Inventory and Production Management in Supply Chains

✍ Scribed by Edward A. Silver, David F. Pyke, Douglas J. Thomas

• Publisher: CRC Press
• Year: 2016
• Tongue: English
• Leaves: 810
• Edition: 4
• Category: Library

✦ Synopsis

Authored by a team of experts, the new edition of this bestseller presents practical techniques for managing inventory and production throughout supply chains. It covers the current context of inventory and production management, replenishment systems for managing individual inventories within a firm, managing inventory in multiple locations and firms, and production management. The book presents sophisticated concepts and solutions with an eye towards today’s economy of global demand, cost-saving, and rapid cycles. It explains how to decrease working capital and how to deal with coordinating chains across boundaries.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
Authors
SECTION I: THE CONTEXT AND IMPORTANCE OF INVENTORY MANAGEMENT AND PRODUCTION PLANNING
1 The Importance of Inventory Management and Production Planning and Scheduling
1.1 Why Aggregate Inventory Investment Fluctuates: The Business Cycle
1.2 Corporate Strategy and the Role of Top Management
1.3 The Relationship of Finance and Marketing to Inventory Management and Production Planning and Scheduling
1.3.1 Finance
1.3.2 Marketing
1.4 Operations Strategy
1.4.1 Mission
1.4.2 Objectives
1.4.3 Management Levers
1.4.4 General Comments
1.5 Measures of Effectiveness for Inventory Management and Production Planning and Scheduling Decisions
1.6 Summary
Problems
References
2 Frameworks for Inventory Management and Production Planning and Scheduling
2.1 The Diversity of Stock-Keeping Units
2.2 The Bounded Rationality of a Human Being
2.3 Decision Aids for Managing Diverse Individual Items
2.3.1 Conceptual Aids
2.3.2 Physical Aids
2.4 Frameworks for Inventory Management
2.4.1 Functional Classifications of Inventories
2.4.2 The A?B?C Classification as a Basis for Designing Individual Item Decision Models
2.5 A Framework for Production Planning and Scheduling
2.5.1 A Key Marketing Concept: The Product Life Cycle
2.5.2 Different Types of Production Processes
2.5.3 The Product-Process Matrix
2.6 Costs and Other Important Factors
2.6.1 Cost Factors
2.6.2 Other Key Variables
2.7 Three Types of Modeling Strategies
2.7.1 Detailed Modeling and Analytic Selection of the Values of a Limited Number of Decision Variables
2.7.2 Broader-Scope Modeling with Less Optimization
2.7.3 Minimization of Inventories with Little Modeling
2.8 The Art of Modeling
2.9 Explicit Measurement of Costs
2.10 Implicit Cost Measurement and Exchange Curves
2.11 The Phases of a Major Study of an Inventory Management or Production Planning and Scheduling System
2.11.1 Consideration
2.11.2 Analysis
2.11.3 Synthesis
2.11.4 Choosing among Alternatives
2.11.5 Control
2.11.6 Evaluation
2.11.7 General Comments
2.11.8 Transient Effects
2.11.9 Physical Stock Counts
2.12 Summary
Problems
Appendix 2A: The Lognormal Distribution
References
3 Forecasting Models and Techniques
3.1 The Components of Time-Series Analysis
3.2 The Three Steps Involved in Statistically Forecasting a Time Series
3.3 Some Aggregate Medium-Range Forecasting Methods
3.3.1 Regression Procedures
3.4 Individual-Item, Short-Term Forecasting: Models and Procedures
3.4.1 The Simple Moving Average
3.4.2 Simple Exponential Smoothing
3.4.3 Exponential Smoothing for a Trend Model
3.4.4 Winters Exponential Smoothing Procedure for a Seasonal Model
3.4.5 Selection of Smoothing Constants
3.5 Measuring the Performance of a Forecasting Process
3.5.1 Measures of Forecast Accuracy
3.5.2 Estimating the Standard Deviation of Forecast Errors over a Lead Time
3.5.3 Monitoring Bias
