Production and operations analysis:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
McGraw-Hill
2009
|
| Ausgabe: | 6. ed., international ed. |
| Schriftenreihe: | The McGraw-Hill/Irwin series operations and decision sciences
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XXVI, 789 S. graph. Darst. |
| ISBN: | 9780071263702 |
Internformat
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| 264 | 1 | |a Boston [u.a.] |b McGraw-Hill |c 2009 | |
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Datensatz im Suchindex
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| DE-BY-FWS_media_number | 083000515794 |
| _version_ | 1806176201432104960 |
| adam_text | Contents
About the Author
xv
Preface
xvi
Introduction
xvii
Chapter
1
Strategy and Competition
1
Chapter Overview
1
Snapshot Application: Apple Adopts a
New Business Strategy and Shifts Its
Core Competency from Computers
to Portable Music
3
1.1
Manufacturing Matters
5
Manufacturing Jobs Outlook
6
1.2
A Framework for Operations Strategy
7
Strategic Dimensions
8
1.3
The Classical View of Operations Strategy
5
Time Horizon
9
Focus
11
Evaluation
12
Consistency
12
1.4
Competing in the Global Marketplace
14
Problems for Sections
1.1-1,4 16
Snapshot Application: Global Manufacturing
Strategies in the Automobile Industry
17
1.5
Strategic Initiatives:
Reengineering
the
Business Process
18
1.6
Strategic Initiatives: Just-in-Time
21
1.7
Strategic Initiatives: Time-Based
Competition
23
1.8
Strategic Initiatives: Competing on
Quality
24
Problems for Sections
1.5-1.8 26
1.9
Matching Process and Product
Life Cycles
27
The Product Life Cycle
2 7
The Process Life Cycle
28
The Product-Process Matrix
29
Problems for Section
1.9 31
1.10
Learning and Experience Curves
31
Learning Curves
32
Experience Curves
34
Learning and Experience Curves and
Manufacturing Strategy
36
Problems for Section
1.10 36
50
1.11
Capacity Growth Planning: A Long-Term
Strategic Problem
38
Economies of Scale and Economies of
Scope
38
Make or Buy: A Prototype Capacity
Expansion Problem
39
Dynamic Capacity Expansion Policy
40
Issues in Plant Location
44
Problems for Section
1.11 46
1.12
Summary
47
Additional Problems for Chapter
1 48
Appendix
1-А
Present Worth Calculations
Bibliography
51
Chapter
2
Forecasting
52
Chapter Overview
52
2.1
The Time Horizon in Forecasting
55
2.2
Characteristics of Forecasts
56
2.3
Subjective Forecasting Methods
56
2.4
Objective Forecasting Methods
57
Causal Models
57
Time Series Methods
58
Snapshot Application: Advanced Forecasting,
Inc., Serves the Semiconductor Industry
59
Problems for Sections
2.1-2.4 59
2.5
Notation Conventions
61
2.6
Evaluating Forecasts
61
Problems for Section
2.6 63
2.7
Methods for Forecasting Stationary Series
64
Moving Averages
64
Problems on Moving Averages
67
Exponential Smoothing
67
Multiple-Step-Ahead Forecasts
71
Comparison of Exponential Smoothing and
Moving Averages
72
Problems for Section
2.7 73
Snapshot Application: Sport Obermeyer Slashes
Costs with Improved Forecasting
74
2.8
Trend-Based Methods
75
Regression Analysis
75
Problems for Section
2.8 76
Double Exponential Smoothing Using Holt
s
Method
77
More Problems for Section
2.8 78
viii
Contents
2.9
Methods for Seasonal Series
79
Seasonal Factors for Stationary Series
79
Seasonal Decomposition Using Moving
Averages
81
Problems for Section
2.9 83
Winters
s
Method for Seasonal Problems
84
More Problems for Section
2.9 89
2.10
Box-Jenkins Models
89
Estimating the Autocorrelation Function
90
The
Autoregressive
Process
93
The Moving-Average Process
94
Mixtures:
ARMA
Models
96
ARMA
Models
96
Using
A RIMA
Models for Forecasting
98
Summary of the Steps Required for Building
ARIMA Models
99
Case Study: Using Box-Jenkins Methodology to
Predict Monthly International Airline
Passenger Totals
100
Snapshot Application: A Simple ARIMA Model
Predicts the Performance of the
U.S. Economy
104
Box-Jenkins Modeling
—
A Critique
104
Problems for Section
2.10 104
2.11
Practical Considerations
105
Model Identification and Monitoring
105
Simple versus Complex Time Series
Methods
106
2.12
Overview of Advanced Topics in
Forecasting
107
Simulation as a Forecasting Tool
107
Forecasting Demand in the Presence of
Lost Sales
108
2.13
Linking Forecasting and Inventory
Management
110
Snapshot Application: Predicting Economic
Recessions 111
2.14
Historical Notes and Additional
Topics
112
2.15
Summary
113
Additional Problems on Forecasting
113
Appendix
2-А
Forecast Errors for
Moving Averages and Exponential
Smoothing
118
Appendix 2-B Derivation of the Equations
for the Slope and Intercept for Regression
Analysis
120
Appendix 2-C Glossary of Notation
for Chapter
2 122
Bibliography
122
Chapter
3
Aggregate Planning
124
Chapter Overview
124
3.1
Aggregate Units of Production
127
3.2
Overview of the Aggregate Planning
Problem
128
3.3
Costs in Aggregate Planning
130
Problems for Sections
3.1-3.3 132
3.4
A Prototype Problem
133
Evaluation of a Chase Strategy
(Zero Inventory Plan)
135
Evaluation of the Constant Workforce Plan
¡36
Mixed Strategies and Additional Constraints
138
Problems for Section
3.4 139
3.5
Solution of Aggregate Planning Problems
by Linear Programming
141
Cost Parameters and Given Information
141
Problem Variables
142
Problem Constraints
142
Rounding the Variables
143
Extensions
144
Other Solution Methods
146
3.6
Solving Aggregate Planning Problems by
Linear Programming: An Example
147
Problems for Sections
3.5
and
3.6 149
3.7
The Linear Decision Rule
152
3.8
Modeling Management Behavior
153
Problems for Sections
3.7
and
3.8 155
3.9
Disaggregating Aggregate Plans
155
Snapshot Application: Welch s Uses Aggregate
Planning for Production Scheduling
157
Problems for Section
3.9 158
3.10
Production Planning on a Global Scale
158
3.11
Practical Considerations
159
3.12
Historical Notes
160
3.13
Summary
161
Additional Problems on Aggregate
Planning
162
Appendix 3-A Glossary of Notation
for Chapter
3 167
Bibliography
168
Supplement
1
Linear Programming
169
S
1.1
Introduction
169
51
.2
A Prototype Linear Programming
Problem
169
5
1.3
Statement of the General Problem
171
Definitions of Commonly Used Terms
172
Features of Linear Programs
173
Contents ix
51.4
Solving
Linear Programming Problems
Graphically
174
Graphing
Linear
Inequalities 1
74
Graphing the Feasible Region
176
Finding the Optimal Solution.
