Decision Support Systems

Characteristics and capabilities of DSS Components of DSS DSS hardware Introduction to DSS Types of decisions Characteristics and capabilities of DSS Components of DSS DSS hardware DSS Classification DSS Classification (Model Based) Model driven DSS Data Driven DSS

11. Group Decision Support System b) Knowledge driven c) Communication driven d) Document driven 8. Architecture of DSS 9. Relation To MIS 10. Relation to BI 11. Group Decision Support System characterstics GDSS Components of GDSS 12. Executive Support System

1. Decision Support Systems Decision support systems (DSS) Offer potential to assist in solving both semi-structured and unstructured problems

Decision Support System (cont.) A Decision Support System (DSS) is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions. Decision Support System is a general term for any computer application that enhances a person or group’s ability to make decisions.

Decision Making as a Component of Problem Solving(cont.) Intelligence Decision making Design Problem solving Choice Implementation Monitoring

Information Requirements by Management Level Strategic Management Tactical Management Information Decisions Operational Management

2. Types of Decisions Organisational theory classifies decision-making into fundamentally three different types: Strategic Management or Tactical Operational Strategic decision-making is concerned with long-term goals & policies for resource allocation/management to meet defined objectives

What types of Decision-Making ? Organisational theory classifies decision-making into fundamentally three different types: Strategic Management or Tactical Operational Tactical decision-making is concerned with the acquisition & efficient utilization of resources to achieve defined goals

What types of Decision-Making ? Organisational theory classifies decision-making into fundamentally three different types: Strategic Management or Tactical Operational Operational decision-making is concerned with the effective & efficient use of resources for execution of specific tasks

Types of Decision-Making Often less detailed data available & so requires good tools for modeling & forecasting More Unstructured Operational Tactical/Managerial Strategic More structured Requires detailed data & uses tools for analysis & integration

Types of Decision-Making Often less detailed data available & so requires good tools for modeling & forecasting More Unstructured Structured Unstructured Semi -Structured More structured Requires detailed data & uses tools for analysis & integration

Global SDI Regional SDI National SDI State SDI Local SDI Example 1: Mapping Malaria Risk in Africa Operational Management/ Tactical Strategic Decision-Making Decision-Making Decision-Making Continental Malaria distribution Maps Global SDI Regional SDI Malaria Data National SDI Spatial Models on geographic distribution, seasonality Malaria Seasonality Data Used for planning, intervention & prevention by national & international health officials State SDI Malaria Occurrence Data Local SDI

Structured vs. Semi-Structured For each decision you make, the decision will fall into one of the following categories: Structured Decisions Unstructured Semi-Structured

Structured Decisions Often called “programmed decisions” because they are routine and there are usually specific policies, procedures, or actions that can be identified to help make the decision “This is how we usually solve this type of problem”

Unstructured Decisions Decision scenarios that often involve new or unique problems and the individual has little or no programmatic or routine procedure for addressing the problem or making a decision

Semi-structured Decisions Decision scenarios that have some structured components and some unstructured components.

The Role of the Decision Maker Decision makers can be Individuals Teams Groups Organizations All of these types of decision makers will differ in their knowledge and experience; therefore, there will be differences in how they will react to a given problem scenario

3. DSS Characteristics and Capabilities Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur

Characteristics of a DSS Handles large amounts of data from different sources Provides report and presentation flexibility Offers both textual and graphical orientation

Characteristics of a DSS (cont) Supports drill down analysis Performs complex, sophisticated analysis and comparisons using advanced software packages Supports optimization, satisficing, and heuristic approaches

Characteristics of a DSS (cont) Performs different types of analyses “What-if” analysis Makes hypothetical changes to problem and observes impact on the results Simulation Duplicates features of a real system Goal-seeking analysis Determines problem data required for a given result

You know the desired result You want to know the required input(s) Goal Seeking Example You know the desired result You want to know the required input(s) Example: Microsoft Excel’s “Goal Seek” and “Solver” functions

Excel demo

Capabilities of a DSS (cont.) Supports Problem solving phases Different decision frequencies Merge with another company? How many widgets should I order? low high Frequency

Capabilities of a DSS (cont.) Highly structured problems Straightforward problems, requiring known facts and relationships. Semi-structured or unstructured problems Complex problems wherein relationships among data are not always clear, the data may be in a variety of formats, and are often difficult to manipulate or obtain

DSS Characteristics and Capabilities (cont.)

