Som485 Exam 1

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DSS: Definitions

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System
Information System
Decision System
Support System

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DSS

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DSS are interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems.” – Sprague & Carlson

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Strategic planning

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rare, long-term

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Management control

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periodic, course corrections

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Operational control

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highly repetitive, brings revenue, short-term

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Types of decisions

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Structured: algorithmic, programmable
Unstructured: subjective, vague problem space
Semistructured: combination of above

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Managerial roles (Mintzberg)

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Interpersonal
Informational
Decisional

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Figurehead

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Is symbolic head; obliged to perform a number of routine duties of a legal or social nature.

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Leader

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Is responsible for the motivation and activation of subordinates; responsible of staffing, training, and associated duties

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Liaison

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Maintain self-developed network of outside contacts and informers who provide favors and information

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Monitor

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seeks and receive a wide variety of special information

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Disseminator

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Transmits information received from outside or from subordinates ot member of the organization; some of this information is factual and some involves interpretation and integration.

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Spokesperson

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Transmits information to outsiders about the organization's plans, policies, actions, results, and so forth; serves as an expert on the organization's industry.

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Entrepreneur

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Searches the organization and its environment for opportunities and initiates improvement project to bring about change; supervise design of certain project

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Disturbance handler

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Is responsible for corrective action when the organization faces important, unexpected disturbances

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Resource allocator

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Is responsible of the allcation of organizational resources of all kinds;

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Negotiator

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Is responsible for representing the organization at major negotiations.

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Decision style

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The manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, and beliefs

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Automated Decision Systems (ADS)

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However, if the Knowledge component is complete (eg. all manager’s decision rules / models are known), a DSS may be use AI to implement (not just recommend) solutions directly without user intervention
- Useful in repetitive decisions

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Model-driven DSS

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quantitative models (statistical, financial, optimization, simulation) used to generate a recommended solution to a problem

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Data-driven DSS

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support ad-hoc reporting and queries on internal & external database

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Communication-driven:

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multiple users, support shared tasks, either cooperative or hostile mode

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Knowledge-driven:

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qualitative models; uses stored rules (Expert Sys & Mining)

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Document-driven

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search, retrieve, analyze, classify text documents (eg. Law firms use it to create a case)

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Business Intelligence (BI)

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Business Intelligence (BI) represents the tools and systems that play a key role in integrating and analyzing all corporate data.

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BI Architecture 3 system components

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Data warehouse
Business analytics
Performance management (BPM)

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Data warehouse

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A repository of cleaned and integrated historical / stable data for the entire business

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Multidimensional models

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A modeling method that involves data analysis in several dimensions

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Iconic (Scale):

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physical replica, Eg. Airplane, building

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Analog

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symbolic representation of reality, Eg. Car dashboard, hierarchy chart

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Quantitative / Mathematical

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uses analytic approach, Eg. LP, EOQ, Regression

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Descriptive / Mental

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narrative, uses heuristics (jury deliberations), cognitive map (Banxia demo), simulation/scenarios

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Rational

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All alternatives will be evaluated
Will look for the best (optimum) solution

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Bounded rationality

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Sub-optimization: failure to look for an overall solution for the organization

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Satisficing (or good enough) solution

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Humans like to simplify problems
consider fewer alternatives, criteria, constraints
they are under time pressure, cost
they have limited processing power

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Scenario analysis

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When some of the model parameters are not controllable; helps to perform computer simulation over a series of periods

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Risk analysis

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Assess level of risk to the outcome associated with each potential alternative being considered (in general, people tend to be overconfident – pull of a slot machine lever)

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Measuring outcomes

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The value of an alternative is evaluated in terms of goal attainment

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Internal data

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Internal data come mainly from the organization’s transaction processing system

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External data

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External data include industry data, market research data, census data, regional employment data, government regulations, tax rate schedules, and national economic data

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Private data

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Private data can include guidelines used by specific decision makers and assessments of specific data and/or situations

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Data extraction

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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

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Database management system (DBMS)

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Software for establishing, adding, updating, deleting and querying a database

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Directory

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A catalog of all the meta-data in a database

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Model Directory

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Index/Catalog/List of all models (meta-data on models)

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Model Base

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Contains the actual collection of available models themselves that can be readily instantiated with data (eg. Excel templates

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Model Base Management

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Tools for creating, manipulating, updating models/templates

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Model integration

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involves combining several models as needed to solve specific problems

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Strategic models

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Models that represent long-term problems for the executive level of management (eg. How many plants should we have five years from now? Uses considerable external data)

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Tactical models

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Models that represent problems for the mid-level of management (eg. Short-term labor recruitment&training, sales promotion planning, budgeting)

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Operational models

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Models that represent problems for the operational (day-to-day activities) level of management (eg. Production scheduling, staffing, inventory control)

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Analytical models

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Helps with analysis of business situations
Eg. Identifying significant variables for prediction, evaluating their sensitivity

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Model building blocks /routines

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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 Eg. Multi-Dimensional Expressions (MDX), XML for Analysis (XMLA) similar to SQL for DBs

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User interface management system (UIMS)

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The DSS component that handles all interaction between users and the system

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Expert tool user

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with skills in the application of one or more types of specialized problem-solving tools

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Facilitator

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who can plan, organize, and electronically control a group in a collaborative computing environment

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Static models

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Models describing a single interval. Parameter values may be considered stable (eg. Interest rate)

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Dynamic models

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Models whose input data are changed over time. E.g., a five-year profit or loss projection; a spreadsheet model may capture inflation, business cycle of economy

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Optimization

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Algorithms (Simplex in LP)

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Decision Analysis

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Decision-Table/Tree

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Simulation

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Uses experimentation, random generator

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Predictive

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Forecasting using regression, time-series analysis

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Heuristics

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Logical deduction using if-then rules (eg. Expert Systems)
This is a qualitative model

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Sensitivity analysis

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A study of the level of effect of a change in an input variable on the overall solution (distinguish this from what-if analysis)

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Certainty

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A condition under which it is assumed that only one result is associated with a decision (easier to model)

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Uncertainty

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For a given decision, possible outcomes are unknown; even if known, probabilities cannot be calculated due to lack of data. (most difficult to model) Eg. Testing a new rocket / product

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Risk

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Possible outcomes are known & data is available to calculate probabilities of occurrence of each outcome for a given decision

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What-if:

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Similar to sensitivity analysis, but focus is on generating the revised solution when an input value is changed.

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Goal-seek

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Calculates the value of an input necessary to achieve a desired level of output (goal). Eg. How many hours to study to get an A?

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Multiple goals

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Finds a compromise solution. Eg. Group decision environments, usually based on utility analysis (Analytical Hierarchy Process)