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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

5.2.2 The reasons for bullwhip effect

Lee et al (1997) identified the four major causes of bullwhip effect as:

• demand forecast updating
• order batching
• price fluctuation
• rationing and shortage gaming

The bullwhip effect is mainly caused by any one or more of these causes. We can see that all these are related to the business processes of the firms. Reading 5.1 explains these points quite adequately. the following will complement your reading and text material.

Demand forecast updating. All firms use standard forecasting techniques for production scheduling, capacity planning, inventory control and materials requirement planning. Firms use order history as a proxy for demand from the firm's immediate customers and forecast demand data is generated on the basis of these past sales data. Mason-Jones and Towill (2000) says that the order information is subject to delay, bias and noise before being transferred onto the supplier. The forecasting technique chosen and the way the demand signal is processed are significant contributors to the bullwhip effect. (Although the terms used by Mason-Jones and Towill are different, Reading 5.1 will have covered these concepts adequately. Forecasting is always subjective, as each firm uses different techniques, and the order quantity for a future period very often includes a very subjective 'hunch' on the part of the planner. These variables are sources of bias and noise in the demand signal.)

Apart from the error due to the forecasting technique chosen, it is also the practice of sales personnel to amend the forecast demand figures according to their gut feelings before the close of the planning period (Reddy 2002). This causes an even greater variance in the demand information as it travels upstream.

Batch ordering. In a supply chain, each company places orders with an upstream organisation using inventory control mechanisms decided by the firm's inventory policy. The demands from customers may be small and frequent which deplete the inventory gradually; firms would wait until the inventory level reaches a predetermined minimum level (the reorder point) before order is initiated. This requires firms to order periodically in batches. This order size is large compared to the regular demand faced by the firm. The supplier receives a large, highly erratic stream of orders with a spike during one cycle, but no orders for the rest of the period. This variability is much larger than the demand faced by the downstream firm.

Batch order can also occur for other reasons, such as orders being held until a greater shipment size is reached, as this will result in better transport rates.

Reddy (2002) points out that one way an artificial demand signal is often generated is by sales and marketing department personnel creating an artificial lower price for a product by foregoing a percentage of their own sales commission. Sales made in this way distort demand information and contribute to uncertainty.

Price fluctuation. Just as sales prompt consumers to buy an item in greater numbers compared with their usual buying patterns, special promotions and price discounts from manufacturers or suppliers result in retailers and wholesalers buying in large quantities and stocking them up. The manufacturers or suppliers often offer these to raise sales volume and to meet sales targets for a period. This is commonly known as 'meeting the quarter' in industry circles. The retailer takes advantage of these promotions to maximise profit. There is a similar buying spree when a price hike is expected. Commonly referred to as 'forwarding buying', the retailers and wholesalers make calculated decisions regarding likely future profits from such a move.

Figure 5.2 Increased order due to price promotion and selling through extra inventory over a longer period

The immediate effect is a larger order just before the discount is to end, and a drop in demand over a longer term while businesses sell through extra inventories.

Rationing and shortage gaming. Shortage gaming occurs in an environment of tight supply and when the manufacturer is expected to ration its products. The customers, wholesalers and retailers may order in large quantities with the expectation that they will receive a greater allocation of products that are in short supply.

The impact on the supply chain is significant as the forecasted demand is greatly, and unrealistically, increased with these inflated orders. Eventually orders disappear and cancellations pour in, making it impossible for the manufacturer to determine the real demand for its products.

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