177
Identifying the Optimal Solution Directly
by Graphical Means
179
51.5 The Simplex Method: An Overview
180
51
.6
Solving Linear Programming Problems
with Excel
181
Entering Large Problems Efficiently
185
51.7 Interpreting the Sensitivity Report
187
Shadow Prices
187
Objective Function Coefficients and Right-
Hand Sides
188
Adding a New Variable
188
Using Sensitivity Analysis
189
51.8 Recognizing Special Problems
191
Unbounded Solutions
191
Empty Feasible Region
192
Degeneracy
194
Multiple Optimal Solutions
194
Redundant Constraints
194
5
1.9
The Application of Linear Programming
to Production and Operations Analysis
195
Bibliography
197
Chapter
4
Inventory Control Subject to Known
Demand
198
Chapter Overview
198
4.1
Types of Inventories
201
4.2
Motivation for Holding Inventories
202
4.3
Characteristics of Inventory Systems
203
4.4
Relevant Costs
204
Holding Cost
204
Order Cost
206
Penalty Cost
207
Problems for Sections
4.1-4.4 208
4.5
The EOQ Model
210
The Basic Model
210
Inclusion of Order Lead Time
213
Sensitivity
214
EOQ and JIT
215
Problems for Section
4.5 216
4.6
Extension to a Finite Production Rate
218
Problems for Section
4.6 219
4.7
Quantity Discount Models
220
Optimal Policy for All-Units Discount Schedule
221
Summary of the Solution Technique for
All-Units Discounts
223
Incremental Quantity Discounts
223
Summary of the Solution Technique
for Incremental Discounts
225
Other Discount Schedules
225
Problems for Section
4.7 226
*4.8 Resource-Constrained Multiple
Product Systems
227
Problems for
Section
4.8 230
4.9
EOQ Models for Production Planning
230
Problems for Section
4.9 234
4.10
Power-of-Two Policies
235
4.11
Historical Notes and Additional Topics
237
Snapshot Application: Mervyn s Recognized
for State-of-the-Art Inventory
Control System
238
4.12
Summary
239
Additional Problems on Deterministic
Inventory Models
240
Appendix
4-А
Mathematical Derivations for
Multiproduct Constrained EOQ Systems
244
Appendix 4-B Glossary of Notation for
Chapter
4 246
Bibliography
246
Chapter
5
Inventory Control Subject to Uncertain
Demand
248
Chapter Overview
248
Overview of Models Treated in This
Chapter
252
5.1
The Nature of Randomness
253
5.2
Optimization Criterion
255
Problems for Sections
5.1
and
5.2 256
5.3
The Newsboy Model
257
Notation
257
Development of the Cost Function
258
Determining the Optimal Policy
259
Optimal Policy for Discrete Demand
261
Extension to Include Starting Inventory
261
Snapshot Application: Using Inventory
Models to Manage the Seed-Corn Supply
Chain at Syngenta
262
Extension to Multiple Planning Periods
263
Problems for Section
5.3 264
5.4
Lot Size-Reorder Point Systems
266
Describing Demand
267
Decision Variables
267
χ
Contents
Derivation of the Expected Cost Function
267
The Cost Function
269
Inventory Level versus Inventory
Position
271
5.5
Service Levels in (Q, R) Systems
272
Type
1
Service
272
Type
2
Service
273
Optimal (Q, R) Policies Subject to
Type
2
Constraint
274
Imputed Shortage Cost
275
Scaling of Lead Time Demand
276
Estimating Sigma When Inventory Control
and Forecasting A re Linked
2 76
*
Lead Time Variability
277
Calculations in Excel
278
Negative Safety Stock
278
Problems for Sections
5.4
and
5.5 279
5.6
Additional Discussion of Periodic-Review
Systems
281
(s, S)
Policies
281
*Service Levels in Periodic-Review
Systems
281
Problems for Section
5.6 282
Snapshot Application: Tropicana Uses
Sophisticated Modeling for Inventory
Management
283
5.7
Multiproduct Systems
283
ABCAnalysis
283
Exchange Curves
285
Problems for Section
5.7 288
*5.8 Overview of Advanced Topics
289
Multi-echelon Systems
289
Perishable Inventory Problems
290
Snapshot Application: Triad s Inventory
Systems Meet Markets
Needs
291
5.9
Historical Notes and Additional
Readings
292
5.10
Summary
293
Additional Problems on Stochastic
Inventory Models
294
Appendix
5-А
Notational Conventions and
Probability Review
300
Appendix 5-B Additional Results and
Extensions for the Newsboy Model
301
Appendix 5-C Derivation of the Optimal
(Q,R) Policy
304
Appendix 5-D Probability Distributions for
Inventory Management
304
Appendix
5-Е
Glossary of Notation for
Chapter
5 308
Bibliography
309
Chapter
6
Supply Chain Management
311
Chapter Overview
311
The Supply Chain as a Strategic
Weapon
315
Snapshot Application: Wal-Mart Wins with Solid
Supply Chain Management
316
6.1
The Transportation Problem
316
The Greedy Heuristic
319
6.2
Solving Transportation Problems with Linear
Programming
320
6.3
Generalizations of the Transportation
Problem
322
Infeasible Routes
323
Unbalanced Problems
323
6.4
More General Network Formulations
324
Problems for Sections
6.1-6.4 327
Snapshot Application: IBM Streamlines
Its Supply Chain for Spare Parts Using
Sophisticated Mathematical Models
328
6.5
Distribution Resource Planning
330
Problems for Section
6.5 332
6.6
Determining Delivery Routes in
Supply Chains
332
Practical Issues in Vehicle Scheduling
336
Snapshot Application: Air Products Saves Big
with Routing and Scheduling Optimizer
337
Problems for Section
6.6 337
6.7
Designing Products for Supply Chain
Efficiency
338
Postponement in Supply Chains
339
Additional Issues in Supply Chain
Design
340
Snapshot Application: Dell Computer Designs
the Ultimate Supply Chain
342
Problems for Section
6.7 342
6.8
The Role
ofinformation
in the Supply
Chain
343
The Bullwhip Effect
344
Snapshot Application: Saturn Emerges as
an Industry Leader with Scientific Supply
Chain Management
347
Electronic Commerce
347
Electronic Data Interchange
348
Web-Based Transactions Systems
349
RFID Technology Provides Faster
Product Flow
350
Problems for Section
6.8 351
6.9
Multilevel Distribution Systems
351
Problems for Section
6.9 354
Contents xi
6.10
Designing
the Supply Chain in a Global
Environment
355
Snapshot Application: Norwegian Company
implements Decision Support System to
Streamline Its Supply Chain
356
Snapshot Application: Timken Battles Imports
with Bundling
358
Supply Chain Management in a Global
Environment
359
Snapshot Application: Digital Equipment
Corporation Uses Mathematical Modeling
to Plan Its Global Supply Chain
360
Trends in Offshore Outsourcing
360
Problems for
Section
6.10 361
6.11
Summary
362
Bibliography
362
Chapter
7
Push and Pull Production Control Systems:
MRP and JIT
364
Chapter Overview
364
MRP Basics
367
JIT Basics
369
7.1
The Explosion Calculus
370
Problems for Section
7.1 374
7.2
Alternative Lot-Sizing Schemes
376
EOQ Lot Sizing
376
The Silver-Meal Heuristic
377
Least Unit Cost
378
Part Period Balancing
3 79
Problems for Section
7.2 380
7.3
Incorporating Lot-Sizing Algorithms into the
Explosion Calculus
382
Problems for Section
7.3 383
7.4
Lot Sizing with Capacity Constraints
384
Problems for Section
7.4 387
7.5
Shortcomings of MRP
388
Uncertainty
388
Capacity Planning
389
Rolling Horizons and System Nervousness
390
Additional Considerations
392
Snapshot Application: Raymond Corporation
Builds World-Class Manufacturing with
MRP II
393
Problems for Section
7.5 394
7.6
JIT Fundamentals
395
The Mechanics of
Kanban
395
Single Minute Exchange of Dies
397
Advantages and Disadvantages of the Just¬
in-Time Philosophy
398
Implementation of JIT in the United States
401
Problems for Section
7.6 402
7.7
A Comparison of MRP and JIT
403
7.8
JIT or Lean Production?
