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© 2011 Pearson Education, Inc. publishing as Prentice Hall F - 1
F Simulation
PowerPoint presentation to accompany
PowerPoint presentation to accompany
Heizer and Render
Heizer and Render
Operations Management, 10e
Operations Management, 10e
Principles of Operations Management, 8e
Principles of Operations Management, 8e
PowerPoint slides by Jeff Heyl
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 2
Outline
Outline
 What Is Simulation?
 Advantages and Disadvantages of
Simulation
 Monte Carlo Simulation
 Simulation of A Queuing Problem
 Simulation and Inventory Analysis
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 3
Learning Objectives
Learning Objectives
When you complete this module you
When you complete this module you
should be able to:
should be able to:
1. List the advantages and disadvantages
of modeling with simulation
2. Perform the five steps in a Monte Carlo
simulation
3. Simulate a queuing problem
4. Simulate an inventory problem
5. Use Excel spreadsheets to create a
simulation
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 4
Computer Analysis
Computer Analysis
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 5
What is Simulation?
What is Simulation?
 An attempt to duplicate the features,
appearance, and characteristics of a
real system
1. To imitate a real-world situation
mathematically
2. To study its properties and operating
characteristics
3. To draw conclusions and make action
decisions based on the results of the
simulation
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 6
Simulation Applications
Simulation Applications
Ambulance location and
dispatching
Assembly-line balancing
Parking lot and harbor design
Distribution system design
Scheduling aircraft
Labor-hiring decisions
Personnel scheduling
Traffic-light timing
Voting pattern prediction
Bus scheduling
Design of library operations
Taxi, truck, and railroad
dispatching
Production facility scheduling
Plant layout
Capital investments
Production scheduling
Sales forecasting
Inventory planning and control
Table F.1
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 7
What Is Simulation?
What Is Simulation?
1. Define the problem
2. Introduce the important variables associated
with the problem
3. Construct a numerical model
4. Set up possible courses of action for testing by
specifying values of variables
5. Run the experiment
6. Consider the results (possibly modifying the
model or changing data inputs)
7. Decide what course of action to take
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 8
Select best course
Examine results
Conduct simulation
Specify values
of variables
Construct model
Introduce variables
The
The
Process of
Process of
Simulation
Simulation
Figure F.1
Define problem
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 9
Advantages of Simulation
Advantages of Simulation
1. Relatively straightforward and flexible
2. Can be used to analyze large and
complex real-world situations that
cannot be solved by conventional
models
3. Real-world complications can be
included that most OM models cannot
permit
4. “Time compression” is possible
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 10
Advantages of Simulation
Advantages of Simulation
5. Allows “what-if” types of questions
6. Does not interfere with real-world
systems
7. Can study the interactive effects of
individual components or variables in
order to determine which ones are
important
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 11
Disadvantages of Simulation
Disadvantages of Simulation
1. Can be very expensive and may take
months to develop
2. It is a trial-and-error approach that may
produce different solutions in repeated
runs
3. Managers must generate all of the
conditions and constraints for
solutions they want to examine
4. Each simulation model is unique
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 12
Monte Carlo Simulation
Monte Carlo Simulation
The Monte Carlo method may be used
when the model contains elements that
exhibit chance in their behavior
1. Set up probability distributions for important
variables
2. Build a cumulative probability distribution for
each variable
3. Establish an interval of random numbers for
each variable
4. Generate random numbers
5. Simulate a series of trials
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 13
Probability of Demand
Probability of Demand
(1) (2) (3) (4)
Demand
for Tires Frequency
Probability of
Occurrence
Cumulative
Probability
0 10 10/200 = .05 .05
1 20 20/200 = .10 .15
2 40 40/200 = .20 .35
3 60 60/200 = .30 .65
