Transcript Systems Development: Chapter 10

Chapter 10
Complex Decisions and
Artificial Intelligence
The Strategic
Management of
Information
Technology
Transaction Processing
System
Input
Process
Systems Development
Communication
Information
Output
Process Flow
Process Flow/Elements
 Components/Elements
 Responsibilities

Overview

Business Problems
– Complex, less structured

Data
– Non-numerical, messy, complex relationship

Artificial Intelligence
– Goal is to make computers “think” like humans
Specialized Problems
Diagnostic
 Speed
 Consistency
 Training

Building Expert Systems
Knowledge Base
 Knowledge Engineers
 Case-Based Reasoning
 Limitations of Expert Systems

Expert System
Expert
 Symbolic and/or Numeric Knowledge
 Knowledge Base
 Expert Decisions made by non-experts

Decision Support System
Compared to Expert System
DSS
ESS
Goal
Help User Make
Decision
Provide Expert
Advice
Method
Data
Model
Presentation
General, limited by
user
Asks Questions
Applies rules and
Explains
Narrow Domain
Type of
Problems
Building Expert Systems
Shell = Tool to Build Expert System
 Knowledge Engineer Builds
 Cooperative Expert Key
 Components:

– Knowledge Base
– Information Engineer applies rules to new data
for each conclusion

Custom Program, Shell, or Pre-packaged
Additional Issues to Consider
Pattern Recognition/Neural Nets
 Voice and Speech Recognition
 Language Comprehension
 Massively Parallel Computers
 Robotics and Motion
 Statistics, Uncertainty, Fuzzy Logic

Expert Systems
Goal: Make same decision an expert would
make with the same data
 Capture and program expert’s knowledge
 Advantage of speed and consistency

Expert Systems Problem Type
Narrow, well-defined domain
 Solutions require an expert
 Complex logical processing
 Handle missing, ill-structured data
 Need a cooperative expert

Limitations of Expert Systems

Fragile Systems
– Small environment changes can force revision
of all of the rules

Mistakes
– Who is responsible?
Expert
 Multiple Expert
 Knowledge Engineer
 Company that uses it

Limitations of Expert Systems

Vague Rules
– Rules can be hard to define

Conflicting Experts
– With multiple opinions, who is right?
– Can diverse methods be combined?
Limitations of Expert Systems

Unforeseen events
– Events outside of domain can lead to nonsense
decisions
– Human experts adapt
– Will human novice recognize a nonsense
result?
AI Research Areas

Computer Science
– Parallel Processing
– Symbolic Processing
– Neural Networks

Robotics Applications
–
–
–
–
Visual Perception
Tactility
Dexterity
Locomotion and Navigation
AI Research Areas

Natural Language
– Speech Recognition
– Language Translation
– Language Comprehension

Cognitive Science
– Expert Systems
– Learning Systems
– Knowledge-Based Systems
Neural Networks
Based on brain design
 Hardware and software
 Recognize patterns

– Design specifications
– Spiegel Catalogs
– Pick stocks
Machine Vision

Advantages of Machine Vision
– Broader spectrum of light
– Will not suffer fatigue
– Damage less easy

Literal
– Problems less detection than processing
Speech Recognition
Voice: primarily ID
 Speech

– Transcripts
– Hands-free operations

Limitations
– Need to train
– Accents and colds
– Synonyms, punctuation, context
AI Questions
What is intelligence?
 Can machines ever think like humans?
 How do humans think?
 Do we really want computers to think like
us?

Other AI Applications

Massively Parallel Processing
– only if task can be split into independent pieces
– math computation and database searches

Robotics and Motion
– welding and painting

Statistics, Unclear, and Fuzzy Logic
– use subjective and incomplete description
The Future

Intelligent Agents
– Learn what you want from what you ask for
and go get it for you
– Automated personal assistant
– Network traffic can be a problem
– Agents are independent of one another
Product-Process Change Matrix
Mass customization
Invention
Dynamic
Product
Change
Mass production
Continuous improvement
Stable
Stable
Process Change
Dynamic
Product-process change matrix
Mass Production
Dynamic
Product
Change
Change conditions
Periodic/forecastable changes in product
market demand and process technology
Strategy
Production
Key organizational tool
Standardized, dedicated production process
Workflows
Serial, linear flow of work, executed to plan
Employee roles
Separate doers and thinkers
Control system
Centralized, hierarchical command system
I/T alignment challenge
Automation of manual processes to achieve cost
justified efficiency enhancement
Reliance on invention form to supply new
product designs and new process tech.; linked
with invention forms in single corporate entity
Stable
Critical synergy
Stable
Process Change
Dynamic
Invention
Dynamic
Product
change
Stable
Change conditions
Constant/unforecastable changes in product
market demand and process technology
Strategy
Production of unique or novel product or
process
Key organization tool
Specialization of creative or high craft skills
Workflows
Independent work
Employee roles
Professionals and craftspeople
Control system
System decentralized to specialized individuals
and groups
I/T alignment
Development and distribution of customized
systems
Critical synergy
Stable
Mass production form supplied with new
processes; operates in market niches too
dynamic or small for mass production;
sometimes incorporated into single corporate
entity with multiproduct mass-production forms
Dynamic
Process change
Figure 3 Product-process change matrix
Mass Customization
Dynamic
Change conditions
Constant/unforecastable changes in market
demand; periodic/forcastable changes in
process technology
Strategy
Low cost process differentiation within new
markets
Key organization tool
Product
change
Workflows
Employee roles
Loosely coupled networks of modular,
flexible processing units
Customer/product unique value chains
Network coordinator and on-demand processors
Control system
Hub and web system; centralized network
coordination, independent processing control
I/T alignment
Integration of constantly changing network info
processing/communication requirements;
interoperability, data communication, and
coprocessing critical to network efficiency
Critical synergy
Reliance on continuous improvement form for
increasing process flexibility within processing
units
Stable
Stable
Dynamic
Process change
Figure 5 Product-process change matrix
Continuous Improvement
Dynamic
Product
change
Change conditions
Constant/unforecastable changes in process
technology, periodic/forecastable changes in
market demand
Strategy
Low cost process differentiation within
mature markets
Key organization tool
Self-managing/cross-functional teams
Workflows
Employee roles
Intensive and reciprocal workflow within teams
Dual, combined doers and thinkers
Control system
Microtransformations; rapid and frequent
switching between decentralized team decision
making and team-managed command systems
I/T alignment
Design of cross-functional info and
communication systems that support microtransformations
Mass-customization form supplied with flexible
new processes; sometimes functions as
transition form in re-engineering to mass
customization
Stable
Critical synergy
Stable
Dynamic
Process change
Figure 6 Product-process change matrix
New core
competence
Phase 3 Redefinition
Value -added
process and services
P
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F
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Phase 2
Enhancement
Excellence
Phase 1
Automation
Transition Barriers
Efficiency
Internal Operations
ORGANIZATIONAL FOCUS
Customer and Supplier
interface
New Business
Units