404
7.9
Historical Notes
405
7.10
Summary
406
Additional Problems for Chapter
7 407
Appendix
7-А
Optimal Lot Sizing for
Time-Varying Demand
411
Appendix 7-B Glossary of Notation for
Chapter
7 415
Bibliography
416
Chapter
8
Operations Scheduling
417
Chapter Overview
417
8.1
Production Scheduling and the Hierarchy
of Production Decisions
420
8.2
Important Characteristics of Job Shop
Scheduling Problems
422
Objectives of Job Shop Management
422
8.3
Job Shop Scheduling Terminology
423
8.4
A Comparison of Specific Sequencing
Rules
425
First-Come, First-Served
425
Shortest Processing Time
426
Earliest Due Date
426
Critical Ratio Scheduling
42 7
8.5
Objectives in Job Shop Management:
An Example
428
Problems for Sections
8.1-8.5 429
8.6
An Introduction to Sequencing Theory for a
Single Machine
430
Shortest-Processing-Time Scheduling
431
Earliest-Due-Date Scheduling
432
Minimizing the Number of Tardy Jobs
432
Precedence Constraints: Lawler s Algorithm
433
Snapshot Application: Millions Saved with
Scheduling System for Fractional Aircraft
Operators
435
Problems for Section
8.6 435
8.7
Sequencing Algorithms for Multiple
Machines
437
Scheduling
η
Jobs on Two Machines
438
Extension to Three Machines
439
The Two-Job Flow Shop Problem
441
Problems for Section
8.7 444
8.8
Stochastic Scheduling: Static Analysis
445
Single Machine
445
Multiple Machines
446
xii Contents
The Two-Machine Flow Shop Case
447
Problems for Section
8.8 448
8.9
Stochastic Scheduling: Dynamic Analysis
449
Selection Disciplines Independent of
Job Processing Times
451
Selection Disciplines Dependent on
Job Processing Times
452
The
сџ
Rule
454
Problems for Section
8.9 454
8.10
Assembly Line Balancing
455
Problems for Section
8.10 459
Snapshot Application: Manufacturing
Divisions Realize Savings with Scheduling
Software
461
8.11
Simulation: A Valuable Scheduling Tool
462
8.12
Post-MRP Production Scheduling
Software
463
8.13
Historical Notes
463
8.14
Summary
464
Additional Problems on Scheduling
465
Bibliography
471
Supplement
2
Queuing Theory
473
52.1 Introduction
473
52.2 Structural Aspects of Queuing
Models
474
52.3 Notation
475
52.4 Little s Formula
476
52.5 The Exponential and
Poisson
Distributions
in Queuing
476
Aside
477
52.6 Birth and Death Analysis for the M/M/l
Queue
478
52.7 Calculation of the Expected System Measures
for the M/M/l Queue
481
52.8 The Waiting Time Distribution
482
52.9 Solution of the General Case
484
52.10 Multiple Servers in Parallel: The M/M/c
Queue
485
52.1
1
The M/M/l Queue with a Finite
Capacity
489
52.12 Results for Nonexponential Service
Distributions
492
52.13 The M/G/oo Queue
493
52.14 Optimization of Queuing Systems
495
Typical Service System Design
Problems
495
Modeling Framework
495
52.15 Simulation of Queuing Systems
498
Bibliography
499
Chapter
9
Project Scheduling
500
Chapter Overview
500
9.1
Representing a Project as a
Network
503
9.2
Critical Path Analysis
505
Finding the Critical Path
508
Problems for Sections
9.1
and
9.2 511
9.3
Time Costing Methods
513
Problems for Section
9.3 517
9.4
Solving Critical Path Problems with Linear
Programming
518
Linear Programming Formulation of the
Cost-Time Problem
521
Problems for Section
9.4 523
9.5
PERT: Project Evaluation and Review
Technique
523
Path Independence
528
Problems for Section
9.5 531
Snapshot Application: Warner Robins Streamlines
Aircraft Maintenance with CCPMProject
Management
533
9.6
Resource Considerations
533
Resource Constraints for Single-Project
Scheduling
533
Resource Constraints for Multiproject
Scheduling
535
Resource Loading Profiles
536
Problems for Section
9.6 538
9.7
Organizational Issues in Project
Management
540
9.8
Historical Notes
541
9.9
Project Management Software
for the PC
542
Snapshot Application: Project
Management Helps United Stay on
Schedule
543
Snapshot Application: Thomas Brothers
Plans Staffing with Project Management
Software
543
Snapshot Application: Florida Power and
Light Takes Project Management
Seriously
543
9.10
Summary
544
Additional Problems on Project
Scheduling
545
Appendix
9-А
Glossary of Notation for
Chapter
9 548
Bibliography
549
Contents XÜi
Chapter
10
Facilities Layout and Location
550
Chapter Overview
550
Snapshot Application: Sun Microsystems
Pioneers New Flex Office System
553
10.1
The Facilities Layout Problem
554
10.2
Patterns of Flow
555
Activity Relationship Chart
555
From-To Chart
557
10.3
Types of Layouts
559
Fixed Position Layouts
559
Product Layouts
559
Process Layouts
560
Layouts Based on Group
Technology
560
Problems for Sections
10.1-І 0.3 562
10.4
A Prototype Layout Problem
and the Assignment Model
564
The Assignment Algorithm
565
Problems for
Section
10.4 567
*10.5 More Advanced Mathematical Programming
Formulations
568
Problem for
Section
10.5 569
10.6
Computerized Layout Techniques
569
CRAFT
570
COFAD
574
ALDE
Ρ
575
CORELAP
576
PLANET
577
Computerized Methods versus Human
Planners
577
Dynamic Plant Layouts
578
Other Computer Methods
578
Problems for Section
10.6 579
10.7
