Bullwhip Effect

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

4-Stage Supply Chain

Effect of Order Variability

Increase in variability in the supply chain

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.

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.

Reasons for Bull-Whip Effect1. 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

• 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

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

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

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

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

Methods for Coping with the Bullwhip3. 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)

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

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

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 CostOutboundInbound

HighLow

LowHigh