CHAPTER 9 : ENABLING THE ORGANIZATION -                                           DECISION MAKING

Decision Making

•Reasons for growth of decision-making information systems

         i.            . People need to analyze large amounts of information—Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity.

       ii.            . People must make decisions quickly—Time is of the essence and people simply do not have time to sift through all the information manually.

      iii.            . People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions—Information systems substantially reduce the time required to perform these sophisticated analysis techniques.

Six - Step Decision Making

      i.            Problem identification: Define the problem as clearly and precisely as possible.

     ii.            Data collection: Gather problem-related data, including who,what,where,when,why, and how. Be sure to gather facts, not rumors or opinions about the problem.

   iii.            Solution generation: Detail every solution possible, including ideas that seem farfetched.

   iv.            Solution test: Evaluate solutions in terms of feasibility (can it be completed?), suitability (is it a permanent or a temporary fix?), and acceptability (can all participants form a consensus?).

    v.            Solution selection: Select the solution that best solves the problem and meets the needs of the business.

   vi.            Solution Implementation: If the solution solves the problem, then the decisions made were correct. If not, then the decision were incorrect and the process begins again.

OPERATING

• At the operational level, employees develop, control, and maintain core business activities required to run the day-to-day operations.
• Operational decisions are considered structured decisions, which arise in situations where established processes offer potential solutions.

MANAGERIAL

•  At the managerial level, employees are continuously  evaluating company operations to hone the firm's abilities to identify, adapt to, and leverage change.
• these types of decision are considered semistructured decisions-occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to a definite recommended decision.

STRATEGIC

• Managers develop overall business strategies, goals, and objectives as part of the company's strategic plan.
• strategic decisions are highly unstructured decisions- occuring in situations which no procedures or rules exist to guide decision makers toward the correct choices.

Enhancing Decision Making with MIS

-          Model  is a simplified representation or abstraction of reality.

Operational Support Systems

• Transactional information encompasses all the information contained within a single business process or unit of work.
• Its primary purpose is to support the performance of daily operational or structured decisions.

Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information

Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision support systems ( DSS )

- Decision support system (DSS) – models information to support managers and business professionals during the decision-making process

- Three quantitative models used by DSSs include:

         i.            Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model

       ii.            What-if analysis – checks the impact of a change in an assumption on the proposed solution

      iii.            Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output

Executive information system ( EIS )

•Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization

•Most EISs offering the following capabilities:

–Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information

–Drill-down – enables users to get details, and details of details, of information

–Slice-and-dice – looks at information from different perspectives

  

 Interaction between a TPS and an EIS

·         Digital dashboard - integrates information from multiple component and present it in a unified display.

ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) stimulates human thinking and behavior, such as the ability to reason and learn.

Intelligence systems are various commercial applications of artificial intelligence. 

CATEGORIES OF AI SYSTEMS :

1. Expert systems - Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.

2. Neural network - Also called an artificial neural network, is a category of AI that attempts to emulate the way the human brain works.  Fuzzy logic is a mathematical method of handling imprecise or subjective information.

3. Genetic algorithms - Is an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.

4. Intelligent agents - Is a special-purpose knowledge-based information system that accomplishes specific tasks on behalf of its users. Shopping boat is software that will search several retailer websites and provide a comparison of each retailer's offerings including price and availability.

5. Virtual reality - Is a computer-simulated environment that can be a simulation of the real world or an imaginary world.

6. DATA MINING 

·         Data-mining software includes many forms of AI such as neural networks and expert systems.

·         Common forms of data-mining analysis capabilities include;

-          Cluster Analysis.

-          Association Detection.

-          Statistical Analysis.

7. CLUSTER ANALYSIS.

·         Cluster Analysis – A technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible.

·         CRM systems depend on cluster analysis to segment customer information and identify behavioral traits.

Eg: Consumer goods by content, brand loyalty or similarity.

8. ASSOCIATION DETECTION

·         Detection – Reveals the degree to which variables are related and the nature Association and frequency of these relationships in the information.

·         Market Basket Analysis – Analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.

9. STATISTICAL ANLYSIS

·         Statistical Analysis – Performs such functions as information correlations, distributions, calculations, and variance analysis.

·                     Forecast – Predictions made on the basis of time-series information.

·                     Time-series Information – Time-stamped information collected at a particular frequency.

Eg: Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods.