DSS

Decision Support Systems (DSS)

Introduction to Decision-Making and Problem-Solving

• Importance: Problem-solving is critical for business organizations.

• Process Overview: The problem-solving process starts with decision-making.

• Simon’s Model: Herbert Simon created a model dividing decision-making into three phases:

  • Intelligence

  • Design

  • Choice

How Decision Making Relates to Problem Solving

• Phases of Decision Making:

  1. Intelligence: Identifying potential problems or opportunities.

  2. Design: Developing potential solutions.

  3. Choice: Selecting a solution to implement.

• Additional Steps:

  • Implementation

  • Monitoring

The First Stage: Intelligence

• Identification of Problems:

  • Must correctly identify and define problems to avoid wasting efforts.

  • Distinguishing between symptoms and actual problems is essential.

• Data Gathering: Gather information to understand the problem thoroughly.

  • Environmental Factors: Investigate resources and constraints affecting the problem.

Data Gathering Techniques

• Analyze the environment: suppliers, customers, competitors.

  • Suppliers: Increasing costs due to external factors (e.g., oil prices).

  • Competitors: Pricing strategies.

  • Customers: Product complaints impacting health.

Overview of Decision Support Systems (DSS)

• Definition: A DSS is used to solve business decision problems, often represented as mathematical programs.

• Purpose: Help in tasks like resource allocation.

Shared Resource Allocation Problems

• Constraints: Rules must be established regarding resource limits.

  • Example: A maximum cotton inventory for production.

• Challenges: Conflicting constraints create complex optimization problems.

Optimization in Decision Making

• Using Solver:

  • Tool to find the best solution among possibilities.

  • Example Constraints:

    • Maximum hours of work per machine.

    • Minimum production goals for specific products.

What-if (Sensitivity) Analysis in DSS

• Purpose of What-if Analysis: To create forecasts for various scenarios (good, bad, stable) based on variable inputs.

• Impact Assessment:

  • Evaluates how changes in variables affect outcomes and identifies critical variables.

Characteristics of Decision Support Systems (DSS)

• Predictive Nature: Outputs focus on future events to minimize risks.

  • Example: Economic forecasts and projections for sales.

• Summary Form: Provides non-detailed, global data insights, focusing on trends rather than specifics.

• Ad Hoc Basis: Information generated irregularly for specific purposes, such as market analysis.

• Unexpected Information: Often reveals surprises or insights not initially anticipated.

• External Data: Primarily gathers data from outside sources, such as government databases.

• Subjectivity: Input data can be subjective, influenced by personal opinions, which may affect accuracy.