3.5.4 Corrective Actions in Statistical Forecasting
3.5.5 Probability Distributions of Forecast Errors
3.6 Handling Anomalous Demand
3.7 Incorporation of Human Judgment
3.7.1 Factors Where Judgment Input Is Needed
3.7.2 Guidelines for the Input and Monitoring of Judgment
3.8 Dealing with Special Classes of Individual Items
3.8.1 Items with Limited History
3.8.2 Intermittent and Erratic Demand
3.8.3 Replacement or Service Parts
3.8.4 Terminal Demand
3.9 Assessing Forecasting Procedures: Tactics and Strategy
3.9.1 Statistical Accuracy of Forecasts
3.9.2 Some Issues of a More Strategic Nature
Problems
Appendix 3A: Derivations
References
SECTION II: REPLENISHMENT SYSTEMS FOR MANAGING INDIVIDUAL ITEM INVENTORIES WITHIN A FIRM
4 Order Quantities When Demand Is Approximately Level
4.1 Assumptions Leading to the Basic EOQ
4.2 Derivation of the EOQ
4.2.1 Numerical Illustration
4.3 Sensitivity Analysis
4.4 Implementation Aids
4.4.1 Numerical Illustration
4.5 Quantity Discounts
4.5.1 Numerical Illustrations
4.5.2 Item A (An Illustration of Case a of Figure 4.5)
4.5.3 Item B (An Illustration of Case b of Figure 4.5)
4.5.4 Item C (An Illustration of Case c of Figure 4.5)
4.6 Accounting for inflation
4.6.1 Price Established Independent of Ordering Policy
4.6.2 Price Set as a Fixed Fractional Markup on Unit Variable Cost
4.7 Limits on order sizes
4.7.1 Maximum Time Supply or Capacity Restriction
4.7.2 Minimum Order Quantity
4.7.3 Discrete Units
4.8 Finite Replenishment Rate: The Economic Production Quantity
4.9 Incorporation of Other Factors
4.9.1 Nonzero Constant Lead Time That Is Known with Certainty
4.9.2 Nonzero Payment Period
4.9.3 Different Types of Carrying Charge
4.9.4 Multiple Setup Costs: Freight Discounts
4.9.5 A Special Opportunity to Procure
4.10 Selection of the Carrying Charge (r), the Fixed Cost per Replenishment (A), or the Ratio A/r Based on Aggregate Considerations: The Exchange Curve
4.10.1 Exchange Curve Illustration
4.11 Summary
Problems
Appendix 4A: Derivations
References
5 Lot Sizing for Individual Items with Time-Varying Demand
5.1 The Complexity of Time-Varying Demand
5.2 The Choice of Approaches
5.3 General Assumptions and a Numerical Example
5.3.1 The Assumptions
5.3.2 A Numerical Example
5.4 Use of a Fixed EOQ
5.5 The Wagner-Whitin Method: An ?Optimal? Solution under an Additional Assumption
5.5.1 The Algorithm
5.5.2 Potential Drawbacks of the Algorithm
5.6 Heuristic Approaches for a Significantly Variable Demand Pattern
5.6.1 The Silver?Meal, or Least Period Cost, Heuristic
5.6.2 The EOQ Expressed as a Time Supply (POQ)
5.6.3 Lot-for-Lot
5.6.4 Least Unit Cost
5.6.5 Part-Period Balancing
5.6.6 Performance of the Heuristics
5.6.7 When to Use Heuristics
5.6.8 Sensitivity to Errors in Parameters
5.6.9 Reducing System Nervousness
5.7 Handling of Quantity Discounts
5.8 Aggregate Exchange Curves
5.9 Summary
Problems
Appendix 5A: Dynamic Programming and Linear Programming Formulations
References
6 Individual Items with Probabilistic Demand
6.1 Some Important Issues and Terminology
6.1.1 Different Definitions of Stock Level
6.1.2 Backorders versus Lost Sales
6.1.3 Three Key Issues to Be Resolved by a Control System under Probabilistic Demand
6.2 The Importance of the Item: A, B, and C Classification
6.3 Continuous versus Periodic Review
6.4 The Form of the Inventory Policy: Four Types of Control Systems
6.4.1 Order-Point, Order-Quantity (s, Q) System
6.4.2 Order-Point, Order-Up-to-Level (s, S) System
6.4.3 Periodic-Review, Order-Up-to-Level (R, S) System
6.4.4 (R, s, S) System
6.5 Specific Cost and Service Objectives
6.5.1 Choosing the Best Approach
6.5.2 SSs Established through the Use of a Simple-Minded Approach
6.5.3 SSs Based on Minimizing Cost
