1. Project and Production Management
module 5: Strategic Decisions
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Project and Production Management
Module 5
Strategic Decisions in Production Management
Prof Arun Kanda & Prof S.G. Deshmukh,
Department of Mechanical Engineering,
Indian Institute of Technology, Delhi

2. MODULE 5: Strategic Decisions in Production Management
1. Life Cycle of a Production System
2. Role of Models in Production Management
3. Financial Evaluation of Capital Investments
4. Project financial appraisal: An example
5. Decision trees and risk evaluation
6. Illustrative Examples
7. Self Evaluation Quiz
8. Problems for Practice
9. Further exploration
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3. 1. LIFE CYCLE OF A PRODUCTION SYSTEM
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1. LIFE CYCLE OF A PRODUCTION SYSTEM

4. SUMMARY OF LAST LECTURE
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SUMMARY OF LAST LECTURE
Notion of Projects and Production
Generalized Model of a Production System
Value addition
Transformations, inputs and outputs
Challenges in Production
Service Systems
Definition of Operations Management

5. Life Cycle of a Production System
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Life Cycle of a Production System
Various stages in the life of a production system
Characteristic problems and challenges arising at each stage
Major managerial decisions to be taken at each stage
Modelling and solution methodologies

6. LIFE CYCLE APPROACH STAGE I
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LIFE CYCLE APPROACH STAGE I

7. STAGE I : MAJOR CONSIDERATIONS
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STAGE I : MAJOR CONSIDERATIONS
Future long term trends of growth
New Products
Technologies
Consumer Preference Patterns
Industrial Climate
Govt. Policies
Basic Desire, Commitment, Experience
Available or Projected Resources

8. LIFE CYCLE APPROACH STAGE II module 5: Strategic Decisions
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LIFE CYCLE APPROACH
STAGE II

9. STAGE II : MAJOR CONSIDERATIONS
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STAGE II : MAJOR CONSIDERATIONS
During Design of Product or Service
Customer Needs
Behavior of Materials
Available Processes of Manufacture
Economics
During Process Selection
Available Technologies
Required Specifications

10. LIFE CYCLE APPROACH STAGE III
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LIFE CYCLE APPROACH STAGE III

11. STAGE III : MAJOR CONSIDERATIONS
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STAGE III : MAJOR CONSIDERATIONS
SURVEYS AND FEASIBILITY STUDIES
ECONOMICS OF
MASS PRODUCTION
BATCH PRODUCTION
JOB PRODUCTION
PROJECT PRODUCTION
LAYOUT PLANNING
QUALITY ASSURANCE
DEMAND FORECASTING

12. LIFE CYCLE APPROACH STAGE IV module 5: Strategic Decisions
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LIFE CYCLE APPROACH
STAGE IV

13. STAGE IV : MAJOR CONSIDERATIONS
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STAGE IV : MAJOR CONSIDERATIONS
WORK MEASURMENT
SETTING OF PRODUCTION STANDARDS
WAGE INCENTIVES
CAPACITY DETERMINATION
PLANNING RESOURCE ACQUISITIONS

14. LIFE CYCLE APPROACH STAGE V Problems of Installation Coordination
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LIFE CYCLE APPROACH
STAGE V
Problems of
Installation
Coordination
Training of Personnel
Learning Phase

15. LIFE CYCLE APPROACH STAGE VI module 5: Strategic Decisions
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LIFE CYCLE APPROACH
STAGE VI

16. STAGE VI : MAJOR CONSIDERATIONS
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STAGE VI : MAJOR CONSIDERATIONS
SETTING PRODUCTION TARGETS
SCHEDULING, SEQUENCING OF JOBS
INVENTORY CONTROL
QUALITY CONTROL
PRODUCTION CONTROL
COST CONTROL
MAINTENANCE

17. LIFE CYCLE APPROACH STAGE VII
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LIFE CYCLE APPROACH STAGE VII

18. STAGE VII : MAJOR CONSIDERATIONS
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STAGE VII :
MAJOR CONSIDERATIONS
New technological Threats / Opportunities
Revision of Market Demand
Newer Products
Technological Innovations
Newer Methods of Manufacture

19. LIFE CYCLE APPROACH STAGE VIII
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LIFE CYCLE APPROACH STAGE VIII

20. STAGE VIII : MAJOR CONSIDERATIONS
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STAGE VIII : MAJOR CONSIDERATIONS
MANNER OF PHASE OUT
SELLOUT / MERGER
START A NEW PRODUCT

21. SUMMARY Various stages in the life of a production system
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SUMMARY
Various stages in the life of a production system
Characteristic problems and challenges arising at each stage
Major managerial decisions to be taken at each stage
Modelling and solution methodologies

22. 2. ROLE OF MODELS IN PRODUCTION MANAGEMENT
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2. ROLE OF MODELS IN PRODUCTION MANAGEMENT

23. ROLE OF MODELS IN DECISION MAKING
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ROLE OF MODELS IN DECISION MAKING
WHAT IS A MODEL ?
WHAT ARE ITS FEATURES OF RELEVANCE TO DECISION MAKERS?
DIFFERENT KINDS OF MODELS
SOME EXAMPLE OF HOW MODELS HELP IN REAL LIFE DECISION MAKING

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WHAT IS A MODEL ?
A model is an abstraction to some degree of the real life thing or process for which we want to predict performance.

