capacity and constraint management
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Capacity and Constraint ManagementTRANSCRIPT
© 2011 Pearson Education, Inc. publishing as Prentice Hall S7 - 1
S7S7 Capacity and Constraint Management
Capacity and Constraint Management
PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer and Render Heizer and Render Operations Management, 10e Operations Management, 10e Principles of Operations Management, 8ePrinciples of Operations Management, 8e
PowerPoint slides by Jeff Heyl
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OutlineOutline
Capacity Design and Effective Capacity
Capacity and Strategy
Capacity Considerations
Managing Demand
Demand and Capacity Management in the Service Sector
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Outline – ContinuedOutline – Continued Bottleneck Analysis and Theory of
Constraints Process Times for Stations,
Systems, and Cycles
Theory of Constraints
Bottleneck Management
Break-Even Analysis Single-Product Case
Multiproduct Case
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Outline – ContinuedOutline – Continued Reducing Risk with Incremental
Changes
Applying Expected Monetary Value to Capacity Decisions
Applying Investment Analysis to Strategy-Driven Investments Investment, Variable Cost, and
Cash Flow
Net Present Value
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Learning ObjectivesLearning Objectives
When you complete this supplement, When you complete this supplement, you should be able to:you should be able to:
1. Define capacity
2. Determine design capacity, effective capacity, and utilization
3. Perform bottleneck analysis
4. Compute break-even analysis
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Learning ObjectivesLearning Objectives
When you complete this supplement, When you complete this supplement, you should be able to:you should be able to:
5. Determine the expected monetary value of a capacity decision
6. Compute net present value
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Process StrategiesProcess Strategies
The objective of a process strategy is The objective of a process strategy is to build a production process that to build a production process that meets customer requirements and meets customer requirements and product specifications within cost product specifications within cost and other managerial constraintsand other managerial constraints
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CapacityCapacity
The throughput, or the number of units a facility can hold, receive, store, or produce in a period of time
Determines fixed costs
Determines if demand will be satisfied
Three time horizons
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Planning Over a Time Planning Over a Time HorizonHorizon
Figure S7.1
Modify capacity Use capacity
Intermediate-range planning
Subcontract Add personnelAdd equipment Build or use inventory Add shifts
Short-range planning
Schedule jobsSchedule personnel Allocate machinery*
Long-range planning
Add facilitiesAdd long lead time equipment *
* Difficult to adjust capacity as limited options exist
Options for Adjusting Capacity
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Design and Effective Design and Effective CapacityCapacity
Design capacity is the maximum theoretical output of a system Normally expressed as a rate
Effective capacity is the capacity a firm expects to achieve given current operating constraints Often lower than design capacity
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Utilization and EfficiencyUtilization and Efficiency
Utilization is the percent of design capacity Utilization is the percent of design capacity achievedachieved
Efficiency is the percent of effective capacity Efficiency is the percent of effective capacity achievedachieved
Utilization = Actual output/Design capacity
Efficiency = Actual output/Effective capacity
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
© 2011 Pearson Education, Inc. publishing as Prentice Hall S7 - 17
Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shiftsEfficiency = 84.6%Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shiftsEfficiency = 84.6%Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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Capacity and StrategyCapacity and Strategy
Capacity decisions impact all 10 decisions of operations management as well as other functional areas of the organization
Capacity decisions must be integrated into the organization’s mission and strategy
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Capacity ConsiderationsCapacity Considerations
1. Forecast demand accurately
