Solution Manual for Operations Management: Sustainability and Supply Chain Management, 13th Edition
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Operations Management
Thirteenth Edition
Principles of Operations
Management
Eleventh Edition
Jay Heizer
Barry Render
Chuck Munson
Solutions Manual
(Download only) for
Operations Management, 13e
and Principles of Operations
Management 11e
Thirteenth Edition
Principles of Operations
Management
Eleventh Edition
Jay Heizer
Barry Render
Chuck Munson
Solutions Manual
(Download only) for
Operations Management, 13e
and Principles of Operations
Management 11e
3
Contents
Chapter 1
Operations and Productivity 1
Discussion Questions 1
Ethical Dilemma 3
American Car Battery Industry 3
End-of-Chapter Problems 3
Case Study 7
Uber Technologies, Inc. 7
Video Case Studies 7
Frito-Lay: Operations Management in Manufacturing 7
Hard Rock Cafe: Operations Management in Services 8
Additional Case Studies 11
National Air Express 11
Zychol Chemicals Corporation 11
Chapter 2
Operations Strategy in a Global Environment 13
Discussion Questions 13
Ethical Dilemma 15
End-of-Chapter Problems 16
Case Study 18
Rapid-Lube 18
Video Case Studies 19
Strategy at Regal Marine 19
Hard Rock Cafe’s Global Strategy 20
Outsourcing Offshore at Darden 20
Additional Case Study 21
Outsourcing to TATA 21
Chapter 3
Project Management 22
Discussion Questions 22
Ethical Dilemma 24
Active Model Exercise 25
ACTIVE MODEL 3.1: Gantt Chart 25
End-of-Chapter Problems 26
Video Case Studies 41
Project Management At Arnold Palmer Hospital 41
Managing Hard Rock’s Rockfest 42
Additional Case Studies 44
Shale Oil Company 44
Southwestern University: A 46
Chapter 4
Forecasting 48
Discussion Questions 48
Ethical Dilemma 51
Active Model Exercises* 51
ACTIVE MODEL 4.1: Moving Averages 51
ACTIVE MODEL 4.2: Exponential Smoothing 51
ACTIVE MODEL 4.3: Exponential Smoothing with Trend Adjustment 51
ACTIVE MODEL 4.4: Trend Projections 52
End-of-Chapter Problems 52
Case Study 82
Contents
Chapter 1
Operations and Productivity 1
Discussion Questions 1
Ethical Dilemma 3
American Car Battery Industry 3
End-of-Chapter Problems 3
Case Study 7
Uber Technologies, Inc. 7
Video Case Studies 7
Frito-Lay: Operations Management in Manufacturing 7
Hard Rock Cafe: Operations Management in Services 8
Additional Case Studies 11
National Air Express 11
Zychol Chemicals Corporation 11
Chapter 2
Operations Strategy in a Global Environment 13
Discussion Questions 13
Ethical Dilemma 15
End-of-Chapter Problems 16
Case Study 18
Rapid-Lube 18
Video Case Studies 19
Strategy at Regal Marine 19
Hard Rock Cafe’s Global Strategy 20
Outsourcing Offshore at Darden 20
Additional Case Study 21
Outsourcing to TATA 21
Chapter 3
Project Management 22
Discussion Questions 22
Ethical Dilemma 24
Active Model Exercise 25
ACTIVE MODEL 3.1: Gantt Chart 25
End-of-Chapter Problems 26
Video Case Studies 41
Project Management At Arnold Palmer Hospital 41
Managing Hard Rock’s Rockfest 42
Additional Case Studies 44
Shale Oil Company 44
Southwestern University: A 46
Chapter 4
Forecasting 48
Discussion Questions 48
Ethical Dilemma 51
Active Model Exercises* 51
ACTIVE MODEL 4.1: Moving Averages 51
ACTIVE MODEL 4.2: Exponential Smoothing 51
ACTIVE MODEL 4.3: Exponential Smoothing with Trend Adjustment 51
ACTIVE MODEL 4.4: Trend Projections 52
End-of-Chapter Problems 52
Case Study 82
3
Contents
Chapter 1
Operations and Productivity 1
Discussion Questions 1
Ethical Dilemma 3
American Car Battery Industry 3
End-of-Chapter Problems 3
Case Study 7
Uber Technologies, Inc. 7
Video Case Studies 7
Frito-Lay: Operations Management in Manufacturing 7
Hard Rock Cafe: Operations Management in Services 8
Additional Case Studies 11
National Air Express 11
Zychol Chemicals Corporation 11
Chapter 2
Operations Strategy in a Global Environment 13
Discussion Questions 13
Ethical Dilemma 15
End-of-Chapter Problems 16
Case Study 18
Rapid-Lube 18
Video Case Studies 19
Strategy at Regal Marine 19
Hard Rock Cafe’s Global Strategy 20
Outsourcing Offshore at Darden 20
Additional Case Study 21
Outsourcing to TATA 21
Chapter 3
Project Management 22
Discussion Questions 22
Ethical Dilemma 24
Active Model Exercise 25
ACTIVE MODEL 3.1: Gantt Chart 25
End-of-Chapter Problems 26
Video Case Studies 41
Project Management At Arnold Palmer Hospital 41
Managing Hard Rock’s Rockfest 42
Additional Case Studies 44
Shale Oil Company 44
Southwestern University: A 46
Chapter 4
Forecasting 48
Discussion Questions 48
Ethical Dilemma 51
Active Model Exercises* 51
ACTIVE MODEL 4.1: Moving Averages 51
ACTIVE MODEL 4.2: Exponential Smoothing 51
ACTIVE MODEL 4.3: Exponential Smoothing with Trend Adjustment 51
ACTIVE MODEL 4.4: Trend Projections 52
End-of-Chapter Problems 52
Case Study 82
Contents
Chapter 1
Operations and Productivity 1
Discussion Questions 1
Ethical Dilemma 3
American Car Battery Industry 3
End-of-Chapter Problems 3
Case Study 7
Uber Technologies, Inc. 7
Video Case Studies 7
Frito-Lay: Operations Management in Manufacturing 7
Hard Rock Cafe: Operations Management in Services 8
Additional Case Studies 11
National Air Express 11
Zychol Chemicals Corporation 11
Chapter 2
Operations Strategy in a Global Environment 13
Discussion Questions 13
Ethical Dilemma 15
End-of-Chapter Problems 16
Case Study 18
Rapid-Lube 18
Video Case Studies 19
Strategy at Regal Marine 19
Hard Rock Cafe’s Global Strategy 20
Outsourcing Offshore at Darden 20
Additional Case Study 21
Outsourcing to TATA 21
Chapter 3
Project Management 22
Discussion Questions 22
Ethical Dilemma 24
Active Model Exercise 25
ACTIVE MODEL 3.1: Gantt Chart 25
End-of-Chapter Problems 26
Video Case Studies 41
Project Management At Arnold Palmer Hospital 41
Managing Hard Rock’s Rockfest 42
Additional Case Studies 44
Shale Oil Company 44
Southwestern University: A 46
Chapter 4
Forecasting 48
Discussion Questions 48
Ethical Dilemma 51
Active Model Exercises* 51
ACTIVE MODEL 4.1: Moving Averages 51
ACTIVE MODEL 4.2: Exponential Smoothing 51
ACTIVE MODEL 4.3: Exponential Smoothing with Trend Adjustment 51
ACTIVE MODEL 4.4: Trend Projections 52
End-of-Chapter Problems 52
Case Study 82
4
Southwestern University: B 82
Video Case Studies 83