6.5.4 SSs Based on Customer Service
6.5.5 SSs Based on Aggregate Considerations
6.6 Two Examples of Finding the Reorder Point s in a Continuous-Review, Order-Point, Order-Quantity (s, Q) System
6.6.1 Protection over the Replenishment Lead Time
6.6.2 An Example Using a Discrete Distribution
6.7 Decision Rules for Continuous-Review, Order-Point, Order-Quantity (s,Q) Control Systems
6.7.1 Common Assumptions and Notation
6.7.2 General Approach to Establishing the Value of s
6.7.3 Common Derivation
6.7.4 Decision Rule for a Specified Safety Factor (k)
6.7.5 Decision Rule for a Specified Cost (B[sub(1)]) per Stockout Occasion
6.7.6 Decision Rule for a Specified Fractional Charge (B[sub(2)]) per Unit Short
6.7.7 Decision Rule for a Specified Fractional Charge (B[sub(3)]) per Unit Short per Unit Time
6.7.8 Decision Rule for a Specified Charge (B[sub(4)]) per Customer Line Item Short
6.7.9 Decision Rule for a Specified Probability (P[sub(1)]) of No Stockout per Replenishment Cycle
6.7.10 Decision Rule for a Specified Fraction (P[sub(2)]) of Demand Satisfied Directly from Shelf
6.7.11 Decision Rule for a Specified Average Time (TBS) between Stockout Occasions
6.7.12 Decision Rule for the Allocation of a TSS to Minimize the ETSOPY
6.7.13 Decision Rule for the Allocation of a TSS to Minimize the ETVSPY
6.7.14 Nonnormal Lead Time Demand Distributions
6.8 Implied Costs and Performance Measures
6.9 Decision Rules for Periodic-Review, Order-Up-to-Level (R, S) Control Systems
6.9.1 The Review Interval (R)
6.9.2 The Order-Up-to-Level (S)
6.9.3 Common Assumptions and Notation
6.9.4 Common Derivation
6.10 Variability in the Replenishment Lead Time Itself
6.10.1 Approach 1: Use of the Total Demand over the Full Lead Time
6.10.2 Approach 2: Use of the Distribution of Demand Rate per Unit Time Combined with the Lead Time Distribution
6.10.3 Nonnormal Distributions
6.11 Exchange Curves Involving SSs for (s,Q) Systems
6.11.1 Single Item Exchange Curve: Inventory versus Service
6.11.2 An Illustration of the Impact of Moving Away from Setting Reorder Points as Equal Time Supplies
6.11.3 Derivation of the SS Exchange Curves
6.11.4 Composite Exchange Curves
6.12 Summary
Problems
Appendix 6A: Some Illustrative Derivations and Approximations
References
SECTION III: SPECIAL CLASSES OF ITEMS
7 Managing the Most Important Inventories
7.1 Nature of Class A Items
7.2 Guidelines for Control of A Items
7.3 Simultaneous Determination of s and Q for Fast-Moving Items
7.3.1 Decision Rules
7.3.2 Cost Penalties
7.3.3 Further Comments
7.4 Decision Rules for (s, S) Systems
7.4.1 Simple Sequential Determination of s and S
7.4.2 Simultaneous Selection of s and S Using the Undershoot Distribution
7.4.3 Comparison of the Methods
7.5 Decision Rules for (R, s, S) Systems
7.5.1 Decision Rule for a Specified Fractional Charge (B[sub(3)]) per Unit Short at the End of Each Period
7.5.2 Decision Rule for a Specified Fraction (P[sub(2)]) of Demand Satis.ed Directly from Shelf
7.6 Coping with Nonstationary Demand
7.7 Comments on Multiple Sources of Supply and Expediting
7.8 Summary
Problems
Appendix 7A: Simultaneous Solutions for Two Control Parameters
References
8 Managing Slow-Moving and Low-Value (Class C) Inventories
8.1 Order-Point, Order-Quantity (s, Q) Systems for Slow-Moving A Items
8.1.1 B[sub(2)] Cost Measure for Very-Slow-Moving, Expensive Items (Q = 1)
8.1.2 Case of Q ? 1 and a B[sub(1)] Cost Structure
8.1.3 Simultaneous Determination of s and Q for Slow-Moving Items
8.2 Controlling the Inventories of Intermittent Demand Items
8.3 Nature of C Items
8.4 Control of C Items Having Steady Demand
8.4.1 Inventory Records
8.4.2 Selecting the Reorder Quantity (or Reorder Interval)
8.4.3 Selecting the Reorder Point (or Order-up-to Level)
