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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

4.3.2 Inventory policy under conditions of uncertainty

There are two basic uncertainties attached with inventory decisions which make the use of the EOQ model and a fixed ROP as seen above unworkable in real business situations. These are:

• uncertainty in demand
• uncertainty in lead time

Firms stock or manufacture on future anticipated demands based on forecasts and there is always uncertainty regarding the actual level of demand. Similarly, there are many reasons for the replenishment lead time to vary. So, a practical and scientific approach must take account of these two uncertainties. There are additional business risks with products with short life cycles, especially when such products are easily substitutable. In these circumstances, the prospects of firms being left with huge surplus stocks of products which are rapidly depreciating in value are very great. Seasonal fashion items and computers or cell phones are examples of such products.

For these reasons, the inventory decisions can be considered from two different angles, for two different kinds of business. The decisions taken by a firm which deals in a seasonal product or one with a short selling season would be different from a firm which sells products on a continuous basis. We should look at these two approaches.

Single period inventory decision with fixed ordering costs and demand uncertainty: In this scenario there is only a single ordering opportunity for the firm. How much inventory should be ordered when there is uncertainty regarding demand? There is a fixed ordering cost associated with the order, which would encourage the firm to order as many units as possible so that this fixed cost can be distributed over these units, making cost per unit lower and thus opening up the possibility of increased profit. This is valid, of course, provided the units can be sold.

The possibility of being left with surplus stock of a product which has a comparatively low salvage value, on the other hand, would discourage the managers from ordering a quantity which is unlikely to be sold during the season. The inventory decisions in this case are quite unique and have no relationship with the EOQ and ROP (reorder point) model. The rational policy would be to aim for a stocking level which is likely to generate maximum profit for the firm.

In inventory management literature the issues related to this problem are often described with the help of a well known inventory model known as the Newsvendor model. It is not necessary for us to get involved in this model as we can gain the same insights with the help of the following case in your text.

In your text

Case: Swimsuit Production, p.49.

This case is self explanatory and highlights the approach appropriate for the single period inventory decision.

The second part of the case adapts the original situation to a scenario in which the firm has a certain level of inventory, still holding products from the previous season which can be sold during the coming season. The managers in this case have make decisions regarding inventory for the coming season. You will notice that the reorder point and the order quantity in this case are not based on the classical ROP and the EOQ model.

The inventory policy which would aptly describe this single period inventory decision is called the single period (s,S) inventory policy. The reorder point in this case is s , and the firm places an order if the inventory position is at or below s and the quantity ordered is what is required to make the inventory level reach S . The main points about the single period (s,S) inventory policy are:

• For some starting inventory levels, it is better to not start production or order new inventory.
• If we start, we always produce or order inventory to the same level.
• Thus, we use an (s,S) policy. If the inventory level is below s , we produce or stock up to S .
• s is the reorder point, and S is the order-up-to level. The difference between the two levels is driven by the fixed costs associated with ordering, transportation, or manufacturing.

There is a similar example of this type of decision making in the following reading.

Reading 4.1

Chopra, S & Meindl, P (2001) extract from Chapter 9 'Determining optimal level of product availability', in Supply Chain Management - Strategy, Planning and Operations, Prentice Hall, pp.222-225.

Activity 4.1

Identify the main concepts in the LL Bean case provided in Reading 4.1 and answer following questions.

What are the main decision parameters in the optimal level of product availability?

What role does forecasting play in deciding the optimal level of product availability?

What relationship does the salvage value of the product have with the optimal level? How would the optimal product availability level change if the salvage values of a parka are assumed to take following figure : $ 20, $ 25, $30. (Other assumptions remain same.)

Inventory policy under uncertain demand: Multiple order situations. Most businesses have regular ordering requirements for a continuing business. How much inventory the firm needs at any time and how much the firm should order depend on following factors:

• the demand for the product
• the uncertainty regarding the level of demand
• the order or replenishment lead time, i.e. the time taken for the order to reach the buyer from the time order is placed
• the inventory holding costs with respect to the fixed order costs.

The uncertainties discussed earlier have a direct impact on a firm's inventory policy. A firm committed to good customer service will keep a safety stock to act as a buffer against the uncertainties. The principal uncertainty is the uncertainty regarding demand during the lead time, when the firm is exposed to the possibility of stock out.