4. Components of DSS Data Management Subsystem Includes the database that contains the data Database management system (DBMS) Can be connected to a data warehouse Model Management Subsystem Model base management system (MBMS) User Interface Subsystem Knowledgebase Management Subsystem Organizational knowledge base

Components of DSS(cont.)

a) Database Management Subsystem Key Data Issues Data quality “Garbage in/garbage out" (GIGO) Data integration “Creating a single version of the truth” Scalability Data Security Timeliness Completeness, …

b) DSS Components Model Management Subsystem Model base MBMS Modeling language Model directory Model execution, integration, and command processor

DSS Components Model Management Subsystem Model base (= database ?) Model Types Strategic models Tactical models Operational models Analytic models Model building blocks Modeling tools

DSS Components Model Management Subsystem The four (4) functions Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks Generation of new routines and reports Model updating and changing Model data manipulation Model directory Model execution, integration and command

Model Base Model Base Models Provides decision makers with access to a variety of models and assists them in decision making Models Financial models Statistical analysis models Graphical models Project management models

Advantages and Disadvantages of Modeling Less expensive than custom approaches or real systems. Faster to construct than real systems Less risky than real systems Provides learning experience (trial and error) Future projections are possible Can test assumptions Disadvantages Assumptions about reality may be incorrect Accuracy of predications often unreliable Requires abstract thinking

c) DSS Components User Interface (Dialog) Subsystem Application interface User Interface Graphical User Interface (GUI) DSS User Interface Portal Graphical icons Dashboard Color coding Interfacing with PDAs, cell phones, etc.

d) DSS Components Knowledgebase Management System Incorporation of intelligence and expertise Knowledge components: Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on Often used to better manage the other DSS components

A Web-Based DSS Architecture

5. DSS Hardware Major Hardware Options Evolved with computer hardware and software technologies Major Hardware Options Mainframe Workstation Personal computer Web server system Internet Intranets Extranets

Internet: a collection of interconnected networks, all freely exchanging information.

Intranets and Extranets Internal corporate network built using Internet and World Wide Web standards and products Slashes the need for paper Provides employees with an easy and intuitive approach to access information that was previously difficult to obtain Principles of Information Systems, Seventh Edition

Intranets and Extranets (continued) Extranet: a network based on Web technologies that links selected resources of a company’s intranet with its customers, suppliers, or other business partners Virtual private network (VPN): a secure connection between two points across the Internet Tunneling: the process by which VPNs transfer information by encapsulating traffic in IP packets over the Internet Principles of Information Systems, Seventh Edition

Table: Summary of Internet, Intranet, and Extranet Users Principles of Information Systems, Seventh Edition

6. DSS Classifications Other DSS Categories Institutional and ad-hoc DSS Personal, group, and organizational support Individual support system versus group support system (GSS) Custom-made systems versus ready-made systems

DSS Classifications(cont.) Holsapple and Whinston's Classification The text-oriented DSS The database-oriented DSS. The spreadsheet-oriented DSS The solver-oriented DSS The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications) The compound DSS

DSS Classifications (cont.) Alter's Output Classification

DSS Classifications (cont.) Holsapple and Whinston's Classification The text-oriented DSS The database-oriented DSS The spreadsheet-oriented DSS The solver-oriented DSS The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications) The compound DSS

7. DSS Classification Model Based A) Model Based DSS Data driven DSS Communication driven DSS Document Driven Knowledge driven

A model of a DSS External and Internal Data Decision Other Maker Knowledge Management Decision Maker Other Information Systems External and Internal Data Data Management Attribute Data Model Management Aspatial Models Dialog Management Attribute-Based Queries and Reports Attribute Data Object

a) Model-driven DSS A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Dicodess is an example of an open source model-driven DSS generator . Other examples: A spread-sheet with formulas in A statistical forecasting model An optimum routing model

b) Data-driven (retrieving) DSS A data-driven DSS or data-oriented DSS 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 provides the elementary level of functionality. Data warehouses provide additional functionality. OLAP provides highest level of functionality. Examples: Accessing AMMIS data base for all maintenance Jan89-Jul94 for CH124 Accessing INTERPOL database for crimes by ……. Accessing border patrol database for all incidents in Sector ...

