Decision Making Models

• Decision Support Systems (DSS) enable better decision making through analyzing information.
• DSS enable activities in decision support, the decision-making process, and utilize strategic management data.

Learning Objectives

• Grasp the core principles of decision making.
• Familiarize with systems-based approaches.
• Review Simon's four-phase decision-making model.
• Understand rationality and its constraints.
• Distinguish between decision choices and establishing a decision principle.
• Know the factors influencing decision making.
• Determine how DSS aids practical decision making.

Decision Making

• Decision making involves selecting among alternatives to achieve goals.
• The decision-making process includes intelligence, design, choice, and implementation phases.

Systems

• Systems consist of inputs, processes, and outputs.
• Systems incorporate feedback and are bounded to separate from their environments.
• The system interacts with its environment through an established boundary.
• Closed systems are independent, take no inputs, and deliver no outputs to the environment.
• Open systems accept inputs and deliver outputs to the environment.

Models for DSS

• Iconic models are small, physical replications of systems like airplane models.
• Analog models are behavioral representations of real systems.
• Analog models are often in two-dimensional diagrams.
• Examples includes organization charts illustrating authority, and colored maps representing bodies of water.
• Quantitative (mathematical) models perform numerical analyses in DSS

Phases of The Decision-Making Process

• Simon's original model contained three phases: intelligence, design, and choice.
• A fourth phase, implementation, was later added.
• Monitoring is listed as a potential fifth phase to the model.
• Searching for conditions that call for decisions is the intelligence gathering phase.
• This phase includes scanning the environment, collecting/analyzing data, identifying problems, categorizing problems, and decomposing problems into smaller parts.
• Assess ownership and responsibility for problem resolution.
• The design phase involves inventing, developing, and analyzing different solutions.
• Generating alternative courses of action, conducting feasibility tests, and validating results.
• The choice phase includes selecting a course of action.
• Normative models help with choosing an actions.
• Normative models evaluate effects on each alternative and rationalize to seek advantages
• Suboptimization involves decisions made in separate part of organization without consideration of whole.
• The implementation phase involves adapting the selected course of action.

Classification of Problems

• Decision-making ranges from structured (programmed) to unstructured problems.
• Structured problems employ standard solutions through abstraction, analysis, and specific categories.
• Unstructured problems are complex with no cut and dried solution methods.
• An unstructured problem has no defined solutions, while a structured problems does.
• Semi-structured problems have elements of both.

Decision Support Frameworks

• Operational control handles accounts, while managerial control involves budget analysis.
• Strategic planning focuses on investment and warehouse locations.
• Semistructured decisions cover production scheduling for operational control.
• Credit evaluation falls under managerial control.
• Mergers are considerations for strategic planning.
• Buying software is unstructured and a consideration of operational control.
• Negotiations fall under managerial control
• Research and development is a consideration for strategic planning

Technologies for Decision-Making Processes

• Structured processes benefits from Management Information Systems (MIS) and transaction processing.
• Semistructured processes use DSS, Knowledge Management Systems (KMS), Group Support Systems (GSS), Customer Relationship Management (CRM), and Supply Chain Management (SCM).
• Unstructured processes benefit from GSS, KMS, Expert Systems (ES), and neural networks.

Introduction to Decision Support Systems

• Key aspects of DSS that include characteristics, applications, capabilities, benefits, and success evaluation.
• DSS are computerized that aids decision-making in organizations.
• A well-designed DSS compiles data, documents, and knowledge to inform decision-making.
• DSS is a methodology that supports decision-making because it is flexible, adaptive, interactive, uses a GUI, is iterative, and employs modeling.
• Key characteristics of DSS include handling unstructured problems and supporting managers at all levels.
• Business intelligence is proactive and accelerates decision-making through real-time data warehousing and anomaly detection.

Components Of DSS

• The Data Base Management System (DBMS), Model Based Management System (MBMS), Dialog Generation & Management System (DGMS), and Knowledge Management make up DSS.

