken into account with each time-slice and more and more knowledge is used in the inference mechanism. A time-slice is the minimum time required for performing a unit of meaningful decision making. The ordering of constraints according to priorities guarantees that the result of planning is as good as time has permitted “so far.” 3. We have studied interagent communication and optimum message routing. Agents communicate at different levels—requesting and providing information, ordering/suggesting/accepting solutions to sub-problems, asking for and offering help, etc. The total knowledge about the environment and agent capabilities is too large to be stored by every agent, and the continual updating about the changes only aggravates the situation. The usual hierarchical organization structure for communication is inflexible, inefficient and error-prone. We have introduced the constrained lattice-like communication structure that permits direct interaction between functionally related agents at any level. The hierarchical and the lattice-like organizational structures may coexist: A transfer of temporary control over resources can be negotiated between the relevant agents directly while higher-level authorities will learn about the decisions, and can also modify or completely reject their implementation.

DSS for Rescheduling of Railway Services under Unplanned Events

In the daily activity of railway transport, the need to make decisions when faced with unforeseen incidents is a common event. Quality of service my be affected by decisions that are made by delays or cancellations. In this multi-objective scenario, there is a need to combine affecting the majority of passengers as little as possible with the minimization of costs. Therefore it is necessary to design planning algorithms taking into account factors such as the availability of engines and the quality of service. This chapter presents the design and implementation experience of a practical case developed for the Spanish Railway Company. With this tool, a DSS was put into service that guides the person in charge as to which measures to adopt with respect to the events that have arisen. The information employed is obtained by means of heuristic search algorithms based on backtracking for the exploration of the solutions space.

From Human Decision Making to DMSS Architecture

In this chapter we begin by featuring the main characteristics of the human decision process. Then, from these traits, we separate the decision process between diagnosis and look-ahead. We explain why DMSSs are mainly look-ahead machines. We claim that look-ahead is generally performed via heuristic search and “what-if analysis” at different cognitive levels. This leads to a functional definition of DMSSs and to different architectures adapted to heuristic search and moreover, paves the way for an analysis of the decision support tools.

Critical Factors in the Development of Executive Systems-Leveraging the Dashboard Approach

Based on the Rockart’s critical success factor (CSF) approach, this chapter puts forward a practical method to guide the development of executive information systems (EIS) in organizations. This method extends the current theory of EIS by using the concept of the dashboard of information to show how an enterprise-wide approach to the development of more effective decision support for managers can deliver tangible benefits without requiring the time-consuming and single-decision focus of the traditional development methods. This method also attempts to leverage the latest computing technologies now available for the development of such systems, notably graphical user interfaces (GUI), data warehousing (DW) and OLAP. The proposed approach is illustrated by examples of dashboard developments, which show how managers should carry out the analysis and development of such a system in their own organizations, business units or functional areas.