Details

Subject(s)

• Marketing—Management [Browse]

Author

• Rabiu, Sani [Browse]
• Mohd Tahir Ismail [Browse]

Summary note

The comprehensive coverage makes the book a valuable reference for practitioners and students, particularly postgraduate and MBA students, who require such insights to improve business functions and make informed decisions. It is also accessible for readers without a strong background in mathematics.

Bibliographic references

Includes bibliographical references and index.

Source of description

• Description based on publisher supplied metadata and other sources.
• Description based on print version record.

Contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• Foreword
• Preface
• Acknowledgements
• About the Authors
• Contributors
• 1. Inventory Management
• 1.1. Introduction to Inventory Management
• 1.2. Key Concepts and Principles
• 1.3. Principles of Inventory Management
• 1.3.1. Inventory Control Systems
• 1.3.2. Demand Forecasting
• 1.3.3. Inventory Planning
• 1.3.4. Supplier Management
• 1.3.5. Inventory Optimization
• 1.3.6. Inventory Auditing
• 1.3.7. Safety Stock Management
• 1.3.8. Inventory Reporting and Analytics
• 1.3.9. Order Management
• 1.3.10. Return Management
• 1.3.11. Cycle Counting
• 1.3.12. Inventory Valuation Methods
• 1.3.13. Warehouse Layout and Design
• 1.3.14. Inventory Segmentation
• 1.3.15. Sustainability and Environmental Considerations
• 1.3.16. Collaboration and Integration With Other Business Functions
• 1.3.17. Technology and Automation
• 1.3.18. Lead Time Management
• 1.3.19. Product Life Cycle Management
• 1.3.20. Service Level Agreements and Performance Metrics
• 1.3.21. Risk Management and Contingency Planning
• 1.3.22. Inventory Accounting and Financial Integration
• 1.3.23. Regulatory Compliance
• 1.3.24. Customer Relationship Management Integration
• 1.3.25. Distribution and Transportation Management
• 1.3.26. Vendor Managed Inventory and Consignment Stock
• 1.3.27. Inventory Replenishment Strategies
• 1.4. Objectives of Inventory Management
• 1.4.1. Ensuring Sufficient Supply to Meet Demand
• 1.4.2. Minimizing Costs Associated With Holding and Managing Inventory
• 1.4.3. Balancing Between Overstocking and Stockouts
• 1.5. Costs Associated with Inventory Management
• 1.5.1. Holding Costs
• 1.5.2. Ordering Costs
• 1.5.3. Stockout Costs
• 1.6. Problems and Challenges in Inventory Management
• 1.6.1. Demand Variability.
• 1.6.2. Strategies to Overcome Demand Variability
• 1.6.3. Lead Time Variability
• 1.6.4. Strategies to Overcome Lead Time Variability
• 1.6.5. Other Common Challenges
• 1.6.6. Strategies to Overcome Other Common Challenges
• 1.7. Types of Inventories
• 1.7.1. Raw Materials
• 1.7.2. Work-in-Progress
• 1.7.3. Finished Goods
• 1.7.4. Maintenance, Repair, and Operations Supplies
• 1.8. Inventory Control and Optimization
• 1.8.1. Definition of Inventory Optimization
• 1.8.2. What Is Inventory Optimization
• 1.8.3. Importance of Inventory Optimization
• 1.8.4. Goals of Inventory Optimization
• 1.9. Relevance to Organizations
• 1.9.1. Impact on Customer Satisfaction and Profitability
• 1.9.2. Importance of Maintaining Optimal Inventory Levels
• 1.9.3. Inventory Control Systems
• 1.9.4. Inventory Optimization Methods
• 1.10. Fundamental Concepts and Practices
• 1.10.1. Inventory Analytics
