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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

5.2.3 Quantifying the bullwhip effect

Your text deals with the quantification process quite extensively and you are expected to read the text very thoroughly. Although the derivation of the mathematical quantification formula may not be very obvious, the underlying principle is quite clear. Note that the formula and explanation are based on certain assumptions:

• The bullwhip effect is mainly due to demand signal processing between various levels of the supply chain.
• One of the main sources of error is the forecasting error and the formula in the text is based on a 'moving average' forecasting technique.

Given the above assumptions, other factors contributing to the bullwhip effect are not considered. This may be a limited view. Nevertheless, it provides insight into one of the main contributors of the bullwhip effect. The explanation is quite simple. The retailer sells a product daily and the daily sales figures represent daily demands for this product. Realistically these daily demands are likely to fluctuate on a day to day basis. When inventory levels approach the reorder point (refer to chapter 4), an order is initiated. The question is: when is this order initiated; what is the quantity; and how does this order quantity relate to the real demand?

You know that the retailer's order decisions are based on inventory policy and this is strongly connected with the estimated demand likely to be faced by the firm in the immediate future. You are aware of the various forecasting techniques used by firms and know that these forecast demand figures are certain to be different from the actual demand experienced by the retailer. How much they will differ will depend on the forecasting technique in use. The points to note are:

• Only the retailer faces real time market demand which originates from the customers.
• The replenishment orders made out by the retailer will be in accordance with the inventory policy followed by the retailer. The inventory policy can be a two parameter continuous review policy which determines when to order and how much to order; or a single parameter continuous review policy which determines how much to order at every period. (See chapter 4 if you need to refresh your memory.)
• The order quantity, whichever policy is chosen, will be affected by the estimated future demand pattern, the likely average demand and the variability of demand. These estimates are the results of the forecasting technique used by the firms and will vary depending on the techniques and parameters chosen.
• So, the order that the retailer sends out does not accurately represent the demand information.

This situation can be best explained by using the single parameter inventory decisions mentioned in chapter 4. Remember that the retailer orders whenever the inventory position falls below the level: .

AVG and STD are forecast average demand and standard deviation of demand during the lead time. The values change from period to period as forecast are updated at each period, the value of S or the target inventory position will change, creating a order pattern which will follow this target inventory which could vary substantially from the demand faced by the retailer.

Figure 5.3 Demand and order - transmission of demand information across a simple supply chain

How much does the order vary from the 'real' demand experienced by the retailer? This is a very basic question and as explained will depend on the inventory policy and the forecasting technique used by the retailer. Experts and academics have faced considerable difficulty in identifying a measure which would adequately represent the way demand information gets amplified. Simchi Levi et al has chosen variance as the appropriate measure, whereas others (Wouters & Fransoo 2000) have based their work on the co-efficient of variation as the preferred measure of bullwhip effect across the supply chain.

The text deals with this problem by assuming that the retailer and other chain members are employing a moving average forecasting technique and following a single parameter continuous review inventory policy. See section 4.2 in your text for a detailed discussion on bullwhip effect and how the effect can be quantified on the basis of these assumption. The derivation of the mathematical formulation is beyond the scope of this study guide and we will conceptually accept the formulae.

In your text the bullwhip effect is given as the ratio between the variances in retailer orders to the manufacturer (Q) and retailer demands (D). This is given by :

where L is the cycle lead time

p is the number of recent most periods used in the forecasting (moving average) to determine the order quantity

The formula is indicative of the likely impact of a long lead time on the bullwhip effect. Conversely, it is also obvious that as 'p', the number of observation periods increases, the bullwhip effect decreases in magnitude.

Information centralisation and bullwhip effect. The bullwhip effect causes distortion in demand information and the distortion is amplified as demand information travels upstream. The magnitude of this amplification, however, depends on whether the supply chain partners share information or not.

We first consider the situation in which all supply chain members act independently without sharing any information. In this case the retailer, the firm situated at the extreme downstream end of the supply chain, does not pass along the actual demand data to the upstream firm (the wholesaler) but transmits orders as discussed earlier based on forecasted demand figures.

The next channel member views these orders as demands and similarly issues its own orders to the next echelon firm. This upstream firms employs a similar forecasting technique to estimate likely average demand for the next period and activates orders to the next echelon when its inventory reaches a predetermined level (ROP). The order quantity is decided in the same way and it does have the same amplifying factors. This process is repeated as orders transmit across the echelons, amplifying the bullwhip effect more and more in the process.

Figure 5.4 Demand amplification across the supply chain

Look at Figure 5.4. Supposing the bullwhip effect amplifies the demand variation by a factor of 1.2 when it reaches the wholesaler, the demand signal processing by the wholesaler amplifies the incoming order variations by a factor of 1.4, and finally the distributor processes the orders from the wholesaler by amplifying the order variation by another factor of 1.5. The cumulative effect is multiplicative when one considers the original demand variation faced by the retailer.

For example, if we denote order variation at the retailer as Var(D), then:

Var( )/Var( D ) = 1.2 or Var( ) = 1.2 Var( D )

Var( )/Var( ) = 1.4 or Var( )/Var( D ) = 1.4 x 1.2 =1.68

Similarly, Var( )/Var( D ) = 1.68 x 1.5 = 2.52

 

This finding can be mathematically represented as :

We can see how original demand variation in transmitted across the supply chain in a multiplicative way when demand information is decentralised. The formula in your text (page 105) mathematically represents this finding when a moving average forecasting technique is used by each firm in the supply chain.

Supply chain with centralised demand information. In this case the firms share demand information across the supply chain. The retailer only transmits an order to the wholesaler, but also transmits his forecast mean demand. The wholesaler uses this forecast mean demand information to calculate his inventory position and order up to level. The distributor receives the order from the wholesaler, but his order to the next level is again based on the retailer's forecast demand. What is the cumulative effect this time?

Figure 5.5 All supply chain members sharing retailer's forecast demand information

In this case each channel member is ordering on the basis of the retailer's forecast average demand. Each firm orders following the usual inventory control technique using this demand information and its replenishment lead time. The order at any stage of the supply chain, when compared with the retailer demand, will have the incidence of total lead time up to that stage. The effect is additive.

The formula demonstrates that increase in demand variability at each stage of the supply chain is additive instead of multiplicative. Chen et al (1999) has shown that if demand information is shared, the increase in variability seen by each stage of the supply chain is the same whether the supply chain follows an echelon inventory policy or not.

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