25. FEATURES OF MODELS MODELS PROVIDE
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FEATURES OF MODELS
MODELS PROVIDE
a focus on relevant factors/variables
opportunity for experimentation without undue cost/hazard
prediction of real life phenomena

26. PREDICTION OF REAL LIFE PHENOMENA
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PREDICTION OF REAL LIFE PHENOMENA
For model to be reliable, model validation is necessary
Judgement,
Experience
Is performance,
prediction O.K?
Model
Simplify
Yes No
Revise
Model
Continue
using model ?
Real World
Try different
simplifying
assumptions

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VARIETY OF MODELS
Wind tunnel & blade planetarium, global, structure, architect’s
model of building design
PHYSICAL
Representation of variables in 2 or 3 dimensional space. (demand history, population, food production, traffic intensity)
GRAPHIC

28. + VARIETY OF MODELS Visual pictures, Cartoons, Road signs PICTORIAL
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VARIETY OF MODELS
Visual pictures, Cartoons, Road signs
PICTORIAL +
Organization chart with authority relationships, information flow, current flow
SCHEMATIC

29. VARIETY OF MODELS Symbols used to represent real world situation
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VARIETY OF MODELS
Symbols used to represent real world situation
MATHEMATICAL
An approximation of the real world generally carried out with a high speed computer
SIMULATION

30. MODELS FOR DECISION MAKING
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MODELS FOR DECISION MAKING
BASED ON STRUCTURE
Scaled up or scaled down versions of reality
( e.g. templates in layout design)
ICONIC
Substitution of one property of interest by another
( e.g. Mechanical & Electrical analogues)
ANALOGUE

31. BASED ON STRUCTURE SYMBOLIC
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BASED ON STRUCTURE
Using Mathematical symbols to establish relations
e.g. LP, Queuing, Inventory
SYMBOLIC

32. BASED ON PURPOSE DESCRIPTIVE Merely describe system
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DESCRIPTIVE
BASED ON PURPOSE
Merely describe system
PRESCRIPTIVE or NORMATIVE
(Tell What to do to maximize profit,minimize cost)
Hence a NORM or OBJECTIVE FUNCTION
IS NEEDED

33. BASED ON ENVIRONMENT DETERMINISTIC CERTAINTY ASSUMED PROBABILISTIC
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BASED ON ENVIRONMENT
DETERMINISTIC
CERTAINTY ASSUMED
PROBABILISTIC
RANDOMNESS ASSUMED

34. EXAMPLES OF SYMBOLIC MODELS FORECASTING MODEL Regression (Descriptive)
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EXAMPLES OF SYMBOLIC
MODELS
FORECASTING
MODEL
Regression
(Descriptive) x x x
Demand x x
Ft = a + bt x x J F M A M J J
Month

35. INVENTORY MODEL actual Approximation, d Inventory Time
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actual
Approximation, d
INVENTORY
MODEL
EOQ =2d Cordering/ iC
Inventory
Time

36. Ordering Cost/annum = Cod /q
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Ordering Cost/annum = Cod /q
Annual Costs
PRESCRIPTIVE
MODEL
Tells what should
the Order quantity
be to Minimize
inventory related
costs per annum
Carrying
Cost /annum
= q iC 2
Time

37. OTHER EXAMPLES LINEAR PROGRAMMING NON- LINEAR PROGRAMMING
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OTHER EXAMPLES
LINEAR PROGRAMMING
NON- LINEAR PROGRAMMING
GOAL PROGRAMMING
MODELS OF PRODUCTION PROCESSES etc.
eg. Product mix; Scheduling

38. A SIMPLE MANUFACTURING PROBLEM
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A Company produces two kinds of products desks & tables
The manufacture of either desk or table requires
1 hour of production capacity in the plant.
Maximum available production capacity
=10 hours/week
Limited Sales capacity : desks 6 per week
tables 8 per week
Gross Margin of Profit from sale of
desk Rs 80; table Rs 40.

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PRIORITY– WISE GOALS
1. Management wants to avoid any under utilization of production capacity.
2. Management wants to sell as many desks and tables as possible, but since the gross margin from the sale of a desk is twice that of a table, there is twice as much desire to achieve sales goal for desk as for tables.
3. Management desire to minimize overtime production of plant as much as possible.

40. 10 x1 6 x2 8 9 A B C 8 PROFIT = 80x1+ 40x2 Pt. Profit O(0,0) = 0
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x1 6
x2 8
Sales
Representation
Profit 9 A B C 8
320
640
800
PROFIT
= 80x1+ 40x2
Pt Profit
O(0,0) = 0
A(0,8) = 320
B(2,8) = 480
C(6,8) = 800
D(6,4) = 640
E(6,0) = 480 7 6
480 5 D 4 3
x2 (tables) 2
Ideal Capacity 1 O E 6 1 2 3 4 5 7 8 9 10
x1 (desk)

41. FOR STATED PRIORITIES : BEST SOLUTION IS x1 = 6, x2 = 8, d1 = 4
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FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
First two goals not achieved
Third goal not achieved,
Since overtime = 4 hours
TRY CHANGING SEQUENCE OF PRIORITIES AND INVESTIGATE IF SOLUTION CHANGES.
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0
FOR STATED PRIORITIES :
BEST SOLUTION IS
x1 = 6, x2 = 8, d1 = 4
d1 = d2 = d3 = 0 +
Point C above - - - - -

