Chapter 3 Study subsections 3 5 10 3

Chapter 3 Study sub-sections: 3. 5 -10, 3. 12(p 118 -120) DECISION SUPPORT SYSTEMS CONCEPTS, METHODOLOGIES, AND TECHNOLOGIES: AN OVERVIEW

Components of a Closed DSS

Components of an Open DSS Network

DSS Classifications (based on dominance of each component) – Model-driven DSS: quantitative models (statistical, financial, optimization, simulation) used to generate a recommended solution to a problem – Data-driven DSS: support ad-hoc reporting and queries on internal & external database – Communication-driven: multiple user interface, support shared tasks, either cooperative or hostile mode – Knowledge-driven: qualitative models; uses stored rules (Expert Sys & Mining) – Document-driven: search, retrieve, analyze, classify text documents (eg. Law firms use it to create a case)

Data Management Subsystem-1

Data Management Subsystem-2 • The Database – Internal data come mainly from the organization’s transaction processing system – External data include industry data, market research data, census data, regional employment data, government regulations, tax rate schedules, and national economic data – Private data can include guidelines used by specific decision makers and assessments of specific data and/or situations

Data Management Subsystem-3 • Data extraction The process of capturing data from several disparate sources, synthesizing them, summarizing them, determining which of them are relevant, and organizing them, resulting in their effective integration

Data Management Subsystem-4 • Database management system (DBMS) • Software for establishing, updating, and querying a database • Directory A catalog of all the meta-data in a database or all the models in a model base

Data Management Subsystem-5 • Key database and database management system issues – – Data quality Data integration Scalability Data security

The Model Management Subsystem-1

The Model Management Subsystem-2 • Model Directory • Index/Catalog/List of all models (meta-data on models) • Model Base Contains the actual collection of available models themselves that can be readily instantiated with data • Model Base Management • Tools for creating, manipulating, updating

The Model Management Subsystem-3 • Four categories of models in the model base (based on Business Functions) – – Strategic models Tactical models Operational models Analytical models Model integration involves combining the operations of several models when needed; Eg. Factor analysis determines which variables are most promising and regression analysis follows up with creating the actual prediction model.

The Model Management Subsystem-4 • Strategic models Models that represent problems for the executive level of management (eg. How many plants should we have five years from now? Uses considerable external data) • Tactical models Models that represent problems for the mid-level of management (eg. Short-term labor recruitment&training, sales promotion planning, budgeting) • Operational models Models that represent problems for the operational (day-to-day activities) level of management (eg. Production scheduling, staffing, inventory control) • Analytical models Mathematical models typically integrated into the above models Egs. Statistical, Financial, MS, data mining algorithms

The Model Management Subsystem-5 • Model building blocks /routines – Helps to create a custom model from smaller components – Preprogrammed software elements that can be used to build computerized models. For example, a random-number generator can be employed in the construction of a simulation model – Models created using blocks /routines can easily be updated – Some programming is required – Modeling languages (MDX, XMLA similar to SQL for DBs) can also be used

User Interface (Dialog) Subsystem-1 • User interface management system (UIMS) The DSS component that handles all interaction between users and the system +GUI

User Interface (Dialog) Subsystem-2 • Since managers are used to verbal interactions and have time constraints, designing DSS interfaces pose a challenge – Voice input and output (no typing) – Natural language processing (typing spoken language) – GUI (more info can be presented compared to text) – Touchscreen (slice & dice data) – Responding to body movements (including face) – Portable devices/ Web interface (as they travel a lot) – Intelligent agents & search engines – Flexibility to suit styles of decision-maker – Ability to support group decision-making

Knowledge-Based Management Subsystem (Chapters 11 -12) • • Optional component of a DSS Typically captures qualitative knowledge mathematical models Static knowledge of a domain Decision rules used in the domain (If-Then) Heuristic / logical reasoning Backward (goal to data)/ Forward chaining (data to goal) Repository of past decisions and outcomes (machine learning) Ability to consult other experts in the field

The User A DSS may be directly used by a decision-maker. But many managers employ an Intermediary/ chauffer: A person who uses a DSS to answer questions for top management The intermediary should be a(n): Expert tool user : with skills in the application of one or more types of specialized problem-solving tools Facilitator: who can plan, organize, and electronically control a group in a collaborative computing environment