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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

5.2.6 Reducing bullwhip effects

The demand amplification or bullwhip effect in the supply chain is system induced and is directly affected by both information and material delays in the supply chain and the feedback process in the decision making process (Mason-Jones & Towill 2000).

We have identified the principal causes of bullwhip effect in supply chains. Additionally we have seen that it is possible to quantify the effect, albeit with certain limitations attached to it. These insights enable us to make effective decisions for minimising or eliminating the bullwhip effect from a supply chain.

In practice, any effort to reduce the bullwhip effect is likely to be difficult and challenging. McCullen and Towill (2001) identify three prime dimensions to the problem of the bullwhip effect. They describe the order aspect of the bullwhip effect as the replenishment dimension affecting the flow of materials and information throughout the system. There are two other prime dimensions which make it difficult to identify and reduce the effect. These are geographical (since activities take place in different locations) and temporal (since activities take place at different times).

Mason-Jones and Towill (2000) provides the following diagram which shows the uncertainties associated with a supply chain and what strategies are available to tackle these.

Demand Side	Planning and Control System
postponement of product customisation partnering schemes information	good decision support system single communication channel between plays push/pull approach
rationalise vendor base partnering scheme information sharing	eliminate waste consistency of product consistency of process times
Supply Side	Manufacturing Process

Figure 5.6 Quadrants of uncertainty in supply chain and improvement strategies
(Source: Mason-Jones & Towill 2000)

We can see from our previous discussion that there are a few options available to management to reduce bullwhip effect across the supply chain. These are :

• Reducing uncertainty by information visibility across the supply chain as this will reduce the bullwhip effect. Practices that support an effort to reduce uncertainty involve the implementation of systems such as electronic data interchange (EDI) and extensible markup language (XML). Both these technologies allow companies to share information (such as consumer sales) with partner companies in the supply chain. EDI uses specific network services with an agreed information protocol while XML supports information sharing over the Internet. POS (point of sale) data can be transmitted to all chain operators, which will enable them to have a clear picture of consumer demand.
  
• Reducing demand variability is an effective approach to reducing the bullwhip effect as this variability causes the forecast error which in turn is the principal cause of the effect. How can we do reduce variability? As demand at the retailer often fluctuates with the price attached to a particular product, a unvarying price such as EDLP (everyday low price) should reduce demand uncertainty. By eliminating price fluctuations a retailer can eliminate much of the demand variability associated with the product.
  
• Decreasing the lead time between each level of the supply chain will aid in reducing the bullwhip effect. In fact it has been recognised that time compression is the key to supply chain excellence. Supply chain lead time is made up of the delays in information processing and materials processing. Mason-Jones and Towill ( 2000) refer this as two distinct lead time pipelines: the order information transfer pipeline , moving upstream from point of sale to raw material supplier and the product transfer downstream from raw material to customer. While in the short term the material flow lead time can be reduced by transportation techniques like crossdocking, information pipeline lead time can be reduced by effective information sharing using technology.
  
• The reduction of the overall supply chain lead time is treated with the utmost priority in contemporary supply chain management. This has resulted in rapid response manufacturing practices which are built on the concept of agile manufacturing . This has been made possible by integrating the physical transfer process with information system integration. This enables an agile enterprise to respond rapidly to customer demand, reducing the lead time. These developments have been seen as a new dimension in supply chain management, often referred to as demand management . We will see the concept of 'pull' system demand management in the next chapter. This has been another development in agile business practices and is contributing to shorter lead times. Many industry commentators prefer to use the term demand management
  
• Eliminate echelons in the supply chain: This involves the elimination of echelons and functional interfaces. This reduces time delays and information distortion which precipitate demand amplification. This can lead to a substantially different channel of distribution.

Strategic partnering with other supply chain actors is a prerequisite for implementing the above policies as each depends on closer relationships between customers and suppliers in order to support transparency and greater information sharing. Practical examples of such partnering are VMI (vendor managed inventory), QR (quick response) and ECR (efficient consumer response). Each of these initiatives offers a means to more closely coordinate supply chain inventories, in some cases making the supplier responsible for inventory levels at customer locations. These concepts will be covered in greater detail in the chapter on strategic partnering.

The following table is provided by Mason-Jones and Towill (2000) as a benchmarking guideline for bullwhip effects in a supply chain. The focus is on concurrent improvement of the information pipeline and the material flow pipeline.

Table 5.1 Benchmarking supply chain bullwhip performance (from Performance Improvement Benchmarking: Shrinking the Uncertainty Circle )

Supply Chain Design Strategy	Bullwhip Performance Measure				Overall Bullwhip Performance Benchmark
peak value	peak time	order recovery	stock recovery
datum design	**	**	*	*	*
information pipeline redesign only	****	**	*	***	***
material pipeline redesign only	*	****	**	****	***
both pipelines redesign	****	****	****	***	****

**** Best bullwhip performance * Worst bullwhip performance

Reading 5.3

Arntzen, BC & Shumway, HM (2002, January/February) 'Driven by demand: A case study', Supply Chain Management Review . [9 pages]

Activity 5.1

Reflecting on lessons learned: answer following questions.

1. What was required to change to a BTO (built to order ) process?
2. Why other change programs failed in the past but not this time?
3. What role does forecasting play in the new BTO model ?
4. What is required for selecting the proper software?
5. What was required from partners and customers to make the new model successful for NMS?

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