42. A GOAL PROGRAMMING FORMULATION OF PROBLEM
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A GOAL PROGRAMMING FORMULATION OF PROBLEM
Decision Variables
x1 = no. of desks produced /week
x2 = no. of tables produced / week
Constraints ( Goal constraints & System Constraints)
d1+= overtime operation (if any)
d1- = idle time when production does not exhaust capacity
Sales capacity
x1 or x1+ d2 - = 6
Constraints x2 8 or x2+ d3 - = 8

43. P1 Minimize underutilization of production capacity (d1- )
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Capacity Constraint:
x1+ x2+ d1- – d2+ = 10
Objective function
P1 Minimize underutilization of
production capacity (d1- )
P2 Min (2d2- + d3-)
P3 Min (d1+)

44. COMPLETE GP MODEL Minimize Z = P1d1- + 2 P2d2- + P2d3- + P3d1+
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COMPLETE GP MODEL
Minimize Z = P1d P2d2- + P2d3- + P3d1+
subject to
(1)…. x1 + x2 + d1- - d1+ = 10
(2)…. x d2- = 6
(3)… x d3- = 8
Non - negativity restrictions
x1, x2, d1-, d2-, d3-, d1+  0

45. PRODUCT MIX MODEL NOTATION: n products (indexed i = 1, …, n)
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PRODUCT MIX MODEL
NOTATION:
n products (indexed i = 1, …, n)
m resources (indexed j = 1, …, m)
aij = Consumption of the jth resource per unit production of the ith product
bj = Availability of the j th resource
pi = Profit contribution/ unit of ith product

46. NOTATION (CONTD.) Ui = Upper limit on sales of ith product
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NOTATION (CONTD.)
Ui = Upper limit on sales of ith product
Li = Lower limit on sales on ith product
xi = Production of the ith product in the
planning horizon (Decision variable)

47. LP MODEL Maximize Z = p1x1+ p2x2 + … +pnxn subject to
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LP MODEL
Maximize Z = p1x1+ p2x2 + … +pnxn
subject to
a11x1+ a12x2+ … + a1nxn < b1
a21x1+ a22x2+ … + a2nxn < b2 …
am1x1+ am2x2+ … + amnxn < bm
Li < xi < Ui (i = 1, …, n)

48. LEONTIEF’S INPUT-OUTPUT MODEL
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LEONTIEF’S INPUT-OUTPUT MODEL
A Macro model of the National Economy
Considers a number of interacting industries
APPLICATIONS INCLUDE
Integrated planning for the whole economy
Target setting for individual industries
Resource allocation to various sectors
Price prediction and control in the economy

49. ASSUMPTIONS The economy consists of a number of interacting industries
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ASSUMPTIONS
The economy consists of a number of interacting industries
Each industry produces a single good and uses only one process of production to make this good
Each industry produces to satisfy the demand in all other industries apart from an exogenous demand

50. NOTATION Industry 1 Industry 2 n = Number of industries
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NOTATION
Industry 1
Industry 2
n = Number of
industries
yi j= Amount of
good i needed
by industry j
bi = Exogenous
demand of
good i
yij
Industry i
Industry j
Industry n
Industry k bi

51. MASS BALANCE EQUATIONS
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MASS BALANCE EQUATIONS
The total amount xi which industry i
must produce to
exactly meet the demands is
xi = yij + bi , i = 1,…n

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PRODUCTION FUNCTIONS
We must relate the inputs yij to the output xj for each industry j xj
yij
Industry i
Industry j
aij = number of units of good i needed to make 1 unit of good j
yij = aijxj for all i,j

53. INPUT-OUTPUT COEFFICIENTS
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INPUT-OUTPUT COEFFICIENTS
aij are known as
Input-Output Coefficients or
Technological Coefficients
Linearity assumed
No economies or
diseconomies
Static (constant aij)
Dynamic (varying aij)
Input of i th industry to industry j ,
yij
Slope = aij
Production of j th industry, xj

54. THE BASIC PRODUCTION MODEL
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THE BASIC PRODUCTION MODEL
Substituting the production function equations
in the mass balance equations, we obtain
the basic production model of LEONTIEF:
x1 = a11x1+ a12x2+ … +a1nxn + b1
x2 = a21x1 + a22x a2nxn + b2 ..
xn = an1x1 + an2x2+ … + annxn + bn
In matrix notation: X = AX + B ,
or X= (I-A)-1 B

55. PRICES IN THE LEONTIEF SYSTEM
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PRICES IN THE LEONTIEF SYSTEM
pj = Unit price of good j
aijpi = Cost of aij units of good i required
to make one unit of good j.
The cost of goods 1, 2, …, n needed to make one unit of good j = i=1n aij pi
If the value added by industry j is rj ,
pj - I=1n aij pi = rj, j = 1, …, n

56. THE PRICE MODEL In Matrix notation (I-A)T P = R or
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THE PRICE MODEL
In Matrix notation
(I-A)T P = R or
P = [(I-A)-1]T R X= (I-A)-1 B
Price model Production model
A is the matrix of technological coefficients
P is the price vector
R is the of value added vector
T denotes transpose & I the Identity matrix