Forecasting Ticket Revenue for Orlando Magic Basketball Games 83
Forecasting at Hard Rock Cafe 84
Additional Case Studies 85
The North-South Airlines 85
Digital Cell Phone, Inc. 87
Chapter 5
Design of Goods and Services 89
Discussion Questions 89
Ethical Dilemma 91
Active Model Exercise 92
Active Model 5.1: Decision Tree 92
End-of-Chapter Problems 92
Case Study 104
De Mar’s Product Strategy 104
Video Case Studies 105
Product Design at Regal Marine 105
Celebrity Cruises Designs A New Ship 106
Supplement 5
Sustainability in the Supply Chain 107
Discussion Questions 107
End-of-Supplement Problems 108
Video Case Studies 112
Building Sustainability at the Orlando Magic’s Amway Center 112
Green Manufacturing And Sustainability at Frito-Lay 113
“Saving The Waves” At Celebrity Cruises 113
Additional Case Study 114
Environmental Sustainability at Walmart 114
Chapter 6
Managing Quality 116
Discussion Questions 116
Ethical Dilemma 118
Active Model Exercise* 119
ACTIVE MODEL 6.1: Pareto Charts 119
End-of-Chapter Problems 119
Case Study 128
Southwestern University: C 128
Video Case Studies 131
The Culture of Quality at Arnold Palmer Hospital 131
Quality Counts at Alaska Airlines 132
Celebrity Cruises: A Premium Experience 133
Additional Case Studies 134
Westover Electrical, Inc. 134
Quality at the Ritz-Carlton Hotel 135
Supplement 6
Statistical Process Control 137
Discussion Questions 137
Active Model Exercises* 139
ACTIVE MODEL S6.1: -X bar Chart 139
ACTIVE MODEL S6.2: p-Chart—with data 139
ACTIVE MODEL S6.3: Process Capability 140
End-of-Supplement Problems 140
Case Study 160
Southwestern University: B 82
Video Case Studies 83
Forecasting Ticket Revenue for Orlando Magic Basketball Games 83
Forecasting at Hard Rock Cafe 84
Additional Case Studies 85
The North-South Airlines 85
Digital Cell Phone, Inc. 87
Chapter 5
Design of Goods and Services 89
Discussion Questions 89
Ethical Dilemma 91
Active Model Exercise 92
Active Model 5.1: Decision Tree 92
End-of-Chapter Problems 92
Case Study 104
De Mar’s Product Strategy 104
Video Case Studies 105
Product Design at Regal Marine 105
Celebrity Cruises Designs A New Ship 106
Supplement 5
Sustainability in the Supply Chain 107
Discussion Questions 107
End-of-Supplement Problems 108
Video Case Studies 112
Building Sustainability at the Orlando Magic’s Amway Center 112
Green Manufacturing And Sustainability at Frito-Lay 113
“Saving The Waves” At Celebrity Cruises 113
Additional Case Study 114
Environmental Sustainability at Walmart 114
Chapter 6
Managing Quality 116
Discussion Questions 116
Ethical Dilemma 118
Active Model Exercise* 119
ACTIVE MODEL 6.1: Pareto Charts 119
End-of-Chapter Problems 119
Case Study 128
Southwestern University: C 128
Video Case Studies 131
The Culture of Quality at Arnold Palmer Hospital 131
Quality Counts at Alaska Airlines 132
Celebrity Cruises: A Premium Experience 133
Additional Case Studies 134
Westover Electrical, Inc. 134
Quality at the Ritz-Carlton Hotel 135
Supplement 6
Statistical Process Control 137
Discussion Questions 137
Active Model Exercises* 139
ACTIVE MODEL S6.1: -X bar Chart 139
ACTIVE MODEL S6.2: p-Chart—with data 139
ACTIVE MODEL S6.3: Process Capability 140
End-of-Supplement Problems 140
Case Study 160
5
Bayfield Mud Company 160
Video Case Studies 162
Frito-Lay’s Quality-Controlled Potato Chips 162
Farm to Fork: Quality at Darden Restaurants 162
Additional Case Study 163
Green River Chemical Co. 163
Chapter 7
Process Strategies 164
Discussion Questions 164
Ethical Dilemma 166
Active Model Exercise 167
ACTIVE MODEL 7.1: Crossover Chart 167
End-of-Chapter Problems 167
Case Study 172
Rochester Manufacturing’s Process Decision 172
Video Case Studies 172
Process Strategy at Wheeled Coach 172
Alaska Airlines 20-Minute Baggage
Process—Guaranteed! 173
Process Analysis at Arnold Palmer Hospital 174
Additional Case Study 176
Matthew Yachts, Inc. 176
Supplement 7
Capacity and Constraint Management 177
Discussion Questions 177
Active Model Exercises 178
ACTIVE MODEL S7.1: Productivity 178
ACTIVE MODEL S7.2: Break-even Analysis 179
End-of-Supplement Problems 179
Video Case Study 191
Capacity Planning at Arnold Palmer Hospital 191
Additional Case Study 192
Southwestern University: D 192
Chapter 8
Location Strategies 194
Discussion Questions 194
Ethical Dilemma 196
Active Model Exercise 196
ACTIVE MODEL 8.1: Center of Gravity 196
End-of-Chapter Problems 196
Case Study 209
Southern Recreational Vehicle Company 209
Video Case Studies 210
Locating the Next Red Lobster Restaurant 210
Where to Place the Hard Rock Cafe 210
Additional Case Study 214
Southwestern University: E 214
Chapter 9
Layout Strategies 216
Discussion Questions 216
Ethical Dilemma 219
Active Model Exercise 219
ACTIVE MODEL 9.1: Process Layout 219
End-of-Chapter Problems 219
Bayfield Mud Company 160
Video Case Studies 162
Frito-Lay’s Quality-Controlled Potato Chips 162
Farm to Fork: Quality at Darden Restaurants 162
Additional Case Study 163
Green River Chemical Co. 163
Chapter 7
Process Strategies 164
Discussion Questions 164
Ethical Dilemma 166
Active Model Exercise 167
ACTIVE MODEL 7.1: Crossover Chart 167
End-of-Chapter Problems 167
Case Study 172
Rochester Manufacturing’s Process Decision 172
Video Case Studies 172
Process Strategy at Wheeled Coach 172
Alaska Airlines 20-Minute Baggage
Process—Guaranteed! 173
Process Analysis at Arnold Palmer Hospital 174
Additional Case Study 176
Matthew Yachts, Inc. 176
Supplement 7
Capacity and Constraint Management 177
Discussion Questions 177
Active Model Exercises 178
ACTIVE MODEL S7.1: Productivity 178
ACTIVE MODEL S7.2: Break-even Analysis 179
End-of-Supplement Problems 179
Video Case Study 191
Capacity Planning at Arnold Palmer Hospital 191
Additional Case Study 192
Southwestern University: D 192
Chapter 8
Location Strategies 194
Discussion Questions 194
Ethical Dilemma 196
Active Model Exercise 196
ACTIVE MODEL 8.1: Center of Gravity 196
End-of-Chapter Problems 196
Case Study 209
Southern Recreational Vehicle Company 209
Video Case Studies 210
Locating the Next Red Lobster Restaurant 210
Where to Place the Hard Rock Cafe 210
Additional Case Study 214
Southwestern University: E 214