Flexible Manufacturing Systems
582
Advantages of Flexible Manufacturing
Systems
584
Disadvantages of Flexible Manufacturing
Systems
584
Decision Making and Modeling of
the FMS
585
The Future of FMS
588
Problems for Section
10.7 590
10.8
Locating New Facilities
590
Snapshot Application: Kraft Foods Uses
Optimization and Simulation to Determine
Best Layout
591
Measures of Distance
592
Problems for Section
10.8 593
10.9
The Single-Facility Rectilinear Distance
Location Problem
593
Contour Lines
596
Minimax Problems
597
Problems for Section
10.9 600
10.10
Euclidean Distance Problems
601
The Gravity Problem
601
The Straight-Line Distance Problem
602
Problems for Section
10.10 603
10.11
Other Location Models
604
Locating Multiple Facilities
605
Further Extensions
606
Problems for Section
10.11 608
10.12
Historical Notes
609
10.13
Summary
610
Additional Problems on Layout and
Location
611
Spreadsheet Problems for
Chapter
10 616
Appendix 10-A Finding Centroids
617
Appendix 10-B Computing Contour
Lines
619
Bibliography
622
Chapter
11
Quality and Assurance
624
Chapter Overview
624
Overview of This Chapter
628
11.1
Statistical Basis of Control
Charts
629
Problems for Section
11.1 631
11.2
Control Charts for Variables: The X and
R
Charts
633
XCharts
636
Relationship to Classical Statistics
636
R
Charts
638
Problems for Section
11.2 639
11.3
Control Charts for Attributes:
The/? Chart
641
p
Charts for Varying Subgroup
Sizes
643
Problems for Section
11.3 644
11.4
The
с
Chart
646
Problems for Section
11.4 648
11.5
Classical Statistical Methods and
Control Charts
649
Problem for Section
11.5 649
*11.6 Economic Design of X Charts
650
Problems for Section
11.6 656
xiv Contents
11.7
Overview of Acceptance Sampling
657
Snapshot Application: Navistar Scores with
Six-Sigma Quality Program
659
11.8
Notation
660
11.9
Single Sampling for Attributes
660
Derivation of the
OC
Curve
662
Problems for Section
11.9 664
* 11.10
Double Sampling Plans for Attributes
665
Problems for
Section
11.10 666
11.11
Sequential Sampling Plans
667
Problems for
Section
11.11 671
11.12
Average Outgoing Quality
672
Snapshot Application: Motorola Leads the Way
with Six-Sigma Quality Programs
674
Problems for Section
11.12 674
11.13
Total Quality Management
675
Definitions
675
Listening to the Customer
675
Competition Based on Quality
677
Organizing for Quality
678
Benchmarking Quality
679
The Deming Prize and the Baldrige
Award
680
ISO
9000 682
Quality: The Bottom Line
683
11.14
Designing Quality into the Product
684
Design, Manufacturing, and Quality
686
11.15
Historical Notes
688
11.16
Summary
689
Additional Problems on Quality and
Assurance
691
Appendix
11-А
Approximating
Distributions
695
Appendix
1
1-B Glossary of Notation for
Chapter
11
on Quality and Assurance
697
Bibliography
698
Chapter
12
Reliability and Maintainability
700
Chapter Overview
700
12.1
Reliability of a Single Component
704
Introduction to Reliability Concepts
704
Preliminary Notation and Definitions
705
The Exponential Failure Law
707
Problems for Section
12.1 710
12.2
Increasing and Decreasing Failure
Rates
712
Problems for Section
12.2 714
12.3
The
Poisson
Process in Reliability
Modeling
715
Series Systems Subject to Purely Random
Failures
718
Problems for
Section
12.3 719
12.4
Failures of Complex Equipment
720
Components in Series
720
Components in Parallel
721
Expected Value Calculations
721
К
Out of
N
Systems
722
Problems for Section
12.4 724
12.5
Introduction to Maintenance Models
724
12.6
Deterministic Age Replacement
Strategies
726
The Optimal Policy in the Basic Case
726
A General Age Replacement Model
728
Problems for Section
12.6 732
12.7
Planned Replacement under
Uncertainty
732
Planned Replacement for a Single Item
732
Block Replacement for a Group of Items
736
Problems for Section
12.7 738
*12.8 Analysis of Warranty Policies
740
The Free Replacement Warranty
740
The Pro
Rata
Warranty
742
Extensions and Criticisms
744
Problems for Section
12.8 744
12.9
Software Reliability
745
Snapshot Application: Reliability-Centered
Maintenance Improves Operations
at Three Mile Island Nuclear Plant
746
12.10
Historical Notes
747
12.11
Summary
748
Additional Problems on Reliability and
Maintainability
749
Appendix 12-A Glossary of Notation on
Reliability and Maintainability
751
Bibliography
753
Appendix: Tables
754
Index
772
|
| adam_txt |
Contents
About the Author
xv
Preface
xvi
Introduction
xvii
Chapter
1
Strategy and Competition
1
Chapter Overview
1
Snapshot Application: Apple Adopts a
New Business Strategy and Shifts Its
Core Competency from Computers
to Portable Music
3
1.1
Manufacturing Matters
5
Manufacturing Jobs Outlook
6
1.2
A Framework for Operations Strategy
7
Strategic Dimensions
8
1.3
The Classical View of Operations Strategy
5
Time Horizon
9
Focus
11
Evaluation
12
Consistency
12
1.4
Competing in the Global Marketplace
14
Problems for Sections
1.1-1,4 16
Snapshot Application: Global Manufacturing
Strategies in the Automobile Industry
17
1.5
Strategic Initiatives:
