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• Module overview
• 1. Introduction to supply chain management
  • Introduction
  • 1.1 What is supply chain management?
    • 1.1.1 Supply chain management: The concept
    • 1.1.2 Supply chain management: Evolution
    • 1.1.3 Supply chain management: The search for a definition
    • 1.1.4 Supply chain management: The drivers:
  • 1.2 Technology and supply chain management
  • 1.3 Supply chain management: New business models and challenges
    • 1.3.1 New supply chain business models
    • 1.3.2 Challenges
  • 1.4 Key issues in supply chain management

• 2. Supply chain management and information systems

  • 2.1 Introduction
  • 2.2 Goals of supply chain IT systems
  • 2.3 Supply chain IT goals: Challenges and means
    • 2.3.1 Standardisation
    • 2.3.2 Infrastructure
    • 2.3.3 Electronic commerce
    • 2.3.4 Supply chain management issues and IT
  • 2.4 Integrating the supply chain information systems
  • 2.5 Decision support systems
    • 2.5.1 The characteristics of decision support systems
    • 2.5.2 DSS components
    • 2.5.3 DSS in supply chain management
  • 2.6 Summary

• 3. Logistic network configuration

  • 3.1 Introduction
  • 3.2 The supply chain network
    • 3.2.1 Network design decisions
    • 3.2.2 Design consideration
  • 3.3 Logistics and facility costs in logistics network configuration
  • 3.4 Logistics network design and decision support systems
    • 3.4.1 The supply chain model
    • 3.4.2 Data and a network model
    • 3.4.3 Model and data validation
    • 3.4.4 Solution techniques
  • 3.5 Key features of a network configuration DSS

• 4. Inventory management and risk pooling

  • 4.1 Introduction
  • 4.2 Inventory: Basic concepts
    • 4.2.1 Why hold inventory: Inventory functionality
    • 4.2.2 Inventory related definitions
    • 4.2.3 Inventory related issues
  • 4.3 The inventory policy
    • 4.3.1 Basic inventory decisions and EOQ
    • 4.3.2 Inventory policy under conditions of uncertainty
  • 4.4 Risk pooling
    • 4.4.1 The Square Root Law of Inventory
    • 4.4.2 Centralised versus the decentralised system
    • 4.4.3 Supply chain inventory
    • 4.4.4 Inventory management: Practical issues
  • 4.5 Summary

• 5. Supply chain integration: The value of information

  • 5.1 Introduction
  • 5.2 Information and supply chain integration
    • 5.2.1 The bullwhip effect
    • 5.2.2 The reasons for bullwhip effect
    • 5.2.3 Quantifying the bullwhip effect
    • 5.2.4 Measuring bullwhip effect: The practical issues
    • 5.2.5 Bullwhip effect: Managerial insight
    • 5.2.6 Reducing bullwhip effects
  • 5.3 Effective forecasts and supply chain integration
  • 5.4 Collaborative supply chains and information integration
  • 5.5 Supply chain systems and information integration
  • 5.6 Information and the synchronised supply chain
  • 5.7 Summary

• 6. Supply chain integration: Distribution strategies

  • 6.1 Introduction
  • 6.2 Transportation and distribution
    • 6.2.1 Transport modes
    • 6.2.2 Inbound transportation
    • 6.2.3 Distribution
  • 6.3 Centralised versus decentralised control
  • 6.4 Distribution strategies
    • 6.4.1 Direct shipment
    • 6.4.2 Cross docking
    • 6.4.3 Warehousing and distribution strategies
    • 6.4.4 Transhipment
  • 6.5 Central versus local facilities
  • 6.6 Push versus pull systems
    • 6.6.1 Push systems
    • 6.6.2 Pull systems
  • 6.7 Summary

• 7. Supply chain simulation games:

  • 7.1 Introduction
  • 7.2 The Beer Game
    • 7.2.1 The purpose
    • 7.2.2 The Beer Game scenario
    • 7.2.3 Features and options
  • 7.3 Playing the Beer Game
    • 7.3.1 Beer Game projects
    • 7.3.2 The Beer Game: The learning tool
  • 7.4 The Risk Pooling Game
    • 7.4.1 Playing the Risk Pooling Game
    • 7.4.2 Risk pooling project
  • 7.5 Summary

• 8. Supply chain integration: Strategic partnering

  • 8.1 Introduction
  • 8.2 Strategic partnering
    • 8.2.1 Strategic partnering: A framework
  • 8.3 Supply chain partnership models
    • 8.3.1 Manufacturer and supplier partnership
    • 8.3.2 Strategic partnering with logistics service providers
    • 8.3.3 RSP or retailer supplier partnership
    • 4.3.4 Distributor integration
  • 8.4 Summary

• 9. International issues in supply chain management

  • 9.1 Introduction
  • 9.2 Globalisation and SCM issues
    • 9.2.1 Global business forces
  • 9.3 International supp ly chain - risks and advantages
    • 9.3.1 Risks in international supply chain
    • 9.3.2 Risk management strategies
    • 9.3.3 Risk management framework
  • 9.4 Issues in intern ational supply chain management
  • 9.5 Logistics infrastructure: Regional issues
  • 9.6 Summary

• 10. Product design and supply chains

  • 10.1 Introduction
    • 10.1.1 Matching the supply chain to the product
  • 10.2 Design for logistics
    • 10.2.1 Packaging
    • 10.2.2 Manufacturing: Concurrent and parallel processing
    • 10.2.3 Postponement or delayed differentiation
  • 10.3 Mass customisation and supply chains
  • 10.4 Supply chain is sues and the push-pull boundary
  • 10.5 Summary

• 11. Supply chain management and customer value

  • 11.1 Introduction
    • 11.1.1 The concept of value
    • 11.1.2 Customer service and value
  • 11.2 Supply chain management and customer value
    • 11.2.1 Tailored logistics
    • 11.2.2 Customer value dimensions
  • 11.3 Customer value measures
    • 11.3.1 Customer based measures
    • 11.3.2 Supply chain performance measures
  • 11.4 Information technology and customer value
  • 11.5 Summary

5.2.5 Bullwhip effect: Managerial insigh

It is obvious that that the way the retailer estimates his future mean demand and variance (by forecasting) affects his orders and is one of the principal causes of the bullwhip effect. In supply chains spanning several echelons, the effect is a practical reality but mangers should look at ways to minimise the effect.

Look at Figure 4.8 on page 108 in your text. It is apparent that the bullwhip effect exists whether demand information across the supply chain is shared or not. However, the effect of information centralisation is positive as it reduces the variability compared to uncentralised information.

We have also seen that the increasing demand information variability across the supply chain is an increasing function of lead time, and safety stocks and a decreasing function of the number of observations used in the demand forecasting. These insights should help managers minimise bullwhip effects.

In your text

Read Case: Barilla Spa (B) and (C).

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