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Operations Management, 1e [Hardback]

  • Formāts: Hardback, 768 pages, height x width x depth: 282x216x33 mm, weight: 1608 g, 216 Illustrations
  • Izdošanas datums: 16-Mar-2016
  • Izdevniecība: McGraw-Hill Education
  • ISBN-10: 1259142205
  • ISBN-13: 9781259142208
Citas grāmatas par šo tēmu:
  • Formāts: Hardback, 768 pages, height x width x depth: 282x216x33 mm, weight: 1608 g, 216 Illustrations
  • Izdošanas datums: 16-Mar-2016
  • Izdevniecība: McGraw-Hill Education
  • ISBN-10: 1259142205
  • ISBN-13: 9781259142208
Citas grāmatas par šo tēmu:

Cachon 1e is designed for undergraduate students taking an introductory course in operations management. This text will share many of the strengths of Matching Supply with Demand: An Introduction to Operations Management (3e).

Operations Management by Cachon comprehensively spans the relevant domain of topics, is accessible to a typical undergraduate student (i.e., limited real world business experience), incorporates the latest research and knowledge, and provides thorough pedagogical support for instructors along with innovative learning support for students.

Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, and how they need it, so that your class time is more engaging and effective.

1 Introduction to Operations Management
1(24)
Introduction
1(1)
The Customer's View of the World
2(3)
A Firm's Strategic Trade-Offs
5(4)
Connections: Airlines
9(1)
Overcoming Inefficiencies: The Three System Inhibitors
10(3)
Operations Management at Work
13(1)
Operations Management: An Overview of the Book
14(3)
Conclusion
17(1)
Summary of Learning Objectives
17(1)
Key Terms
18(1)
Conceptual Questions
19(1)
Solved Example Problems
20(1)
Problems and Applications
21(3)
References
24(1)
2 Introduction to Processes
25(15)
Introduction
25(1)
Process Definition, Scope, and Flow Units
26(2)
Three Key Process Metrics: Inventory, Flow Rate, and Flow Time
28(2)
Little's Law---Linking Process Metrics Together
30(3)
Connections: Little's Law
33(1)
Conclusion
33(1)
Summary of Learning Objectives
33(1)
Key Terms
34(1)
Key Formulas
34(1)
Conceptual Questions
34(1)
Solved Example Problems
35(1)
Problems and Applications
36(3)
Case: Cougar Mountain
39(1)
3 Process Analysis
40(27)
Introduction
40(1)
How to Draw a Process Flow Diagram
41(4)
Capacity for a One-Step Process
45(2)
How to Compute Flow Rate, Utilization, and Cycle Time
47(3)
How to Analyze a Multistep Process and Locate the Bottleneck
50(4)
The Time to Produce a Certain Quantity
54(2)
Conclusion
56(1)
Summary of Learning Objectives
57(1)
Key Terms
58(1)
Conceptual Questions
59(1)
Solved Example Problems
60(2)
Problems and Applications
62(4)
Case: Tesla
66(1)
References
66(1)
4 Process Improvement
67(36)
Introduction
67(2)
Measures of Process Efficiency
69(4)
How to Choose a Staffing Level to Meet Demand
73(7)
Off-Loading the Bottleneck
80(1)
How to Balance a Process
81(2)
The Pros and Cons of Specialization
83(1)
Connections: The History of Specialization
84(1)
Understanding the Financial Impact of Process Improvements
85(4)
Conclusion
89(1)
Summary of Learning Objectives
90(1)
Key Terms
91(1)
Key Formulas
92(1)
Conceptual Questions
93(1)
Solved Example Problems
94(4)
Problems and Applications
98(3)
Reference
101(1)
Case: Xootr
102(1)
5 Process Analysis with Multiple Flow Units
103(36)
Introduction
103(1)
Generalized Process Flow Patterns
104(4)
How to Find the Bottleneck in a General Process Flow
108(4)
Attrition Losses, Yields, and Scrap Rates
112(4)
