Introduction Organizational Decision Support Systems example essay topic

Document-Driven DSS is a relatively new field in Decision Support. Document-Driven DSS is focused on the retrieval and management of unstructured documents. Documents can take many forms, but can be broken down into three categories: Oral, written, and video. Examples of oral documents are conversations that are transcribed; video can be news clips, or television commercials; written documents can be written reports, catalogs, letters from customers, memos, and even e-mail.

Jane Fedorowicz (1996) estimated that American businesses use store up to 1.3 trillion documents which can eat up to 50% of their floor space. Yet only 5 to 10 percent of these documents are available to managers for use in decision making. Fedorowicz defined document as a 'chunk' of information. Unfortunately documents are not standardized in a uniform pattern or structure. Managers and IT / IS people need away to correlate these documents into usable formats that can be compared and processed, as well as incorporating existing databases, to support decision making.

New technology and software is making this concept into a reality. Basic Document-Driven systems exist in the form of web-based search engines, such as Excite, Alta Vista, and Lycos. Many commercial based web pages contain search engines which allow users to input words or phrases to specify and limit the documents they wish to see. Further advances in client / server technology will allow managers to store, manage, and access these documents This web page contains links to many sites that contain information related to Data-driven DSS, especially Data Warehousing (DW) and On-line Analytical Processing (OLAP). Data-driven DSS is a type of DSS that emphasizes access to and manipulation of a time-series of internal company data and sometimes external data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality.

Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-driven DSS with On-line Analytical Processing (OLAP) provides the highest level of functionality and decision support that is linked to analysis of large collections of historical data. Executive Information Systems (EIS) and Geographic Information Systems (GIS) are special purpose Data-Driven DSS. A Data Warehouse is a database designed to support decision making in organizations. It is batch updated and structured for rapid online queries and managerial summaries. Data warehouses contain large amounts of data.

A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management's decision making process. On-line Analytical Processing (OLAP) software is used for manipulating data from a variety of sources that has been stored in a static data warehouse. The software can create various views and representations of the data. For a software product to be considered an OLAP application it must contain three key features: 1. multidimensional views of data; 2. complex calculations; and 3. time oriented processing capabilities. Executive Information Systems (EIS) are computerized systems intended to provide current and appropriate information to support executive decision making for managers using a networked workstation. The emphasis is on graphical displays and an easy to use interface that present information from the corporate database.

They are tools to provide canned reports or briefing books to top-level executives. EIS offer strong reporting and drill-down capabilities. A Geographic Information System (GIS) or Spatial DSS is a support system that represents data using maps. It helps people access, display and analyze data that have geographic content and meaning. Chapter 1 Introduction Part 1 Three Case Studies of Decision Support Systems Case 1 Connoisseur Foods: The Introduction of Modeling and Data Retrieval Capabilities Case 2 Great Eastern Bank: A Portfolio Management System Case 3 Go taas-Larsen Shipping Corporation: A Corporate Planning System Part 2 Choices for the User and Implementation Chapter 2 A Taxonomy of Decision Support Systems Chapter 3 Using Decision Support Systems to Increase the Effectiveness of Individuals Chapter 4 Patterns of System Usage Part 3 Toward Successful Implementation Chapter 5 Difficulties in System Usage Chapter 6 Implementation Patterns Chapter 7 Implantation Risk Factors and Implementation Strategies Chapter 8 Trends for the Future Part 4 Additional Case Studies Case 4 Equitable Life: A Computer-Assisted Underwriting System Case 5 Interactive Market Systems: A Media Decision Support System Case 6 The Great Northern Bank: A System for Budgeting, Planning and Control Case 7 The Cost of Living Council: Decision Support in a Regulatory Setting Case 8 A AIMS: An Analytic Information Management System Leadership Principles of Strategy Development & Implementation As the leader, you are accountable for establishing your organization's strategic direction. You can involve many people in various levels of responsibility, but at the end of the day, your most important job is building strategy: making it clear, getting people committed to it, and making sure it gets implemented.

If you don't do it, it will not get done. GDSS has been helping leaders of IBM, The World Bank, DuPont, People's Bank, The U.S. Government, AOL, and dozens of premier organizations establish and implement wide-ranging, transformational strategies. GDSS continuously consolidates its knowledge and makes it available to you through essential and unique service offerings. We know how to successfully build principle-based, strategy-focused, and results-oriented organizations. And we are ready to help you. Turning strategy into measurable results!

