Production and Operations Analytics, Eight Edition 8th edition by Steven Nahmias , Tava Lennon Olsen – Ebook PDF Instant Download/Delivery: 1478639261 978-1478639268
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ISBN 10: 1478639261
ISBN 13: 978-1478639268
Author: Steven Nahmias , Tava Lennon Olsen
Nahmias and Olsen skillfully blend comprehensive coverage of topics with careful integration of mathematics. The authors’ decades of experience in the field contributed to the success of previous editions; the eighth edition continues the long tradition of excellence. Clearly written, reasonably priced, with an abundance of expertly formulated practice problems and updated examples, this textbook is essential reading for analyzing and improving all facets of operations.
Some of the material in the newest edition has been reorganized. For example, the first chapter introduces service strategy, the product/process matrix and flexible manufacturing systems, benchmarking, the productivity frontier, the innovation curve, and lean production as a strategy. The focus is slightly more international. The analysis of capacity growth planning now appears in the chapter on supply chain analytics. Aggregate planning details were added to chapter 3, including chase and level strategies in an appendix to the chapter. There is an expanded discussion on risk pooling in the chapter on supply chain strategy. The mechanics behind lean production are included in the chapter on push and pull production systems. The chapter on quality and assurance downplays sampling in favor of discussions of quality management, process capability, and the waste elimination side of lean. The separate chapter on facilities layout and location was eliminated and the information redistributed throughout the text.
Production and Operations Analytics, Eight Edition 8th Table of contents:
CHAPTER 1: Strategy and Competition
Chapter Overview
Key Points
Snapshot Application: Apple Adopts a New Business Strategy and Shifts Its Core Competency from Compu
1.1 Macroeconomic Trends
Manufacturing Matters
Offshoring and Reshoring
1.2 Competing in the Global Marketplace
Snapshot Application: Global Manufacturing Strategies in theAutomobile Industry
Problems for Sections 1.1–1.2
1.3 A Framework for Operations Strategy
Strategic Dimensions
Order Qualifiers and Order Winners
Measuring Performance
The Productivity Frontier
Problems for Section 1.3
1.4 Process Types and Layouts
The Product–Process Matrix
Plant Layout
Layout Strategies
Layouts Based on Group Technology
Problems for Section 1.4
1.5 Product, Process, and Innovation Life Cycles
The Product Life Cycle
The Process Life Cycle
1.6 The Innovation Life Cycle
Problems for Sections 1.5–1.6
1.7 Learning and Experience Curves
Learning Curves
Experience Curves
Learning and Experience Curves and Manufacturing Strategy
Economies of Scale and Economies of Scope
Problems for Section 1.7
1.8 Strategic Initiatives
Business Process Reengineering
Just-in-time, Lean Production, and the Toyota Production System
1.9 Flexible Manufacturing Systems
Competing on Quality
Benchmarking Quality
Time-based Competition
Servicization
Snapshot Application: The IBM Story
Automation
Problems for Sections 1.8–1.9
1.10 Summary
Appendix 1–A Layout Decisions
Activity Relationship Chart
From-To Chart
Problems for Appendix 1–A
Appendix 1–B Using Centroids For Layout Decisions
Bibliography
CHAPTER 2: Forecasting
Chapter Overview
Key Points
2.1 The Time Horizon in Forecasting
2.2 Characteristics of Forecasts
2.3 Subjective Forecasting Methods
2.4 Objective Forecasting Methods
Causal Models
Time Series Methods
Problems for Sections 2.1–2.4
2.5 Notation Conventions
2.6 Evaluating Forecasts
Snapshot Application: Pfizer Bets Big on Forecasts of Drug Sales
Problems for Section 2.6
2.7 Methods for Forecasting Stationary Series
Moving Averages
Moving Average Lags behind the Trend
Problems on Moving Averages
Exponential Smoothing
Multiple-Step-Ahead Forecasts
Comparison of Exponential Smoothing and Moving Averages
Problems for Section 2.7
2.8 Trend-Based Methods
Regression Analysis
Problems for Section 2.8
Double Exponential Smoothing Using Holt’s Method
More Problems for Section 2.8
2.9 Methods for Seasonal Series