Connections: TV Shows
116(2)
Flow Unit-Dependent Processing Times
118(6)
Rework
124(3)
Conclusion
127(1)
Summary of Learning Objectives
128(1)
Key Terms
129(1)
Conceptual Questions
129(2)
Solved Example Problems
131(5)
Problems and Applications
136(1)
Case: Airport Security
137(1)
References
138(1)
6 Learning Curves
139(35)
Introduction
139(1)
Various Forms of the Learning Curve
140(3)
Connections: Learning Curves in Sports
143(1)
The Power Law
144(2)
Estimating the Learning Curve Using a Linear Log-Log Graph
146(4)
Using Learning Curve Coefficients to Predict Costs
150(3)
Using Learning Curve Coefficients to Predict Cumulative Costs
153(1)
Employee Turnover and Its Effect on Learning
154(3)
Standardization as a Way to Avoid "Relearning"
157(2)
Connections: Process Standardization at Intel
159(1)
Drivers of Learning
160(2)
Conclusion
162(1)
Summary of Learning Objectives
163(1)
Key Terms
164(1)
Key Formulas
165(1)
Conceptual Questions
165(3)
Solved Example Problems
168(3)
Problems and Applications
171(2)
Case: Ford's Highland Plant
173(1)
References
173(1)
7 Process Interruptions
174(36)
Introduction
174(1)
Setup Time
175(3)
Capacity of a Process with Setups
178(4)
Batches and the Production Cycle
178(1)
Capacity of the Setup Resource
178(2)
Capacity and Flow Rate of the Process
180(2)
Utilization in a Process with Setups
182(3)
Connections: U.S. Utilization
185(1)
Inventory in a Process with Setups
185(4)
Choose the Batch Size in a Process with Setups
189(1)
Setup Times and Product Variety
190(3)
Connections: LEGO
193(1)
Managing Processes with Setup Times
194(3)
Why Have Setup Times: The Printing Press
194(1)
Reduce Variety or Reduce Setups: SMED
195(1)
Smooth the Flow: Heijunka
196(1)
Connections: Formula 1
197(1)
Conclusion
198(1)
Summary of Learning Objectives
199(1)
Key Terms
200(1)
Key Formulas
201(1)
Conceptual Questions
201(2)
Solved Example Problems
203(2)
Problems and Applications
205(4)
Case: Bonaire Salt
209(1)
8 Lean Operations and the Toyota Production System
210(40)
Introduction
210(2)
What Is Lean Operations?
212(1)
Wasting Time at a Resource
212(6)
Wasting Time of a Flow Unit
218(1)
The Architecture of the Toyota Production
System
219(1)
TPS Pillar 1 Single-Unit Flow and Just-in-Time Production
220(10)
Pull Systems
222(3)
Transferring on a Piece-by-Piece Basis
225(2)
Takt Time
227(1)
Demand Leveling
228(2)
TPS Pillar 2 Expose Problems and Solve Them When They Occur: Detect-Stop-Alert (Jidoka)
230(4)
Exposing Problems
231(1)
Jidoka: Detect-Stop-Alert
232(2)
Root-Cause Problem Solving and Defect Prevention
234(1)
Conclusion
234(1)
Summary of Learning Objectives
235(2)
Key Terms
237(1)
Key Formulas
238(1)
Conceptual Questions
239(3)
Solved Example Problems
242(4)
Problems and Applications
246(2)
Case: Nike
248(1)
References
249(1)
9 Quality and Statistical Process Control
250(42)
Introduction
250(1)
The Statistical Process Control Framework
251(4)
Connections: Lost Luggage
255(1)
Capability Analysis
255(1)
Determining a Capability Index
256(3)
Predicting the Probability of a Defect
259(2)
Setting a Variance Reduction Target
261(1)
Process Capability Summary and Extensions
262(1)
Connections: Apple iPhone Bending
263(1)
Conformance Analysis
264(3)
Investigating Assignable Causes
267(4)
How to Eliminate Assignable Causes and Make the Process More Robust
271(1)
Connections: Left and Right on a Boat
272(1)
Defects with Binary Outcomes: Event Trees
272(3)
Capability Evaluation for Discrete Events
272(3)
Defects with Binary Outcomes: p-Charts
275(1)
Connections: Some free cash from Citizens Bank?