Chapter 9
Layout Strategies 216
Discussion Questions 216
Ethical Dilemma 219
Active Model Exercise 219
ACTIVE MODEL 9.1: Process Layout 219
End-of-Chapter Problems 219
6
Case Study 243
State Automobile License Renewals 243
Video Case Studies 245
Laying out Arnold Palmer Hospital’s
New Facility 245
Facility Layout at Wheeled Coach 246
Additional Case Study 247
Microfix, inc. 247
Chapter 10
Human Resources, Job Design, and Work Measurement 248
Discussion Questions 248
Active Model Exercise 250
ACTIVE MODEL 10.1: Work Sampling 250
Ethical Dilemma 250
End-of-Chapter Problems 250
Case Study 270
Jackson Manufacturing Co. 270
Video Case Studies 271
The “People” Focus: Human Resources at Alaska Airlines 271
Hard Rock’s Human Resource Strategy 272
Additional Case Studies 272
Chicago Southern Hospital 272
The Fleet that Wanders 273
Chapter 11
Supply Chain Management 274
Discussion Questions 274
Ethical Dilemma 276
End-of-Chapter Problems 276
Video Case Studies 279
Darden’s Global Supply Chains 279
Supply Chain Management at Regal Marine 280
Arnold Palmer Hospital’s Supply Chain 280
Supplement 11
Supply Chain Management Analytics 282
Discussion Questions 282
End-of-Supplement Problems 284
Chapter 12
Inventory Management 293
Discussion Questions 293
Ethical Dilemma 295
Active Model Exercises 295
ACTIVE MODEL 12.1: Economic Order Quantity (EOQ) Model 295
ACTIVE MODEL 12.2: Production Order Quantity Model 296
End-of-Chapter Problems 296
Case Study 312
Zhou Bicycle Company 312
Video Case Studies 313
Managing Inventory at Frito-Lay 313
Inventory Management At Celebrity Cruises 314
Inventory Control at Wheeled Coach 315
Additional Case Studies 315
Southwestern University: F 315
Laplace Power and Light 317
Case Study 243
State Automobile License Renewals 243
Video Case Studies 245
Laying out Arnold Palmer Hospital’s
New Facility 245
Facility Layout at Wheeled Coach 246
Additional Case Study 247
Microfix, inc. 247
Chapter 10
Human Resources, Job Design, and Work Measurement 248
Discussion Questions 248
Active Model Exercise 250
ACTIVE MODEL 10.1: Work Sampling 250
Ethical Dilemma 250
End-of-Chapter Problems 250
Case Study 270
Jackson Manufacturing Co. 270
Video Case Studies 271
The “People” Focus: Human Resources at Alaska Airlines 271
Hard Rock’s Human Resource Strategy 272
Additional Case Studies 272
Chicago Southern Hospital 272
The Fleet that Wanders 273
Chapter 11
Supply Chain Management 274
Discussion Questions 274
Ethical Dilemma 276
End-of-Chapter Problems 276
Video Case Studies 279
Darden’s Global Supply Chains 279
Supply Chain Management at Regal Marine 280
Arnold Palmer Hospital’s Supply Chain 280
Supplement 11
Supply Chain Management Analytics 282
Discussion Questions 282
End-of-Supplement Problems 284
Chapter 12
Inventory Management 293
Discussion Questions 293
Ethical Dilemma 295
Active Model Exercises 295
ACTIVE MODEL 12.1: Economic Order Quantity (EOQ) Model 295
ACTIVE MODEL 12.2: Production Order Quantity Model 296
End-of-Chapter Problems 296
Case Study 312
Zhou Bicycle Company 312
Video Case Studies 313
Managing Inventory at Frito-Lay 313
Inventory Management At Celebrity Cruises 314
Inventory Control at Wheeled Coach 315
Additional Case Studies 315
Southwestern University: F 315
Laplace Power and Light 317
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7
Chapter 13
Aggregate Planning and S&OP 319
Discussion Questions 319
Ethical Dilemma 321
Active Model Exercise 321
ACTIVE MODEL 13.1: Aggregate Planning 321
End-of-Chapter Problems 322
Case Study 339
Andrew-Carter, Inc. 339
Video Case Study 340
Using Revenue Management to Set Orlando Magic Ticket Prices 340
Additional Case Studies 340
Cornwell Glass 340
Southwestern University: (G) 342
Chapter 14
Material Requirements Planning (MRP) and ERP 344
Discussion Questions 344
Ethical Dilemma 347
Active Model Exercise 347
ACTIVE MODEL 14.1: Order Releases 347
End-of-Chapter Problems 347
Video Case Studies 372
When 18,500 Orlando Magic Fans Come to Dinner 372
MRP At Wheeled Coach 374
Additional Case Studies 374
IKON’S Attempt at ERP 374
Hill’s Automotive, Inc. 375
Chapter 15
Short-Term Scheduling 376
Discussion Questions 376
Ethical Dilemma 377
Active Model Exercise 377
ACTIVE MODEL 15.1: Job Shop Sequencing 377
End-of-Chapter Problems 378
Case Study 398
Old Oregon Wood Store 398
Video Case Studies 400
From the Eagles to The Magic: Converting the Amway Center 400
Scheduling at Hard Rock Cafe 401
Additional Case Study 402
Payroll Planning, Inc. 402
Chapter 16
Lean Operations 403
Discussion Questions 403
Ethical Dilemma 404
End-of-Chapter Problems 404
Video Case Studies 407
Lean Operations at Alaska Airlines 407
JIT at Arnold Palmer Hospital 408
Additional Case Studies 408
JIT After a Catastrophe 408
Mutual Insurance Company of Iowa 409
Chapter 13
Aggregate Planning and S&OP 319
Discussion Questions 319
Ethical Dilemma 321
Active Model Exercise 321
ACTIVE MODEL 13.1: Aggregate Planning 321
End-of-Chapter Problems 322
Case Study 339
Andrew-Carter, Inc. 339
Video Case Study 340
Using Revenue Management to Set Orlando Magic Ticket Prices 340
Additional Case Studies 340
Cornwell Glass 340
Southwestern University: (G) 342
Chapter 14
Material Requirements Planning (MRP) and ERP 344
Discussion Questions 344
Ethical Dilemma 347
Active Model Exercise 347
ACTIVE MODEL 14.1: Order Releases 347
End-of-Chapter Problems 347
Video Case Studies 372
When 18,500 Orlando Magic Fans Come to Dinner 372
MRP At Wheeled Coach 374
Additional Case Studies 374
IKON’S Attempt at ERP 374
Hill’s Automotive, Inc. 375
Chapter 15
Short-Term Scheduling 376
Discussion Questions 376
Ethical Dilemma 377
Active Model Exercise 377
ACTIVE MODEL 15.1: Job Shop Sequencing 377
End-of-Chapter Problems 378
Case Study 398
Old Oregon Wood Store 398
Video Case Studies 400
From the Eagles to The Magic: Converting the Amway Center 400
Scheduling at Hard Rock Cafe 401
Additional Case Study 402
Payroll Planning, Inc. 402
Chapter 16
Lean Operations 403
Discussion Questions 403
Ethical Dilemma 404
End-of-Chapter Problems 404
Video Case Studies 407
Lean Operations at Alaska Airlines 407
JIT at Arnold Palmer Hospital 408
Additional Case Studies 408
JIT After a Catastrophe 408