Model and data-retrieving DSS Examples: Collect weather observations at all stations and forecast tomorrow’s weather Collect data on all civilian casualties to predict casualties over the next month

c) Communication-driven DSS A communication-driven DSS use network and comminication technologies to faciliate collaboartion on decision making. It supports more than one person working on a shared task. examples include integrated tools like Microsoft's NetMeeting or Groove (Stanhope 2002), Vide conferencing. It is related to group decision support systems.

d) Document-driven DSS A document-driven DSS uses storage and processing technologies to document retrieval and analysis. It manages, retrieves and manipulates unstructured information in a variety of electronic formats. Document database may include: Scanned documents, hypertext documents, images, sound and video. A search engine is a primary tool associated with document drivel DSS.

e)Knowledge-driven DSS A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. It suggest or recommend actions to managers. MYCIN: A rule based reasoning program which help physicians diagnose blood disease.

8. Architecture Three fundamental components of DSS: the database management system (DBMS), the model management system (MBMS), and the dialog generation and management system (DGMS). the Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is of course the component that allows a user to interact with the system. Once again, different authors identify different components in a DSS

A Detailed Architecture Even though different authors identify different components in a DSS, academics and practitioners have come up with a generalized architecture made of six distinct parts: the data management system, the model management system, the knowledge engine, The user interface, the DSS architecture and network, and the user(s)

Typical Architecture TPS: transaction processing system MODEL: representation of a problem OLAP: on-line analytical processing USER INTERFACE: how user enters problem & receives answers DSS DATABASE: current data from applications or groups DATA MINING: technology for finding relationships in large data bases for prediction TPS EXTERNAL DATA DSS DATA BASE DSS SOFTWARE SYSTEM MODELS OLAP TOOLS DATA MINING USER INTERFACE

Applications There are theoretical possibilities of building such systems in any knowledge domain. Clinical decision support system for medical diagnosis. a bank loan officer verifying the credit of a loan applicant an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs. DSS is extensively used in business and management. Executive dashboards and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward. A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package, developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase. DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.

9. Information Systems to support decisions Management Information Systems Decision Support Systems Decision support provided Provide information about the performance of the organization Provide information and techniques to analyze specific problems Information form and frequency Periodic, exception, demand, and push reports and responses Interactive inquiries and responses Information format Prespecified, fixed format Ad hoc, flexible, and adaptable format Information processing methodology Information produced by extraction and manipulation of business data Information produced by analytical modeling of business data

10. DSS and BI DSS is not quite synonymous with BI DSS are generally built to solve a specific problem and include their own database(s) BI applications focus on reporting and identifying problems by scanning data stored in data warehouses Both systems generally include analytical tools (BI called business analytics systems) Although some may run locally as a spreadsheet, both DSS and BI uses Web

11. Group Decision Support System Group Decision Support System (GDSS) Contains most of the elements of DSS plus software to provide effective support in group decision-making settings

External database access Databases Model base GDSS processor GDSS software External databases Access to the internet and corporate intranet, networks, and other computer system Dialogue manager External database access Users

Characteristics of a GDSS (1) Special design Ease of use Flexibility Decision-making support Delphi approach (decision makers are geographically dispersed) Brainstorming Group consensus Nominal group technique

Characteristics of a GDSS (2) Anonymous input Reduction of negative group behaviour Parallel communication Automated record keeping Cost, control, complexity factors

Components of a GDSS and GDSS Software Database Model base Dialogue manager Communication capability Special software (also called GroupWare) E.g., Lotus Notes people located around the world work on the same project, documents, and files, efficiently and at the same time

GDSS Alternatives high Local area decision network Wide area decision network Decision frequency Decision room Teleconferencing low close distant Location of group members

Decision Room Decision Room For decision makers located in the same geographic area or building Use of computing devices, special software, networking capabilities, display equipment, and a session leader Collect, coordinate, and feed back organized information to help a group make a decision Combines face-to-face verbal interaction with technology-aided formalization

Wide Area Decision Network Characteristics Location of group members is distant Decision frequency is high Virtual workgroups Groups of workers located around the world working on common problems via a GDSS

12. Executive Support System Characteristics A specialized DSS that includes all the hardware, software, data, procedures, and people used to assist senior-level executives within the organization Board of directors President Function area vice presidents Function area managers

Characteristics of ESSs Tailored to individual executives Easy to use Drill down capabilities Support the need for external data Help with situations with high degree of uncertainty Futures orientation (predictions, forecasting) Linked with value-added business processes