4 40 40/200 = .20 .85
5 30 30/ 200 = .15 1.00
200 days 200/200 = 1.00
Table F.2
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 14
Assignment of Random
Assignment of Random
Numbers
Numbers
Daily
Demand Probability
Cumulative
Probability
Interval of
Random
Numbers
0 .05 .05 01 through 05
1 .10 .15 06 through 15
2 .20 .35 16 through 35
3 .30 .65 36 through 65
4 .20 .85 66 through 85
5 .15 1.00 86 through 00
Table F.3
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 15
Table of Random Numbers
Table of Random Numbers
52 50 60 52 05
37 27 80 69 34
82 45 53 33 55
69 81 69 32 09
98 66 37 30 77
96 74 06 48 08
33 30 63 88 45
50 59 57 14 84
88 67 02 02 84
90 60 94 83 77
Table F.4
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 16
Simulation Example 1
Simulation Example 1
Select random
numbers from
Table F.3
Day
Number
Random
Number
Simulated
Daily Demand
1 52 3
2 37 3
3 82 4
4 69 4
5 98 5
6 96 5
7 33 2
8 50 3
9 88 5
10 90 5
39 Total
3.9 Average
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 17
Simulation Example 1
Simulation Example 1
Day
Number
Random
Number
Simulated
Daily Demand
1 52 3
2 37 3
3 82 4
4 69 4
5 98 5
6 96 5
7 33 2
8 50 3
9 88 5
10 90 5
39 Total
3.9 Average
Expected
demand = ∑ (probability of i units) x
(demand of i units)
= (.05)(0) + (.10)(1) + (.20)(2)
+ (.30)(3) + (.20)(4) + (.15)
(5)
= 0 + .1 + .4 + .9 + .8 + .75
= 2.95 tires
5
i =1
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 18
Queuing Simulation
Queuing Simulation
Number
of Arrivals Probability
Cumulative
Probability
Random-Number
Interval
0 .13 .13 01 through 13
1 .17 .30 14 through 30
2 .15 .45 31 through 45
3 .25 .70 46 through 70
4 .20 .90 71 through 90
5 .10 1.00 91 through 00
1.00
Overnight barge arrival rates
Table F.5
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 19
Queuing Simulation
Queuing Simulation
Daily
Unloading
Rates Probability
Cumulative
Probability
Random-Number
Interval
1 .05 .05 01 through 05
2 .15 .20 06 through 20
3 .50 .70 21 through 70
4 .20 .90 71 through 90
5 .10 1.00 91 through 00
1.00
Barge unloading rates
Table F.6
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 20
Queuing Simulation
Queuing Simulation
(1)
Day
(2)
Number
Delayed from
Previous Day
(3)
Random
Number
(4)
Number
of Nightly
Arrivals
(5)
Total
to Be
Unloaded
(6)
Random
Number
(7)
Number
Unloaded
1 0 52 3 3 37 3
2 0 06 0 0 63 0
3 0 50 3 3 28 3
4 0 88 4 4 02 1
5 3 53 3 6 74 4
6 2 30 1 3 35 3
7 0 10 0 0 24 0
8 0 47 3 3 03 1
9 2 99 5 7 29 3
10 4 37 2 6 60 3
11 3 66 3 6 74 4
12 2 91 5 7 85 4
13 3 35 2 5 90 4
14 1 32 2 3 73 3
15 0 00 5 5 59 3
20 41 39
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 21
Queuing Simulation
Queuing Simulation
Average number of barges
delayed to the next day =
= 1.33 barges delayed per day
20 delays
15 days
Average number of
nightly arrivals =
= 2.73 arrivals per night
41 arrivals
15 days
Average number of barges
unloaded each day =
= 2.60 unloadings per day
39 unloadings
15 days
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 22
Inventory Simulation
Inventory Simulation
(1)
Demand for
Ace Drill
(2)
Frequency
(3)
Probability
(4)
Cumulative
Probability
(5)
Interval of
Random Numbers
0 15 .05 .05 01 through 05
1 30 .10 .15 06 through 15
2 60 .20 .35 16 through 35
3 120 .40 .75 36 through 75
4 45 .15 .90 76 through 90
5 30 .10 1.00 91 through 00
300 1.00
Table F.8
Daily demand for Ace Drill
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 23
Inventory Simulation
Inventory Simulation
(1)
Demand for
Ace Drill
(2)
Frequency
(3)
Probability
(4)
Cumulative
Probability
(5)
Interval of
Random Numbers
1 10 .20 .20 01 through 20
2 25 .50 .70 21 through 70
3 15 .30 1.00 71 through 00
50 1.00
Table F.9
Reorder lead time
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 24
Inventory Simulation
Inventory Simulation
1. Begin each simulation day by checking to see if
ordered inventory has arrived. If it has, increase
current inventory by the quantity ordered.
2. Generate daily demand using probability
distribution and random numbers.
3. Compute ending inventory. If on-hand is
insufficient to meet demand, satisfy as much as
possible and note lost sales.
4. Determine whether the day's ending inventory has
reached the reorder point. If it has, and there are
no outstanding orders, place an order. Choose
lead time using probability distribution and
random numbers.
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 25
Inventory Simulation
Inventory Simulation
(1)
Day
(2)
Units
Received
(3)
Beginning
Inventory
(4)
Random
Number
(5)
Demand
(6)
Ending
Inventory
(7)
Lost
Sales
(8)
Order?