276(1)
Conclusion
277(1)
Summary of Learning Objectives
278(1)
Key Terms
279(2)
Key Formulas
281(1)
Conceptual Questions
281(3)
Solved Example Problems
284(4)
Problems and Applications
288(2)
Case: The Production of M&M's
290(1)
References
291(1)
10 Introduction to Inventory Management
292(24)
Introduction
292(1)
Inventory Management
293(5)
Types of Inventory
293(1)
Inventory Management Capabilities
294(1)
Reasons for Holding Inventory
295(3)
How to Measure Inventory: Days-of-Supply and Turns
298(3)
Days-of-Supply
298(1)
Inventory Turns
299(1)
Benchmarks for Turns
300(1)
Connections: U.S. Inventory
301(1)
Evaluate Inventory Turns and Days-of-Supply from Financial Reports
302(5)
Inventory Stockout and Holding Costs
304(1)
Inventory Stockout Cost
304(1)
Inventory Holding Cost
305(1)
Inventory Holding Cost Percentage
306(1)
Inventory Holding Cost per Unit
306(1)
Conclusion
307(1)
Summary of Learning Objectives
308(1)
Key Terms
309(1)
Key Formulas
310(1)
Conceptual Questions
310(1)
Solved Example Problems
311(2)
Problems and Applications
313(2)
Case: Linking Turns to Gross Margin
315(1)
11 Supply Chain Management
316(1)
Introduction
316(1)
Supply Chain Structure and Roles
317(1)
Tier 2 Suppliers, Tier 1 Suppliers, and Manufacturers
317(2)
Distributors and Retailers
319(2)
Metrics of Supply Chain Performance
321(1)
Cost Metrics
321(2)
Service Metrics
323(1)
Supply Chain Decisions
324(3)
Tactical Decisions
324(1)
Strategic Decisions
325(2)
Sources of Variability in a Supply Chain
327(9)
Variability Due to Demand: Level, Variety, and Location
327(2)
Variability Due to the Bullwhip Effect
329(4)
Variability Due to Supply Chain Partner Performance
333(2)
Variability Due to Disruptions
335(1)
Supply Chain Strategies
336(7)
Mode of Transportation
336(3)
Overseas Sourcing
339(4)
Connections: Nike
343(1)
Connections: Zara
344(3)
Make-to-Order
344(3)
Connections: Dell
347(4)
Online Retailing
348(3)
Connections: Amazon
351(2)
Conclusion
353(1)
Summary of Learning Objectives
353(1)
Key Terms
354(2)
Key Formulas
356(1)
Conceptual Questions
356(2)
Solved Example Problems
358(2)
Problems and Applications
360(1)
Case: TIMBUK2
360(2)
12 Inventory Management with Steady Demand
362(27)
Introduction
362(1)
The Economic Order Quantity
363(3)
The Economic Order Quantity Model
364(2)
Connections: Consumption
366(5)
EOO Cost Function
367(2)
Optimal Order Quantity
369(1)
EOQ Cost and Cost per Unit
370(1)
Economies of Scale and Product Variety
371(3)
Connections: Girl Scout Cookies
374(1)
Quantity Constraints and Discounts
374(6)
Quantity Constraints
374(2)
Quantity Discounts
376(4)
Conclusion
380(1)
Summary of Learning Objectives
381(1)
Key Terms
381(1)
Key Formulas
382(1)
Conceptual Questions
382(1)
Solved Example Problems
383(2)
Problems and Applications
385(2)
Case: J&J and Walmart
387(2)
13 Inventory Management with Perishable Demand
389(57)
Introduction
389(1)
The Newsvendor Model
390(13)
O'Neill's Order Quantity Decision
391(4)
The Objective of and Inputs to the Newsvendor Model
395(1)
The Critical Ratio
396(2)
How to Determine the Optimal Order Quantity
398(5)
Connections: Flexible Spending Accounts
403(1)
Newsvendor Performance Measures
404(7)
Expected Inventory
404(3)
Expected Sales
407(1)
Expected Profit
408(1)
In-Stock and Stockout Probabilities
409(2)
Order Quantity to Achieve a Service Level
411(1)
Mismatch Costs in the Newsvendor Model
412(5)