Seasonal Factors for Stationary Series
Determining the Deseasonalized Series
Problems for Section 2.9
Winters’s Method for Seasonal Problems
More Problems for Section 2.9
2.10 Box-Jenkins Models
Snapshot Application: Sport Obermeyer Slashes Costs with Improved Forecasting
Estimating the Autocorrelation Function
The Autoregressive Process
The Moving-Average Process
Mixtures: ARMA Models
ARIMA Models
Using ARIMA Models for Forecasting
Summary of the Steps Required for Building ARIMA Models
Snapshot Application: A Simple Arima Model Predicts the Performance of the U.S. Economy
Box-Jenkins Modeling—A Critique
Problems for Section 2.10
2.11 Practical Considerations
Model Identification and Monitoring
Simple versus Complex Time Series Methods
Snapshot Application: Nate Silver Presidential Election Forecasts
2.12 Overview of Advanced Topics in Forecasting
Simulation as a Forecasting Tool
Forecasting Demand in the Presence of Lost Sales
2.13 Linking Forecasting and Inventory Management
2.14 Historical Notes and Additional Topics
2.15 Summary
Appendix 2–A Forecast Errors for Moving Averages and Exponential Smoothing
Case 1. Moving Averages
Case 2. Exponential Smoothing
Appendix 2–B Derivation of the Equations for the Slope and Intercept for Regression Analysis
Appendix 2–C
Bibliography
CHAPTER 3: Sales and Operations Planning
Chapter Overview
Key Points
3.1 The S&OP Process
Snapshot Application
Problems for Section 3.1
3.2 Key Performance Indicators
Problems for Section 3.2
3.3 The Role of Uncertainty
Problems for Section 3.3
3.4 Aggregate Planning of Capacity
Aggregate Units
Costs in Aggregate Capacity Planning
Snapshot Application: HP Enterprise Services Uses Optimization for Workforce Planning
A Prototype Problem
Chase, Level, and Mixed Strategies
Problems for Section 3.4
3.5 Solving Aggregate Planning Problems
Cost Parameters and Given Information
Problem Decision Variables
Problem Constraints
Rounding the Variables
Extensions
Problems for Section 3.5
3.6 Disaggregating Plans
Snapshot Application: Welch’s Uses Aggregate Planning for Production Scheduling
Problems for Section 3.6
3.7 Sales and Operation Planning on a Global Scale
3.8 Historical Notes
3.9 Summary
Appendix 3–A Aggregate Planning Heuristics
Evaluation of a Chase Strategy (Zero Inventory Plan)
Evaluation of a Constant Workforce Plan
Appendix 3–B
Bibliography
Supplement 1: Linear Programming
S1.1 Introduction
S1.2 A Prototype Linear Programming Problem
S1.3 Statement of the General Problem
Definitions of Commonly Used Terms
Features of Linear Programs
S1.4 Solving Linear Programming Problems Graphically
Graphing Linear Inequalities
Graphing the Feasible Region
Finding the Optimal Solution
Identifying the Optimal Solution Directly by Graphical Means
S1.5 The Simplex Method: An Overview
S1.6 Solving Linear Programming Problems with Excel
Entering Large Problems Efficiently
S1.7 Interpreting the Sensitivity Report
Shadow Prices
Objective Function Coefficients and Right-Hand Sides
Adding a New Variable
Using Sensitivity Analysis
S1.8 Recognizing Special Problems
Unbounded Solutions
Empty Feasible Region
Degeneracy
Multiple Optimal Solutions
Redundant Constraints
S1.9 The Application of Linear Programming to Production and Operations Analysis
S1.10 A Prototype Layout Problem and the Assignment Model
The Assignment Algorithm
S1.11 More Advanced Mathematical Programming Formulations
Bibliography
CHAPTER 4: Inventory Control Subject to Known Demand
Chapter Overview
Key Points
4.1 Types of Inventories
4.2 Motivation for Holding Inventories
4.3 Characteristics of Inventory Systems
4.4 Relevant Costs
Holding Cost
Order Cost
Penalty Cost
Problems for Sections 4.1–4.4
4.5 The EOQ Model
The Basic Model
Inclusion of Order Lead Time
Sensitivity
EOQ and Lean
Problems for Section 4.5
4.6 Extension to a Finite Production Rate
Problems for Section 4.6
4.7 Quantity Discount Models
Optimal Policy for All-Units Discount Schedule
Snapshot Application: SmartOps Assists in Designing Caterpillar’s Inventory Control System
Summary of the Solution Technique for All-Units Discounts
Incremental Quantity Discounts
Summary of the Solution Technique for Incremental Discounts
Other Discount Schedules
Problems for Section 4.7