(9)
Random
Number
(10)
Lead
Time
1 10 06 1 9 0 No
2 0 9 63 3 6 0 No
3 0 6 57 3 3 0 Yes 02 1
4 0 3 94 5 0 2 No
5 10 10 52 3 7 0 No
6 0 7 69 3 4 0 Yes 33 2
7 0 4 32 2 2 0 No
8 0 2 30 2 0 0 No
9 10 10 48 3 7 0 No
10 0 7 88 4 3 0 Yes 14 1
41 2
Table F.10
Order quantity = 10 units Reorder point = 5 units
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 26
Inventory Simulation
Inventory Simulation
Average ending inventory = = 4.1 units/day
41 total units
10 days
Average lost sales = = .2 unit/day
2 sales lost
10 days
= = .3 order/day
3 orders
10 days
Average number
of orders placed
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 27
Inventory Simulation
Inventory Simulation
Daily order cost = (cost of placing
1 order) x
(number of orders placed per day)
= $10 per order
x .3 order per day = $3
Daily holding cost = (cost of holding 1
unit for 1 day) x
(average ending inventory)
= 50¢ per unit per
day x 4.1 units per day
= $2.05
Daily stockout cost= (cost per lost
sale) x
(average number of lost sales per day)
= $8 per lost sale
x .2 lost sales per day
= $1.60
Total daily inventory cost= Daily
order cost + Daily holding cost +
Daily stockout cost
=
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 28
Using Software in Simulation
Using Software in Simulation
 Computers are critical in simulating
complex tasks
 General-purpose languages - BASIC, C++
 Special-purpose simulation languages -
GPSS, SIMSCRIPT
1. Require less programming time for large
simulations
2. Usually more efficient and easier to check
for errors
3. Random-number generators are built in
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 29
Using Software in Simulation
Using Software in Simulation
 Commercial simulation programs are
available for many applications - Extend,
Modsim, Witness, MAP/1, Enterprise
Dynamics, Simfactory, ProModel, Micro
Saint, ARENA
 Spreadsheets such as Excel can be used
to develop some simulations
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 30
Using Software in Simulation
Using Software in Simulation
© 2011 Pearson Education, Inc. publishing as Prentice Hall
F - 31
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
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.

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Jay heizer operation management 10 modF.ppt

  • 1. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 1 F Simulation PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer and Render Heizer and Render Operations Management, 10e Operations Management, 10e Principles of Operations Management, 8e Principles of Operations Management, 8e PowerPoint slides by Jeff Heyl
  • 2. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 2 Outline Outline  What Is Simulation?  Advantages and Disadvantages of Simulation  Monte Carlo Simulation  Simulation of A Queuing Problem  Simulation and Inventory Analysis
  • 3. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 3 Learning Objectives Learning Objectives When you complete this module you When you complete this module you should be able to: should be able to: 1. List the advantages and disadvantages of modeling with simulation 2. Perform the five steps in a Monte Carlo simulation 3. Simulate a queuing problem 4. Simulate an inventory problem 5. Use Excel spreadsheets to create a simulation
  • 4. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 4 Computer Analysis Computer Analysis
  • 5. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 5 What is Simulation? What is Simulation?  An attempt to duplicate the features, appearance, and characteristics of a real system 1. To imitate a real-world situation mathematically 2. To study its properties and operating characteristics 3. To draw conclusions and make action decisions based on the results of the simulation
  • 6. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 6 Simulation Applications Simulation Applications Ambulance location and dispatching Assembly-line balancing Parking lot and harbor design Distribution system design Scheduling aircraft Labor-hiring decisions Personnel scheduling Traffic-light timing Voting pattern prediction Bus scheduling Design of library operations Taxi, truck, and railroad dispatching Production facility scheduling Plant layout Capital investments Production scheduling Sales forecasting Inventory planning and control Table F.1
  • 7. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 7 What Is Simulation? What Is Simulation? 1. Define the problem 2. Introduce the important variables associated with the problem 3. Construct a numerical model 4. Set up possible courses of action for testing by specifying values of variables 5. Run the experiment 6. Consider the results (possibly modifying the model or changing data inputs) 7. Decide what course of action to take
  • 8. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 8 Select best course Examine results Conduct simulation Specify values of variables Construct model Introduce variables The The Process of Process of Simulation Simulation Figure F.1 Define problem