57. SUMMARY Purpose and Types of Models
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SUMMARY
Purpose and Types of Models
Aid decision making in Production Management
Examples of Models
Forecasting
Lot sizing
Product Mix (Graphic and LP)
Leontief’s Input Output Model

58. 3. Financial Evaluation Of Capital Investments
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3. Financial Evaluation Of Capital Investments

59. ISSUES FOR DISCUSSION A new venture as a stream of cash flows
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ISSUES FOR DISCUSSION
A new venture as a stream of cash flows
The time value of money
The six rate of return formulas
Notion of depreciation
Computation of taxes
Computation of the before/after tax
NPV
IRR
Payback period
Evaluating investments on financial criteria

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CASH FLOWS
Suppose a m/c costs Rs 10 lacs. It produces 100 pieces/day and each piece is sold for
Rs 25/. Maintenance expenses average Rs 500/day. Assuming 300 working days in a year, what would be profile of expenditure for the next five years?
All figures in Lacs of Rs

61. module 5: Strategic Decisions
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TIME VALUE OF MONEY
A rupee today is more valuable than a rupee one year hence. Why?
Current possession preferred to promises of future
Money can earn positive returns
During inflation a rupee today represents a greater real purchasing power than a future rupee

62. NOTION OF COMPONDING AND DISCOUNTING OF CASH FLOWS
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NOTION OF COMPONDING AND DISCOUNTING OF CASH FLOWS
Compounding
Discounting
A= P(1+i)n
Present
Sum, P
Future
Amount, A

63. COMPOUNDING FACTOR FOR SINGLE PAYMENT (F/P)
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COMPOUNDING FACTOR FOR SINGLE PAYMENT (F/P) F
n= no of periods
i= interest rate per period P
… n n
F = P (1+ i )n = P (CFsp)

64. PRESENT VALUE FACTOR FOR FUTURE PAYMENT (P/F)
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PRESENT VALUE FACTOR FOR FUTURE PAYMENT (P/F) F
n= no of periods
i= interest rate per period P
… n n
P = F 1/ (1+ i )n = F (PVsp)

65. EQUIVALENT ANNUITY FOR A FUTURE SUM (A/F)
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EQUIVALENT ANNUITY FOR A FUTURE SUM (A/F) F
A A A A A
… n n
A = F. i/ [(1+i)n- 1] = F (Sinking fund deposit factor)

66. PRESENT VALUE OF AN ANNUITY (P/A)
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PRESENT VALUE OF AN ANNUITY (P/A) P
A A A A A
… n n
P = A [1-(1+i)-n ]/i = A (present value factor
for annuity)

67. EQUIVALENT ANNUITY FOR A PRESENT SUM (A/P)
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EQUIVALENT ANNUITY FOR A PRESENT SUM (A/P) P
A A A A A
… n n
A = P i/ [1-(1+i)-n ] = P (capital recovery factor)

68. SAMPLE PROJECT Suppose a project has the following data:
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SAMPLE PROJECT
Suppose a project has the following data:
Initial investment (I) = Rs 3,00,000
Annual costs of operation =Rs 20,000
Expected annual revenues
Rs 1,00,000 per annum for the first two years
Rs 200,000 per annum for the next three years
Planning horizon of 5 years

69. INVESTMENT, YEARLY COSTS & REVENUES
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INVESTMENT, YEARLY COSTS & REVENUES
Revenues
Costs
Time (yrs)
(All revenues and costs are in thousand Rupees)

70. GROSS CASH FLOWS 0 1 2 3 4 5 Time(yrs)
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GROSS CASH FLOWS
Time(yrs)
(All cash flows are in thousands of rupees.)

71. UNDISCOUNTED CASH FLOWS BEFORE TAX
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UNDISCOUNTED CASH FLOWS BEFORE TAX
Year
Cash flow
Cumulative
cash flow
Net Present Value = (in thousands)
Payback period = years

72. DISCOUNTED CASH FLOWS FOR INTEREST RATE= 10%
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DISCOUNTED CASH FLOWS FOR INTEREST RATE= 10%
Year
Cash flows
Discount
factor
DCF
Cum
DCF
Net Present Value = (in thousands)
Payback period = years

73. DISCOUNTED CASH FLOWS FOR INTEREST RATE= 20%
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DISCOUNTED CASH FLOWS FOR INTEREST RATE= 20%
Year
Cash flows
Discount
factor
DCF
Cum
DCF
Net Present Value = 85.5 (in thousands)
Payback period = years

74. DISCOUNTED CASH FLOWS FOR INTEREST RATE= 25%
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DISCOUNTED CASH FLOWS FOR INTEREST RATE= 25%
Year
Cash flows
Discount
factor
DCF
Cum
DCF
Net Present Value = 40.4 (in thousands)
Payback period = years

75. DISCOUNTED CASH FLOWS FOR INTEREST RATE= 30%
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DISCOUNTED CASH FLOWS FOR INTEREST RATE= 30%
Year
Cash flows
Discount
factor
DCF
Cum
DCF
Net Present Value = 2.2 (in thousands)
Payback period = years