Mutual Insurance Company of Iowa 409
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8
Chapter 17
Maintenance and Reliability 411
Discussion Questions 411
Ethical Dilemma 413
Active Model Exercises 413
ACTIVE MODEL 17.1: Series Reliability 413
ACTIVE MODEL 17.2: Redundancy 413
ACTIVE MODEL 17.3: Parallel Systems 413
End-of-Chapter Problems 414
Video Case Study 417
Maintenance Drives Profits at Frito-Lay 417
Additional Case Studies 418
Cartak’s Department Store 418
Worldwide Chemical Company 418
Business Analytics Module A
Decision-Making Tools 420
Discussion Questions 420
End-of-Module Problems 421
Case Study 433
Tom Thompson’s Liver Transplant 433
Additional Case Studies 433
Arctic, Inc. 433
Ski Right Corp. 434
Warehouse Tenting At The Port Of Miami 435
Business Analytics Module B
Linear Programming 436
Discussion Questions 436
Active Model Exercise 437
ACTIVE MODEL B.1: LP Graph 437
End-of-Module Problems 438
Case Study 460
Quain Lawn and Garden Inc. 460
Video Case Study 461
Using LP to Meet Scheduling Challenges at
Alaska Airlines 461
Additional Case Studies 462
Chase Manhattan Bank 462
Coastal States Chemical 464
Business Analytics Module C
Transportation Models 466
Discussion Questions 466
End-of-Module Problems 467
Case Study 480
Custom Vans, Inc. 480
Additional Case Study 482
Consolidated Bottling: B 482
Business Analytics Module D
Waiting-Line Models 484
Discussion Questions 484
Active Model Exercises 488
ACTIVE MODEL D.1: Single Server Model 488
ACTIVE MODEL D.2: Multiple Server System
with Costs 488
Chapter 17
Maintenance and Reliability 411
Discussion Questions 411
Ethical Dilemma 413
Active Model Exercises 413
ACTIVE MODEL 17.1: Series Reliability 413
ACTIVE MODEL 17.2: Redundancy 413
ACTIVE MODEL 17.3: Parallel Systems 413
End-of-Chapter Problems 414
Video Case Study 417
Maintenance Drives Profits at Frito-Lay 417
Additional Case Studies 418
Cartak’s Department Store 418
Worldwide Chemical Company 418
Business Analytics Module A
Decision-Making Tools 420
Discussion Questions 420
End-of-Module Problems 421
Case Study 433
Tom Thompson’s Liver Transplant 433
Additional Case Studies 433
Arctic, Inc. 433
Ski Right Corp. 434
Warehouse Tenting At The Port Of Miami 435
Business Analytics Module B
Linear Programming 436
Discussion Questions 436
Active Model Exercise 437
ACTIVE MODEL B.1: LP Graph 437
End-of-Module Problems 438
Case Study 460
Quain Lawn and Garden Inc. 460
Video Case Study 461
Using LP to Meet Scheduling Challenges at
Alaska Airlines 461
Additional Case Studies 462
Chase Manhattan Bank 462
Coastal States Chemical 464
Business Analytics Module C
Transportation Models 466
Discussion Questions 466
End-of-Module Problems 467
Case Study 480
Custom Vans, Inc. 480
Additional Case Study 482
Consolidated Bottling: B 482
Business Analytics Module D
Waiting-Line Models 484
Discussion Questions 484
Active Model Exercises 488
ACTIVE MODEL D.1: Single Server Model 488
ACTIVE MODEL D.2: Multiple Server System
with Costs 488
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9
ACTIVE MODEL D.3: Constant Service Times 489
End-of-Module Problems 489
Case Studies 505
New England Foundry 505
The Winter Park Hotel 506
Additional Case Study 506
Pantry Shopper 506
Business Analytics Module E
Learning Curves 508
Discussion Questions 508
Active Model Exercise 509
ACTIVE MODEL E.1: Unit Curve, Cumulative Curve, and Costs 508
End-of-Module Problems 509
Case Study 515
SMT’S Negotiation with IBM 515
Business Analytics Module F
Simulation 516
Discussion Questions 516
End-of-Module Problems 518
Case Study 531
Alabama Airlines Call Center 531
Additional Case Study 533
Saigon Transport 533
Business Analytics Module G
Applying Analytics to Big Data in Operations Management 534
Discussion Questions 534
End-of-Module Problems 535
Online Tutorial 1
Statistical Tools for Managers 538
Discussion Questions 538
End-of-Tutorial Problems 538
Online Tutorial 2
Acceptance Sampling 543
Discussion Questions 543
End-of-Tutorial Problems 543
Online Tutorial 3
The Simplex Method of Linear Programming 545
Discussion Questions 545
End-of-Tutorial Problems 545
Online Tutorial 4
The MODI and VAM Methods of Solving Transportation Problems 551
Discussion Questions 551
End-of-Tutorial Problems 551
Online Tutorial 5
Vehicle Routing and Scheduling 557
Discussion Questions 557
End-of-Tutorial Problems 558
Case Study 559
Routing And Scheduling Of Phlebotomists 559
ACTIVE MODEL D.3: Constant Service Times 489
End-of-Module Problems 489
Case Studies 505
New England Foundry 505
The Winter Park Hotel 506
Additional Case Study 506
Pantry Shopper 506
Business Analytics Module E
Learning Curves 508
Discussion Questions 508
Active Model Exercise 509
ACTIVE MODEL E.1: Unit Curve, Cumulative Curve, and Costs 508
End-of-Module Problems 509
Case Study 515
SMT’S Negotiation with IBM 515
Business Analytics Module F
Simulation 516
Discussion Questions 516
End-of-Module Problems 518
Case Study 531
Alabama Airlines Call Center 531
Additional Case Study 533
Saigon Transport 533
Business Analytics Module G
Applying Analytics to Big Data in Operations Management 534
Discussion Questions 534
End-of-Module Problems 535
Online Tutorial 1
Statistical Tools for Managers 538
Discussion Questions 538
End-of-Tutorial Problems 538
Online Tutorial 2
Acceptance Sampling 543
Discussion Questions 543
End-of-Tutorial Problems 543
Online Tutorial 3
The Simplex Method of Linear Programming 545
Discussion Questions 545
End-of-Tutorial Problems 545
Online Tutorial 4
The MODI and VAM Methods of Solving Transportation Problems 551
Discussion Questions 551
End-of-Tutorial Problems 551
Online Tutorial 5
Vehicle Routing and Scheduling 557
Discussion Questions 557
End-of-Tutorial Problems 558
Case Study 559
Routing And Scheduling Of Phlebotomists 559
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1
11C H A P T E R
Operations and Productivity
DISCUSSION QUESTIONS
1. The text suggests four reasons to study OM. We want to understand (1) how people organize themselves for productive enterprise,
(2) how goods and services are produced, (3) what operations managers do, and (4) this costly part of our economy and most enterprises.