  • 9. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 9 Advantages of Simulation Advantages of Simulation 1. Relatively straightforward and flexible 2. Can be used to analyze large and complex real-world situations that cannot be solved by conventional models 3. Real-world complications can be included that most OM models cannot permit 4. “Time compression” is possible
  • 10. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 10 Advantages of Simulation Advantages of Simulation 5. Allows “what-if” types of questions 6. Does not interfere with real-world systems 7. Can study the interactive effects of individual components or variables in order to determine which ones are important
  • 11. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 11 Disadvantages of Simulation Disadvantages of Simulation 1. Can be very expensive and may take months to develop 2. It is a trial-and-error approach that may produce different solutions in repeated runs 3. Managers must generate all of the conditions and constraints for solutions they want to examine 4. Each simulation model is unique
  • 12. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 12 Monte Carlo Simulation Monte Carlo Simulation The Monte Carlo method may be used when the model contains elements that exhibit chance in their behavior 1. Set up probability distributions for important variables 2. Build a cumulative probability distribution for each variable 3. Establish an interval of random numbers for each variable 4. Generate random numbers 5. Simulate a series of trials
  • 13. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 13 Probability of Demand Probability of Demand (1) (2) (3) (4) Demand for Tires Frequency Probability of Occurrence Cumulative Probability 0 10 10/200 = .05 .05 1 20 20/200 = .10 .15 2 40 40/200 = .20 .35 3 60 60/200 = .30 .65 4 40 40/200 = .20 .85 5 30 30/ 200 = .15 1.00 200 days 200/200 = 1.00 Table F.2
  • 14. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 14 Assignment of Random Assignment of Random Numbers Numbers Daily Demand Probability Cumulative Probability Interval of Random Numbers 0 .05 .05 01 through 05 1 .10 .15 06 through 15 2 .20 .35 16 through 35 3 .30 .65 36 through 65 4 .20 .85 66 through 85 5 .15 1.00 86 through 00 Table F.3
  • 15. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 15 Table of Random Numbers Table of Random Numbers 52 50 60 52 05 37 27 80 69 34 82 45 53 33 55 69 81 69 32 09 98 66 37 30 77 96 74 06 48 08 33 30 63 88 45 50 59 57 14 84 88 67 02 02 84 90 60 94 83 77 Table F.4
  • 16. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 16 Simulation Example 1 Simulation Example 1 Select random numbers from Table F.3 Day Number Random Number Simulated Daily Demand 1 52 3 2 37 3 3 82 4 4 69 4 5 98 5 6 96 5 7 33 2 8 50 3 9 88 5 10 90 5 39 Total 3.9 Average
  • 17. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 17 Simulation Example 1 Simulation Example 1 Day Number Random Number Simulated Daily Demand 1 52 3 2 37 3 3 82 4 4 69 4 5 98 5 6 96 5 7 33 2 8 50 3 9 88 5 10 90 5 39 Total 3.9 Average Expected demand = ∑ (probability of i units) x (demand of i units) = (.05)(0) + (.10)(1) + (.20)(2) + (.30)(3) + (.20)(4) + (.15) (5) = 0 + .1 + .4 + .9 + .8 + .75 = 2.95 tires 5 i =1
  • 18. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 18 Queuing Simulation Queuing Simulation Number of Arrivals Probability Cumulative Probability Random-Number Interval 0 .13 .13 01 through 13 1 .17 .30 14 through 30 2 .15 .45 31 through 45 3 .25 .70 46 through 70 4 .20 .90 71 through 90 5 .10 1.00 91 through 00 1.00 Overnight barge arrival rates Table F.5
  • 19. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 19 Queuing Simulation Queuing Simulation Daily Unloading Rates Probability Cumulative Probability Random-Number Interval 1 .05 .05 01 through 05 2 .15 .20 06 through 20 3 .50 .70 21 through 70 4 .20 .90 71 through 90 5 .10 1.00 91 through 00 1.00 Barge unloading rates Table F.6
  • 20. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 20 Queuing Simulation Queuing Simulation (1) Day (2) Number Delayed from Previous Day (3) Random Number (4) Number of Nightly Arrivals (5) Total to Be Unloaded (6) Random Number (7) Number Unloaded 1 0 52 3 3 37 3 2 0 06 0 0 63 0 3 0 50 3 3 28 3 4 0 88 4 4 02 1 5 3 53 3 6 74 4 6 2 30 1 3 35 3 7 0 10 0 0 24 0 8 0 47 3 3 03 1 9 2 99 5 7 29 3 10 4 37 2 6 60 3 11 3 66 3 6 74 4 12 2 91 5 7 85 4 13 3 35 2 5 90 4 14 1 32 2 3 73 3 15 0 00 5 5 59 3 20 41 39
  • 21. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 21 Queuing Simulation Queuing Simulation Average number of barges delayed to the next day = = 1.33 barges delayed per day 20 delays 15 days Average number of nightly arrivals = = 2.73 arrivals per night 41 arrivals 15 days Average number of barges unloaded each day = = 2.60 unloadings per day 39 unloadings 15 days
  • 22. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 22 Inventory Simulation Inventory Simulation (1) Demand for Ace Drill (2) Frequency (3) Probability (4) Cumulative Probability (5) Interval of Random Numbers 0 15 .05 .05 01 through 05 1 30 .10 .15 06 through 15 2 60 .20 .35 16 through 35 3 120 .40 .75 36 through 75 4 45 .15 .90 76 through 90 5 30 .10 1.00 91 through 00 300 1.00 Table F.8 Daily demand for Ace Drill