The demand estimation is based on forecasts and the likelihood that demand during the lead time would exceed the forecast demand prompts firms to keep a safety stock. How much inventory to be kept as safety stock? Bowersox et al (2000) says that the task of planning safety stock requires three steps. Firstly, the likelihood of stock out be gauged. Secondly, the demand potential during a stock out period must be estimated. Finally, a policy decision is required concerning the desired level of stock out protection. In most cases the error associated with demand forecasting will cause stock out, if at all, towards the end of the replenishment cycle.

Inventory decisions regarding when to order and how much to order can be addressed in several ways. The two principal inventory decision models applicable for firms with multiple order situations and in conditions of uncertainty are the continuous review model and the periodic review model . These two models are also referred to as two parameter decision models . When and how much are the two parameters.

The continuous review model. Under the continuous review system, firms continuously monitor the inventory position and whenever the position is at or below the reorder point, an order is initiated to bring the inventory level to a predetermined level or order-up-to level. The main questions here are: what is the reorder point and what is order-up-to level (S in Figure 4.2)?

Figure 4.2 Continuous review inventory policy (s,S)

When ? The reorder point ( s ) in this case takes account of the demand uncertainty during the replenishment lead time, L . Assuming average demand during lead time is normally distributed, the usual reorder point at which the firm (a retailer or a wholesaler) will place an order is:

Where z is the constant associated with service level.

STD is the standard deviation of daily demand.

L is the replenishment lead time.

STD x is the standard deviation of demand during lead time

In your text

See Section 3.2.5 in text for a detailed discussion.

How much ? The second part of the equation is the safety stock. How much should the business order? The quantity ordered is based on the EOQ model since total cost is minimised by ordering this quantity:

And the level of inventory at order-up-to level (S) is:

This inventory policy in this multiple order situation requires that every time the inventory position is below s, an order is initiated to take the inventory level to S. This is the principle of (s, S) inventory policy. S in this case is Q+ safety stock.. What is important is to note that the reorder point (s) is a function of:

• the lead time
• average demand
• demand variability
• service level.

Single parameter inventory decision. There is another useful inventory model which is known as the single parameter inventory decision . The single parameter in this case is how much to order. The question of when to order is ignored since firms order at frequent intervals. This model is used when there are no order related costs, and so firms can order as frequently as possible in order to keep their inventory levels as low as possible. In such a situation, the EOQ model is of no relevance and the reorder point and the order-up-to point become the same. Another application of this model is when the lead time is very long or when

L x AVG > Q

In these cases:

And whenever the inventory position falls below this position, firms initiate an order to bring inventory position up to this level. This means frequent ordering and in many businesses, especially in the retail sector, this kind of frequent order, sometimes a daily order, is not uncommon.

In your text

See Sections 3.2.4 and 3.2.5 in your text.

Variable demand and variable lead time and inventory. We have considered a fixed lead time or replenishment time until now, but this assumption is unlikely to be useful in practical life. Just as there is uncertainty related with daily demand, there is also uncertainty related with lead time. These uncertainties can be mainly attributable to order processing time variations, manufacturers' performance uncertainty, and transit related uncertainty. It is therefore important that we address this issue. As in other cases the approach is based on statistics and on the notion of probability with replenishment lead time assumed to be following the normal distribution pattern.

All text books in logistics provide the formula which is used to account for both demand and lead time uncertainty.

In your text

See Section 3.2.6 in your text.

AVG x AVGL represents average demand during lead time and the second part is the standard deviation during lead time.

The other important inventory policy is the periodic review policy, by which a firm reviews the level of its inventory at predetermined intervals and initiates an order when the level of stock is below a certain point.

Periodic review policy. Ballou (1999) provides a good description of this policy. The decisions related to order quantity and reorder point are related to the length of the review interval. If the review period is small, it is possible that order quantity will follow the EOQ model whenever the stock falls below a certain predetermined level. On the other hand, when the review period is long, the order must be large enough to cover demand during the review period and the lead time and the quantity will be larger than the EOQ.

Figure 4.3 Periodic review inventory policy

Ballou (1999) provides the optimum review interval (R): µ

R = Q/Annual demand

And the order-up-to level (S) is

Where AVG is the mean daily demand (forecast) during the period R+L and STD is the standard deviation of daily demand.

In your text

See Section 3.2.7 in your text.

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