2. Understand the technology and capacity increments
3. Find the optimum operating level (volume)
4. Build for change
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Economies and Economies and Diseconomies of ScaleDiseconomies of Scale
Economies of scale
Diseconomies of scale
25 - room roadside motel 50 - room
roadside motel
75 - room roadside motel
Number of Rooms25 50 75
Av
era
ge
un
it c
os
t(d
olla
rs p
er
roo
m p
er n
igh
t)
Figure S7.2
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Managing DemandManaging Demand Demand exceeds capacity
Curtail demand by raising prices, scheduling longer lead time
Long term solution is to increase capacity
Capacity exceeds demand Stimulate market
Product changes
Adjusting to seasonal demands Produce products with complementary
demand patterns
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Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Jet ski engine sales
Figure S7.3
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Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
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Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Combining both demand patterns reduces the variation
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
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Tactics for Matching Tactics for Matching Capacity to DemandCapacity to Demand
1. Making staffing changes
2. Adjusting equipment Purchasing additional machinery
Selling or leasing out existing equipment
3. Improving processes to increase throughput
4. Redesigning products to facilitate more throughput
5. Adding process flexibility to meet changing product preferences
6. Closing facilities
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Demand and Capacity Demand and Capacity Management in the Service Management in the Service
SectorSector Demand management
Appointment, reservations, FCFS rule
Capacity management Full time,
temporary, part-time staff
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Bottleneck Analysis and Bottleneck Analysis and Theory of ConstraintsTheory of Constraints
Each work area can have its own unique capacity
Capacity analysis determines the throughput capacity of workstations in a system
A bottleneck is a limiting factor or constraint
A bottleneck has the lowest effective capacity in a system
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Process Times for Stations, Process Times for Stations, Systems, and CyclesSystems, and Cycles
The process time of a stationprocess time of a station is the time to produce a unit at that single workstation
The process time of a systemprocess time of a system is the time of the longest process in the system … the bottleneck
The process cycle timeprocess cycle time is the time it takes for a product to go through the production process with no waiting
These two might be quite
different!
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A Three-Station A Three-Station Assembly LineAssembly Line
Figure S7.4
2 min/unit 4 min/unit 3 min/unit
A B C
QuickTime™ and a decompressor
are needed to see this picture.
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Process Times for Stations, Process Times for Stations, Systems, and CyclesSystems, and Cycles
The system process timesystem process time is the process time of the bottleneck after dividing by the number of parallel operations
The system capacitysystem capacity is the inverse of the system process time
The process cycle timeprocess cycle time is the total time through the longest path in the system
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Capacity AnalysisCapacity Analysis Two identical sandwich lines
Lines have two workers and three operations
All completed sandwiches are wrapped
Wrap
37.5 sec/sandwich
Order
30 sec/sandwich
Bread Fill Toast
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
Bread Fill Toast
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
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Capacity Capacity AnalysisAnalysis Wrap
37.5 sec
Order
30 sec
Bread Fill Toast
15 sec 20 sec 40 sec
Bread Fill Toast
15 sec 20 sec 40 sec
Toast work station has the longest processing time – 40 seconds
The two lines each deliver a sandwich every 40 seconds so the process time of the combined lines is 40/2 = 20 seconds
At 37.5 seconds, wrapping and delivery has the longest processing time and is the bottleneck
Capacity per hour is 3,600 seconds/37.5 seconds/sandwich = 96 sandwiches per hour
Process cycle time is 30 + 15 + 20 + 40 + 37.5 = 142.5 seconds
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Capacity AnalysisCapacity Analysis Standard process for cleaning teeth