LO 1.1: Define operations management
AACSB: Application of knowledge
2. With some 40% of all jobs being in the OM field, the career opportunities are prolific. The text suggests many career opportunities. OM
students find initial jobs throughout the OM field, including supply chain, logistics, purchasing, production planning and scheduling, plant layout,
maintenance, quality control, inventory management, etc.
LO 1.3: Identify career opportunities in operations management
AACSB: Application of knowledge
3. Possible responses include: Adam Smith (work specialization/ division of labor), Charles Babbage (work specialization/division of labor),
Frederick W. Taylor (scientific management), Walter Shewart (statistical sampling and quality control), Henry Ford (moving assembly line),
Charles Sorensen (moving assembly line), Frank and Lillian Gilbreth (motion study), Eli Whitney (standardization).
LO 1.1: Define operations management
AACSB: Application of knowledge
4. See references in the answer to Question 3.
LO 1.1: Define operations management
AACSB: Application of knowledge
5. The actual charts will differ, depending on the specific organization the student chooses to describe. The important thing is for students
to recognize that all organizations require, to a greater or lesser extent, (a) the three primary functions of operations, finance/accounting,
and marketing; and (b) that the emphasis or detailed breakdown of these functions is dependent on the specific competitive strategy
employed by the firm.
LO 1.1: Define operations management
AACSB: Application of knowledge
6. The answer to this question may be similar to that for Question 5. Here, however, the student should be encouraged to utilize a more
detailed knowledge of a past employer and indicate on the chart additional information such as the number of persons employed to perform
the various functions and, perhaps, the position of the functional areas within the overall organization hierarchy.
LO 1.1: Define operations management
AACSB: Application of knowledge
11C H A P T E R
Operations and Productivity
DISCUSSION QUESTIONS
1. The text suggests four reasons to study OM. We want to understand (1) how people organize themselves for productive enterprise,
(2) how goods and services are produced, (3) what operations managers do, and (4) this costly part of our economy and most enterprises.
LO 1.1: Define operations management
AACSB: Application of knowledge
2. With some 40% of all jobs being in the OM field, the career opportunities are prolific. The text suggests many career opportunities. OM
students find initial jobs throughout the OM field, including supply chain, logistics, purchasing, production planning and scheduling, plant layout,
maintenance, quality control, inventory management, etc.
LO 1.3: Identify career opportunities in operations management
AACSB: Application of knowledge
3. Possible responses include: Adam Smith (work specialization/ division of labor), Charles Babbage (work specialization/division of labor),
Frederick W. Taylor (scientific management), Walter Shewart (statistical sampling and quality control), Henry Ford (moving assembly line),
Charles Sorensen (moving assembly line), Frank and Lillian Gilbreth (motion study), Eli Whitney (standardization).
LO 1.1: Define operations management
AACSB: Application of knowledge
4. See references in the answer to Question 3.
LO 1.1: Define operations management
AACSB: Application of knowledge
5. The actual charts will differ, depending on the specific organization the student chooses to describe. The important thing is for students
to recognize that all organizations require, to a greater or lesser extent, (a) the three primary functions of operations, finance/accounting,
and marketing; and (b) that the emphasis or detailed breakdown of these functions is dependent on the specific competitive strategy
employed by the firm.
LO 1.1: Define operations management
AACSB: Application of knowledge
6. The answer to this question may be similar to that for Question 5. Here, however, the student should be encouraged to utilize a more
detailed knowledge of a past employer and indicate on the chart additional information such as the number of persons employed to perform
the various functions and, perhaps, the position of the functional areas within the overall organization hierarchy.
LO 1.1: Define operations management
AACSB: Application of knowledge
Loading page 10...
2 CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y
7. The basic functions of a firm are marketing, accounting/ finance, and operations. An interesting class discussion: “Do all firms/organizations
(private, government, not-for-profit) perform these three functions?” The authors’ hypothesis is yes, they do.
LO 1.1: Define operations management
AACSB: Application of knowledge
8. The 10 strategic decisions of operations management are product design, quality, process, location, layout, human resources, supply-chain
management, inventory, scheduling (intermediate and short-term), and maintenance. We find this structure an excellent way to help students
organize and learn the material.
LO 1.1: Define operations management
AACSB: Application of knowledge
9. Four areas that are important to improving labor productivity are (1) basic education (basic reading and math skills), (2) diet of the labor
force, (3) social overhead that makes labor available (water, sanitation, transportation, etc.), and (4) maintaining and expanding the skills
necessary for changing technology and knowledge, as well as for teamwork and motivation.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Application of knowledge
10. Productivity is harder to measure when the task becomes more intellectual. A knowledge society implies that work is more
intellectual and therefore harder to measure. Because the U.S. and many other countries are increasingly “knowledge” societies,
productivity is harder to measure. Using labor-hours as a measure of productivity for a postindustrial society versus an industrial or
agriculture society is very different. For example, decades spent developing a marvelous new drug or winning a very difficult legal case on
intellectual property rights may be significant for postindustrial societies, but not show much in the way of productivity improvement
measured in labor-hours.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
11. Productivity is difficult to measure because precise units of measure may be lacking, quality may not be consistent, and exogenous
variables may change.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Reflective thinking
12. Mass customization is the flexibility to produce to meet specific customer demands, without sacrificing the low cost of a product-oriented
process. Rapid product development is a source of competitive advantage. Both rely on agility within the organization.
LO 1.1: Define operations management
AACSB: Application of knowledge
13. Labor productivity in the service sector is hard to improve because (1) many services are labor intensive and (2) they are individually
(personally) processed (the customer is paying for that service—the haircut), (3) it may be an intellectual task performed by professionals,
(4) it is often difficult to mechanize and automate, and (5) it is often difficult to evaluate for quality.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Reflective thinking
14. Taco Bell designed meals that were easy to prepare; with actual cooking and food preparation done elsewhere; automation to save
preparation time; reduced floor space; manager training to increase span of control.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Application of knowledge
7. The basic functions of a firm are marketing, accounting/ finance, and operations. An interesting class discussion: “Do all firms/organizations
(private, government, not-for-profit) perform these three functions?” The authors’ hypothesis is yes, they do.
LO 1.1: Define operations management
AACSB: Application of knowledge
8. The 10 strategic decisions of operations management are product design, quality, process, location, layout, human resources, supply-chain
management, inventory, scheduling (intermediate and short-term), and maintenance. We find this structure an excellent way to help students
organize and learn the material.
LO 1.1: Define operations management
AACSB: Application of knowledge
9. Four areas that are important to improving labor productivity are (1) basic education (basic reading and math skills), (2) diet of the labor
force, (3) social overhead that makes labor available (water, sanitation, transportation, etc.), and (4) maintaining and expanding the skills
necessary for changing technology and knowledge, as well as for teamwork and motivation.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Application of knowledge
10. Productivity is harder to measure when the task becomes more intellectual. A knowledge society implies that work is more
intellectual and therefore harder to measure. Because the U.S. and many other countries are increasingly “knowledge” societies,
productivity is harder to measure. Using labor-hours as a measure of productivity for a postindustrial society versus an industrial or
agriculture society is very different. For example, decades spent developing a marvelous new drug or winning a very difficult legal case on
intellectual property rights may be significant for postindustrial societies, but not show much in the way of productivity improvement
measured in labor-hours.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
11. Productivity is difficult to measure because precise units of measure may be lacking, quality may not be consistent, and exogenous
variables may change.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Reflective thinking
12. Mass customization is the flexibility to produce to meet specific customer demands, without sacrificing the low cost of a product-oriented
process. Rapid product development is a source of competitive advantage. Both rely on agility within the organization.