76. DISCOUNTED CASH FLOWS FOR INTEREST RATE= 35%
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DISCOUNTED CASH FLOWS FOR INTEREST RATE= 35%
Year
Cash flow
Discount factor
DCF
Cum
DCF
Net Present Value = (in thousands)
Payback period > 5 years

77. INTERNAL RATE OF RETURN (GROSS CASH FLOWS)
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INTERNAL RATE OF RETURN (GROSS CASH FLOWS)
NPV
400
400
300
IRR = 30.35%
208.7
200
85.5
100
40.4
2.2
Interest i
10% % % %
-29.4
-100

78. PAYBACK PERIOD (Gross Cash Flows)
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PAYBACK PERIOD (Gross Cash Flows)
NPV
2.78, 3.21, 3.85, 4.32, 4.95, >5 years
0% 10% % 25% 30% %
300
200
100
Years
-100
-200
-300
Interest rate, i

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TAX CONSIDERATIONS
Notion of Depreciation used in computing after tax cash flows.
Straight line method (Here the amount to be depreciated is I/n in each period)
Sum of digits (Here the depreciated amounts are I.n/S,I.(n-1)/S, ... I./S in the 1st, 2nd, ... nth period, where S is the sum of the digits = n. (n+1)/2)

80. TAX CONSIDERATIONS (Continued)
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TAX CONSIDERATIONS (Continued)
Declining balance method
(Here the depreciation in any period is a constant percentage of the opening value. If the initial value of the asset is I, the values after depreciation in successive periods are
a.I, a.a.I, a.a.a.I, ... ,a.a...a.I)

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DEPRECIATION
Depreciation : I Straight line ; II Sum of digits ; III Declining balance
(YRS) I
II ( 5/ / / / /15) =
III ( ) =

82. COMPUTATION OF NET CASH FLOWS
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COMPUTATION OF NET CASH FLOWS
Year
Gross
income
Deprec
Taxable
income
Tax (30%)
After tax
cash flows

83. NET CASH FLOWS (INTEREST =0%)
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NET CASH FLOWS (INTEREST =0%)
Year
Net cash
flow
Cumulative net cash
flow
Net Present value = 280 (in thousand Rs)
Payback period = years

84. NET DISCOUNTED CASH FLOWS ( INTEREST RATE= 10%)
module 5: Strategic Decisions
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NET DISCOUNTED CASH FLOWS ( INTEREST RATE= 10%)
Year
Cash flows
After tax
cash flows
Dis. factor
DCF
Cum DCF
Net Present Value = (in thousands)
Payback period = years

85. NET DISCOUNTED CASH FLOWS (INTEREST RATE= 20%)
module 5: Strategic Decisions
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NET DISCOUNTED CASH FLOWS (INTEREST RATE= 20%)
Year
Cash flows
After tax
cash flows
Disc factor
DCF
Cum DCF
Net Present Value = (in thousands)
Payback period = 4.6 years

86. NET DISCOUNTED CASH FLOWS ( INTEREST RATE= 30%)
module 5: Strategic Decisions
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NET DISCOUNTED CASH FLOWS ( INTEREST RATE= 30%)
Year
Cash flows
After tax
cash flows
Disc. factor
DCF
Cum DCF
Net Present Value = (in thousands)
Payback period > 5 years

87. INTERNAL RATE OF RETURN (NET CASH FLOWS)
module 5: Strategic Decisions
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INTERNAL RATE OF RETURN (NET CASH FLOWS)
NPV
280
300
IRR = 23.5%
200
124.31
100
23.67
Interest i
10% % %
-44.63

88. PAYBACK PERIOD (Net Cash Flows)
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PAYBACK PERIOD (Net Cash Flows)
NPV
300
3.06, 3.65, , >5years
0% % 20% %
200
100
Years
-100
-200
-300

89. BEFORE & AFTER TAX CASH FLOWS
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BEFORE & AFTER TAX CASH FLOWS
BEFORE TAX AFTER TAX
NPV Payback Interest Payback NPV % % %
% >

90. module 5: Strategic Decisions
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SUMMARY
Vital role of financial appraisal in overall project evaluation
Accounting for the time value of money
Estimation of investment, yearly costs and revenues to obtain gross cash flows
Depreciation and tax concepts to obtain net cash flows
Computation of NPV, IRR , benefit /cost ratio and payback

91. 4. Project Financial Appraisal: An Example
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4. Project Financial Appraisal: An Example

92. module 5: Strategic Decisions
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PROBLEM STATEMENT
A project requires an initial investment of Rs 10 Lakhs and the anticipated income at the end of each of five successive years is likely to be Rs 2 lakhs, Rs 4 lakhs, Rs 6 lakhs, Rs 6 lakhs and Rs 4 lakhs respectively. The company is planning to take a loan of Rs 5 lakhs to be returned in equal annual instalments at a rate of interest of 12% over the planning horizon of 5 years. For such cases sum of digits depreciation is allowed. The company is in the 30% tax bracket. Considering the net cash returns after taxes compute (a) Net Present Value
(b) Discounted Payback period
(c) Debt Service Coverage Ratio
(Assume a rate of interest of 8% for discounting cash flows)
What is your recommendation on going ahead with the project based on these three financial parameters?