4.8 Resource-Constrained Multiple Product Systems
Problems for Section 4.8
4.9 EOQ Models for Production Planning
Problems for Section 4.9
4.10 Power-of-Two Policies
4.11 Historical Notes and Additional Topics
4.12 Summary
Appendix 4–A Mathematical Derivations for Multiproduct Constrained EOQ Systems
Appendix 4–B
Bibliography
CHAPTER 5: Inventory Control Subject to Uncertain Demand
Chapter Overview
Key Points
5.1 The Nature of Randomness
5.2 Optimization Criterion
Problems for Sections 5.1 and 5.2
5.3 The Newsvendor Model
Notation
Development of the Cost Function
Determining the Optimal Policy
Optimal Policy for Discrete Demand
Extension to Include Starting Inventory
Snapshot Application: Using Inventory Models to Manage the Seed-Corn Supply Chain at Syngenta
Extension to Multiple Planning Periods
Problems for Section 5.3
5.4 Lot Size–Reorder Point Systems
Describing Demand
Decision Variables
Derivation of the Expected Cost Function
The Cost Function
Inventory Level versus Inventory Position
Snapshot Application: Inventory Management Software for the Small Business
5.5 Service Levels In (Q, R) Systems
Type 1 Service
Type 2 Service
Optimal (Q, R) Policies Subject to Type 2 Constraint
Imputed Shortage Cost
Scaling of Lead Time Demand
Estimating Sigma When Inventory Control and Forecasting Are Linked
Lead Time Variability
Negative Safety Stock
Problems for Sections 5.4 and 5.5
5.6 Additional Discussion of Periodic-Review Systems
(s, S) Policies
Snapshot Application: Tropicana Uses Sophisticated Modeling for Inventory Management
Service Levels in Periodic-Review Systems
Fixed Order Size Model
Problems for Section 5.6
5.7 Multiproduct Systems
ABC Analysis
Exchange Curves
Problems for Section 5.7
5.8 Overview of Advanced Topics
Multi-echelon Systems
Perishable Inventory Problems
Snapshot Application: Intel Uses Multiechelon Inventory Modelling to Manage the Supply Chain for Box
5.9 Historical Notes and Additional Readings
5.10 Summary
Appendix 5–A Notational Conventions and Probability Review
Appendix 5–B Additional Results and Extensions for the Newsvendor Model
Appendix 5–C Derivation of the Optimal (Q, R) Policy
Appendix 5–D Probability Distributions for Inventory Management
Appendix 5–E
Bibliography
CHAPTER 6: Supply Chain Strategy
Chapter Overview
Key Points
Snapshot Application: Walmart Wins with Solid Supply Chain Management
6.1 Supply Chain Strategy
Snapshot Application: Anheuser-Busch Re-Engineers Their Supply Chain
6.2 The Role of Information and Technology in the Supply Chain
Electronic Commerce
Barcodes and QR Codes
RFID Technology
Blockchain Technology
6.3 The Bullwhip Effect
Problems for Sections 6.1, 6.2, and 6.3
6.4 Incentives in the Supply Chain
Problems for Section 6.4
6.5 Risk Pooling
The Theory of Pooling
Inventory/Location Pooling
Product Pooling and Postponement
Capacity Pooling
6.6 Multilevel Distribution Systems
6.7 Designing Products for Supply Chain Efficiency
Additional Issues in Supply Chain Design
Snapshot Application: Dell Computer Designs the Ultimate Supply Chain
Problems for Sections 6.5–6.7
6.8 Global Supply Chain Management
Problems for Section 6.8
6.9 Summary
Bibliography
CHAPTER 7: Supply Chain Analytics
Chapter Overview
Key Points
7.1 Capacity Growth Planning
Snapshot Application: IBM Streamlines Its Semiconductor Supply Chain Using Sophisticated Mathematica
Make or Buy: A Prototype Capacity Expansion Problem
Dynamic Capacity Expansion Policy
Problems for Section 7.1
7.2 Locating New Facilities
Measures of Distance
Problems for Section 7.2
7.3 The Single-Facility Rectilinear Distance Location Problem
Contour Lines
Minimax Problems
Problems for Section 7.3
7.4 Euclidean Distance Problems
The Gravity Problem
The Straight-Line Distance Problem
Problems for Section 7.4
7.5 Other Location Models
Locating Multiple Facilities
Further Extensions
Problems for Section 7.5
Extra Problems for Sections 7.3–7.5
7.6 The Transportation Problem
7.7 Generalizations of the Transportation Problem
Infeasible Routes
Unbalanced Problems
7.8 More General Network Formulations
Problems for Sections 7.6–7.8
7.9 Determining Delivery Routes in Supply Chains
Snapshot Application: J. B. HUNT Saves Big with Routing and Scheduling Algorithm
Practical Issues in Vehicle Scheduling