The Need Rapidly identify critical drivers of organizational success Decrease the amount of time spent looking for critical information Clearly link the organization's strategy to key indicators According to Best Practices, LLC., "71% of companies surveyed believe that the scorecard should be the central method for communicating strategy and goals" The Problem Leaders are not given the information they need to make informed, strategic decisions, and are overwhelmed with too much data and conflicting data Leadership, executives and managers are unable to track progress against goals and objectives due to a lack of accepted strategic performance indicators The SolutionGDSS assists clients to reduce costs and increase their return on management time by systematically designing and building measurement systems 1. Introduction Organizational decision support systems (ODSS) are a class of decision support systems that promise to provide support at a higher organizational level for businesses than preceding forms of decision support. The existing literature provides many different descriptions of ODSS and its functionality. This leads to considerable confusion as to what is necessary for a system to be called an ODSS.

This confusion has inhibited the development of ODSS both conceptually and in terms of implementation. We begin by considering the existing conceptual base upon which ODSS is built and proposes a means of distinguishing organizational decisions from executive and group decisions. Several different conceptions of ODSS have been provided in the literature but all of them are essentially modifications of group decision support systems (GDSS). In so far as there is a distinction, it should be based on the difference between the characteristics of groups and organizations. People within groups share norms and typically work toward consensus building through decision making, while the key characteristic of heterogeneous organizations is that they require assistance to reconcile divergent perceptions as their first step toward decision making. The purpose of the conceptualization is to provide the means to build upon GDSS functionality in a way that takes organizational features seriously.

This may come in the form of better interfaces or different functionality, but in essence, it is the emphasis on the organizational features of hierarchy and heterogony that will make ODSS advantageous. Our semiotics-based approach will focus on these characteristics and provide a means of using the analysis of organizational communications to model information to support organizational decision-making. The next section presents various definitions of ODSS. This is followed by a consideration of the key differences between groups and organizations. Next, the semiotic approach we take will be explained in more detail.

The paper ends with a hospital-based example that shows how the semiotic approach can be beneficially used to identify and refine organizational level problems that are suitable for ODSS support. George [7] summarized the conceptualization and development of ODSS and emphasized the way the organizational perspective was first introduced into the domain of decision support. The opportunities afforded by data communication and the renewed emphasis on cheaper business computing created the means to integrate decision support to include first small groups of decision makers (as in GDSS) and, later, many different groups (as in ODSS). In its earliest conceptualization, the primary emphasis of ODSS was upon the communication and coordination function [3 and 8]. The subsequent history of decision support system developments in the 1980's rests on the difference between technology- and business-driven approaches. DSS approaches were seduced by technology-driven opportunities afforded by personal computers, network computing, and related software developments.

Because technology-driven approaches became the mainstream, all efforts were directed to exploiting these new functionalities at the expense of a focus on organizational problem solving. Although GDSS and ODSS are types of multi participant [9] decision support systems, we agree with King and Star [14] who claim that we cannot simply scale up from DSS to GDSS or from GDSS to ODSS. They imply that there are more than contextual differences between these different kinds of systems because group representatives tend to operate in an organizationally sub-optimal way as their first loyalty is to their own groups rather than to the organization. However, Miller and Nilakanta [19] do regard DSS and ODSS to be closely related: 'Note that the difference between DSS and ODSS is in the context of the decision and it is believed that much of the technical design aspects of ODSS (e. g., data and model management capabilities and the interfaces among the subsystems) will not be very different.

' Aggarwal and M irani [1], however, propose a broader definition of an ODSS which includes all systems that provide 'border less' and 'seamless' decision-making support across functional, divisional, and national boundaries. The technological features of these different types of decision support systems are exemplified by the difference between single machine operations, which is the minimal necessary unit for DSS, a decision room which is typical for GDSS, and the possibilities of dispersed computers, perhaps even spread among different organizations, which can be said to characterize ODSS. The application of these concepts to the architectures of multi participant decision support systems, in general, can be seen in the work of Hols apple and Winston [10]. Similarly, Jacob and Pir kul [11] address the organizational perspective from the point of view of multi participant decision making which can be structured around nodes within networks of knowledge-based systems. Each of these relies upon different technical structures, but all are implicitly intended to serve the business functions. What are the real differences beyond this manifestation?

