Decisions and Artificial Intelligence in Management Systems

Learning Objectives

• Types of decisions and decision-making process
• Role of business intelligence and analytics in decision making
• Definition and distinction of artificial intelligence (AI)
• Major AI techniques and their organizational benefits

Business Value of Improved Decision Making

• Decision impacts span all organizational levels
• Small individual decision improvements aggregate to significant value
• Quantifiable measurement of decision-making value

Types of Decisions

• Unstructured Decisions: Novel and important; require judgment without predefined procedures.
• Structured Decisions: Routine and repetitive; follow definite procedures.
• Semi-structured Decisions: Involve both clear-cut and ambiguous elements.

Decision-Making Process

1. Intelligence: Identifying problems.
2. Design: Exploring solutions.
3. Choice: Selecting from alternatives.
4. Implementation: Executing and monitoring solutions.

High-Velocity Automated Decision Making

• Involves algorithms replacing human decision-making.
• Fast processing and predefined solution parameters.

Quality Dimensions of Decisions

• Accuracy: Reflect reality.
• Comprehensiveness: Consider all relevant facts.
• Fairness: Reflects interests of concerned parties.
• Speed (Efficiency): Uses time and resources effectively.
• Coherence: Rational explanation of decisions.
• Due Process: Can be appealed.

Business Intelligence (BI) Overview

• Strategies and technologies for collecting and analyzing business data.
• Business analytics tools support meaningful data interpretation.

Elements of the Business Intelligence Environment

1. Data from the business environment.
2. Business intelligence infrastructure.
3. Business analytics toolset.
4. Managerial methods and users.
5. Delivery platforms (MIS, DSS, ESS).
6. User interface for data visualization.

BI and Analytics Capabilities

• Production & parameterized reports, dashboards, ad-hoc reporting.
• Forecasting, scenario analysis, and drill-down functionalities.

Predictive Analytics

• Uses statistical techniques to predict future trends.
• Analyzes customer behavior responses to marketing, pricing changes, etc.

Operational Intelligence and Analytics

• Focused on real-time monitoring for day-to-day operations.
• Leverages IoT data for decision support.

Location Analytics and GIS

• Analyzes geographic data for business insights.
• Useful in optimizing operational logistics, such as ATM placements.

Support for Semi-Structured Decisions

• Decision-support systems (DSS) assist in complex analysis.
• Techniques include what-if analysis and sensitivity analysis.

Decision Support for Senior Management

• Executive support systems (ESS) provide critical performance information.
• Utilize balanced scorecard frameworks to measure organizational performance.

AI Techniques

• Expert Systems: Capture human expertise via rules.
• Machine Learning: Algorithms learn from data patterns.
• Neural Networks: Model human brain processing for complex problems.
• Genetic Algorithms: Optimize solutions using techniques from evolutionary biology.
• Natural Language Processing & Computer Vision: Enable understanding of human language and image data.
• Intelligent Agents: Automate repetitive tasks based on learned knowledge.