Dr. Merle P. Martin
MIS Department
CSU Sacramento

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Ski Resort Planning
What is a DSS?
Unstructured Problems
DSS Components
DSS Examples
Group DSS

Ski Area Planning
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Ski area designs require
 same input
 same decision model
Each resort offers :
 different types of trails
 to different skill levels
Long-range objective: maximize profits for given terrain & market mix

Ski Area Planning
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Optimum design concentrates on balancing downhill / uphill capacities
System of trails cannot easily be changed once carved
Summer activities complicate design
Industry is capital intensive

Ski Resort Planning
Primary Objective
Downhill Capacity
(Trails)
Production Capacity
=
Uphill Capacity
(Lifts)
Market Demand

Ski Area Planning
Terrain Capacity Analysis:
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Examine physical attributes of mountain
Create initial set of trails
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Determine mountain's downhill capacity (i.e., trail system)

Ski Area Planning
Terrain Capacity Analysis:
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Examine physical attributes of the mountain
Create initial set of trails
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Determine mountain's downhill capacity (i.e., trail system)
Market Analysis:
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Match trail system to market mix

Topography Map (Terrain)
Expert and advanced trails
Steep slope
Gentle slope
Lifts
Novice & beginner trail

Physical Design
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Physical terrain and constraints
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Slope of mountain sides
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Physical obstacles
(e.g., cliffs, boulders, creeks, etc.)
Aesthetics (i.e., forest scenery)
Designer selects initial layout
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Initial set of trails

Physical Design
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Downhill capacity of skiers calculated
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Number of skiers per acre
(judgmental)
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Type of skier (i.e., skill level)
Regional density

Market Analysis
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Objective: match trail system to market demands
Seven skier skill levels:
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Beginner
Novice
Low intermediate
Intermediate
High intermediate
Advance
Expert
Market Mix:
Percentage from each category

Decision Support System
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Calculates trail capacity
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Matches skill levels to trail via slope grades
Takes into account skier density per acre by skill level
Calculates market mix of skier skill levels
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Provides expected numbers from a given market mix distribution

Decision Support System
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Balances trail system to market mix
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Changes input parameters:
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Trail attributes
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Density levels
Market mix distribution
Examines uphill capacity

Terrain Capacity Analysis
Slope Inventory
S v u r l L p h g % /
N 1 0 1 n d n d
0 1
0 1 n d
0 4 1
0 4 1
0 4 1

Market Display:
Design for 3837 Skiers
L m
I m h d
N k l G r
9 2
8 1
6 1
3 1 4
6 2
8 1
9 1
Market percent estimated by the planner
Computed by the DSS

Skill Balance
L m
I m h d
A k n
4 0 3
9 0
7 0
3 0 1
4 0 7
6 0 2
7 0
7 0

Sprague / Carlson:
“Computer-based system that helps the decision maker confront ill-structured problems through direct interaction with data and analysis models.”

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Aid decision maker
 not replace ( ES )
Decision maker remains in control
Not always best decision
Change / flexibility
Quick response

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Large amounts of data
Different data sources
Tailored to decision maker:
 judgment
 knowledge
 intuition
 style
 personal traits

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Graphically oriented
Optimize / heuristics
“What if”(sensitivity) analysis
Goal-seeking analysis
Unstructured problems

Data Base
Decision
Maker
DSS
Software
Graphical
Interface
Model
Base
Models
• Financial
• Statistical analysis
• Graphical
• Project mgmt

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Financial
Statistical Analysis
Graphical
Project Management
Management Science
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Operations research

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Cash Flow
 e.g., discounted payback
Internal ROI
Portfolio Analysis
 stock market
 advertising
Spreadsheets

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Descriptive (summary)
Trend projection
Hypothesis testing
Regression analysis

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Inventory
Queuing (Line)
Network
Search

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American Airlines
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Price / rate selection
Frito-Lay
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Advertising / promotion selection
Juniper Lumber
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Production scheduling

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Kmart
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Price evaluation
Southern Railway
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Train dispatching and forecasting
Texas Oil & Gas
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Potential drilling sites

Does your firm use DSS?
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How?
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How could your firm use a DSS?
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What problems do you see with DSS?

) interactive computer-based system facilitating solution of unstructured problems by a set of decision makers working together as a group.

Database
Model Base
GDSS
Processor
Groupware
Dialogue
Manager
User

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Brainstorming tools
Idea organization
Prioritization / voting
Electronic questionnaires
 pre-meeting
Group Dictionary
Stakeholder identification

Projection Screen
Facilitator
Console &
Network
Server
Projector
Workstations

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Efficiency of group meetings
Quality of decisions reached
 alternatives examined
 participation / contribution
 those who would otherwise be silent
 decision outcomes
Leverage (way meetings run)
 e.g., human parallel processing

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Anonymity
 provides sense of equality
 encourages participation by all
 reduces:
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 problems with “group think” dominance by strong personalities
 heightens conflict / impoliteness

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Facility Design
 lighting and layout
Multiple public screens
Knowledge bases / databases
Network speed

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Fixed versus customized tools
Software design
 ease of use
Group size / composition
 individual satisfaction increases with group size
Satisfaction
 participants not blocked out of group

same place
Face-to-Face
Meetings
(electronic copyboards)
Teams in
Place
(team room tools) different places
Cross-Distance
Meetings
(audio/video conferencing) same time
Ongoing
Coordination
(voice mail, e-mail) different times

GDSS is based on the assumption that anonymity is desired. How important is anonymity in groups?
 at work?
 in class?
 in distance-learning?
WHAT DO YOU THINK?

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What is a DSS?
DSS Components
DSS Examples
Group DSS