93. ANALYSIS OF LOAN P = A/(1+i) + A/ (1+i)2 + . . . + A/(1+i)n Years
module 5: Strategic Decisions
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ANALYSIS OF LOAN
P = A/(1+i) + A/ (1+i) A/(1+i)n
Years
… n
P A A A
Capital
Recovery
Factor
P = A (1+i)n – or A = P i(1+i)n
i(1+i)n (1+i)n – 1
For i = 12% = 0.12; n=5 years and P=Rs 5 Lakhs
A = 5 * = Rs lakhs.

94. COMPUTATION OF INTEREST AND PRINCIPLE OF LOAN
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COMPUTATION OF INTEREST AND PRINCIPLE OF LOAN
Year
Opening principal
(in Lakhs of Rs.)
Interest Paid
in year 12%)
Principal paid
in year
Total Instalment

95. COMPUTING TAX AND AFTER TAX CASH FLOWS
module 5: Strategic Decisions
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COMPUTING TAX AND AFTER TAX CASH FLOWS
10L L L L L L
Gross
income
Depreciation
(SOD) (5/15) (4/15) (3/15) (2/15) (1/15)
Interest on
Loan
Taxable
Income
Tax
After Tax

96. DISCOUNTING AFTER TAX CASH FLOWS
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DISCOUNTING AFTER TAX CASH FLOWS
10L L L L L L
Gross
income
Tax
After tax
Income
Discount
Factor(8%)
DCF
Cum DCF
NPV = – 10 = Discounted Payback = 3.3 years

97. DEBT SERVICE COVERAGE RATIO
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DEBT SERVICE COVERAGE RATIO
10L L L L L L
Gross
income
After tax
Income
Loan
Instalment
Cash/Loan
Average Debt Service Coverage Ratio = 13.26/5 = 2.652

98. CONCLUSIONS 10L 2L 4L 6L 6L 4L Gross 2 4 6 6 4 income
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CONCLUSIONS
10L L L L L L
Gross
income
This project has favourable performance on all
the three financial indicators and is thus
financially sound
NPV = Rs Lakhs
Discounted Payback = 3.3 years
DSCR = 2.652

99. 5. Decision Trees andRisk Evaluation
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5. Decision Trees andRisk Evaluation

100. RISK EVALUATION AND DECISION TREES
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RISK EVALUATION AND DECISION TREES
Business Decisions involve risk
What is risk and how to measure it
Use of decision trees to represent real life investment, return and risk
An example of a product launch to illustrate the use of decision trees
Determination of optimal strategies.

101. PRODUCT LAUNCH WITH Decision Trees
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PRODUCT LAUNCH WITH Decision Trees
A new consumer product has been developed by a company and preliminary marketing potential has been estimated as a chance of for successful launch which would result in an annual profit with the present worth of Rs.2,000,000.Failure of product would mean a loss of Rs.300,000.

102. (a) Less than 10% of the public try the new product ,
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The company can either drop the idea , or launch the product immediately , or try in a test market ( at a cost of Rs.40,000) before deciding to drop or launch. The test market would classify the sample response in one of the following three categories:-
(a) Less than 10% of the public try the new product ,

103. module 5: Strategic Decisions
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(b) More than 10% try the product , but less than 25% of those who try buy it on a second or subsequent occasion, (c) more 10% try the product and the repeat purchase rate is 25% or more Based on the subjective estimates of experts the following joint probability table is available.
Test Market response
(a) (b) (c)
Success(S)
Failure(F)

104. module 5: Strategic Decisions
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Use a decision tree to determine what is best for the company to do Also obtain the probability distribution of outcomes for each choice , and comment on the risk in each choice.

105. DECISION TREE 1 2 4 (a) 7 3 (b) 5 8 (c) 6 9 Drop Success Direct
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DECISION TREE
Drop
Success
Direct
Failure 1
Drop
Success 2
Launch
Launch
Failure
Test market
(a) 4 7
Drop
Success
(b) 3
Launch
Failure 5
(c) 8
Drop
Success
Launch
Failure 6 9

106. OPTIMAL DECISION TREE 1 2 4 (a) 7 3 (b) 5 8 (c) 6 9 Drop(0) Success
module 5: Strategic Decisions
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OPTIMAL DECISION TREE
Drop(0)
Success
Drop(0)
Direct
Failure 1
Success 2
Launch
Launch
Failure
Test market
(a) 4 7
Drop
Success
(b) 3
Launch
Failure 5
(c) 8
Drop
Success
Launch
Failure 6 9

107. PROBABILITY DATA P(S,a) = 0.05 P(S,b) = 0.10 P(S,c) = 0.25
module 5: Strategic Decisions
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PROBABILITY DATA
P(S,a) = P(S,b) = P(S,c) = 0.25
P(F,a) = P(F,b) = P(F,c) = 0.00
P(S) = ( ) = 0.40
P(F) = ( ) = 0.60
P(a) = ( ) = 0.50
P(b) = ( ) = 0.25
P(c) = ( ) = 0.25

108. TREE EVALUATION Let annual profit be (P) = 2,000,000
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TREE EVALUATION
Let annual profit be (P) = 2,000,000
Let loss due to failure be (F)=300,000
Let test market cost be (T) = 40,000
If product is launched direct then,
At node 2, value (E2) = P(S)*P + P(F)*F
= 0.4*2,000, *(-300,000)
(E2)= 620,000