LO 1.1: Define operations management
AACSB: Application of knowledge
13. Labor productivity in the service sector is hard to improve because (1) many services are labor intensive and (2) they are individually
(personally) processed (the customer is paying for that service—the haircut), (3) it may be an intellectual task performed by professionals,
(4) it is often difficult to mechanize and automate, and (5) it is often difficult to evaluate for quality.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Reflective thinking
14. Taco Bell designed meals that were easy to prepare; with actual cooking and food preparation done elsewhere; automation to save
preparation time; reduced floor space; manager training to increase span of control.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Application of knowledge
Loading page 11...
CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y 3
15. Bureau of Labor Statistics (stats.bls.gov) is a good place to start. Results will vary for each year, but overall data for the economy will
range from 0.9% to 4.8%, and mfg. could be as high as 5% and services between 1% and 2%. The data will vary even more for months or
quarters. The data are frequently revised, often substantially.
LO 1.7: Compute multifactor productivity
AACSB: Application of knowledge
ETHICAL DILEMMA
AMERICAN CAR BATTERY INDUSTRY
You may want to begin the discussion by asking how ethical it is for you to be in the lead battery business when you know that any batteries
you recycle will very likely find their way to an overseas facility (probably Mexico) with, at best, marginal pollution containment. Then after a
likely conclusion of “Well someone has to provide batteries,” you can move to the following discussion.
(a) As owner of an independent auto repair shop trying to dispose of a few old batteries each week, your options may be limited. But as
an ethical operator, your first option is to put pressure on your battery supplier to take your old batteries. Alternatively, shop for a
battery supplier who wants your business enough to dispose of your old batteries. Third, because there is obviously a market for the
lead in old batteries, some aggressive digging may uncover an imaginative recycler who can work out an economical arrangement for
pickup or delivery of your old batteries. Another option is, of course, to discontinue the sale of batteries. (This is a problem for many
small businesses; ethical decisions and regulation may be such that they often place an expensive and disproportionate burden on a
small firm.)
(b) As manager of a large retailer responsible for disposal of thousands of used batteries each week, you should have little trouble finding
a battery supplier with a reverse supply chain suitable for disposal of old batteries. Indeed, a sophisticated retailer, early on in any
supply-chain development process, includes responsible disposal of environmentally dangerous material as part of the negotiations.
Disposal of old batteries should be a minor issue for a large retailer.
(c) For both a small and large retailer, the solution is to find a “sustainable” solution or get out of the battery business. Burying the batteries
behind the store is not an option. Supplement 5: Sustainability in the Supply Chain provides some guidelines for a deeper class
discussion.
END-OF-CHAPTER PROBLEMS
120 boxes
(a) = 3.0 boxes/hour
40 hours
1.1
125 boxes
(b) = 3.125 boxes/hour
40 hours
(c) Change in productivity = 0.125 box/hour
(d) 0.125 box
Percentage change = = 4.167%
3.0
1.2 (a) Labor productivity is 160 valves/80 hours = 2 valves per hour
(b) New labor productivity = 180 valves/80 hours = 2.25 valves per hour
(c) Percentage change in productivity = .25 valve/2 valves = 12.5%
1.3
So, 57,600
= = 200
(160)(12)(0.15)
L laborers employed
57,600
0.15 = , where number of laborers
(160)(12)( ) employed at the plant
L
L =
15. Bureau of Labor Statistics (stats.bls.gov) is a good place to start. Results will vary for each year, but overall data for the economy will
range from 0.9% to 4.8%, and mfg. could be as high as 5% and services between 1% and 2%. The data will vary even more for months or
quarters. The data are frequently revised, often substantially.
LO 1.7: Compute multifactor productivity
AACSB: Application of knowledge
ETHICAL DILEMMA
AMERICAN CAR BATTERY INDUSTRY
You may want to begin the discussion by asking how ethical it is for you to be in the lead battery business when you know that any batteries
you recycle will very likely find their way to an overseas facility (probably Mexico) with, at best, marginal pollution containment. Then after a
likely conclusion of “Well someone has to provide batteries,” you can move to the following discussion.
(a) As owner of an independent auto repair shop trying to dispose of a few old batteries each week, your options may be limited. But as
an ethical operator, your first option is to put pressure on your battery supplier to take your old batteries. Alternatively, shop for a
battery supplier who wants your business enough to dispose of your old batteries. Third, because there is obviously a market for the
lead in old batteries, some aggressive digging may uncover an imaginative recycler who can work out an economical arrangement for
pickup or delivery of your old batteries. Another option is, of course, to discontinue the sale of batteries. (This is a problem for many
small businesses; ethical decisions and regulation may be such that they often place an expensive and disproportionate burden on a
small firm.)
(b) As manager of a large retailer responsible for disposal of thousands of used batteries each week, you should have little trouble finding
a battery supplier with a reverse supply chain suitable for disposal of old batteries. Indeed, a sophisticated retailer, early on in any
supply-chain development process, includes responsible disposal of environmentally dangerous material as part of the negotiations.
Disposal of old batteries should be a minor issue for a large retailer.
(c) For both a small and large retailer, the solution is to find a “sustainable” solution or get out of the battery business. Burying the batteries
behind the store is not an option. Supplement 5: Sustainability in the Supply Chain provides some guidelines for a deeper class
discussion.
END-OF-CHAPTER PROBLEMS
120 boxes
(a) = 3.0 boxes/hour
40 hours
1.1
125 boxes
(b) = 3.125 boxes/hour
40 hours
(c) Change in productivity = 0.125 box/hour
(d) 0.125 box
Percentage change = = 4.167%
3.0
1.2 (a) Labor productivity is 160 valves/80 hours = 2 valves per hour
(b) New labor productivity = 180 valves/80 hours = 2.25 valves per hour
(c) Percentage change in productivity = .25 valve/2 valves = 12.5%
1.3
So, 57,600
= = 200
(160)(12)(0.15)
L laborers employed
57,600
0.15 = , where number of laborers
(160)(12)( ) employed at the plant
L
L =
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4 CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y
Units produced 100 pkgs
(a) = = 20 pkgs/hour
Input 5
1.4
133 pkgs
(b) = 26.6 pkgs per hour
5
6.6
(c) Increase in productivity = = 33.0%
20
1.5 Resource Last Year This Year Change Percentage Change
Labor 1, 000 = 3.33
300
1, 000 = 3.64
275
0.31 0.31 = 9.3%
3.33
Resin 1, 000 = 20
50
1, 000 = 22.22
45
2.22 2.22 = 11.1%
20
Capital 1, 000 = 0.1
10, 000
1, 000 = 0.09
11, 000 –0.01 0.01 = 10.0%
0.1
− −
Energy 1, 000 = 0.33
3, 000
1, 000 = 0.35
2, 850 0.02 0.02 = 6.1%
0.33
1.6 Last Year This Year
Production 1,000 1,000
Labor hr. @ $10 $3,000 $2,750
Resin @ $5 250 225
Capital cost/month 100 110
Energy 1,500 1,425
$4,850 $4,510
[(1,000 / 4,510) (1,000 / 4,850)]
(1,000 / 4,850)
− =
−0.222 0.206 0.016
= = 7.8% improvement*
0.206 0.206
*with rounding to 3 decimal places.