To make the various forms of decision support truly distinct, we should look beyond the technical features of these operating architectures. If we see the original purpose of ODSS as emphasizing communication and coordination, we should regard this as addressing the problem of reconciliation. Later work by Weis band and Galegher [24] emphasized the need to address organizational diversity and flexibility, which we can regard as addressing the problem of organizational heterogony. We propose that organizational communication and its attendant problems of reconciliation, along with heterogony, constitute the two domains which ODSS should address. We will now examine the significance of these two organizational features vis-a-vis the characteristics that are typical of groups in relation to organizations.

2. Groups and organizations Groups meet with the belief that they have sufficient understanding of the goals and assumptions of their distinct problem-solving environment. We can refer to these assumptions and understandings as 'norms' and this allows us to apply norm-based analyses upon group decision-making processes. The most important function of a GDSS would be to build consensus so that the shared norms can effectively be used in the decision-making. The architecture and software capabilities of GDSS emphasize this importance. Much effort is given to allow for voting-type behavior and other group consensus techniques.

Similarly, the software supports the work of a group moving through the draft stages of a text. Some GDSS are especially good at providing techniques, usually through well-designed interfaces and presentational graphics, to promote focused discussion. Groups usually agree that key organizational issues such as strategy, structure, and boundaries are given. They are usually concerned with operational level decisions such as procedures, production techniques, and business efficiency. Pinsonneault and Kraemer [21] report on a number of experimental and other empirical works which show how 'GDSS focus the attention and efforts of group members on task-related activities', and refer to findings which show increased depth of analysis, task-oriented communication, and clarification efforts. They conclude that these results demonstrate the efficacy of GDSS in increasing consensus in groups.

Organizations, in contrast to groups, are usually heterogeneous. So the most important function, which any computer system that supports decision-making can do, is to recognize the common requirements of different groups, and to reconcile different perceptions. Organizations generally behave in ways different from groups. We can take the example of the Canadian telecommunications company described by Marche [17 and 18], which, in trying to determine what a building is, illustrates the differences which arise among groups when an organization tries to reconcile norms. In this company, a seemingly straightforward effort to model data about corporate real estate holdings revealed extraordinary difficulties when it was clear that each group within the organization needed to conform to conflicting otologies. For the accountants, a building could be defined as something that was depreciating.

For the planners responsible for assigning space to people, a distinct building was something that could be conceived as a contiguous space. The architects saw the building in planning terms. Others were concerned with problems such as the definition of temporary or mobile structures. So many cases arose which yielded entirely different solutions, and Marche gives the example of a building which, having been originally erected in 1958, extended in 1969, and further built upon in 1985, was seen by different groups to be one, two, or even three buildings. As we can see in the example above, organizations are required to integrate differing functions, usually within hierarchies, for common goals. Inherently, organizations transcend groups.

When organizations make decisions, they must take into account that different perceptions are brought in by different interested persons. Each organizational component brings with it a different set of norms. This heterogony is the result of a variety of functionalities for which organizations take responsibility. For instance, they need to accommodate widely divergent functions such as the production, distribution and sale of goods, the maintenance of solid legal footing, and the requirements of accounting activities. Without this heterogony, this necessary variety cannot function effectively.

Many of the differing perceptions brought in by heterogony will conflict with one another, despite an overall shared organizational goal (i. e., the purpose of the business). What is required for effective ODSS will then be a means to analyze these differing perceptions and to provide the tools to allow participants to recognize mutually acceptable approaches to reach common goals. The semiotic approach to information can provide one such tool. 3. A semiotic approach to organizations semiotic approach to information systems provides a tool to represent organizational knowledge and activity. The theory of signs originates in the work of Peirce [20] who shows that a sign must be capable of evoking responses from the interpreter or a person.

Semiotics makes us recognize the importance of an agent as being responsible for the existence of a sign and its meaning. Organizations can be seen as complexes of signs communicated among people who, acting as semiotic agents, are responsible for assigning meaning [2, 5, 12, 15, 17, 18 and 23]. This approach provides a conceptual breakdown of information systems into four levels. This helps us to identify the range of issues from the cultural environment and problems of meaning to formal structures and the physical characteristics of codes and signals [16]. Level one, called 'pragmatics', is concerned with the actual context of activity and those characteristics of people, organizations, and acts of communication which affect information.