8.4.4 Two-Bin System Revisited
8.4.5 Simple Form of the (R, S) System
8.4.6 Grouping of Items
8.5 Control of Items with Declining Demand Patterns
8.5.1 Establishing the Timing and Sizes of Replenishments under Deterministic Demand
8.5.2 Sizing of the Final Replenishment under Probabilistic Demand
8.6 Reducing Excess Inventories
8.6.1 Review of the Distribution by Value
8.6.2 Rule for the Disposal Decision
8.6.3 Options for Disposing of Excess Stock
8.7 Stocking versus Not Stocking an Item
8.7.1 Relevant Factors
8.7.2 Simple Decision Rule
8.7.3 Some Extensions
8.8 Summary
Problems
Appendix 8A: Poisson Distribution and Some Derivations
References
9 Style Goods and Perishable Items
9.1 Style Goods Problem
9.2 Simplest Case: Unconstrained, Single-Item, Newsvendor Problem
9.2.1 Determination of the Order Quantity by Marginal Analysis
9.2.2 An Equivalent Result Obtained through Profit Maximization
9.2.3 Case of Normally Distributed Demand
9.2.4 Case of a Fixed Charge to Place the Order
9.2.5 Case of Discrete Demand
9.3 Single-Period, Constrained, Multi-Item Situation
9.3.1 Numerical Illustration
9.4 Postponed Product Differentiation
9.4.1 Value of Delayed Financial Commitment
9.4.2 Value of Flexibility
9.5 More than One Period in Which to Prepare for the Selling Season
9.6 Multiperiod Newsvendor Problem
9.7 Other Issues Relevant to the Control of Style Goods
9.7.1 Updating of Forecasts
9.7.2 Reorders and Markdowns
9.7.3 Reserving Capacity Ahead of Time
9.7.4 Inventory Policies for Common Components
9.7.5 Other Research
9.8 Inventory Control of Perishable Items
9.9 Summary
Problems
Appendix 9A: Derivations
References
SECTION IV: MANAGING INVENTORY ACROSS MULTIPLE LOCATIONS AND MULTIPLE FIRMS
10 Coordinated Replenishments at a Single Stocking Point
10.1 Advantages and Disadvantages of Coordination
10.2 Deterministic Case: Selection of Replenishment Quantities in a Family of Items
10.2.1 Assumptions
10.2.2 Decision Rule
10.2.3 A Bound on the Cost Penalty of the Heuristic Solution
10.3 Deterministic Case with Group Discounts
10.3.1 Numerical Illustration
10.4 Case of Probabilistic Demand and No Quantity Discounts
10.4.1 (S, c, s), or Can-Order, Systems
10.4.2 Periodic Review System
10.5 Probabilistic Demand and Quantity Discounts
10.5.1 A Full Truckload Application
10.5.2 Numerical Illustration
10.6 Production Environment
10.6.1 Case of Constant Demand and Capacity: Economic Lot Scheduling Problem
10.6.2 Case of Time-Varying Demand and Capacity: Capacitated Lot Sizing
10.6.3 Probabilistic Demand: The Stochastic Economic Lot Scheduling Problem
10.7 Shipping Consolidation
10.8 Summary
Problems
Appendix 10A: Derivation of Results in Section 10.2
References
11 Multiechelon Inventory Management
11.1 Multiechelon Inventory Management
11.2 Structure and Coordination
11.3 Deterministic Demand
11.3.1 Sequential Stocking Points with Level Demand
11.3.2 Other Results for the Case of Level Demand
11.3.3 Multiechelon Stocking Points with Time-Varying Demand
11.4 Probabilistic Demand
11.4.1 Base Stock Control System
11.4.2 Serial Situation
11.4.3 Arborescent Situation
11.5 Remanufacturing and Product Recovery
11.5.1 Multiechelon Situation with Probabilistic Usage and One-for-One Ordering
11.5.2 Some Extensions of the Multiechelon Repair Situation
11.5.3 Some Insights and Results for the More General Context of Remanufacturing and Product Recovery
11.6 Additional Insights
11.6.1 Economic Incentives to Centralize Stocks
11.6.2 Where to Deploy Stock
11.6.3 Lateral Transshipments
11.7 Summary
Problems
Appendix 11A: Derivation of the Logic for Computing the Best Replenishment Quantities in a Deterministic, Two-Stage Process
References
12 Coordinating Inventory Management in the Supply Chain
