The StrategicManagement of

InformationTechnology

Chapter 10Chapter 10Complex Decisions andComplex Decisions and

Artificial IntelligenceArtificial Intelligence

Transaction Processing Transaction Processing SystemSystem

Input OutputProcess

Information

Communication

Systems Development

Process FlowProcess Flow

Process Flow/Elements Components/Elements Responsibilities

OverviewOverview

Business Problems– Complex, less structured

Data– Non-numerical, messy, complex relationship

Artificial Intelligence– Goal is to make computers “think” like humans

Specialized ProblemsSpecialized Problems

Diagnostic Speed Consistency Training

Building Expert SystemsBuilding Expert Systems

Knowledge Base Knowledge Engineers Case-Based Reasoning Limitations of Expert Systems

Expert SystemExpert System

Expert Symbolic and/or Numeric Knowledge Knowledge Base Expert Decisions made by non-experts

Decision Support System Decision Support System Compared to Expert SystemCompared to Expert System

DSS ESS

Goal Help User MakeDecision

Provide ExpertAdvice

Method DataModelPresentation

Asks QuestionsApplies rules andExplains

Type ofProblems

General, limited byuser

Narrow Domain

Building Expert SystemsBuilding 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 ConsiderAdditional Issues to Consider

Pattern Recognition/Neural Nets Voice and Speech Recognition Language Comprehension Massively Parallel Computers Robotics and Motion Statistics, Uncertainty, Fuzzy Logic

Expert SystemsExpert 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 TypeExpert 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 SystemsLimitations 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 SystemsLimitations 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 SystemsLimitations 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 AreasAI Research Areas Computer Science

– Parallel Processing

– Symbolic Processing

– Neural Networks Robotics Applications

– Visual Perception

– Tactility

– Dexterity

– Locomotion and Navigation

AI Research AreasAI Research Areas

Natural Language– Speech Recognition– Language Translation– Language Comprehension

Cognitive Science– Expert Systems– Learning Systems– Knowledge-Based Systems

Neural NetworksNeural Networks

Based on brain design Hardware and software Recognize patterns

– Design specifications– Spiegel Catalogs– Pick stocks

Machine VisionMachine Vision

Advantages of Machine Vision– Broader spectrum of light– Will not suffer fatigue– Damage less easy

Literal– Problems less detection than processing

Speech RecognitionSpeech Recognition

Voice: primarily ID Speech

– Transcripts– Hands-free operations

Limitations– Need to train– Accents and colds– Synonyms, punctuation, context

AI QuestionsAI 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 ApplicationsOther 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 FutureThe 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

ProductChange

Process Change

Dynamic

Stable

Stable Dynamic

Mass customization Invention

Mass production Continuous improvement

Product-Process Change Matrix

Product Change

Process Change

Dynamic

Stable

Stable Dynamic

Mass ProductionChange 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 costjustified efficiency enhancement

Critical synergy Reliance on invention form to supply new product designs and new process tech.; linked with invention forms in single corporate entity

Product-process change matrix

Product change

Process change

Dynamic

Stable

Stable Dynamic

InventionChange 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 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

Figure 3 Product-process change matrix

Product change

Process change

Dynamic

Stable

Stable Dynamic

Mass CustomizationChange conditions Constant/unforecastable changes in market

demand; periodic/forcastable changes in process technology

Strategy Low cost process differentiation within new markets

Key organization tool Loosely coupled networks of modular,flexible processing units

Workflows Customer/product unique value chains

Employee roles 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

Figure 5 Product-process change matrix

Product change

Process change

Dynamic

Stable

Stable Dynamic

Continuous ImprovementChange 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 Intensive and reciprocal workflow within teams

Employee roles 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 micro-transformations

Critical synergy Mass-customization form supplied with flexible new processes; sometimes functions as transition form in re-engineering to mass customization

Figure 6 Product-process change matrix

PERFORMANCE FOCUS ORGANIZATIONAL FOCUS

New core competence Phase 3 Redefinition

Phase 2 Enhancement

Transition BarriersPhase 1 Automation

Value -added process and services

Excellence

Efficiency

Internal Operations Customer and Supplier interface

New Business Units