Architecture Of DSS

• DSS contains a model base, DBMS, dialogue generation management systems (DGMS) and DSS user.
• DSS architecture integrates data from external sources, the Internet, and internal systems through data management.
• Model management and knowledge-based subsystems facilitate decision-making.

Data Management Subsystem

Components includes databases, management systems, directories, and query facilities.

• Databases stores interrelated data from internal Transaction Processing Systems (TPS) and external sources.
• Database management systems extracts and manages and modifies data.
• Data directories catalogs and defines data availability.

Model Management Subsystem

• Subsystem includes model base with modeling language, a model directory, and model execution capabilities.
• Strategic models support top-level decisions, while tactical models aid middle management.
• Operational models support daily activities, and analytical models perform data analyses.
• A model base management system functions through model creation, updates, and new routine generation.
• The Model command processor receives instructions from the user interface.

User Interface System

• The User Interface Management System (UIMS) includes a GUI and natural language processor.
• This system interacts with model management and data management subsystems.

Knowledge-Based Management System

• The intelligent agent system component helps with complex problem solving.
• DSS hardware is based on a web server with a DBMS and supports mobile access across various networks.
• DSS can be classified by if it is Alter based (data/model oriented), Holsapple and Whinston based text, database, or is intelligent.

DSS Classifications

• Institutional DSS handle recurring issues, while ad hoc DSS address unanticipated problems.
• Custom DSS are vendor-made, while generic DSS can be modified and include built-in databases, models, and user interfaces for repeated industry issues at reduced costs.

Web and DSS

• DSS can use web capabilities to enable data collection, communications, collaborations, download capabilities, run on Web servers, simplify integration problems and enhance usability features.

Evaluating the Success/Failure of Decision Support Systems

• Decision Making under Certainty assumes complete knowledge and short time horizons.
• Decision Making under Risk Analysis assesses expected outcomes with known or estimated probabilities.
• Risk Analysis calculates the expected value of each alternative and select the alternative with the best expected value.
• Decision Making under Uncertainty operates under the assumption that several outcomes possible.

Measuring Outcomes

• Measuring outcomes includes goal attainment and customer satisfaction.
• Important Decision Making Issues include personality types, gender, human cognition, and decision styles.

Decision Style

• It varies from individual and situational.
• Directive styles favor concrete data and written communication due to low requirements of detailed information.
• Analytical styles need great data analysis for taking decisions.
• Conceptual styles are for people-oriented thinkers.
• Behavioral styles focus on short-range vision with limited data input.

The Decision makers:

• Individuals may face conflicting objectives with decisions that are automated.
• Groups face conflicting objectives and can be complicated to come to an agreement.

Groups and DSS

• A GDSS supports group decision-making through computer-based systems.
• System is used by members in the same area to share a PC and have access to display
• Systems are used in LANs, remote decision rooms, and WANs.

Benefits of DSS

• DSS can improve personal efficiency, speed up problem-solving, promote interpersonal communication, support learning, and increase organizational control.

Developing Decision Support Systems

Strategies for DSS Analysis and Design

• Option to program a customized DSS with general purpose programming language such as C++.
• Alternatively, use a DSS generator software or spreadsheet.

DSS Analysis and Design Process

• Approaches include system development life cycle, which starts with problem definition and ends with implementation.
• Planning identifies business value and develops the project plan, while analysis gathers information and models.
• Design focuses on the physical system and databases.
• Implementation involves construction and installation.
• The primary advantage of SDLC offers structure, although challenges may arise with rigidity.
• DSS can ROMC (representations, operations, memory aids, and controls) analyses
• Analyzing by functional category analyses is option.

System Functions

• Locating Knowledge base and aggregation for statistics, estimating modeling.
• Simulation creates expected outcomes, and Equalization maintains consistency.

DSS Development Process

• System requires problem diagnosis to contextualize, followed by identifying objectives and resources.
• Analysis categorizes requirements, and design determines components and platform.
• Construction then applies prototyping, and implementation tests deployment.
• Incremental adaptation then refines the system.

Explore the nuances of decision-making versus establishing principles, the impact of emotions, and factors influencing choices. Learn about Decision Support Systems (DSS) and different model characteristics, structured vs unstructured problems.