12.1 Information Distortion in a Supply Chain
12.2 Collaboration and Information Sharing
12.2.1 Sales and Operations Planning
12.2.2 Collaborative Forecasting
12.3 Vendor-Managed Inventory
12.4 Aligning Incentives
12.4.1 Wholesale Price Contract
12.4.2 Buyback Contract
12.4.3 Revenue-Sharing Contract
12.4.4 Service-Level Agreements
12.4.5 Challenges Implementing Coordinating Agreements
12.5 Summary
Problems
References
SECTION V: PRODUCTION MANAGEMENT
13 An Overall Framework for Production Planning and Scheduling
13.1 Characteristics of Different Production Processes
13.2 A Framework for Production Decision Making
13.2.1 A Review of Anthony's Hierarchy of Managerial Decisions
13.2.2 Integration at the Operational Level
13.2.3 The Framework
13.3 Options in Dealing with the Hierarchy of Decisions
13.3.1 Monolithic Modeling Approach
13.3.2 Implicit Hierarchical Planning
13.3.3 Explicit Hierarchical Planning
13.3.4 The Hax?Meal Hierarchical Planning System
13.4 Summary
Problems
References
14 Medium-Range Aggregate Production Planning
14.1 The Aggregate Planning Problem
14.2 The Costs Involved
14.2.1 Costs of Regular-Time Production
14.2.2 Overtime Costs
14.2.3 Costs of Changing the Production Rate
14.2.4 Inventory Associated Costs
14.2.5 Costs of Insufficient Capacity in the Short Run
14.3 The Planning Horizon
14.4 Two Pure Strategies: Level and Chase
14.5 Feasible Solution Methods
14.5.1 General Comments
14.5.2 An Example of a Graphic?Tabular Method
14.6 Linear Programming Models
14.6.1 Strengths and Weaknesses
14.6.2 The Inclusion of Integer Variables in LP Formulations
14.6.3 The Land Algorithm
14.7 Simulation Search Procedures
14.8 Modeling the Behavior of Managers
14.8.1 Management Coefficients Models
14.8.2 Manpower Decision Framework
14.9 Planning for Adjustments Recognizing Uncertainty
14.9.1 The Production-Switching Heuristic
14.10 Summary
Problems
References
15 Material Requirements Planning and Its Extensions
15.1 The Complexity of Multistage Assembly Manufacturing
15.2 The Weaknesses of Traditional Replenishment Systems in a Manufacturing Setting
15.3 Closed-Loop MRP
15.4 Material Requirements Planning
15.4.1 Some Important Terminology
15.4.2 Information Required for MRP
15.4.3 The General Approach of MRP
15.4.4 A Numerical Illustration of the MRP Procedure
15.4.5 The Material Requirements Plan and Its Uses
15.4.6 Low-Value, Common-Usage Items
15.4.7 Pegging
15.4.8 Handling Requirements Updates
15.4.9 Coping with Uncertainty in MRP
15.5 Capacity Requirements Planning
15.6 Distribution Requirements Planning
15.7 Weaknesses of MRP
15.8 ERP Systems
15.8.1 Enhancements to ERP Systems
15.9 Summary
Problems
References
16 Just-in-Time, Optimized Production Technology and Short-Range Production Scheduling
16.1 Production Planning and Scheduling in Repetitive Situations: Just-in-Time
16.1.1 Philosophy of JIT
16.1.2 Kanban Control System
16.1.3 Benefits and Weaknesses of JIT
16.2 Planning and Scheduling in Situations with Bottlenecks: Optimized Production Technology
16.2.1 Philosophy of OPT
16.2.2 Drum-Buffer-Rope Scheduling
16.2.3 A Related System: CONWIP
16.2.4 Benefits and Weaknesses of OPT
16.3 Short-Range Production Scheduling
16.3.1 Issues in Short-Term Scheduling
16.3.2 Techniques for Short-Term Scheduling
16.3.3 Deterministic Scheduling of a Single Machine: Priority Sequencing Rules
16.3.4 General Job Shop Scheduling
16.4 Summary
Problems
Appendix 16A: Proof that SPT Minimizes Total Flowtime
References
17 Summary
17.1 Operations Strategy
17.2 Changing the Givens
17.3 Future Developments
Appendix I: Elements of Lagrangian Optimization
Appendix II: The Normal Probability Distribution
Appendix III: Approximations and Excel Functions
Author Index
Subject Index