109. If test market of product is done then,
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If test market of product is done then,
At node 9, value = [P(S,c)*P+P(F,c)*F]/P(c)
=(0.25*2,000, *(-300,000))/0.25
(E9) = 2,000,000
At node 8, value = [P(S,b)*P+P(F,b)*F]/P(b)
=(0.10*2,000, *(-300,000))/0.25
(E8) = 620,000

110. module 5: Strategic Decisions
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At node 7, value = [P(S,a)*P+P(F,a)*F]/P(a)
=(0.05*2,000, *(-300,000))/0.50
(E7) = -70,000
Since nodal node 7 is negative indicating loss thereby it is better to drop the node 4 but launch the node 5 & node 6 since nodal node 8 & 9 are positive indicating profit .

111. The value @ node 6 (E6) = value @ node 9 =2,000,000
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The node 6 (E6) = node =2,000,000
The node 5 (E5) = node =620,000
The node 4 (E4) = 0
Thereby P(F)=P(F,b)+P(F,c)=0.15+0=0.15
At node 3,value(E3)=P(a)*E4+P(b)*E5+P(c)*E6
= 0.5* *620, *2,000,000
= 655,000

112. module 5: Strategic Decisions
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If the company drop the idea then expected return is Rs 0
If the company directly launches the product then the expected return is Rs 620,000
If company tests the products potential by market survey then expected return is Rs (655,000-40,000)=615,000

113. module 5: Strategic Decisions
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Thereby to get some return company should not drop the idea, instead it should go for test market since expected return in direct launch and test market is near about same but the chance of failure in direct launch =0.6 while in test market it is only 0.15

114. ENUMERATION OF ALL STRATEGIES
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ENUMERATION OF ALL STRATEGIES
The various possible strategies can be
(1) Drop or Status quo.
(2) Direct Launch
(3) Test Market

115. TEST MARKET STRATEGIES
module 5: Strategic Decisions
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TEST MARKET STRATEGIES
There are three possible cases(a),(b)&(c) each with two possibilities drop(D) or launch(L), leading to totally 23=8 strategies. They are: Cases Possibilities
(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)
(a) D D D D L L L L
(b) D D L L L L D D
(c) D L L D L D D L

116. RISK EVALUATION STRATEGIES (1) DROP OR STATUS QUO E(return) = E(1) = 0
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RISK EVALUATION
STRATEGIES
(1) DROP OR STATUS QUO
E(return) = E(1) = 0
P(S)= P(F)= P(0)= 1
(2) DIRECT LAUNCH
E(2) =[0.4*2+0.6*(-0.3)]x 106 = Rs 0.62x106
P(S)= P(F)= P(0)= 0

117. P(S)=P(S,c)=0.25 P(F)=P(F,c)=0 P(0)=0.75
module 5: Strategic Decisions
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(3) TEST MARKET
(i) D D D
E(3(i)) =0*0.5+0*0.25+0* =Rs -40,000
P(S)= P(F)= P(-0.04x106 )= 1
(ii) D D L
E(3(ii)) =0*0.5+0*0.25+2x106*
=Rs =0.46x106
P(S)=P(S,c)= P(F)=P(F,c)=0 P(0)=0.75

118. P(S)=P(S,b)=0.10 P(F)=P(F,b)=0.15 P(0)=0.75 =Rs 0.615x106
module 5: Strategic Decisions
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(iii) D L L
E(3(iii))=(0* *0.25+2* )x106
P(S)= P(S,b)+P(S,c)= = P(F)= P(F,b)+P(F,c)= = P(0)= = 0.50
(iv) D L D
E(3(iv)) =(0* X106*0.25+0*0.25)-40000
=0.115x106
P(S)=P(S,b)= P(F)=P(F,b)= P(0)=0.75
=Rs 0.615x106

119. P(S)= P(S,a)+P(S,b)+P(S,c) =0.05+0.10+0.25=0.4
module 5: Strategic Decisions
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(v) L L L
E(3(iii))=(-0.07* *0.25+2* )x106
P(S)= P(S,a)+P(S,b)+P(S,c) = =0.4
P(F)= P(F,a)+P(F,b)+P(F,c) = =0.6
P(0)= 0
=Rs 0.58x106

120. (vi) L L D E(3(iv)) =(-0.07*0.5+.62*0.25+0*0.25-0.04)x106 =0.08x106
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(vi) L L D
E(3(iv)) =(-0.07* *0.25+0* )x106
=0.08x106
P(S)= P(S,a)+ P(S,b)= =0.15
P(F)= P(F,a)+P(F,b) = =0.6
P(0)= =0.25

121. P(S)=P(S,a)=0.05 P(F)= P(F,a) =0.45 P(0)= 0.5
module 5: Strategic Decisions
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(vii) L D D
E(3(vii))=(-0.07*0.5+0*0.25+0* )x106
= x106
P(S)=P(S,a)=0.05 P(F)= P(F,a) = P(0)= 0.5
(viii) L D L
E(3(viii))=(-0.07*0.5+0*0.25+2* )x106
= 0.425x106
P(S)= P(S,a) +P(S,c) = = P(F)= P(F,a) +P(F,c) = = P(0)= 0.25