Reengineering
the
Business Process
18
1.6
Strategic Initiatives: Just-in-Time
21
1.7
Strategic Initiatives: Time-Based
Competition
23
1.8
Strategic Initiatives: Competing on
Quality
24
Problems for Sections
1.5-1.8 26
1.9
Matching Process and Product
Life Cycles
27
The Product Life Cycle
2 7
The Process Life Cycle
28
The Product-Process Matrix
29
Problems for Section
1.9 31
1.10
Learning and Experience Curves
31
Learning Curves
32
Experience Curves
34
Learning and Experience Curves and
Manufacturing Strategy
36
Problems for Section
1.10 36
50
1.11
Capacity Growth Planning: A Long-Term
Strategic Problem
38
Economies of Scale and Economies of
Scope
38
Make or Buy: A Prototype Capacity
Expansion Problem
39
Dynamic Capacity Expansion Policy
40
Issues in Plant Location
44
Problems for Section
1.11 46
1.12
Summary
47
Additional Problems for Chapter
1 48
Appendix
1-А
Present Worth Calculations
Bibliography
51
Chapter
2
Forecasting
52
Chapter Overview
52
2.1
The Time Horizon in Forecasting
55
2.2
Characteristics of Forecasts
56
2.3
Subjective Forecasting Methods
56
2.4
Objective Forecasting Methods
57
Causal Models
57
Time Series Methods
58
Snapshot Application: Advanced Forecasting,
Inc., Serves the Semiconductor Industry
59
Problems for Sections
2.1-2.4 59
2.5
Notation Conventions
61
2.6
Evaluating Forecasts
61
Problems for Section
2.6 63
2.7
Methods for Forecasting Stationary Series
64
Moving Averages
64
Problems on Moving Averages
67
Exponential Smoothing
67
Multiple-Step-Ahead Forecasts
71
Comparison of Exponential Smoothing and
Moving Averages
72
Problems for Section
2.7 73
Snapshot Application: Sport Obermeyer Slashes
Costs with Improved Forecasting
74
2.8
Trend-Based Methods
75
Regression Analysis
75
Problems for Section
2.8 76
Double Exponential Smoothing Using Holt
s
Method
77
More Problems for Section
2.8 78
viii
Contents
2.9
Methods for Seasonal Series
79
Seasonal Factors for Stationary Series
79
Seasonal Decomposition Using Moving
Averages
81
Problems for Section
2.9 83
Winters
s
Method for Seasonal Problems
84
More Problems for Section
2.9 89
2.10
Box-Jenkins Models
89
Estimating the Autocorrelation Function
90
The
Autoregressive
Process
93
The Moving-Average Process
94
Mixtures:
ARMA
Models
96
ARMA
Models
96
Using
A RIMA
Models for Forecasting
98
Summary of the Steps Required for Building
ARIMA Models
99
Case Study: Using Box-Jenkins Methodology to
Predict Monthly International Airline
Passenger Totals
100
Snapshot Application: A Simple ARIMA Model
Predicts the Performance of the
U.S. Economy
104
Box-Jenkins Modeling
—
A Critique
104
Problems for Section
2.10 104
2.11
Practical Considerations
105
Model Identification and Monitoring
105
Simple versus Complex Time Series
Methods
106
2.12
Overview of Advanced Topics in
Forecasting
107
Simulation as a Forecasting Tool
107
Forecasting Demand in the Presence of
Lost Sales
108
2.13
Linking Forecasting and Inventory
Management
110
Snapshot Application: Predicting Economic
Recessions 111
2.14
Historical Notes and Additional
Topics
112
2.15
Summary
113
Additional Problems on Forecasting
113
Appendix
2-А
Forecast Errors for
Moving Averages and Exponential
Smoothing
118
Appendix 2-B Derivation of the Equations
for the Slope and Intercept for Regression
Analysis
120
Appendix 2-C Glossary of Notation
for Chapter
2 122
Bibliography
122
Chapter
3
Aggregate Planning
124
Chapter Overview
124
3.1
Aggregate Units of Production
127
3.2
Overview of the Aggregate Planning
Problem
128
3.3
Costs in Aggregate Planning
130
Problems for Sections
3.1-3.3 132
3.4
A Prototype Problem
133
Evaluation of a Chase Strategy
(Zero Inventory Plan)
135
Evaluation of the Constant Workforce Plan
¡36
Mixed Strategies and Additional Constraints
138
Problems for Section
3.4 139
3.5
Solution of Aggregate Planning Problems
by Linear Programming
141
Cost Parameters and Given Information
141
Problem Variables
142
Problem Constraints
142
Rounding the Variables
143
Extensions
144
Other Solution Methods
146
3.6
Solving Aggregate Planning Problems by
Linear Programming: An Example
147
Problems for Sections
3.5
and
3.6 149
3.7
The Linear Decision Rule
152
3.8
Modeling Management Behavior
153
Problems for Sections
3.7
and
3.8 155
3.9
Disaggregating Aggregate Plans
155
Snapshot Application: Welch's Uses Aggregate
Planning for Production Scheduling
157
Problems for Section
3.9 158
3.10
Production Planning on a Global Scale
158
3.11
Practical Considerations
159
3.12
Historical Notes
160
3.13
Summary
161
Additional Problems on Aggregate
Planning
162
Appendix 3-A Glossary of Notation
for Chapter
3 167
Bibliography
168
Supplement
1
Linear Programming
169
S
1.1
Introduction
169
51
.2
A Prototype Linear Programming
Problem
169
5
1.3
Statement of the General Problem
171
Definitions of Commonly Used Terms
172
Features of Linear Programs
173
Contents ix
51.4
Solving
Linear Programming Problems
Graphically
174
Graphing
Linear
Inequalities 1
74
Graphing the Feasible Region
176
Finding the Optimal Solution.