Problems for Section 7.9
7.10 Summary
Appendix 7–A Present Worth Calculations
Appendix 7–B Computing Contour Lines
Bibliography
CHAPTER 8: Service Operations Management
Chapter Overview
Key Points
8.1 Service Operations Strategy
The Service Economy
Service Quality
Measuring Quality
Controlling Quality
Paying for Quality
Key Decisions for Service Businesses
Managing Variability
Servicization and Leasing
Snapshot Application: Southwest Airlines Competes with Service
Service Competition
Problems for Section 8.1
8.2 Flow Systems
Process Flow Diagrams
Capacity
Flow Rates and Utilization
Problems for Section 8.2
8.3 Modeling Unscheduled Arrivals
Poisson Process
Exponential Interarrival Times
General Arrival Processes
Pooling in Services
Probability of Delay
Problems for Section 8.3
8.4 Queueing Systems
Structural Aspects of Queueing Models
Notation
Little’s Law
The M/M/1 Queue
Problems for Section 8.4
8.5 General Queueing Models
Expected Time in System for a Single Server System
Multiple Parallel Servers
Systems with Abandonment
Priorities
Snapshot Application: Using Queueing to Make Staffing Decisions Saves Lives
Other Queueing Extensions
Simulation
Improving a Service Process
Problems for Section 8.5
8.6 The Human Element in Service Systems
The Psychology of Queueing
Snapshot Application: Disney Uses Both the Science and the Psychology of Queueing
Introducing Technology into Services
Guidelines for Service Guarantees and Refunds
8.7 Call and Contact Centers
Call Center Basics
Metrics
Call Routing
8.8 Revenue Management
Airline Revenue Management Overview
Revenue Management Basics
Lead Time Pricing
Nontraditional Applications for Revenue Management
Problems for Sections 8.6–8.8
8.9 Historical Notes and Additional Readings
8.10 Summary
Appendix 8–A Simulation Implementation
Random Number Generation
Entity Driven Logic
Bibliography
Supplement 2: Queueing Techniques
S2.1 Details of the Poisson Process and Exponential Distribution
S2.2 Analysis of the M/M/1 Queue
Waiting Time Distribution
S2.3 Further Results for M/M Queues
The M/M/s Queue
The M/M/1 Queue with a Finite Capacity
S2.4 Infinite Server Results
The M/G/∞ queue
Infinite Server Limits
S2.5 Queueing Networks
S2.6 Optimization of Queueing Systems
Typical Service System Design Problems
Modeling Framework
Bibliography
CHAPTER 9: Production Control Systems: Push and Pull
Chapter Overview
Key Points
9.1 Enterprise Systems
9.2 MRP Basics
9.3 The Explosion Calculus
Problems for Section 9.3
9.4 Alternative Lot-Sizing Schemes
EOQ Lot Sizing
The Silver–Meal Heuristic
Other Lot-Sizing Methods
Incorporating Lot-Sizing Algorithms into the Explosion Calculus
Problems for Section 9.4
9.5 Lot Sizing with Capacity Constraints
Problems for Section 9.5
9.6 Shortcomings of MRP
Uncertainty
Capacity Planning
Rolling Horizons and System Nervousness
Additional Considerations
Problems for Section 9.6
9.7 Pull Scheduling Fundamentals
The Mechanics of Kanban
Single Minute Exchange of Dies
Heijunka
Relationship with Suppliers
Advantages and Disadvantages of Pull Production
Problems for Section 9.7
9.8 A Comparison of MRP, Pull, and Hybrid Systems
9.9 Flexible Manufacturing Systems
Advantages of Flexible Manufacturing Systems
Disadvantages of Flexible Manufacturing Systems
9.10 Historical Notes
9.11 Summary
Appendix 9–A Lot Sizing for Time-Varying Demand
Least Unit Cost
Part-Period Balancing
The Wagner–Whitin Algorithm
Solution by Dynamic Programming
Appendix 9–B
Bibliography
CHAPTER 10: Quality and Assurance
Chapter Overview
Key Points
10.1 Quality Basics
The Deming Prize and the Baldrige Award
ISO 9000
Six Sigma Quality
Snapshot Application: Motorola Leads the Way with Six-sigma Quality Programs
Lean Quality
10.2 Statistical Basis of Control Charts
Problems for Section 10.2
10.3 Control Charts for Variables: the X and R Charts
X Charts
Relationship to Classical Statistics
R Charts
Problems for Section 10.3
10.4 Control Charts for Attributes: The p Chart
p Charts for Varying Subgroup Sizes
Problems for Section 10.4
10.5 The c Chart
Problems for Section 10.5
10.6 Process Capability
Snapshot Application: Navistar Scores with Six-sigma Quality Program
Problems for Section 10.6
10.7 Overview of Acceptance Sampling
10.8 Single Sampling for Attributes