• 1.10.2. Inventory Models
• 1.11. Impact of Inventory Management on Organizational Operations
• 1.11.1. Optimized Inventory Levels
• 1.11.2. Reduced Costs
• 1.11.3. Improved Supply Chain Collaboration
• 1.11.4. Enhanced Customer Service
• 1.11.5. Toyota Production System
• 1.11.6. Amazon's Fulfillment Centers
• 1.12. Practical Applications
• 1.12.1. Practical Tips for Practitioners
• 1.12.2. Tools and Technologies
• 1.13. Conclusion
• Exercises
• 2. Inventory Control Techniques
• 2.1. Introduction to Inventory Control Techniques
• 2.1.1. Definition of Inventory Control Techniques
• 2.1.2. Importance of Inventory Control Techniques
• 2.1.3. Contribution to Effective Inventory Management
• 2.2. Just-In-Time
• 2.2.1. Key Principles of JIT
• 2.2.2. Benefits of JIT
• 2.2.3. Challenges of JIT
• 2.2.4. Applications of JIT
• 2.3. ABC Analysis
• 2.3.1. Categories in ABC Analysis
• 2.3.2. Methodology for Categorizing Inventory Items.
• 2.3.3. Optimization Strategies
• 2.3.4. Apply Control Policies
• 2.3.5. Benefits
• 2.3.6. Challenges of ABC Analysis
• 2.3.7. Limitations
• 2.4. VED Analysis
• 2.4.1. Vital (V)
• 2.4.2. Essential (E)
• 2.4.3. Desirable (D)
• 2.4.4. Integration with ABC Analysis
• 2.4.5. Advantages of VED Analysis
• 2.4.6. Disadvantages of VED Analysis
• 2.5. Safety Stock Management
• 2.5.1. Key Points About Safety Stock Management
• 2.5.2. Methods for Calculating Safety Stock Levels
• 2.5.3. Advantages of Safety Stock
• 2.5.4. Disadvantages of Safety Stock
• 2.5.5. Balancing Safety Stock
• 2.6. Inventory Turnover
• 2.6.1. Benefits of Calculating Inventory Turnover
• 2.7. Reorder Point (ROP)
• 2.7.1. Definition of Reorder Point and Its Role in Inventory Management
• 2.7.2. Calculation of Reorder Point Based on Lead Time and Demand
• 2.7.3. Impact of Reorder Points on Inventory Control and Cost Management
• 2.8. Economic Order Quantity (EOQ) Model
• 2.8.1. Mathematical Formulation and Assumptions of the EOQ Model
• 2.8.2. Economic Production Quantity (EPQ)
• 2.8.3. Advantages
• 2.8.4. Disadvantages
• 2.9. Comparison of Inventory Control Techniques
• 2.9.1. ABC Analysis
• 2.9.2. Economic Order/Production Quantity (EOQ/EPQ)
• 2.9.3. Safety Stock
• 2.9.4. Reorder Point (ROP)
• 2.10. Criteria for Selecting Appropriate Techniques Based on Organizational Needs
• 2.10.1. Integrated Inventory Management in a Retail Chain
• 2.10.2. Manufacturing Company Using Multiple Techniques
• 2.11. Importance of Inventory Control from an Organizational Perspective
• 2.12. Functioning of Inventory Management Systems Within Organizations
• 2.12.1. Role of Coordination Among Different Departments in Inventory Management
• 2.12.2. Strategies for Optimizing Expenditure on Materials and Inventory
• 2.13. Coordination and Optimal Expenditure on Materials.
• 2.13.1. Techniques for Optimizing Material Expenditure
• 2.14. Multi-Echelon Inventory Optimization (MEIO)
• 2.14.1. Overview of MEIO
• 2.14.2. Key Benefits of MEIO
• 2.14.3. Components of MEIO Models
• 2.14.4. MEIO Techniques and Approaches
• 2.14.5. Challenges in MEIO Implementation
• 2.15. Conclusion
• 3. Advanced Inventory Optimization Models
• 3.1. Introduction
• 3.1.1. Multi-Echelon Inventory Model
• 3.2. Sensitivity Analysis
• 3.2.1. Conducting Sensitivity Analysis
• 3.3. Inventory Optimization for Seasonal Demand