Shared assumptions among people, and problems arising from ambiguities in communications, and the informal nature of most organizational interactions form the concern of pragmatics. 'Semantics' and tools of semantic analysis, which form level two of the semiotic framework, deal with problems of meaning. They also help us create models of networks of meaning in organizations. Level three, termed 'syntactics', deals with the use of formalisms, and level four, 'empiric's', is concerned with the physical characteristics of codes and signals. There are other structured and semi-structured techniques, which also provide analysts with tools to interpret the uses of information within organizations. However, approaches such as Peter Checkland's [5] 'soft systems methodology' are intended as means to facilitate systems design by eliciting user requirements.

Other more formal approaches seek to associate efficient data flow structures with organizational structures. Though they may be useful for systems design, they are unable to provide a representation of organizational activity. Cecez-Kecmanovic [4], in proposing a conceptualization of what she calls organizational activity support systems, uses a semiotic approach to the problem. According to her, a semiotic approach 'recognizes organizational entities and their respective roles, persons as role holders, patterns of actions and types of activities as well as documents and information, and their flows within an activity and between activities' [4]. Further, the approach is based on the principle that different kinds of knowledge are assumed in the performance of activities in an organizational context. Cecez-Kecmanovic demonstrates a technique of knowledge representation, which captures these dimensions and show their interrelationships.

She goes one step further by creating a formal model of organizational activity (i. e., she creates schemes / models of organizational entities, roles, personnel and their activities, documents and data; these models can then be used to simulate solutions to organizational decision-making problems). Such a model of organizational activity is fundamentally based on the semiotics of organizational communications and upon speech act analysis. Her formalization convincingly demonstrates that semiotic techniques can provide one pillar of support for ODSS design. Now let us consider how we can bring together our understanding of the organizational problems that ODSS can ameliorate with the semiotic technique.

We do this by separating our approach into the distinct concepts which we call 'prioritization', 'contextualization', 'formalization', and 'building in functionality'. These are drawn from the semiotic levels described above, 'pragmatics', 'semantics', 'syntactics', and 'empiric's'. First, we will have to clearly specify the tangible problems that people face at the organizational level. 3.1. Prioritization: identifying 'organizational level' problems Keen [13] identifies a number of needs for DSS research and lists first the question, 'What decisions really do matter in an organization and how should we build better environments to help decision makers handle them?' For the purposes of this argument, let us identify three typical decision problems that must be addressed at the organizational level: boundaries, structure, and strategy. What constitutes the organization and its boundaries is increasingly problematic, because of the extensive interlink ages among organizations and the rapidly growing opportunities which businesses have to outsource common functions. Most large organizations recognize the need to decide on which functions should be internalized and which ones outsourced.

Similarly, the boundaries between linked organizations need to be continuously negotiated. Another typical problem that must be solved at the organizational level concerns the internal structure that the organization should have. Companies regularly reassess the shape of their hierarchies and attempt to maximize the opportunities offered by new organizational forms such as networked or distributed structures. A third set of organizational concerns comes under the category of business strategy.

This involves the decision upon how resources are allocated to advantageously position the firm for future opportunities. True strategic decisions implicate the whole organization and not only subsets thereof (although such decisions may be made by the top management). Decisions of these kinds are especially appropriate for ODSS. These are the kind of problems that ODSS should be focusing on, precisely because they are organizational issues as distinct from the kind of problems which groups within organizations typically have to encounter. 3.2. Contextualization: understanding why perceptions of organizational level problems differ Having examined some of the most important organizational level problems, we need to understand why different groups have conflicting perceptions of these concerns.

We can do this by making the 'organizational' characteristics of these problems explicit through the use of organizational theory and semiotics. There are some recent efforts to incorporate elements of this approach into ODSS, although they need to be more explicitly grounded in organizational theory. One good case is presented by Miller and Nilakanta [19] in their example of the Boone Cannery. As the cannery extended its business and added several trading partners, EDI made it possible for the firm to extend its information handling tasks. However, the key decisions that had to be taken affected a number of people at various functional and hierarchical levels.

In addition to procedural matters, 'organizational norms and policies need to be enforced or changed. At the departmental or functional unit level, inter-unit communication becomes a necessity. Communication infrastructures and protocols must be established so that conflicts are reduced. Because of self preservation tendencies, organizational units may sub optimize or act in discordance with the overall objective of the firm [19]. ' Different groups may have conflicting perceptions of the above-mentioned problems because their interests vary.