  • 23. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 23 Inventory Simulation Inventory Simulation (1) Demand for Ace Drill (2) Frequency (3) Probability (4) Cumulative Probability (5) Interval of Random Numbers 1 10 .20 .20 01 through 20 2 25 .50 .70 21 through 70 3 15 .30 1.00 71 through 00 50 1.00 Table F.9 Reorder lead time
  • 24. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 24 Inventory Simulation Inventory Simulation 1. Begin each simulation day by checking to see if ordered inventory has arrived. If it has, increase current inventory by the quantity ordered. 2. Generate daily demand using probability distribution and random numbers. 3. Compute ending inventory. If on-hand is insufficient to meet demand, satisfy as much as possible and note lost sales. 4. Determine whether the day's ending inventory has reached the reorder point. If it has, and there are no outstanding orders, place an order. Choose lead time using probability distribution and random numbers.
  • 25. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 25 Inventory Simulation Inventory Simulation (1) Day (2) Units Received (3) Beginning Inventory (4) Random Number (5) Demand (6) Ending Inventory (7) Lost Sales (8) Order? (9) Random Number (10) Lead Time 1 10 06 1 9 0 No 2 0 9 63 3 6 0 No 3 0 6 57 3 3 0 Yes 02 1 4 0 3 94 5 0 2 No 5 10 10 52 3 7 0 No 6 0 7 69 3 4 0 Yes 33 2 7 0 4 32 2 2 0 No 8 0 2 30 2 0 0 No 9 10 10 48 3 7 0 No 10 0 7 88 4 3 0 Yes 14 1 41 2 Table F.10 Order quantity = 10 units Reorder point = 5 units
  • 26. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 26 Inventory Simulation Inventory Simulation Average ending inventory = = 4.1 units/day 41 total units 10 days Average lost sales = = .2 unit/day 2 sales lost 10 days = = .3 order/day 3 orders 10 days Average number of orders placed
  • 27. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 27 Inventory Simulation Inventory Simulation Daily order cost = (cost of placing 1 order) x (number of orders placed per day) = $10 per order x .3 order per day = $3 Daily holding cost = (cost of holding 1 unit for 1 day) x (average ending inventory) = 50¢ per unit per day x 4.1 units per day = $2.05 Daily stockout cost= (cost per lost sale) x (average number of lost sales per day) = $8 per lost sale x .2 lost sales per day = $1.60 Total daily inventory cost= Daily order cost + Daily holding cost + Daily stockout cost =
  • 28. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 28 Using Software in Simulation Using Software in Simulation  Computers are critical in simulating complex tasks  General-purpose languages - BASIC, C++  Special-purpose simulation languages - GPSS, SIMSCRIPT 1. Require less programming time for large simulations 2. Usually more efficient and easier to check for errors 3. Random-number generators are built in
  • 29. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 29 Using Software in Simulation Using Software in Simulation  Commercial simulation programs are available for many applications - Extend, Modsim, Witness, MAP/1, Enterprise Dynamics, Simfactory, ProModel, Micro Saint, ARENA  Spreadsheets such as Excel can be used to develop some simulations
  • 30. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 30 Using Software in Simulation Using Software in Simulation
  • 31. © 2011 Pearson Education, Inc. publishing as Prentice Hall F - 31 All rights reserved. No part of this publication may be reproduced, stored in a retrieval 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.

Editor's Notes

  • #4: This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity.
  • #5: This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity.
  • #6: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #8: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #9: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #10: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #11: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #12: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #13: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #14: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #15: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #16: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #17: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #18: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #19: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #20: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #21: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #22: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #23: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #24: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #25: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #26: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #27: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #28: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #29: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.
  • #30: This slide can be used to frame a discussion of capacity. Points to be made might include: - capacity definition and measurement is necessary if we are to develop a production schedule - while a process may have “maximum” capacity, many factors prevent us from achieving that capacity on a continuous basis. Students should be asked to suggest factors which might prevent one from achieving maximum capacity.