This document outlines a PowerPoint presentation on capacity and constraint management. The presentation covers topics such as design capacity versus effective capacity, bottleneck analysis, break-even analysis, and applying expected monetary value and investment analysis to capacity decisions. It lists learning objectives related to defining capacity, performing bottleneck analysis, and using various analytical tools for evaluating capacity and investment decisions.

1. 10/16/2010
Capacity and Constraint Outline
S7 Management
Capacity
Design and Effective Capacity
Capacity and Strategy
PowerPoint presentation to accompany
Heizer and Render Capacity Considerations
Operations Management, 10e
Principles of Operations Management, 8e Managing Demand
PowerPoint slides by Jeff Heyl Demand and Capacity
Management in the Service Sector
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Outline – Continued Outline – Continued
Bottleneck Analysis and Theory of Reducing Risk with Incremental
Constraints Changes
Process Times for Stations, Applying Expected Monetary
Systems, and Cycles Value to Capacity Decisions
Theory of Constraints Applying Investment Analysis to
Bottleneck Management Strategy-Driven Investments
Break-Even Analysis Investment, Variable Cost, and
Single-Product Case Cash Flow
Multiproduct Case Net Present Value
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Learning Objectives Learning Objectives
When you complete this supplement, When you complete this supplement,
you should be able to: you should be able to:
1. Define capacity 5. Determine the expected monetary
2. Determine design capacity, effective value of a capacity decision
capacity, and utilization 6. Compute net present value
3. Perform bottleneck analysis
4. Compute break-even analysis
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2. 10/16/2010
Process Strategies Capacity
The throughput, or the number of
The objective of a process strategy is units a facility can hold, receive,
to build a production process that store, or produce in a period of time
meets customer requirements and
Determines
ete es
product specifications within cost
fixed costs
and other managerial constraints
Determines if
demand will
be satisfied
Three time horizons
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Planning Over a Time Design and Effective
Horizon Capacity
Options for Adjusting Capacity
Long-range Add facilities
Design capacity is the maximum
planning Add long lead time equipment * theoretical output of a system
Intermediate- Subcontract Add personnel
range Add equipment Build or use inventory Normally expressed as a rate
N ll d t
planning Add shifts
Effective capacity is the capacity a
Short-range
Schedule jobs
firm expects to achieve given current
planning
* Schedule personnel
Allocate machinery
operating constraints
Modify capacity Use capacity
Often lower than design capacity
* Difficult to adjust capacity as limited options exist
Figure S7.1
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Utilization and Efficiency Bakery Example
Utilization is the percent of design capacity Actual production last week = 148,000 rolls
achieved Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Utilization = Actual output/Design capacity
p g p y Bakery operates 7 days/week, 3 - 8 hour shifts
Efficiency is the percent of effective capacity Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
achieved
Efficiency = Actual output/Effective capacity
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3. 10/16/2010
Bakery Example Bakery Example
Actual production last week = 148,000 rolls Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls 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 Example Bakery Example
Actual production last week = 148,000 rolls Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4% Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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Bakery Example Bakery Example
Actual production last week = 148,000 rolls Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts Bakery operates 7 days/week, 3 - 8 hour shifts
Efficiency = 84.6%
Effi i 84 6%
Efficiency of new line = 75%
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Expected Output = (Effective Capacity)(Efficiency)
Utilization = 148,000/201,600 = 73.4%
= (175,000)(.75) = 131,250 rolls
Efficiency = 148,000/175,000 = 84.6%
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4. 10/16/2010
Bakery Example Capacity and Strategy
Actual production last week = 148,000 rolls Capacity decisions impact all 10
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour decisions of operations
Bakery operates 7 days/week, 3 - 8 hour shifts management as well as other
Efficiency = 84.6%
Effi i 84 6% functional areas of the organization
f ti l f th i ti
Efficiency of new line = 75%