They may also be characterized by varying levels of access to information, and differences in their ability to understand issues that span the entire organization. Semiotics and, in particular, the notions of pragmatics and semantics within it, provide a means to examine these issues. Pragmatics identifies different kinds of organizational norms. Semantics provides a preliminary step to the presentation of semantic schema which are based on norm analytic structures. Both norm analysis and schema design are well-recognized techniques of the semiotic approach that can be used to analyze and model divergent organizational views [2 and 16]. The purpose of drawing schema is to capture the relationships between those elements that make up the entirety of the system being examined.

Also, by drawing the schema, the analyst is forced to assign unique relationships among elements. This discipline assists in revealing ambiguities and reconciling conflicts. To do this, we must at least capture the basic characteristics of relationships. These are: the relationship which shows how one element is dependent on another to exist, called 'ontological dependency'; the specification of an individual; the relationship between parts and wholes; the roles in relationships; and the relationship between specific and generic characteristics. Fig. 1 describes one notational system for drawing semantic schema and we shall use this in an example in the next section.

(8 K) Fig. 1. Graphical notation symbols for semiotic she mata, taken from [2]. 3.3. Formalization: modeling of divergent organizational perceptions The next step is to be able to analyze and model divergent organizational perceptions. This can be accomplished through the approach used by Cecez-Kecmanovic [4] to model organizational communications. Once these divergent perceptions are formalized, they can then be manipulated.

This is an essential step towards building in functionality. 3.4. Building in functionality Once the various organizational perceptions have been identified and modeled, we need to be able to build in the necessary functionality as Miller and Nilakanta [19] do in their designing of a data extraction scheme for ODSS. While the work of Cecez-Kecmanovic [4] and that of Miller and Nilakanta [19] are important and useful in themselves, their scope is limited. The scope of Miller and Nilakanta's work is limited because they are primarily interested in data-handling issues. The key issues that emerge from their work are at the level of norms. Cecez-Kecmanovic's work takes this approach further towards a model and shows how it can be used in the designing of ODSS.

Her work takes a semiotic approach to the problem of creating formal models of communications based on the norm structures of organizations. Thus, we could say that Cecez-Kecmanovic's work has formalization without functionality, whereas Miller and Nilakanta provide us with this functionality without explicitly relating it to organizational theory. Without clear links to organizational theory, their work does not sufficiently differentiate organizational level and group level issues. We therefore need to place the issue of ODSS in perspective and locate it within organizational theory using semiotics and, in particular, on distinguishing between organizational and group level problems. Now the challenge is to use the semiotic technique in organizational analysis to satisfy the requirements of prioritization (of organizational level decision problems), contextualization, formalization, and functionality.

This could go some way towards providing a clearer conceptualization of organizational decision problems and, therefore, what an ODSS should actually be. In the following section, we demonstrate its utility through an example. 4. Organizational decision making in a British hospital: staff scheduling for ambulatory care Let us take the case of decision-making in a British hospital.

This hospital has embarked on building an ambulatory care and diagnostic center and the staff scheduling decisions there lend themselves to consideration as an organizational decision-making model. The purpose in using this example is to demonstrate the continuity of conceptual, as well as functional elements, through the whole of the analytical process. It must hold its integrity through the processes of determining the priorities, interpreting the context, designing the system, and ensuring that it functions in a way which satisfies all of the interested parties. Ambulatory care is distinct from emergency and long-term in-patient care. The hospital's expectation is to increase the number of patients and improve the quality and access to service.

The intention is also to make considerable financial savings over existing hospital practices. One of the key features of this distinction is that staff members are allocated differently from previously, where a third work schedule is required, in addition to that for emergency coverage and normal ward duties. A number of departments will move to the new center and this will involve substantial differences in working practices for medical staff, and different access to treatment and diagnosis for patients. The center will depend entirely on effective scheduling services.

This dependence can be exemplified by the substitution of normal reception services with highly skilled people that are able to assess individual treatment needs and their staffing requirements [22]. Here, we take the requirements of the staff scheduling problem and apply our approach to ODSS using organizational theory. 4.1. Prioritization The new center needs to have systems that can cope with referrals from, and other relationships with, the existing health care institutions. This will include other hospitals, general practitioners, clinics, private health care providers, and other elements of the health system. This not only defines the center's boundary in relation to the main hospital, it also establishes its procedures in relation to other bodies which may wish to use or be used by the center. One of the key features of ambulatory care is that the entire procedure, which is to be applied towards patient care, should be reduced from typically 4.