177
Identifying the Optimal Solution Directly
by Graphical Means
179
51.5 The Simplex Method: An Overview
180
51
.6
Solving Linear Programming Problems
with Excel
181
Entering Large Problems Efficiently
185
51.7 Interpreting the Sensitivity Report
187
Shadow Prices
187
Objective Function Coefficients and Right-
Hand Sides
188
Adding a New Variable
188
Using Sensitivity Analysis
189
51.8 Recognizing Special Problems
191
Unbounded Solutions
191
Empty Feasible Region
192
Degeneracy
194
Multiple Optimal Solutions
194
Redundant Constraints
194
5
1.9
The Application of Linear Programming
to Production and Operations Analysis
195
Bibliography
197
Chapter
4
Inventory Control Subject to Known
Demand
198
Chapter Overview
198
4.1
Types of Inventories
201
4.2
Motivation for Holding Inventories
202
4.3
Characteristics of Inventory Systems
203
4.4
Relevant Costs
204
Holding Cost
204
Order Cost
206
Penalty Cost
207
Problems for Sections
4.1-4.4 208
4.5
The EOQ Model
210
The Basic Model
210
Inclusion of Order Lead Time
213
Sensitivity
214
EOQ and JIT
215
Problems for Section
4.5 216
4.6
Extension to a Finite Production Rate
218
Problems for Section
4.6 219
4.7
Quantity Discount Models
220
Optimal Policy for All-Units Discount Schedule
221
Summary of the Solution Technique for
All-Units Discounts
223
Incremental Quantity Discounts
223
Summary of the Solution Technique
for Incremental Discounts
225
Other Discount Schedules
225
Problems for Section
4.7 226
*4.8 Resource-Constrained Multiple
Product Systems
227
Problems for
Section
4.8 230
4.9
EOQ Models for Production Planning
230
Problems for Section
4.9 234
4.10
Power-of-Two Policies
235
4.11
Historical Notes and Additional Topics
237
Snapshot Application: Mervyn 's Recognized
for State-of-the-Art Inventory
Control System
238
4.12
Summary
239
Additional Problems on Deterministic
Inventory Models
240
Appendix
4-А
Mathematical Derivations for
Multiproduct Constrained EOQ Systems
244
Appendix 4-B Glossary of Notation for
Chapter
4 246
Bibliography
246
Chapter
5
Inventory Control Subject to Uncertain
Demand
248
Chapter Overview
248
Overview of Models Treated in This
Chapter
252
5.1
The Nature of Randomness
253
5.2
Optimization Criterion
255
Problems for Sections
5.1
and
5.2 256
5.3
The Newsboy Model
257
Notation
257
Development of the Cost Function
258
Determining the Optimal Policy
259
Optimal Policy for Discrete Demand
261
Extension to Include Starting Inventory
261
Snapshot Application: Using Inventory
Models to Manage the Seed-Corn Supply
Chain at Syngenta
262
Extension to Multiple Planning Periods
263
Problems for Section
5.3 264
5.4
Lot Size-Reorder Point Systems
266
Describing Demand
267
Decision Variables
267
χ
Contents
Derivation of the Expected Cost Function
267
The Cost Function
269
Inventory Level versus Inventory
Position
271
5.5
Service Levels in (Q, R) Systems
272
Type
1
Service
272
Type
2
Service
273
Optimal (Q, R) Policies Subject to
Type
2
Constraint
274
Imputed Shortage Cost
275
Scaling of Lead Time Demand
276
Estimating Sigma When Inventory Control
and Forecasting A re Linked
2 76
*
Lead Time Variability
277
Calculations in Excel
278
Negative Safety Stock
278
Problems for Sections
5.4
and
5.5 279
5.6
Additional Discussion of Periodic-Review
Systems
281
(s, S)
Policies
281
*Service Levels in Periodic-Review
Systems
281
Problems for Section
5.6 282
Snapshot Application: Tropicana Uses
Sophisticated Modeling for Inventory
Management
283
5.7
Multiproduct Systems
283
ABCAnalysis
283
Exchange Curves
285
Problems for Section
5.7 288
*5.8 Overview of Advanced Topics
289
Multi-echelon Systems
289
Perishable Inventory Problems
290
Snapshot Application: Triad's Inventory
Systems Meet Markets
'
Needs
291
5.9
Historical Notes and Additional
Readings
292
5.10
Summary
293
Additional Problems on Stochastic
Inventory Models
294
Appendix
5-А
Notational Conventions and
Probability Review
300
Appendix 5-B Additional Results and
Extensions for the Newsboy Model
301
Appendix 5-C Derivation of the Optimal
(Q,R) Policy
304
Appendix 5-D Probability Distributions for
Inventory Management
304
Appendix
5-Е
Glossary of Notation for
Chapter
5 308
Bibliography
309
Chapter
6
Supply Chain Management
311
Chapter Overview
311
The Supply Chain as a Strategic
Weapon
315
Snapshot Application: Wal-Mart Wins with Solid
Supply Chain Management
316
6.1
The Transportation Problem
316
The Greedy Heuristic
319
6.2
Solving Transportation Problems with Linear
Programming
320
6.3
Generalizations of the Transportation
Problem
322
Infeasible Routes
323
Unbalanced Problems
323
6.4
More General Network Formulations
324
Problems for Sections
6.1-6.4 327
Snapshot Application: IBM Streamlines
Its Supply Chain for Spare Parts Using
Sophisticated Mathematical Models
328
6.5
Distribution Resource Planning
330
Problems for Section
6.5 332
6.6
Determining Delivery Routes in
Supply Chains
332
Practical Issues in Vehicle Scheduling
336
Snapshot Application: Air Products Saves Big
with Routing and Scheduling Optimizer
337
Problems for Section
6.6 337
6.7
Designing Products for Supply Chain
Efficiency
338
Postponement in Supply Chains
339
Additional Issues in Supply Chain
Design
340
Snapshot Application: Dell Computer Designs
the Ultimate Supply Chain
342
Problems for Section
6.7 342
6.8
The Role
ofinformation
in the Supply
Chain
343
The Bullwhip Effect
344
Snapshot Application: Saturn Emerges as
an Industry Leader with Scientific Supply
Chain Management
347
Electronic Commerce
347
Electronic Data Interchange
348
Web-Based Transactions Systems
349
RFID Technology Provides Faster
Product Flow
350
Problems for Section
6.8 351
6.9
Multilevel Distribution Systems
351
Problems for Section
6.9 354
Contents xi
6.10
Designing
the Supply Chain in a Global
Environment
355
Snapshot Application: Norwegian Company
implements Decision Support System to
Streamline Its Supply Chain
356
Snapshot Application: Timken Battles Imports
with Bundling
358
Supply Chain Management in a Global
Environment
359
Snapshot Application: Digital Equipment
Corporation Uses Mathematical Modeling
to Plan Its Global Supply Chain
360
Trends in Offshore Outsourcing
360
Problems for
Section
6.10 361
6.11
Summary
362
Bibliography
362
Chapter
7
Push and Pull Production Control Systems:
MRP and JIT
364
Chapter Overview
364
MRP Basics
367
JIT Basics
369
7.1
The Explosion Calculus
370
Problems for Section
7.1 374
7.2
Alternative Lot-Sizing Schemes
376
EOQ Lot Sizing
376
The Silver-Meal Heuristic
377
Least Unit Cost
378
Part Period Balancing
3 79
Problems for Section
7.2 380
7.3
Incorporating Lot-Sizing Algorithms into the
Explosion Calculus
382
Problems for Section
7.3 383
7.4
Lot Sizing with Capacity Constraints
384
Problems for Section
7.4 387
7.5
Shortcomings of MRP
388
Uncertainty
388
Capacity Planning
389
Rolling Horizons and System Nervousness
390
Additional Considerations
392
Snapshot Application: Raymond Corporation
Builds World-Class Manufacturing with
MRP II
393
Problems for Section
7.5 394
7.6
JIT Fundamentals
395
The Mechanics of
Kanban
395
Single Minute Exchange of Dies
397
Advantages and Disadvantages of the Just¬
in-Time Philosophy
398
Implementation of JIT in the United States
401
Problems for Section
7.6 402
7.7
A Comparison of MRP and JIT
403
7.8
JIT or Lean Production?