Derivation of the OC Curve
Problems for Section 10.8
10.9 Designing Quality into the Product
The Design Cycle
Ease of Manufacturability
Listening to the Customer
10.10 Historical Notes
10.11 Summary
Appendix 10–A Approximating Distributions
Appendix 10–B Classical Statistical Methods and Control Charts
Appendix 10–C Economic Design of X Charts
Appendix 10–D Double Sampling Plans for Attributes
Appendix 10–E Sequential Sampling Plans
Appendix 10–F Average Outgoing Quality
Appendix 10–G
Bibliography
CHAPTER 11: Operations Scheduling
Chapter Overview
Key Points
11.1 Production Scheduling and the Hierarchy of Production Decisions
11.2 Important Characteristics of Job Shop Scheduling Problems
11.3 Job Shop Scheduling Terminology
11.4 A Comparison of Specific Sequencing Rules
First-Come, First-Served
Shortest Processing Time
Earliest Due Date
Critical Ratio Scheduling
11.5 Objectives in Job Shop Management: An Example
Problems for Sections 11.1–11.5
11.6 An Introduction to Sequencing Theory for a Single Machine
Shortest-Processing-Time Scheduling
Earliest-Due-Date Scheduling
Minimizing the Number of Tardy Jobs
Precedence Constraints: Lawler’s Algorithm
Snapshot Application: Millions Saved with Scheduling System for Fractional Aircraft Operators
Problems for Section 11.6
11.7 Sequencing Algorithms for Multiple Machines
Scheduling n Jobs on Two Machines
Extension to Three Machines
The Two-Job Flow Shop Problem
Problems for Section 11.7
11.8 Stochastic Scheduling: Static Analysis
Single Machine
Multiple Machines
The Two-Machine Flow Shop Case
Problems for Section 11.8
11.9 Stochastic Scheduling: Dynamic Analysis
Selection Disciplines Independent of Job Processing Times
Selection Disciplines Dependent on Job Processing Times
The cμ Rule
Problems for Section 11.9
11.10 Assembly Line Balancing
Snapshot Application: Manufacturing Divisions Realize Savings with Scheduling Software
Problems for Section 11.10
11.11 Historical Notes
11.12 Summary
Bibliography
CHAPTER 12: Project Scheduling
Chapter Overview
Key Points
12.1 Representing a Project as a Network
12.2 Critical Path Analysis
Finding the Critical Path
Problems for Sections 12.1 and 12.2
12.3 Time Costing Methods
Problems for Section 12.3
12.4 Solving Critical Path Problems with Linear Programming
Linear Programming Formulation of the Cost–Time Problem
Problems for Section 12.4
12.5 PERT: Project Evaluation and Review Technique
Path Independence
Problems for Section 12.5
Snapshot Application: Warner Robins Streamlines Aircraft Maintenance with CCPM Project Management
12.6 Resource Considerations
Resource Constraints for Single-Project Scheduling
Resource Constraints for Multiproject Scheduling
Resource Loading Profiles
Problems for Section 12.6
12.7 Organizational Issues in Project Management
12.8 Historical Notes
12.9 Project Management Software for the PC
Snapshot Applications: Project Management Helps United Stay on Schedule
12.10 Summary
Appendix 12–A
Bibliography
CHAPTER 13: Reliability and Maintainability
Chapter Overview
Key Points
13.1 Reliability of a Single Component
Introduction to Reliability Concepts
Preliminary Notation and Definitions
The Exponential Failure Law
Problems for Section 13.1
13.2 Increasing and Decreasing Failure Rates
Problems for Section 13.2
13.3 The Poisson Process in Reliability Modeling
Series Systems Subject to Purely Random Failures
Problems for Section 13.3
13.4 Failures of Complex Equipment
Components in Series
Components in Parallel
Expected Value Calculations
K Out of N Systems
Problems for Section 13.4
13.5 Introduction to Maintenance Models
13.6 Deterministic Age Replacement Strategies
The Optimal Policy in the Basic Case
A General Age Replacement Model
Problems for Section 13.6
13.7 Planned Replacement Under Uncertainty
Snapshot Application: Reliability-Centered Maintenance Improves Operations at Three Mile Island Nucl
Planned Replacement for a Single Item
Block Replacement for a Group of Items
Problems for Section 13.7
13.8 Analysis of Warranty Policies
The Free Replacement Warranty
The Pro Rata Warranty
Extensions and Criticisms
Problems for Section 13.8
13.9 Software Reliability
13.10 Historical Notes
13.11 Summary
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