Strategies to Manage the Newsvendor Environment: Product Pooling, Quick Response, and Make-to-Order
417(9)
Product Pooling
417(5)
Quick Response
422(2)
Make-to-Order
424(2)
Connections: Make-to-Order-Dell to Amazon
426(1)
Conclusion
427(1)
Summary of Learning Objectives
427(1)
Key Terms
428(2)
Key Formulas
430(1)
Conceptual Questions
430(3)
Solved Example Problems
433(3)
Problems and Applications
436(7)
Case: Le Club Francias du Vin
443(2)
Appendix 13A
445(1)
14 Inventory Management with Frequent Orders
446(41)
Introduction
446(1)
Medtronic's Supply Chain
447(2)
The Order-up-to Model
449(6)
Design of the Order-up-to Model
449(1)
The Order-up-to Level and Ordering Decisions
450(1)
Demand Forecast
451(4)
Connections: Poisson
455(1)
Performance Measures
456(5)
Expected On-Hand Inventory
456(3)
In-Stock and Stockout Probability
459(1)
Expected On-Order Inventory
460(1)
Choosing an Order-up-to Level
461(2)
Inventory and Service in the Order-up-to Level Model
463(3)
Improving the Supply Chain
466(7)
Location Pooling
466(3)
Lead-Time Pooling
469(2)
Delayed Differentiation
471(2)
Conclusion
473(1)
Summary of Learning Objectives
474(1)
Key Terms
475(1)
Key Formulas
475(1)
Conceptual Questions
476(3)
Solved Example Problems
479(2)
Problems and Applications
481(1)
Case: Warkworth Furniture
482(2)
Appendix 14A
484(3)
15 Forecasting
487(41)
Introduction
487(2)
Forecasting Framework
489(3)
Connections: Predicting the Future?
492(1)
Evaluating the Quality of a Forecast
493(4)
Eliminating Noise from Old Data
497(6)
Naive Model
497(1)
Moving Averages
498(1)
Exponential Smoothing Method
499(3)
Comparison of Methods
502(1)
Time Series Analysis---Trends
503(6)
Time Series Analysis---Seasonality
509(66)
Expert Panels and Subjective Forecasting
575(2)
Sources of Forecasting Biases
517(1)
Conclusion
517(1)
Summary of Learning Objectives
518(1)
Key Terms
519(1)
Key Formulas
520(1)
Conceptual Questions
521(1)
Solved Example Problems
522(3)
Problems and Applications
525(2)
Case: International Arrivals
527(1)
Literature/Further Reading
527(1)
16 Service Systems with Patient Customers
528(43)
Introduction
528(1)
Queues When Demand Exceeds Supply
529(4)
Length of the Queue
530(1)
Time to Serve Customers
531(1)
Average Waiting Time
532(1)
Managing Peak Demand
533(1)
Connections: Traffic and Congestion Pricing
533(1)
Queues When Demand and Service Rates Are Variable---One Server
534(11)
The Arrival and Service Processes
537(3)
A Queuing Model with a Single Server
540(2)
Utilization
542(1)
Predicting Time in Queue, Tq; Time in Service; and Tote Time in the System
543(1)
Predicting the Number of Customers Waiting and in Service
543(1)
The Key Drivers of Waiting Time
544(1)
Connections: The Psychology of Waiting
545(2)
Queues When Demand and Service Rates Are Variable---Multiple Servers
547(5)
Utilization, the Number of Servers, and Stable Queues
548(3)
Predicting Waiting Time in Queue, Tq; Waiting Time in Service; and Total Time in the System
551(1)
Predicting the Number of Customers Waiting and in Service
551(1)
Connections: Self-Service Queues
552(1)
Queuing System Design---Economies of Scale and Pooling
553(5)
The Power of Pooling
555(3)
Connections: The Fast-Food Drive-Through
558(1)
Conclusion
559(1)
Summary of Learning Objectives
560(1)
Key Terms
561(1)
Key Formulas
561(1)
Conceptual Questions
562(2)
Solved Example Problems
564(2)
Problems and Applications
566(3)
Case: Potty Parity
569(2)
