Quantitative Analysis for Management with CD

1. Introduction to Quantitative Analysis

     Introduction. What Is Quantitative Analysis? The Quantitative Analysis Approach. How to Develop a Quantitative Analysis Model.
     The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach. Possible Problems in the Quantitative Analysis
     Approach. Implementation—Not Just the Final Step 
       


2. Probability Concepts and Applications

     Introduction. Fundamental Concepts. Mutually Exclusive and Collectively Exhaustive Events. Statistically Independent Events. 
     Statistically Dependent Events. Revising Probabilities with Bayes’ Theorem. Further Probability Revisions. Random Variables. 
     Probability Distributions. The Binomial Distribution. The Normal Distribution. The Exponential Distribution.  The Poisson Distribution.

 
3. Decision Models and Decision Trees

     Introduction. The Six Steps in Decision Making. Types of Decision-Making Environments. Decision Making under Uncertainty. 
     Decision Making under Risk. Decision Trees. How Probability Values Are Estimated by Bayesian Analysis.  Utility Theory. 

 

4. Regression Models
   
     Introduction. Scatter Diagrams. Simple Linear Regression. Measuring the Fit of the Regression Model. Assumptions of the Regression
     Model. Testing the Model for Significance. Multiple Regression Analysis.  Binary or Dummy Variables. Model Building. Nonlinear 
     Regression. Cautions and Pitfalls in Regression Analysis.

     

5. Forecasting 
   
     Introduction. Types of Forecasts. Scatter Diagrams and Time Series. Measures of Forecast Accuracy. Time-Series Forecasting Models. 
     Monitoring and Controlling Forecasts. Using the Computer to Forecast.

    

6. Inventory Control Models 
   
     Introduction. Importance of Inventory Control. Inventory Decisions. Economic Order Quantity: Determining How Much to Order
     Reorder Point: Determining When to Order. EOQ Without the Instantaneous Receipt Assumption. Quantity Discount Models.
     Use of Safety Stock. ABC Analysis. Dependent Demand: The Case for Materials Requirements Planning. Just-In-Time Inventory Control.
     Enterprise Resource Planning.

    

7. Linear Programming Models: Graphical and Computer Methods

     Introduction. Requirements of a Linear Programming Problem. Formulating LP Problems. Graphical Solution to a LP Problem. Solving Flair 
     Furniture’s LP Problem Using QM for Windows and Excel. Solving Minimization Problems. Four Special Cases in LP. Sensitivity Analysis. 

 

8. Linear Programming Modeling Applications and Computer Analyses 
   
     Introduction. Marketing Applications. Manufacturing Applications. Employee Scheduling Applications. Financial Applications. Transportation 
     Applications. Transshipment Applications. Ingredient Blending Applications.

  

9. Linear Programming: The Simplex Method

     Introduction. How to Set Up the Initial Simplex Solution. Simplex Solution Procedures. The Second Simplex Tableau Developing the Third 
     Tableau. Review of Procedures for Solving LP Maximization Problems. Surplus and Artificial Variables Solving Minimization Problems. 
     Review of Procedures for Solving LP Minimization Problems. Special Cases.Sensitivity Analysis with the Simplex Tableau. The Dual.  
     Karmarkar’s Algorithm.
 
    

10. Transportation and Assignment Models

        Introduction. Setting Up a Transportation Problem. Developing an Initial Solution: Northwest Corner Rule. Stepping-Stone Method: 
        Finding a Least-Cost Solution.  MODI Method. Vogel’s Approximation Method: Another Way to  Find an Initial Solution.  Unbalanced 
        Transportation Problems. Degeneracy in Transportation Problems.  More Than One Optimal Solution. Unacceptable or Unacceptable or 
        Prohibited Routes. Facility Location Analysis. Approach of the Assignment Model. Unbalanced Assignment Problems. Maximization 
        Assignment Problems

      

11. Integer Programming, Goal Programming, and Nonlinear Programming

       Introduction. Integer Programming. Modeling with 0–1 Variables. Goal Programming. Nonlinear Programming. 
 
  

12. Network Models 
    
       Introduction. Minimal-Spanning Tree Technique. Maximal-Flow Technique. Shortest-Route Technique.
 
      

13. Project Management

       Introduction. PERT. PERT/Cost. Critical Path Method. Other Topics in Project Management.

      

14. Waiting Lines and Queuing Theory Models

       Introduction. Waiting Line Costs. Characteristics of a Queuing System. Single-Channel Queuing Model with Poisson Arrivals and 
       Exponential Service Times (M/M/1). Multiple-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m). 
       Constant Service Time Model (M/D/1). Finite Population Model  (M/M/1 with Finite Source). Some General Operating Characteristic 
       Relationships. More Complex Queuing Models and  the Use of Simulation


     

15. Simulation Modeling

       Introduction. Advantages and Disadvantages of Simulation. Monte Carlo Simulation. Simulation and Inventory Analysis. Simulation of a 
       Queuing Problem. Fixed Time Increment and Next Event Increment Simulation Models. Simulation Model for a Maintenance Policy.
       Two Other Types of Simulation Models. Verification and Validation. Role of Computers in Simulation.
  
      

16. Markov Analysis

       Introduction. States and State Probabilities. Matrix of Transition Probabilities. Predicting Future Market Shares. Markov Analysis 
       of Machine Operations. Equilibrium Conditions. Absorbing States and the Fundamental Matrix: Accounts Receivable Application.

 

17. Statistical Quality Control

       Introduction. Defining Quality and TQM. Statistical Process Control. Control Charts for Variables. Control Charts for Attributes.
      
       

Appendices 
   

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