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

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I project selection non numeric models
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
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 models1
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 models2
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 models3
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 models4
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 models5
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 models6
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 models7
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
Summary

Numeric Methods Can Assist in:

  • Project Selection

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


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