17 Service Systems with Impatient Customers
571(1)
Introduction
571(1)
Lost Demand in Queues with No Buffers
572(1)
Connections: Ambulance Diversion
573(2)
The Erlang Loss Model
574(1)
Connections: Agner Krarup Erlang
575(7)
Capacity and Implied Utilization
576(1)
Performance Measures
576(1)
Percentage of Time All Servers Are Busy and the Denial of Service Probability
577(2)
Amount of Lost Demand, the Flow Rate, Utilization, and Occupied Resources
579(2)
Staffing
581(1)
Managing a Queue with Impatient Customers: Economies of Scale, Pooling, and Buffers
582(7)
Economies of Scale
582(2)
Pooling
584(2)
Buffers
586(3)
Lost Capacity Due to Variability
589(4)
Conclusion
593(1)
Summary of Learning Objectives
594(1)
Key Terms
594(1)
Key Formulas
595(1)
Conceptual Questions
596(1)
Solved Example Problems
597(2)
Problems and Applications
599(1)
References
600(1)
Case: Bike Sharing
601(2)
Appendix 17A Erlang Loss Tables
603(4)
18 Scheduling to Prioritize Demand
607(37)
Introduction
607(1)
Scheduling Timeline and Applications
608(62)
Resource Scheduling---Shortest Processing Time
670(7)
Performance Measures
611(1)
First-Come-First-Served vs. Shortest Processing Time
611(5)
Limitations of Shortest Processing Time
616(1)
Resource Scheduling with Priorities---Weighted Shortest Processing Time
617(4)
Connections: Net Neutrality
621(1)
Resource Scheduling with Due Dates---Earliest Due Date
622(3)
Theory of Constraints
625(2)
Reservations and Appointments
627(6)
Scheduling Appointments with Uncertain Processing Times
628(2)
No-Shows
630(3)
Connections: Overbooking
633(2)
Conclusion
635(1)
Summary of Learning Objectives
635(1)
Key Terms
636(1)
Key Formulas
637(1)
Conceptual Questions
637(2)
Solved Example Problems
639(2)
Problems and Applications
641(2)
References
643(1)
Case: Disney Fastpass
643(1)
19 Project Management
644(37)
Introduction
644(1)
Creating a Dependency Matrix for the Project
645(4)
The Activity Network
649(2)
The Critical Path Method
651(3)
Slack Time
654(3)
The Gantt Chart
657(2)
Uncertainty in Activity Times and Iteration
659(5)
Random Activity Times
659(3)
Iteration and Rework
662(1)
Unknown Unknowns (Unk-unks)
662(2)
Project Management Objectives
664(1)
Reducing a Project's Completion Time
665(1)
Organizing a Project
666(2)
Conclusion
668(1)
Summary of Learning Objectives
668(2)
Key Terms
670(1)
Key Formulas
671(1)
Conceptual Questions
672(2)
Solved Example Problems
674(3)
Problems and Applications
677(3)
Case: Building a House in Three Hours
680(1)
References
680(1)
Literature/Further Reading
680(1)
20 New Product Development
681(1)
Introduction
681(3)
Types of Innovations
684(1)
Connections: Innovation at Apple
685(2)
The Product Development Process
687(1)
Understanding User Needs
688(5)
Attributes and the Kano Model
688(2)
Identifying Customer Needs
690(1)
Coding Customer Needs
691(2)
Concept Generation
693(1)
Prototypes and Fidelity
693(1)
Connections: Crashing Cars
694(6)
Generating Product Concepts Using Attribute-Based Decomposition
694(2)
Generating Product Concepts Using User Interaction-Based Decomposition
696(3)
Concept Selection
699(1)
Rapid Validation/Experimentation
700(2)
Connections: The Fake Back-end and the Story of the First Voice Recognition Software
702(1)
Forecasting Sales
703(2)
Conclusion
705(2)
Summary of Learning Objectives
707(1)
Key Terms
708(2)
Key Formulas
710(1)
Conceptual Questions
710(2)
Solved Example Problems
712(4)
Problems and Applications
