Advanced Organizer

D ecision Mak ing

P lanning

O rganizing

Leading

C ontro lling

Managem ent Functions

R esearch

D esign

Production

Q uality

Marketing

Project Managem ent

Managing Technology

Tim e Managem ent

E thics

C areer

Personal Technology

Managing Engineering and Technology

1. Recognize Problem / Opportunity

2. Define Goals/Objectives

3. Assemble Relevant Data

4. Identify Feasible Alts

7. Predict Alts’ Outcomes

8. Choose the Best Alt.9. Audit the Results

Overall Mission / Objectives

5. Select the Criterion

6. Construct a Model

Planning/ Decision-Making Process

Planning Tools &Techniques

Techniques for Assessing the environment• Environmental Scanning• Forecasting-• Benchmarking-Techniques for allocating Resources• Budgeting• Scheduling- Gantt charts, PERT Network analysis, CPM• Breakeven Analysis• Linear programming

Mathematical Model

• A model is usually refers to a representation of an actual object to reduced scale such as a wooden model of a building. These models are used to study the behavior of the actual object. For example a scale model of an aero plane can be used in a wind tunnel to simulate the behavior of the actual aero plane in flight. Mathematical model perform the similar function. This mathematical modeling is the aid to decision making

Tools for Decision Making under Certainty

• Linear programming– Graphical solution– Simplex method– Computer software

• Non-linear programming• Engineering Economic Analysis

Linear Programming

• Linear programming is the simplest and most widely used technique for solving decision making problems. The aim of this method is to determine how to meet the desired goals while taking constraints into account.

• The term linear implies proportionately

Decision Variables Objective Function (Maximizing or Minimizing)

Example:A factory produces two products, product X and product Y. If we can realize $10 profit per unit of product X and $14 per unit of Y, what should be the production level for product X and product Y?

Maximize P = 10x + 14y

Linear Programming

Linear Programming

• Constrains– Example:

• 3 machinists• 2 assemblers• Each works 40 hours/week• Product X requires 3 hours of machining and 1 hour of

assembly per unit• Product Y requires 2 hours of machining and 2 hours of

assembly per unit– For machining time: 3x + 2y 3(40)– For assembly time: 1x + 2y 2(40)

Linear programming Graphical solution (Constraints)

3x+2y≤120

(40,0)

(0,60)

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Y

X

x+2y≤80

(80,0)

(0,40)

Feasible Region

Corner Solutions

Linear programming Graphical solution (Objective Function)

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Y

X

P=10x+14y

P=1050

P=700

P=350

Linear programming Graphical solution (Objective Function)

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Y

X

P=10x+14y

P=1050

P=700

P=350

Linear programming Graphical solution

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Y

X

Optimal Solution(20, 30)

Engineering Economic Analysis

• Time Value of Money• Minimum Acceptable Rate of Return• Decision Criteria

– Net Present Worth– Equivalent Annual Worth– Internal Rate of Return– Benefit / Cost Ratio