Capacity decisions must be
Expected Output = (Effective Capacity)(Efficiency) integrated into the organization’s
= (175,000)(.75) = 131,250 rolls mission and strategy
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Capacity Considerations Economies and
Diseconomies of Scale
1. Forecast demand accurately
(dollars per room per night)
2. Understand the technology and
Average uni cost
capacity increments
p y 25 - room 75 - room
it
roadside motel roadside motel
m
50 - room
3. Find the optimum roadside motel
operating level
(volume)
Economies Diseconomies
4. Build for change of scale of scale
25 50 75
Number of Rooms
Figure S7.2
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Managing Demand Complementary Demand
Demand exceeds capacity
Patterns
Curtail demand by raising prices,
scheduling longer lead time
Long term solution is to increase capacity 4,000 –
Sales in units
Capacity exceeds demand 3,000 –
Stimulate market 2,000 –
Product changes Jet ski
1,000 –
engine
Adjusting to seasonal demands sales
Produce products with complementary JFMAMJJASONDJFMAMJJASONDJ
demand patterns Time (months)
Figure S7.3
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5. 10/16/2010
Complementary Demand Complementary Demand
Patterns Patterns
Combining both
demand patterns
reduces the
variation
4,000 – 4,000 –
Sales in units
Sales in units
Snowmobile
S bil Snowmobile
S bil
3,000 – motor sales 3,000 – motor sales
2,000 – 2,000 –
1,000 – Jet ski 1,000 – Jet ski
engine engine
sales sales
JFMAMJJASONDJFMAMJJASONDJ JFMAMJJASONDJFMAMJJASONDJ
Time (months) Time (months)
Figure S7.3 Figure S7.3
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Tactics for Matching Demand and Capacity
Capacity to Demand Management in the
1. Making staffing changes
Service Sector
2. Adjusting equipment Demand management
Purchasing additional machinery Appointment, reservations, FCFS rule
Selling or leasing out existing equipment
3. Improving processes to increase throughput Capacity
4. Redesigning products to facilitate more
management
throughput Full time,
5. Adding process flexibility to meet changing temporary,
product preferences part-time
6. Closing facilities staff
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Bottleneck Analysis and Process Times for Stations,
Theory of Constraints Systems, and Cycles
Each work area can have its own unique The process time of a station is the
capacity time to produce a unit at that single
Capacity analysis determines the workstation
throughput capacity of workstations in a The process time of a system is the
system time of the longest process in the
A bottleneck is a limiting factor or system … the bottleneck
constraint The process cycle time is the time it
A bottleneck has the lowest effective takes for a product to go through the
capacity in a system production process with no waiting
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6. 10/16/2010
Process Times for Stations,
A Three-Station
Three- Systems, and Cycles
Assembly Line
The system process time is the
QuickTime™ and a
decompressor
are needed to see this picture.
process time of the bottleneck after
dividing by the number of parallel
operations
The system capacity is the inverse
A B C of the system process time
2 min/unit 4 min/unit 3 min/unit The process cycle time is the total
time through the longest path in the
Figure S7.4 system
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Capacity Bread Fill Toast
Capacity Analysis Analysis Order
30 sec
15 sec 20 sec 40 sec
Wrap
37.5 sec
Bread Fill Toast
15 sec 20 sec 40 sec
Two identical sandwich lines
Toast work station has the longest processing
Lines have two workers and three operations time – 40 seconds
All completed sandwiches are wrapped The two lines each deliver a sandwich every 40
seconds so the process time of the combined
p
lines is 40/2 = 20 seconds
Bread Fill Toast
At 37.5 seconds, wrapping and delivery has the
Order
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
Wrap
longest processing time and is the bottleneck
30 sec/sandwich 37.5 sec/sandwich Capacity per hour is 3,600 seconds/37.5
Bread Fill Toast seconds/sandwich = 96 sandwiches per hour
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
Process cycle time is 30 + 15 + 20 + 40 + 37.5 =
142.5 seconds
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Capacity Cleaning
Capacity Analysis Analysis
Check
in
2 min/unit
Takes
X-ray
2 min/unit
Develops
X-ray
4 min/unit
24 min/unit
X-ray
exam
Dentist
8 min/unit
Check
out
6 min/unit
5 min/unit
Standard process for cleaning teeth
All possible paths must be compared
Cleaning and examining X-rays can happen
simultaneously Cleaning path is 2 + 2 + 4 + 24 + 8 + 6 = 46
minutes
X-ray exam path is 2 + 2 + 4 + 5 + 8 + 6 = 27
Cleaning minutes
Takes Develops 24 min/unit Check Longest path involves the hygienist cleaning the