Cleaning and examining X-rays can happen simultaneously
Checkout
6 min/unit
Check in
2 min/unit
DevelopsX-ray
4 min/unit 8 min/unit
DentistTakesX-ray
2 min/unit
5 min/unit
X-rayexam
Cleaning
24 min/unit
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Capacity Capacity AnalysisAnalysis All possible paths must be compared
Cleaning path is 2 + 2 + 4 + 24 + 8 + 6 = 46 minutes
X-ray exam path is 2 + 2 + 4 + 5 + 8 + 6 = 27 minutes
Longest path involves the hygienist cleaning the teeth
Bottleneck is the hygienist at 24 minutes
Hourly capacity is 60/24 = 2.5 patients
Patient should be complete in 46 minutes
Checkout
6 min/unit
Check in
2 min/unit
DevelopsX-ray
4 min/unit 8 min/unit
DentistTakesX-ray
2 min/unit
5 min/unit
X-rayexam
Cleaning
24 min/unit
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Theory of ConstraintsTheory of Constraints
Five-step process for recognizing and managing limitations
Step 1:Step 1: Identify the constraint
Step 2:Step 2: Develop a plan for overcoming the constraints
Step 3:Step 3: Focus resources on accomplishing Step 2
Step 4:Step 4: Reduce the effects of constraints by offloading work or expanding capability
Step 5:Step 5: Once overcome, go back to Step 1 and find new constraints
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Bottleneck ManagementBottleneck Management
1. Release work orders to the system at the pace of set by the bottleneck
2. Lost time at the bottleneck represents lost time for the whole system
3. Increasing the capacity of a non-bottleneck station is a mirage
4. Increasing the capacity of a bottleneck increases the capacity of the whole system
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Break-Even AnalysisBreak-Even Analysis
Technique for evaluating process and equipment alternatives
Objective is to find the point in dollars and units at which cost equals revenue
Requires estimation of fixed costs, variable costs, and revenue
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Break-Even AnalysisBreak-Even Analysis Fixed costs are costs that continue
even if no units are produced Depreciation, taxes, debt, mortgage
payments
Variable costs are costs that vary with the volume of units produced Labor, materials, portion of utilities
Contribution is the difference between selling price and variable cost
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Break-Even AnalysisBreak-Even Analysis
Costs and revenue are linear functions Generally not the case in the
real world
We actually know these costs Very difficult to verify
Time value of money is often ignored
AssumptionsAssumptions
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Profit corri
dor
Loss
corridor
Break-Even AnalysisBreak-Even AnalysisTotal revenue line
Total cost line
Variable cost
Fixed cost
Break-even pointTotal cost = Total revenue
–
900 –
800 –
700 –
600 –
500 –
400 –
300 –
200 –
100 –
–| | | | | | | | | | | |
0 100 200 300 400 500 600 700 800 900 10001100
Co
st in
do
llars
Volume (units per period)Figure S7.5
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Break-Even AnalysisBreak-Even Analysis
BEPx = break-even point in unitsBEP$ = break-even point in dollarsP = price per unit (after all discounts)
x = number of units producedTR= total revenue = PxF = fixed costsV = variable cost per unitTC= total costs = F + Vx
TR = TCor
Px = F + Vx
Break-even point occurs whenBreak-even point occurs when
BEPx =F
P - V
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Break-Even AnalysisBreak-Even Analysis
BEPx = break-even point in unitsBEP$ = break-even point in dollarsP = price per unit (after all discounts)
x = number of units producedTR= total revenue = PxF = fixed costsV = variable cost per unitTC= total costs = F + Vx
BEP$ = BEPx P
= P
=
=
F(P - V)/P
FP - V
F1 - V/P
Profit = TR - TC= Px - (F + Vx)= Px - F - Vx= (P - V)x - F
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Break-Even ExampleBreak-Even Example
Fixed costs = $10,000 Material = $.75/unitDirect labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = =F
1 - (V/P)$10,000
1 - [(1.50 + .75)/(4.00)]
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Break-Even ExampleBreak-Even Example
Fixed costs = $10,000 Material = $.75/unitDirect labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = =F
1 - (V/P)$10,000
1 - [(1.50 + .75)/(4.00)]
= = $22,857.14$10,000
.4375
BEPx = = = 5,714F
P - V$10,000
4.00 - (1.50 + .75)
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Break-Even ExampleBreak-Even Example
50,000 –
40,000 –
30,000 –
20,000 –
10,000 –
–| | | | | |
0 2,000 4,000 6,000 8,000 10,000
Do
llars
Units
Fixed costs
Total costs
Revenue
Break-even point
