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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

5.2.4 Measuring bullwhip effect: The practical issues

The above explanation, when read in conjunction with your text should provide a good theoretical base on the effects of information transparency across the supply chain. However, practical measurement of the bullwhip effect is not easy. Wouters and Fransoo (1999) carried out the most recent work in the Netherlands and noted the following difficulties in practical measurement of the effect:

• In many supply chains demand data is not available at the level of detail necessary for a good analysis. Instead, data are sometimes incomplete or only available at a highly aggregated product or time level.
• As supply chains are complex, data co-relation is often difficult. A wholesaler may give an order to its supplier based on several retailers' orders. It will be impossible to disaggregate the wholesaler's order to study the relationship with respect to orders made by one retailer.
• The measurement of total bullwhip effect should provide insight into the specific causes of the bullwhip effect. Specifying which part of the total bullwhip effect can be attributed to a specific cause can be problematic because of:
• unclear ordering policies where rules regarding order batching are unclear
• unavailability of real demand data which requires separating out the effects of price fluctuations and of shortage gaming from sales figures. Such real data are often not available.
• unavailability of real demand data when shortage occurs and price changes as sales at that time do not represent real demand.

The following reading will provide you with an account of practical measurement of bullwhip effect or demand amplification as it is referred to in this case.

Reading 5.2

Taylor, DH (1999) 'Measurement and analysis of demand amplification across the supply chain', The International Journal of Logistics Management , vol. 10, no. 2, pp.55-70.

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