Check in Dentist
X-ray X-ray out teeth
2 min/unit 2 min/unit 4 min/unit X-ray 8 min/unit 6 min/unit Bottleneck is the hygienist at 24 minutes
exam
Hourly capacity is 60/24 = 2.5 patients
5 min/unit
Patient should be complete in 46 minutes
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7. 10/16/2010
Theory of Constraints Bottleneck Management
Five-step process for recognizing and 1. Release work orders to the system at the
managing limitations pace of set by the bottleneck
Step 1: Identify the constraint 2. Lost time at the bottleneck represents
Step 2: Develop a plan for overcoming the
p p p g y
lost time for the whole system
constraints 3. Increasing the capacity of a non-
Step 3: Focus resources on accomplishing Step 2 bottleneck station is a mirage
Step 4: Reduce the effects of constraints by 4. Increasing the capacity of a bottleneck
offloading work or expanding capability increases the capacity of the whole
Step 5: Once overcome, go back to Step 1 and find system
new constraints
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Break-
Break-Even Analysis Break-
Break-Even Analysis
Fixed costs are costs that continue
Technique for evaluating process even if no units are produced
and equipment alternatives
Depreciation, taxes, debt, mortgage
Objective is to find the point in payments
dollars and units at which cost Variable costs are costs that vary
equals revenue with the volume of units produced
Requires estimation of fixed costs, Labor, materials, portion of utilities
variable costs, and revenue Contribution is the difference between
selling price and variable cost
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Break-
Break-Even Analysis Break-
Break-Even Analysis
–
Assumptions 900 –
Total revenue line
Costs and revenue are linear 800 –
Break-even point Total cost line
functions 700 – Total cost = Total revenue
Cost in dollars
600 –
Generally not the case in the
y
d
500 –
real world
400 – Variable cost
We actually know these costs 300 –
Very difficult to verify 200 –
100 – Fixed cost
Time value of money is often –
| | | | | | | | | | | |
ignored 0 100 200 300 400 500 600 700 800 900 1000 1100
Figure S7.5 Volume (units per period)
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8. 10/16/2010
Break-
Break-Even Analysis Break-
Break-Even Analysis
BEPx = break-even point in x = number of units BEPx = break-even point in x = number of units
units produced units produced
BEP$ = break-even point in TR = total revenue = Px BEP$ = break-even point in TR = total revenue = Px
dollars F = fixed costs dollars F = fixed costs
P = price per unit (after V = variable cost per unit P = price per unit (after V = variable cost per unit
all discounts) TC = total costs = F + Vx all discounts) TC = total costs = F + Vx
BEP$ = BEPx P
Break-
Break-even point occurs when
= F P Profit = TR - TC
P-V = Px - (F + Vx)
TR = TC F F
or BEPx = = = Px - F - Vx
P-V (P - V)/P
Px = F + Vx F = (P - V)x - F
=
1 - V/P
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Break-
Break-Even Example Break-
Break-Even Example
Fixed costs = $10,000 Material = $.75/unit Fixed costs = $10,000 Material = $.75/unit
Direct labor = $1.50/unit Selling price = $4.00 per unit Direct labor = $1.50/unit Selling price = $4.00 per unit
F $10,000 F $10,000
BEP$ = = BEP$ = =
1 - (V/P) 1 - [(1.50 + .75)/(4.00)] 1 - (V/P) 1 - [(1.50 + .75)/(4.00)]
$10,000
= = $22,857.14
.4375
F $10,000
BEPx = = = 5,714
P-V 4.00 - (1.50 + .75)
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Break-
Break-Even Example Break-
Break-Even Example
50,000 –
Multiproduct Case
Revenue
40,000 –
F
Break-even BEP$ =
point
30,000 – Total
∑ 1-
Vi
x (Wi)
Dollars
s
costs
t
Pi
20,000 –
Fixed costs where V = variable cost per unit
10,000 –
P = price per unit
F = fixed costs
–
| | | | | |
W = percent each product is of total dollar sales
0 2,000 4,000 6,000 8,000 10,000
Units
i = each product
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9. 10/16/2010
Multiproduct Example Multiproduct Example
Fixed costs = $3,000 per month Fixed costs = $3,000 per month
Annual Forecasted Annual Forecasted
Item Price Cost Sales Units Item Price Cost Sales Units
Sandwich $5.00 $3.00 9,000 Sandwich $5.00 $3.00 9,000
Drink 1.50 .50 9,000 Drink 1.50 .50 9,000
Baked potato 2.00
2 00 1.00
1 00 7,000
7 000 Baked potato 2.00
2 00 1.00
1 00 7,000
7 000
Annual Weighted
Selling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248
Drinks 1.50 .50 .33 .67 13,500 .186 .125
Baked 2.00 1.00 .50 .50 14,000 .193 .096
potato
$72,500 1.000 .469
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Multiproduct Example F
Reducing Risk with
BEP =
∑ 1 - P x (W )
V
$
i
i
Incremental Changes
Fixed costs = $3,000 per month i
(a) Leading demand with (b) Capacity lags demand with
incremental expansion incremental expansion