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Break-Even ExampleBreak-Even Example
BEP$ =F
∑ 1 - x (Wi)Vi
Pi
Multiproduct CaseMultiproduct Case
where V = variable cost per unitP = price per unitF = fixed costs
W = percent each product is of total dollar salesi = each product
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248Drinks 1.50 .50 .33 .67 13,500 .186 .125Baked 2.00 1.00 .50 .50 14,000 .193 .096 potato
$72,500 1.000 .469
Annual WeightedSelling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248Drinks 1.50 .50 .33 .67 13,500 .186 .125Baked 2.00 1.00 .50 .50 14,000 .193 .096 potato
$72,500 1.000 .469
Annual WeightedSelling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
BEP$ =F
∑ 1 - x (Wi)Vi
Pi
= = $76,759$3,000 x 12
.469
Daily sales = = $246.02
$76,759312 days
.621 x $246.02$5.00 = 30.6 31
sandwichesper day
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Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(a) Leading demand with incremental expansion
Dem
and
Expected demand
New capacity
(c) Attempts to have an average capacity with incremental expansion
Dem
and
New capacity Expected
demand
(b) Capacity lags demand with incremental expansion
Dem
and
New capacity
Expected demand
Figure S7.6
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Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(a) Leading demand with incremental expansion
Expected demand
Figure S7.6
New capacity
Dem
and
Time (years)1 2 3
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Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(b) Capacity lags demand with incremental expansion
Expected demand
Figure S7.6
Dem
and
Time (years)1 2 3
New capacity
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Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes(c) Attempts to have an average capacity
with incremental expansion
Expected demand
Figure S7.6
New capacity
Dem
and
Time (years)1 2 3
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
Determine states of nature Future demand
Market favorability
Analyzed using decision trees Hospital supply company
Four alternatives
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
EMV = (.4)($100,000) + (.6)(-$90,000)
Large Plant
EMV = -$14,000
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
-$14,000
$13,000
$18,000
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Strategy-Driven InvestmentStrategy-Driven Investment
Operations may be responsible for return-on-investment (ROI)
Analyzing capacity alternatives should include capital investment, variable cost, cash flows, and net present value
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Net Present Value (NPV)Net Present Value (NPV)
where F = future valueP = present valuei = interest rate
N = number of years
P =F
(1 + i)N
F = P(1 + i)N
In general:
Solving for P:
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Net Present Value (NPV)Net Present Value (NPV)
where F = future valueP = present valuei = interest rate
N = number of years
P =F
(1 + i)N
F = P(1 + i)N
In general:
Solving for P:
While this works fine, it is
cumbersome for larger values of N
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NPV Using FactorsNPV Using Factors
P = = FXF
(1 + i)N
where X = a factor from Table S7.1 defined as = 1/(1 + i)N and F = future value
Portion of Table S7.1
Year 6% 8% 10% 12% 14%
1 .943 .926 .909 .893 .8772 .890 .857 .826 .797 .7693 .840 .794 .751 .712 .6754 .792 .735 .683 .636 .5925 .747 .681 .621 .567 .519
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Present Value of an AnnuityPresent Value of an Annuity
An annuity is an investment which generates uniform equal payments
S = RX
where X = factor from Table S7.2S = present value of a series of uniform annual receiptsR = receipts that are received every year of the life of the investment
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Present Value of an AnnuityPresent Value of an Annuity
Portion of Table S7.2
Year 6% 8% 10% 12% 14%
1 .943 .926 .909 .893 .8772 1.833 1.783 1.736 1.690 1.6473 2.676 2.577 2.487 2.402 2.3224 3.465 3.312 3.170 3.037 2.9145 4.212 3.993 3.791 3.605 3.433
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Present Value of an AnnuityPresent Value of an Annuity
$7,000 in receipts per for 5 yearsInterest rate = 6%
From Table S7.2X = 4.212
S = RXS = $7,000(4.212) = $29,484
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LimitationsLimitations
1. Investments with the same NPV may have different projected lives and salvage values
2. Investments with the same NPV may have different cash flows
3. Assumes we know future interest rates
4. Payments are not always made at the end of a period
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