122. STRATEGY 1 1 P(x) -0.3 0 2 Rs(millions) DROP OR STATUS QUO .
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STRATEGY 1
DROP OR STATUS QUO .
Expected return (E1)= Therefore probability of success or failure=0 while probability of zero return =1. 1
P(x)
failure
Rs(millions)
success

123. STRATEGY 2 1 0.6 0.4 P(x) 0.62 -0.3 0 2 Rs(millions) DIRECT LAUNCH .
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STRATEGY 2
DIRECT LAUNCH .
Expected return (E1)=E(2)=0.62 millions The probability of success =0.4 and of failure=0.60 while probability of zero return =0. 1
0.6
0.4
P(x)
0.62
Rs(millions)
success
failure

124. STRATEGY 3(i) 1 P(x) -0.3 2 -0.04 Rs(millions) TEST MARKET( D D D )
module 5: Strategic Decisions
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STRATEGY 3(i)
TEST MARKET( D D D )
Expected return (E1)=E(3(i))= millions The probability of success and failure=0 while probability of zero return =1. 1
P(x)
-0.04
failure
Rs(millions)
success

125. STRATEGY 3(ii) 1 0.75 P(x) 0.25 0.46 -0.3 0 2 Rs(millions)
module 5: Strategic Decisions
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STRATEGY 3(ii)
TEST MARKET( D D L )
Expected return E(3(ii)) = 0.46x106 millions The probability of success =0.25 and of failure=0 while probability of zero return =0.75 1
0.75
P(x)
0.25
0.46
failure
Rs(millions)
success

126. STRATEGY 3(iii) 1 0.50 P(x) 0.35 0.15 0.615 -0.3 0 2 Rs(millions)
module 5: Strategic Decisions
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STRATEGY 3(iii)
TEST MARKET( D L L )
Expected return E(3(iii)) = 0.615x106 millions The probability of success =0.35 and of failure=0.15 while probability of zero return=0.5 1
0.50
P(x)
0.35
0.15
0.615
failure
Rs(millions)
success

127. STRATEGY 3(iv) 1 0.75 P(x) 0.15 0.10 0.115 -0.3 0 2 Rs(millions)
module 5: Strategic Decisions
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STRATEGY 3(iv)
TEST MARKET( D L D )
Expected return E(3(iv)) = 0.115x106 millions The probability of success=0.1 and of failure=0.15 while probability of zero return =0.75 1
0.75
P(x)
0.15
0.10
0.115
failure
Rs(millions)
success

128. STRATEGY 3(v) 1 0.6 0.4 P(x) 0.58 -0.3 0 2 Rs(millions)
module 5: Strategic Decisions
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STRATEGY 3(v)
TEST MARKET( L L L )
Expected return E(3(v)) = 0.58x106 millions The probability of success=0.4and of failure=0.6 while probability of zero return =0 1
0.6
0.4
P(x)
0.58
Rs(millions)
success
failure

129. STRATEGY 3(vi) 1 0.6 P(x) 0.25 0.15 -0.3 0 2 Rs(millions) 0.08
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STRATEGY 3(vi)
TEST MARKET( L L D )
Expected return E(3(vi)) = 0.08x106 millions The probability of success=0.15and of failure= while probability of zero return =0.25 1
0.6
P(x)
0.25
0.15
Rs(millions)
failure
0.08
success

130. STRATEGY 3(vii) 1 0.5 0.45 P(x) 0.05 -0.3 0 2 -0.075 Rs(millions)
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STRATEGY 3(vii)
TEST MARKET( L D D )
Expected return E(3(vii)) =-0.075x106 millions The probability of success=0.05and of failure= while probability of zero return =0.5 1
0.5
0.45
P(x)
0.05
failure
-0.075
Rs(millions)
success

131. STRATEGY 3(viii) 1 0.45 P(x) 0.3 0.25 0.425 -0.3 0 2 Rs(millions)
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STRATEGY 3(viii)
TEST MARKET( L D L )
Expected return E(3(viii)) =0.425x106 millions The probability of success=0.3 and of failure=0.45 while probability of zero return =0.25 1
0.45
P(x)
0.3
0.25
0.425
failure
Rs(millions)
success

132. SUMMARY OF ALL TEN STRATEGIES
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SUMMARY OF ALL TEN STRATEGIES
S.No.
Strategy
Profit(Prob)
Loss (Prob)
Expected gain
Risk 1 D
0(1) 2 L
2(0.4)
0.3(0.6)
0.62
0.6
3(i)
-0.04
3(ii)
DDL
2(0.25)
0(.75)
0.46
0.75
3(iii)
DLL
2(0.35)
0.3(0.15)
0.615
0.15
3(iv)
DLD
2(0.10)
0.115
3(v)
LLL
0.6(0.6)
0.58
3(vi)
LLD
2(0.15)
0.08
3(vii)
LDD
2(0.05)
0.3(0.45)
-0.075
0.45
3(viii)
LDL )
0.425

133. module 5: Strategic Decisions
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CONCLUSIONS
Risk defined as the probability of a negative payoff (loss)
Business situations abound in capital inflows and risky revenue realization
Decision trees capture such situations with finite number of decision alternatives
Example of Product launch to assess optimal strategy