$3,000 x Forecasted
Annual 12
New
Item Price Cost = Sales Units
= $76,759 New
.469 capacity capacity
Demand
Sandwich $5.00 $3.00 9,000
Demand
Expected Expected
demand demand
Drink 1.50 .50
Daily 9,000
$76,759
Baked potato 2.00
2 00 1sales = 312 days = $246.02
1.00
00 7 000 $246 02
7,000
Annual Weighted
Selling Variable .621 x $246.02 of Contribution
Forecasted % (c) Attempts to have an average
Item (i)
Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ = 30.6 ≈ 31
Sales (col 5 x col 7) capacity with incremental
$5.00 sandwiches expansion
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 per day
.248
New
Drinks 1.50 .50 .33 .67 13,500 .186 .125
Demand
capacity Expected
Baked 2.00 1.00 .50 .50 14,000 .193 .096 demand
potato
$72,500 1.000 .469
Figure S7.6
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Reducing Risk with Reducing Risk with
Incremental Changes Incremental Changes
(a) Leading demand with incremental (b) Capacity lags demand with incremental
expansion expansion
New New
capacity capacity
Expected
Demand
Demand
Expected demand
demand
1 2 3 1 2 3
Time (years) Time (years)
Figure S7.6 Figure S7.6
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10. 10/16/2010
Reducing Risk with Expected Monetary Value
Incremental Changes (EMV) and Capacity Decisions
(c) Attempts to have an average capacity
with incremental expansion
Determine states of nature
New
capacity Future demand
Expected
Market favorability
Demand
demand
Analyzed using decision trees
Hospital supply company
Four alternatives
1 2 3
Time (years)
Figure S7.6
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Expected Monetary Value Expected Monetary Value
(EMV) and Capacity Decisions (EMV) and Capacity Decisions
Market favorable (.4) Market favorable (.4)
$100,000 $100,000
Market unfavorable (.6) Market unfavorable (.6)
-$90,000 -$90,000
Market favorable ( 4)
(.4) Market favorable ( 4)
(.4)
$60,000 $60,000
Medium plant Medium plant
Market unfavorable (.6)
-$10,000 Large Plant Market unfavorable (.6)
-$10,000
Market favorable (.4)
EMV = (.4)($100,000) Market favorable (.4)
$40,000 + (.6)(-$90,000) $40,000
Market unfavorable (.6) Market unfavorable (.6)
-$5,000 EMV = -$14,000 -$5,000
$0 $0
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Expected Monetary Value
(EMV) and Capacity Decisions Strategy-
Strategy-Driven Investment
-$14,000
Market favorable (.4)
$100,000
Market unfavorable (.6)
-$90,000
Operations may be responsible
$18,000 for return-on-investment (ROI)
Market favorable ( 4)
(.4)
Medium plant
$60,000
Analyzing capacity alternatives
Market unfavorable (.6)
-$10,000 should include capital
$13,000
Market favorable (.4)
investment, variable cost, cash
$40,000
flows, and net present value
Market unfavorable (.6)
-$5,000
$0
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11. 10/16/2010
Net Present Value (NPV) Net Present Value (NPV)
In general: In general:
F = P(1 + i)N F = P(1 + i)N
where F = future value where F = future value
P = present value
t l P While this works
= present value
t l
i = interest rate i fine, it is
= interest rate
N = number of years N cumbersome for
= number of years
larger values of N
Solving for P: Solving for P:
F F
P= P=
(1 + i)N (1 + i)N
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NPV Using Factors Present Value of an Annuity
F
P=
(1 + i)N
= FX An annuity is an investment which
generates uniform equal payments
where X = a factor from Table S7.1
defined as = 1/(1 + i)N and S = RX
F = future value
where X = factor from Table S7.2
Year 6% 8% 10% 12% 14% S = present value of a series of
1 .943 .926 .909 .893 .877 uniform annual receipts
Portion of
Table S7.1 2 .890 .857 .826 .797 .769 R = receipts that are received every
3 .840 .794 .751 .712 .675 year of the life of the investment
4 .792 .735 .683 .636 .592
5 .747 .681 .621 .567 .519
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Present Value of an Annuity Present Value of an Annuity
Portion of Table S7.2
$7,000 in receipts per for 5 years
Interest rate = 6%
Year 6% 8% 10% 12% 14%
1 .943 .926 .909 .893 .877
2 1.833
1 833 1.783
1 783 1.736
1 736 1.690
1 690 1.647
1 647
From Table S7.2
3 2.676 2.577 2.487 2.402 2.322 X = 4.212
4 3.465 3.312 3.170 3.037 2.914
5 4.212 3.993 3.791 3.605 3.433 S = RX
S = $7,000(4.212) = $29,484
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12. 10/16/2010
Limitations
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 All rights reserved. No part of this publication may be reproduced, stored in a retrieval
3. Assumes we know future interest rates system, or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording, or otherwise, without the prior written permission of the publisher.
Printed in the United States of America.
4. Payments are not always made at the
end of a period
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