404
7.9
Historical Notes
405
7.10
Summary
406
Additional Problems for Chapter
7 407
Appendix
7-А
Optimal Lot Sizing for
Time-Varying Demand
411
Appendix 7-B Glossary of Notation for
Chapter
7 415
Bibliography
416
Chapter
8
Operations Scheduling
417
Chapter Overview
417
8.1
Production Scheduling and the Hierarchy
of Production Decisions
420
8.2
Important Characteristics of Job Shop
Scheduling Problems
422
Objectives of Job Shop Management
422
8.3
Job Shop Scheduling Terminology
423
8.4
A Comparison of Specific Sequencing
Rules
425
First-Come, First-Served
425
Shortest Processing Time
426
Earliest Due Date
426
Critical Ratio Scheduling
42 7
8.5
Objectives in Job Shop Management:
An Example
428
Problems for Sections
8.1-8.5 429
8.6
An Introduction to Sequencing Theory for a
Single Machine
430
Shortest-Processing-Time Scheduling
431
Earliest-Due-Date Scheduling
432
Minimizing the Number of Tardy Jobs
432
Precedence Constraints: Lawler 's Algorithm
433
Snapshot Application: Millions Saved with
Scheduling System for Fractional Aircraft
Operators
435
Problems for Section
8.6 435
8.7
Sequencing Algorithms for Multiple
Machines
437
Scheduling
η
Jobs on Two Machines
438
Extension to Three Machines
439
The Two-Job Flow Shop Problem
441
Problems for Section
8.7 444
8.8
Stochastic Scheduling: Static Analysis
445
Single Machine
445
Multiple Machines
446
xii Contents
The Two-Machine Flow Shop Case
447
Problems for Section
8.8 448
8.9
Stochastic Scheduling: Dynamic Analysis
449
Selection Disciplines Independent of
Job Processing Times
451
Selection Disciplines Dependent on
Job Processing Times
452
The
сџ
Rule
454
Problems for Section
8.9 454
8.10
Assembly Line Balancing
455
Problems for Section
8.10 459
Snapshot Application: Manufacturing
Divisions Realize Savings with Scheduling
Software
461
8.11
Simulation: A Valuable Scheduling Tool
462
8.12
Post-MRP Production Scheduling
Software
463
8.13
Historical Notes
463
8.14
Summary
464
Additional Problems on Scheduling
465
Bibliography
471
Supplement
2
Queuing Theory
473
52.1 Introduction
473
52.2 Structural Aspects of Queuing
Models
474
52.3 Notation
475
52.4 Little's Formula
476
52.5 The Exponential and
Poisson
Distributions
in Queuing
476
Aside
477
52.6 Birth and Death Analysis for the M/M/l
Queue
478
52.7 Calculation of the Expected System Measures
for the M/M/l Queue
481
52.8 The Waiting Time Distribution
482
52.9 Solution of the General Case
484
52.10 Multiple Servers in Parallel: The M/M/c
Queue
485
52.1
1
The M/M/l Queue with a Finite
Capacity
489
52.12 Results for Nonexponential Service
Distributions
492
52.13 The M/G/oo Queue
493
52.14 Optimization of Queuing Systems
495
Typical Service System Design
Problems
495
Modeling Framework
495
52.15 Simulation of Queuing Systems
498
Bibliography
499
Chapter
9
Project Scheduling
500
Chapter Overview
500
9.1
Representing a Project as a
Network
503
9.2
Critical Path Analysis
505
Finding the Critical Path
508
Problems for Sections
9.1
and
9.2 511
9.3
Time Costing Methods
513
Problems for Section
9.3 517
9.4
Solving Critical Path Problems with Linear
Programming
518
Linear Programming Formulation of the
Cost-Time Problem
521
Problems for Section
9.4 523
9.5
PERT: Project Evaluation and Review
Technique
523
Path Independence
528
Problems for Section
9.5 531
Snapshot Application: Warner Robins Streamlines
Aircraft Maintenance with CCPMProject
Management
533
9.6
Resource Considerations
533
Resource Constraints for Single-Project
Scheduling
533
Resource Constraints for Multiproject
Scheduling
535
Resource Loading Profiles
536
Problems for Section
9.6 538
9.7
Organizational Issues in Project
Management
540
9.8
Historical Notes
541
9.9
Project Management Software
for the PC
542
Snapshot Application: Project
Management Helps United Stay on
Schedule
543
Snapshot Application: Thomas Brothers
Plans Staffing with Project Management
Software
543
Snapshot Application: Florida Power and
Light Takes Project Management
Seriously
543
9.10
Summary
544
Additional Problems on Project
Scheduling
545
Appendix
9-А
Glossary of Notation for
Chapter
9 548
Bibliography
549
Contents XÜi
Chapter
10
Facilities Layout and Location
550
Chapter Overview
550
Snapshot Application: Sun Microsystems
Pioneers New Flex Office System
553
10.1
The Facilities Layout Problem
554
10.2
Patterns of Flow
555
Activity Relationship Chart
555
From-To Chart
557
10.3
Types of Layouts
559
Fixed Position Layouts
559
Product Layouts
559
Process Layouts
560
Layouts Based on Group
Technology
560
Problems for Sections
10.1-І 0.3 562
10.4
A Prototype Layout Problem
and the Assignment Model
564
The Assignment Algorithm
565
Problems for
Section
10.4 567
*10.5 More Advanced Mathematical Programming
Formulations
568
Problem for
Section
10.5 569
10.6
Computerized Layout Techniques
569
CRAFT
570
COFAD
574
ALDE
Ρ
575
CORELAP
576
PLANET
577
Computerized Methods versus Human
Planners
577
Dynamic Plant Layouts
578
Other Computer Methods
578
Problems for Section
10.6 579
10.7
Flexible Manufacturing Systems
582
Advantages of Flexible Manufacturing
Systems
584
Disadvantages of Flexible Manufacturing
Systems
584
Decision Making and Modeling of
the FMS
585
The Future of FMS
588
Problems for Section
10.7 590
10.8
Locating New Facilities
590
Snapshot Application: Kraft Foods Uses
Optimization and Simulation to Determine
Best Layout
591
Measures of Distance
592
Problems for Section
10.8 593
10.9
The Single-Facility Rectilinear Distance
Location Problem
593
Contour Lines
596
Minimax Problems
597
Problems for Section
10.9 600
10.10
Euclidean Distance Problems
601
The Gravity Problem
601
The Straight-Line Distance Problem
602
Problems for Section
10.10 603
10.11
Other Location Models
604
Locating Multiple Facilities
605
Further Extensions
606
Problems for Section
10.11 608
10.12
Historical Notes
609
10.13
Summary
610
Additional Problems on Layout and
Location
611
Spreadsheet Problems for
Chapter
10 616
Appendix 10-A Finding Centroids
617
Appendix 10-B Computing Contour
Lines
619
Bibliography
622
Chapter
11
Quality and Assurance
624
Chapter Overview
624
Overview of This Chapter