716(2)
Case: Innovation at Toyota
718(1)
References
718(1)
Glossary 719(14)
Index 733
Professor Cachon is the Fred R. Sullivan Professor of Operations, Information, and Decisons at The Wharton School of the University of Pennsylvania, where he teaches a variety of undergraduate, MBA, executive, and Ph.D. courses in operations management. His research focuses on operations strategy, and in particular, on how operations are used to gain competitive advantage.  His administrative responsibilities have included Chair of the Operations, Information and Decisions Department, Vice Dean of Strategic Initiatives for the Wharton School, and President of the Manufacturing and Service Operations Society. He has been named an INFORMS Fellow and a Distinguished Fellow of the Manufacturing and Service Operations Society.  His articles have appeared in Harvard Business Review, Management Science, Marketing Science, Manufacturing & Service Operations Management, and Operations Research. He is the former editor-in-chief of Manufacturing & Service Operations Management, and Management Science. He has consulted with a wide range of companies, including 4R Systems, Ahold, Americold, Campbell Soup, Gulfstream Aerospace, IBM, Medtronic, and ONeill.  Before joining The Wharton School in July 2000, Professor Cachon was on the faculty at the Fuqua School of Business, Duke University. He received a Ph.D. from The Wharton School in 1995.  He is an avid proponent of bicycle commuting (and other environmentally friendly modes of transportation). Along with his wife and four children he enjoys hiking, scuba diving and photography.





Professor Terwiesch is the Andrew M. Heller Professor at the Wharton School of the University of Pennsylvania. He also is a professor in Whartons Operations and Information Management Department as well as a Senior Fellow at  the Leonard Davis Institute for Health Economics. His research on operations management, research and development, and innovation management appears in many of the leading academic journals, including Management Science, Operations Research, Marketing Science, and Organization Science. He has received numerous teaching awards for his courses in Whartons MBA and executive education programs. Professor Terwiesch has researched with and consulted for various organizations, including a project on concurrent engineering for BMW, supply chain management for Intel and Medtronic, and product customization for Dell. Most of his current work relates to health care and innovation management. In the health care arena, some of Professor Terwieschs recent projects include the analysis of capacity allocation for cardiac surgery procedures at the University of CaliforniaSan Francisco and at Penn Medicine, the impact of emergency room crowding on hospital capacity and revenues (also at Penn Medicine), and the usage of intensive care beds in the Childrens Hospital of Philadelphia. In the innovation area, recent projects include the management of the clinical development portfolio at Merck, the development of open innovation systems, and the design of patient-centered care processes in the Veterans Administration hospital system.  Professor Terwieschs latest book, Innovation Tournaments, outlines a novel, process-based approach to innovation management. The book was featured by BusinessWeek, the Financial Times, and the Sloan Management Review.