1. Bullwhip Effect

2. Bull-Whip Effect
Increase in Variability as we go up in the supply
Chain is called Bull-Whip Effect
• Customer demand for specific products does
not vary much, Inventory levels fluctuate
considerably across their supply chain

3. 4-Stage Supply Chain

4. Effect of Order Variability
Increase in variability in the supply chain

5. Proctor & Gamble Case
• Disturbing and often inexplicable variations in
supply and ordering figures on diapers,
• Relatively stable demand from consumers
• Variability increased further when examining its
own orders to its suppliers.

6. Example:
• The Barilla company(Italy)a major pasta
producer
• Offered special discounts to customer who
ordered full truckload of goods
Results:
• Created highly peaked and volatile customer
demand-patterns
• The supply chain costs outstripped the
benefits from full truckload transportation.

7. Reasons for Bull-Whip Effect
1. Demand Forecasting
In almost all forecasts, estimates of mean and standard
deviation(variability) of customer demand get modified as
more and more data becomes available

8. • Increase in variability is magnified with increasing lead
time.
• Safety stock and base-stock levels have a lead time
component in their estimations.
• With longer lead times:
– a small change in the estimate of demand variability implies
a significant change in safety stock and base-stock level, which
implies
– significant changes in order quantities
– Which in turn leads to an increase in variability
2. Lead Time

9. 3. Batch Ordering
• If Retailer uses batch ordering, as with a (Q,R) policy
• Then Wholesaler observes a large order, followed by
several periods of no orders, followed by another
large order, and so on.
• Wholesaler sees a distorted and highly variable
pattern of orders.
• Firms use Batch Ordering because:
– Firms faced with fixed ordering costs need to apply
(Q,R)inventory policy which leads to batch ordering
– Transportation discounts with large orders
– Periodic sales quotas/incentives

10. 4. Price Fluctuations
• Retailers often attempt to stock up when prices
are lower.
– Accentuated by promotions and discounts at certain
times or for certain quantities.
– Such Forward Buying results in:
• Large order during the discounts
• Relatively small orders at other time periods

11. 5. Inflated Orders
• Inflated orders during shortage periods
• Common when retailers and distributors
suspect that a product will be in short supply
and therefore anticipate receiving supply
proportional to the amount ordered.
• After period of shortage, retailer goes back to
its standard orders
– leads to all kinds of distortions and variations in
demand estimates

12. Methods for Coping with the Bullwhip
1. Reducing uncertainty. Centralizing information
Quick Response Strategy
• Suppliers receive POS data from retailers
• Suppliers use this information to synchronize their
production and inventory activities with actual sales at the
retailer
Ex. Milliken & Company (a textile & chemicals company)
2. Reducing variability.
– Reducing variability inherent in the customer demand process.
– “Everyday low pricing” (EDLP) strategy.
– Ex. WALMART

13. Methods for Coping with the Bullwhip
3. Lead-time reduction
– Lead times magnify the increase in variability due to
demand forecasting.
– Two components of lead times:
• order lead times i.e time to produce & ship the item [can be
reduced through the use of cross-docking]
• Information lead times i.e time it takes to process an order [can be
reduced through the use of electronic data interchange (EDI).]
4. Strategic partnerships
– Vendor managed inventory (VMI)
• Manufacturer manages the inventory of its product at the retailer
outlet
• VMI the manufacturer does not rely on the orders placed by a
retailer, thus avoiding the bullwhip effect entirely.
Wal-Mart (buyer) and Procter & Gamble (supplier)

14. Risk Pooling
• A tool for reducing variability in Supply Chain
• It suggests that demand variability is reduced if
one aggregates demand across locations, as high
demand from one customer will be offset by low
demand from other
• Reduction in variability allows decrease in safety
stock and therefore reduces average inventory

15. Few Critical Points:
• Centralized Inventory reduces both safety stock
and average inventory in the system as there are
possibilities of reallocation of inventory from the
centralized warehouse from one market area of
having low demand to other having high demand
• Higher the coefficient of variation, greater the
benefit from centralized systems or risk pooling
• Benefit from risk pooling depends on behavior of
demand from one market relative to other. It
decreases if demand from both is showing very
high positive correlation

16. Centralized Vs. Decentralized Systems
Parameter Centralized Decentralized Remarks
Safety Stock Low High Amount of decrease
depends on coefficient of
variation & correlation
between demand from
different markets
Service Level (at same
total safety level
stock)
High Low Amount of increase
depends on coefficient of
variation & correlation
between demand from
different markets
Overhead Costs Low High* *due to low economies of
scale
Customer Lead Time High Low
Transportation Cost
Outbound
Inbound
High
Low
Low
High