Output
Productivity = Input
1.7
65 65
(a) Labor productivity = =
(520 × 13) $6,760
= .0096 rug per labor $
65Multifactor(b) =
productivity (520 × $13) + (100 × $5) + (20 × $50)
65
= = .00787 rug per $
$8, 260
Units produced 100 pkgs
(a) = = 20 pkgs/hour
Input 5
1.4
133 pkgs
(b) = 26.6 pkgs per hour
5
6.6
(c) Increase in productivity = = 33.0%
20
1.5 Resource Last Year This Year Change Percentage Change
Labor 1, 000 = 3.33
300
1, 000 = 3.64
275
0.31 0.31 = 9.3%
3.33
Resin 1, 000 = 20
50
1, 000 = 22.22
45
2.22 2.22 = 11.1%
20
Capital 1, 000 = 0.1
10, 000
1, 000 = 0.09
11, 000 –0.01 0.01 = 10.0%
0.1
− −
Energy 1, 000 = 0.33
3, 000
1, 000 = 0.35
2, 850 0.02 0.02 = 6.1%
0.33
1.6 Last Year This Year
Production 1,000 1,000
Labor hr. @ $10 $3,000 $2,750
Resin @ $5 250 225
Capital cost/month 100 110
Energy 1,500 1,425
$4,850 $4,510
[(1,000 / 4,510) (1,000 / 4,850)]
(1,000 / 4,850)
− =
−0.222 0.206 0.016
= = 7.8% improvement*
0.206 0.206
*with rounding to 3 decimal places.
Output
Productivity = Input
1.7
65 65
(a) Labor productivity = =
(520 × 13) $6,760
= .0096 rug per labor $
65Multifactor(b) =
productivity (520 × $13) + (100 × $5) + (20 × $50)
65
= = .00787 rug per $
$8, 260
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CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y 5
1.8 (a) Labor productivity = 1,000 tires/400 hours = 2.5 tires/hour.
(b) Multifactor productivity is 1,000 tires/(400 × $12.50 + 20,000 × $1 + $5,000 + $10,000) = 1,000 tires/$40,000 = 0.025 tire/dollar.
(c) Multifactor productivity changes from 1,000/40,000 to 1,000/39,000, or from 0.025 to 0.02564; the ratio is 1.0256, so the change
is a 2.56% increase.
1.9 Last Year This Year Change Percentage Change
Labor hrs. 1,500 = 4.29
350
1,500 = 4.62
325
0.33
4.29
= 7.7%
Capital invested 1,500 = 0.10
15, 000
1,500 = 0.08
18, 000
0.02
0.1
− = –20%
Energy (btu) 1,500 = 0.50
3, 000
1,500 = 0.55
2, 750
0.05
0.50
= 10%
Productivity of capital did drop; labor productivity increased as did energy, but by less than the anticipated 15%.
1.10 Multifactor productivity is:
375 autos/[($20 × 10,000) + ($1,000 × 500) + ($3 × 100,000)] = 375/(200,000 + 500,000 +300,000) = 375/1,000,000
= .000375 auto per dollar of inputs
1.11 (a) Before: 500/20 = 25 boxes per hour;
1.12 1,500 × 1.25 = 1,875 (new demand)
≅
≅
Outputs = Productivity
Inputs
1,875 = 2.344
Labor-hours
1,875
New process = 800 labor-hours
2.344
800 = 5 workers
160
1,500
Current process = = 2.344
labor-hours
1,500 = labor-hours 640
2.344
640 = 4 workers
160
Add one worker.
After, 650/24 = 27.08
(b) 27.08/25
= 1.083, or an increase of 8.3% in productivity
(c) New labor productivity = 700/24 = 29.167
boxes per hour
1.8 (a) Labor productivity = 1,000 tires/400 hours = 2.5 tires/hour.
(b) Multifactor productivity is 1,000 tires/(400 × $12.50 + 20,000 × $1 + $5,000 + $10,000) = 1,000 tires/$40,000 = 0.025 tire/dollar.
(c) Multifactor productivity changes from 1,000/40,000 to 1,000/39,000, or from 0.025 to 0.02564; the ratio is 1.0256, so the change
is a 2.56% increase.
1.9 Last Year This Year Change Percentage Change
Labor hrs. 1,500 = 4.29
350
1,500 = 4.62
325
0.33
4.29
= 7.7%
Capital invested 1,500 = 0.10
15, 000
1,500 = 0.08
18, 000
0.02
0.1
− = –20%
Energy (btu) 1,500 = 0.50
3, 000
1,500 = 0.55
2, 750
0.05
0.50
= 10%
Productivity of capital did drop; labor productivity increased as did energy, but by less than the anticipated 15%.
1.10 Multifactor productivity is:
375 autos/[($20 × 10,000) + ($1,000 × 500) + ($3 × 100,000)] = 375/(200,000 + 500,000 +300,000) = 375/1,000,000
= .000375 auto per dollar of inputs
1.11 (a) Before: 500/20 = 25 boxes per hour;
1.12 1,500 × 1.25 = 1,875 (new demand)
≅
≅
Outputs = Productivity
Inputs
1,875 = 2.344
Labor-hours
1,875
New process = 800 labor-hours
2.344
800 = 5 workers
160
1,500
Current process = = 2.344
labor-hours
1,500 = labor-hours 640
2.344
640 = 4 workers
160
Add one worker.
After, 650/24 = 27.08
(b) 27.08/25
= 1.083, or an increase of 8.3% in productivity
(c) New labor productivity = 700/24 = 29.167
boxes per hour
Loading page 14...
6 CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y
1.13 (a) Labor change:
1,500 1,500
= = .293 loaf/$
(640 × $8) 5,120
1,875 = 0.293 loaf/$
(800 × $8)
(b) Investment change:
1,500 1,500
= = .293 loaf/$
(640 × $8) 5,120
1,875 1,875
= = .359 loaf/$
(640 × 8) + (100) 5,220
.293 – .293
(c) Percentage change : = 0 (labor)
.293
.359 – .293
Percentage change : = .225
.293
= 22.5% (investment)
The better option is to purchase a new blender because it generates more loaves per dollar.
1,500
Old process = (640 8) + 500 + (1,500 0.35)
1,500
= = 0.244 loaf/$
6,145
1,875
New process = (800 8) + 500 + (1,875 0.35)
1,875
= = 0.248 loaf/$
7,556.25
0.248 – 0.244
Percentage change = = 1.6%
0.244
× ×
× ×
41.1
labor-hours
labor-hours
6,600 vans
(a) = 0.10
= 66,000
x
x
1.15
There are 300 laborers. So,
66,000 labor-hours = 220 labor-hours/laborer
300 laborers on average, per month
$ output 52($90) + 80($198)
labor-hours 8(45)
$20,520 = $57.00 per labor-hour
360
1.16 =
=
=
=
6,600 vans
(b) Now = 0.11, so 60,000 labor-hours
labor-hours
60,000 labor-hours
so, 200 labor-hours/laborer
300 laborers on average, per month
x
x
1.13 (a) Labor change:
1,500 1,500
= = .293 loaf/$
(640 × $8) 5,120
1,875 = 0.293 loaf/$
(800 × $8)
(b) Investment change:
1,500 1,500
= = .293 loaf/$
(640 × $8) 5,120
1,875 1,875
= = .359 loaf/$
(640 × 8) + (100) 5,220
.293 – .293
(c) Percentage change : = 0 (labor)
.293
.359 – .293
Percentage change : = .225
.293
= 22.5% (investment)
The better option is to purchase a new blender because it generates more loaves per dollar.
