This course introduces to students to the field of production management and it represents a mixed concept of scientific and quantitative methods. Production and operation activities start from forecasting, planning for facilities and equipment, designing the best work system, procuring materials and equipment, measuring productivity levels and operational research.
In order to achieve success and desired results, it is important for the project management team to effectively manage the day to day activities involved in a project. Projects consist of a number of separate but interdependent tasks that have the specific objective of creating and developing some new entity. In this aspect project management plays a crucial role. Project management has been in existence for several years and has become an integral area of management over the last three decades. Project Management Institute defines the discipline of project management as the art of planning and controlling various human and non-human resources to be used during the entire project life cycle in order to accomplish already set objectives of scope, cost, quality, and time by using modern management techniques (Barrie and Paulson, 1984). In simple terms, project management can be defined as the process of planning, executing, controlling and monitoring the various aspects of a project in order to achieve the project objectives and deliverables within limited time, costs and resources.
1. Introduction to Quantitative Analysis/Operational Research
1.1) The quantitative analysis approach
1.2) how to develop a quantitative analysis approach
1.3) the role of computers and Spreadsheet Models in the quantitative analysis approach
2. Probability Concepts and Application
2.1) Bayes’ Theorem
2.2) The Normal Distribution
2.3) The Exponential Distribution
2.4) The Poisson Distribution
3. Linear Programming: The Graphical and Simplex Method
3.1) Graphically solve any linear programming problem that has only two Variables-Maximization problems
3.2) Solving minimization problems with more than two constraints
4. Transportation Method
4.1) Setting up a transportation problem
4.2) Solving a problem using the shadow costs method
4.3) Solving an unbalanced transportation problem
4.4) Explanation on degeneracy in transportation problem
5. Assignment Model
5.1) Approach of the assignment mode
5.2) Using Koenig Method to solve minimization and balanced problem
5.3) Solving a maximization problem and unbalanced problem
6. Decision Theory
6.1) Explanation on the six steps in decision theory
6.2) Discussion on the types of Decision-Making Environment
6.3) Decision making under risk
6.4) Decision making under uncertainty
7. Decision Tree and Utility Tree
7.1) Explanation on the five steps of Decision Tree analysis
7.2) Develop accurate and useful decision trees
7.3) Solving a problem by computing expected monetary values (EMVs)Solving a more complex or multi-stage problem
7.4) Application of utility theory to solve problems using decision tree model
8. Introduction to Production Management
8.1) Overview of production management
8.2) Functions within business organization
8.3) Classifying production systems
8.4) Types of operation
9. Forecasting
9.1) Introduction Steps in the forecasting process
9.2) Approaches to forecasting
9.3) Forecasts based on judgement and opinion
9.4) Forecasts based on historical data Associative forecast techniques
9.5) Accuracy and control of forecasts
9.6) Elements of good forecasts
10. Design of Production System
10.1) Capacity Planning
10.2) Introduction Importance of capacity decisions
10.3) Defining and measuring capacity
10.4) Determinants of effective capacity
10.5) Determining of capacity requirements
10.6) Evaluating alternatives
10.7) Design of Work System Job Design
10.8) Work Measurement Labor standards and incentives
11. Operating and Controlling Production System
11.1) Aggregate Planning
11.2) Introduction and overview of aggregate planning
11.3) The concepts of aggregate planning
11.4) Purpose and scope of aggregate planning
11.5) Demand and capacity
11.6) Inputs to aggregate planning
11.7) Decision variables and costs
11.8) Techniques for aggregate planning