628
11.1
Statistical Basis of Control
Charts
629
Problems for Section
11.1 631
11.2
Control Charts for Variables: The X and
R
Charts
633
XCharts
636
Relationship to Classical Statistics
636
R
Charts
638
Problems for Section
11.2 639
11.3
Control Charts for Attributes:
The/? Chart
641
p
Charts for Varying Subgroup
Sizes
643
Problems for Section
11.3 644
11.4
The
с
Chart
646
Problems for Section
11.4 648
11.5
Classical Statistical Methods and
Control Charts
649
Problem for Section
11.5 649
*11.6 Economic Design of X Charts
650
Problems for Section
11.6 656
xiv Contents
11.7
Overview of Acceptance Sampling
657
Snapshot Application: Navistar Scores with
Six-Sigma Quality Program
659
11.8
Notation
660
11.9
Single Sampling for Attributes
660
Derivation of the
OC
Curve
662
Problems for Section
11.9 664
* 11.10
Double Sampling Plans for Attributes
665
Problems for
Section
11.10 666
11.11
Sequential Sampling Plans
667
Problems for
Section
11.11 671
11.12
Average Outgoing Quality
672
Snapshot Application: Motorola Leads the Way
with Six-Sigma Quality Programs
674
Problems for Section
11.12 674
11.13
Total Quality Management
675
Definitions
675
Listening to the Customer
675
Competition Based on Quality
677
Organizing for Quality
678
Benchmarking Quality
679
The Deming Prize and the Baldrige
Award
680
ISO
9000 682
Quality: The Bottom Line
683
11.14
Designing Quality into the Product
684
Design, Manufacturing, and Quality
686
11.15
Historical Notes
688
11.16
Summary
689
Additional Problems on Quality and
Assurance
691
Appendix
11-А
Approximating
Distributions
695
Appendix
1
1-B Glossary of Notation for
Chapter
11
on Quality and Assurance
697
Bibliography
698
Chapter
12
Reliability and Maintainability
700
Chapter Overview
700
12.1
Reliability of a Single Component
704
Introduction to Reliability Concepts
704
Preliminary Notation and Definitions
705
The Exponential Failure Law
707
Problems for Section
12.1 710
12.2
Increasing and Decreasing Failure
Rates
712
Problems for Section
12.2 714
12.3
The
Poisson
Process in Reliability
Modeling
715
Series Systems Subject to Purely Random
Failures
718
Problems for
Section
12.3 719
12.4
Failures of Complex Equipment
720
Components in Series
720
Components in Parallel
721
Expected Value Calculations
721
К
Out of
N
Systems
722
Problems for Section
12.4 724
12.5
Introduction to Maintenance Models
724
12.6
Deterministic Age Replacement
Strategies
726
The Optimal Policy in the Basic Case
726
A General Age Replacement Model
728
Problems for Section
12.6 732
12.7
Planned Replacement under
Uncertainty
732
Planned Replacement for a Single Item
732
Block Replacement for a Group of Items
736
Problems for Section
12.7 738
*12.8 Analysis of Warranty Policies
740
The Free Replacement Warranty
740
The Pro
Rata
Warranty
742
Extensions and Criticisms
744
Problems for Section
12.8 744
12.9
Software Reliability
745
Snapshot Application: Reliability-Centered
Maintenance Improves Operations
at Three Mile Island Nuclear Plant
746
12.10
Historical Notes
747
12.11
Summary
748
Additional Problems on Reliability and
Maintainability
749
Appendix 12-A Glossary of Notation on
Reliability and Maintainability
751
Bibliography
753
Appendix: Tables
754
Index
772 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Nahmias, Steven |
| author_GND | (DE-588)133417344 |
| author_facet | Nahmias, Steven |
| author_role | aut |
| author_sort | Nahmias, Steven |
| author_variant | s n sn |
| building | Verbundindex |
| bvnumber | BV023260932 |
| callnumber-first | T - Technology |
| callnumber-label | TS155 |
| callnumber-raw | TS155 |
| callnumber-search | TS155 |
| callnumber-sort | TS 3155 |
| callnumber-subject | TS - Manufactures |
| classification_rvk | QP 500 |
| classification_tum | WIR 837f |
| ctrlnum | (OCoLC)187418596 (DE-599)BVBBV023260932 |
| dewey-full | 658.5 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.5 |
| dewey-search | 658.5 |
| dewey-sort | 3658.5 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 6. ed., international ed. |
| format | Book |
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| id | DE-604.BV023260932 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:32:00Z |
| indexdate | 2024-08-01T11:17:33Z |
| institution | BVB |
| isbn | 9780071263702 |
| language | English |
| lccn | 2008000034 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016446139 |
| oclc_num | 187418596 |
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| physical | XXVI, 789 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | McGraw-Hill |
| record_format | marc |
| series2 | The McGraw-Hill/Irwin series operations and decision sciences |
| spellingShingle | Nahmias, Steven Production and operations analysis Production management Produktion (DE-588)4047347-8 gnd Produktionswirtschaft (DE-588)4123986-6 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Produktionstheorie (DE-588)4121520-5 gnd Ablaufplanung (DE-588)4122751-7 gnd Produktmanagement (DE-588)4125960-9 gnd |
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| title | Production and operations analysis |
| title_auth | Production and operations analysis |
| title_exact_search | Production and operations analysis |
| title_exact_search_txtP | Production and operations analysis |
| title_full | Production and operations analysis Steven Nahmias |
| title_fullStr | Production and operations analysis Steven Nahmias |
| title_full_unstemmed | Production and operations analysis Steven Nahmias |
| title_short | Production and operations analysis |
| title_sort | production and operations analysis |
| topic | Production management Produktion (DE-588)4047347-8 gnd Produktionswirtschaft (DE-588)4123986-6 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd Produktionstheorie (DE-588)4121520-5 gnd Ablaufplanung (DE-588)4122751-7 gnd Produktmanagement (DE-588)4125960-9 gnd |
| topic_facet | Production management Produktion Produktionswirtschaft Management Operations Research Produktionstheorie Ablaufplanung Produktmanagement Lehrbuch |
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