Project Selection

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# Project Selection - PowerPoint PPT Presentation

Project Selection. I. Project Selection: Non-Numeric Models. Sacred Cow Operating Necessity Competitive Necessity Product Line Extension Comparative Benefit

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## PowerPoint Slideshow about 'Project Selection' - alana-huff

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Presentation Transcript
I. Project Selection: Non-Numeric Models
• Sacred Cow
• Operating Necessity
• Competitive Necessity
• Product Line Extension
• Comparative Benefit

(E.g. Q-SORT: Projects are Divided into Rated Groups. If a Group Has More than Eight Members, It is Divided into Two Groups. Then Projects within Groups are Ranked).

II. Project Selection: Numeric Models
• Payback Period

Initial Fixed Investment / Annual Cash Inflow E.g. \$10,000 / \$2000 = 5 Years

• Mean Rate of Return

Annual Return / Initial Investment E.g. \$3,000 / \$10,000 = 0.30

II. Project Selection: Numeric Models
• Present Value (Discounted Cash Flow)

1. In One Year:

(Net Present Value)(1+k) = (Future Value)

Where k is Interest Rate

E.g. (\$10,000 or NPV) (1.1) = \$11,000 = F

2. In t Years:

NPV (1+k)t = F E.g. (\$10,000) (1.1) (1.1) = \$12,100

II. Project Selection: Numeric Models
• Present Value (Discounted Cash Flow)

3. Solving for NPV:

NPV = F / (1+k)t

E.g. NPV = \$12,100 / 1.21 = \$10,000

4. If You Have F’s in Different Years (or Periods)

NPV = -A0 + [F1/(1+k)1] + [F2/(1+k)2] + Etc. NPV = -\$7,000+(\$5,000/1.1)+(\$5,000/1.21) NPV = \$1,677.68

II. Project Selection: Numeric Models
• Profitability Index (Cost-Benefit)

Index = NPV / Initial Investment (A0)

E.g. Index = \$1,677.68 / \$7000.00 = 0.24

II. Project Selection: Numeric Models
• Scoring Methods

1. Unweighted 0-1 Factor

2. Unweighted Factor Scoring

Example – Project A

Qualify No Qualify S Environmental Impact x 8

Need for Consultants x 3

Impact on Image x 7

Totals 2 1 18

II. Project Selection: Numeric Models
• Scoring Methods

3. Weighted Factor Scoring

For Each Project i: Si = Si1W1 + Si2W2 + Si3W3 + Etc. E.g. S1 = (10)(0.5) + (10)(0.3) + (5)(0.2) = 9

Select Projects with Highest Scores (Si’s)

II. Project Selection: Numeric Models
• Scoring Methods

4. Linear (Integer) Programming

Maximize Z = S1X1 + S2X2 + S3X3 + Etc. Subject to: m1X1 + m2X2+ Etc.  M Xi = 0 or 1

E.g. Max. Z = 10 X1 + 10X2 + 5X3 s.t. 2X1 + 5X2+ 3X3 7 Workers X1,X2,X3 = 0 or 1

II. Project Selection: Numeric Models
• Analysis of Projects Under Uncertainty
• Primary Source of Uncertainty: Time and Cost
• We Can Use Monte Carlo Simulation
• Computer Can Generate Typical (E.g. Normally Distributed) Activity Times and Costs. After 1000’s of Runs a Cost Probability Distribution Can be Generated for Each Proposed Project.

Probability Cost.3 \$1000 .4 2000

.3 3000

Summary

Numeric Methods Can Assist in:

• Project Selection
• Bidding through Cost Estimation and ComputerSoftware Such as Quickest (Constructive Computing)