• 3.3.1. Seasonal Forecasting Models
• 3.3.2. Dynamic Safety Stock Strategies
• 3.3.3. Flexible Capacity Planning
• 3.4. Applications of Advanced Inventory Optimization Techniques
• 3.4.1. Retail Industry
• 3.4.2. Manufacturing Industry
• 3.4.3. Healthcare Industry
• 3.4.4. E-commerce Industry
• 3.5. Conclusion
• 4. Inventory Management in the Manufacturing Sector
• 4.1. Introduction to Inventory Management in Manufacturing
• 4.1.1. Definition and Scope of Inventory Management Specific to the Manufacturing Sector
• 4.1.2. Importance of Inventory Management in Manufacturing Operations
• 4.1.3. Impact on Production
• 4.2. Significance of Inventory Management in Manufacturing
• 4.3. Role of Inventory Management
• 4.3.1. Strategizing Inventory Policies
• 4.3.2. Allocating Resources Efficiently
• 4.3.3. Ensuring the Availability of Materials for Continuous Production
• 4.3.4. Practical Applications
• 4.4. Inventory Analytics
• 4.4.1. Descriptive Analytics
• 4.4.2. Predictive Analytics
• 4.4.3. Key Performance Indicators (KPIs) and Metrics for Inventory Performance in Manufacturing
• 4.4.4. Techniques for Forecasting Demand and Lead Times Specific to Manufacturing
• 4.5. Conclusion
• 5. Emerging Trends in Inventory Management
• 5.1. Introduction.
• 5.2. Integration of Cutting-Edge Technologies
• 5.2.1. Artificial Intelligence and Machine Learning in Inventory Management
• 5.2.2. Enhanced Data Analysis Capabilities
• 5.2.3. Inventory Optimization Using Machine Learning Algorithms
• 5.2.4. Linear Regression
• 5.2.5. Decision Trees
• 5.2.6. Classification and Regression
• 5.2.7. Visualization
• 5.2.8. Neural Networks
• 5.2.9. Robotics in Warehouse Operations
• 5.2.10. Automated Picking and Packing
• 5.2.11. Autonomous Mobile Robots
• 5.2.12. Collaborative Robots (Cobots)
• 5.3. Cloud-Based Inventory Systems
• 5.3.1. Real-Time Monitoring and Decision-Making
• 5.3.2. Enhanced Collaboration and Flexibility
• 5.3.3. Flexibility and Collaboration Across Multiple Locations
• 5.4. Conclusion
• 6. Inventory Forecasting
• 6.1. Introduction to Inventory Forecasting
• 6.1.1. Historical Background
• 6.2. Expectation
• 6.3. Variance
• 6.4. Covariance
• 6.5. Exponential Smoothing
• 6.6. MAPE (Mean Absolute Percentage Error)
• 6.7. Linear Regression
• 6.7.1. SSE (Sum of Squared Errors)
• 6.7.2. Ridge Regression
• 6.8. LASSO (Least Absolute Shrinkage and Selection Operator)
• 6.9. Elastic Net
• 6.10. Random Forest
• 6.11. Support Vector Machine (SVM)
• 6.12. Role of Inventory Forecasting in Inventory Management
• 6.12.1. Predicting Future Inventory Requirements
• 6.12.2. Time Series Analysis
• 6.12.3. Causal Models
• 6.12.4. Implications for Supply Chain Management
• 6.12.5. Bullwhip Effect Reduction
• 6.12.6. Supplier Relationship Management
• 6.12.7. Inventory Optimization
• 6.12.8. Logistics and Operational Efficiency
• 6.12.9. Transportation Planning
• 6.12.10. Warehouse Space Utilization
• 6.12.11. Labor Planning
• 6.13. Inventory Forecasting Techniques
• 6.13.1. Demand Planning
• 6.13.2. Demand Forecasting.
• 6.13.3. Comparative Analysis of Techniques.

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ISBN

• 1-04-035222-7
• 1-003-53486-4
• 1-04-035218-9
• 9781003534860

OCLC

1519977688

Doi

10.1201/9781003534860

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