1,500
Old process = (640 8) + 500 + (1,500 0.35)
1,500
= = 0.244 loaf/$
6,145
1,875
New process = (800 8) + 500 + (1,875 0.35)
1,875
= = 0.248 loaf/$
7,556.25
0.248 – 0.244
Percentage change = = 1.6%
0.244
× ×
× ×
41.1
labor-hours
labor-hours
6,600 vans
(a) = 0.10
= 66,000
x
x
1.15
There are 300 laborers. So,
66,000 labor-hours = 220 labor-hours/laborer
300 laborers on average, per month
$ output 52($90) + 80($198)
labor-hours 8(45)
$20,520 = $57.00 per labor-hour
360
1.16 =
=
=
=
6,600 vans
(b) Now = 0.11, so 60,000 labor-hours
labor-hours
60,000 labor-hours
so, 200 labor-hours/laborer
300 laborers on average, per month
x
x
Loading page 15...
CHAPTER 1 O P E R A T I O N S A N D P R O D U C T I V I T Y 7
1
1,500
Last year = (350 8) + (15,000 0.0083) + (3,000 0.6)
1.17 × × ×
= + +
1,500
2,800 124.50 1,800
= =
1,500 0.317 doz / $
4,724.5
× × ×
1500
This year = (325 8) + (18,000 0.0083) + (2,750 0.6)
0.341 doz / $=
0.341 0.317
Percentage change = 0.317
0.076, or 7.6% increase
−
=
CASE STUDY
UBER TECHNOLOGIES, INC.
1. First, some drivers (maybe most) may not require a wage that equals those fully engaged in the “taxi” business. It truly could be a
supplemental income. . . . “I’m going that way anyhow so let’s make a few dollars while on the way.” Similarly, the capital investment cost
approaches zero as the car is going that direction anyhow. These are idle or underutilized resources.
From society’s perspective, Uber and its like competitors are desirable because both idle or wasted labor and capital resources are being
utilized. At the same time, as a bonus, Uber is reducing traffic and auto pollution while speeding up the transport of individuals and local
commerce.
As a competitor for the traditional taxi service, Uber seems to be an enhancement in efficiency.
For those faculty who what to spend some time on the larger productivity message, this case provides such an opportunity. Uber, as
Joseph Schumpeter would suggest, has developed a disruptive technology (creative destruction, in a Schumpeterian translation).
Innovations such as this are exactly how economic efficiency is enhanced. The traditional taxi services, with some imagination, could have
developed and adopted this technology, but most were ensconced in their own regulatory cocoon. As is often the case, it takes an outsider,
such as Uber et al. to be creative by putting unused resources to use and providing society greater efficiency.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
2. Perhaps a business model similar to Uber’s can be applied to the trucking industry. And, indeed, Uber has established an Uber app for the
trucking industry. An estimated 30% of trucking backhauls are empty. However, the number of independent truckers or truckers with the latitude
to alter their route may be very small. And this number must be a tiny fraction of independent automobile drivers. So, the ability to “Uberize”
trucking may be very difficult, but utilizing that idle 30% would be huge benefit to society.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
3. Perhaps the Uber model can be used for package delivery, documents, and everything from flowers to groceries. Airbnb (www.airbnb.com)
is applying a similar model to short-term rentals of rooms, apartments, and homes—competing with more traditional bed and breakfast facilities
and hotels.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
VIDEO CASE STUDIES
FRITO-LAY: OPERATIONS MANAGEMENT IN MANUFACTURING
This case provides a great opportunity for an instructor to stimulate a class discussion early in the course about the pervasiveness of the 10
decisions of OM with this case alone or in conjunction with the Hard Rock Cafe case. There is a short video (7 minutes) available in MyLab
Operations Management that is filmed specifically for this text and supplements this case.
1
1,500
Last year = (350 8) + (15,000 0.0083) + (3,000 0.6)
1.17 × × ×
= + +
1,500
2,800 124.50 1,800
= =
1,500 0.317 doz / $
4,724.5
× × ×
1500
This year = (325 8) + (18,000 0.0083) + (2,750 0.6)
0.341 doz / $=
0.341 0.317
Percentage change = 0.317
0.076, or 7.6% increase
−
=
CASE STUDY
UBER TECHNOLOGIES, INC.
1. First, some drivers (maybe most) may not require a wage that equals those fully engaged in the “taxi” business. It truly could be a
supplemental income. . . . “I’m going that way anyhow so let’s make a few dollars while on the way.” Similarly, the capital investment cost
approaches zero as the car is going that direction anyhow. These are idle or underutilized resources.
From society’s perspective, Uber and its like competitors are desirable because both idle or wasted labor and capital resources are being
utilized. At the same time, as a bonus, Uber is reducing traffic and auto pollution while speeding up the transport of individuals and local
commerce.
As a competitor for the traditional taxi service, Uber seems to be an enhancement in efficiency.
For those faculty who what to spend some time on the larger productivity message, this case provides such an opportunity. Uber, as
Joseph Schumpeter would suggest, has developed a disruptive technology (creative destruction, in a Schumpeterian translation).
Innovations such as this are exactly how economic efficiency is enhanced. The traditional taxi services, with some imagination, could have
developed and adopted this technology, but most were ensconced in their own regulatory cocoon. As is often the case, it takes an outsider,
such as Uber et al. to be creative by putting unused resources to use and providing society greater efficiency.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
2. Perhaps a business model similar to Uber’s can be applied to the trucking industry. And, indeed, Uber has established an Uber app for the
trucking industry. An estimated 30% of trucking backhauls are empty. However, the number of independent truckers or truckers with the latitude
to alter their route may be very small. And this number must be a tiny fraction of independent automobile drivers. So, the ability to “Uberize”
trucking may be very difficult, but utilizing that idle 30% would be huge benefit to society.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
3. Perhaps the Uber model can be used for package delivery, documents, and everything from flowers to groceries. Airbnb (www.airbnb.com)
is applying a similar model to short-term rentals of rooms, apartments, and homes—competing with more traditional bed and breakfast facilities
and hotels.
LO 1.8: Identify the critical variables in enhancing productivity
AACSB: Analytical thinking
VIDEO CASE STUDIES
FRITO-LAY: OPERATIONS MANAGEMENT IN MANUFACTURING
This case provides a great opportunity for an instructor to stimulate a class discussion early in the course about the pervasiveness of the 10
decisions of OM with this case alone or in conjunction with the Hard Rock Cafe case. There is a short video (7 minutes) available in MyLab
Operations Management that is filmed specifically for this text and supplements this case.
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Subject
Operations Management