Bullwhip effect ch3
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The bullwhip effect occurs when demand variability increases at each stage of the supply chain, despite relatively stable consumer demand. As orders move upstream, fluctuations in ordering patterns are amplified, making it difficult for suppliers to match production to customer needs. This summary effect is caused by factors like forecasting errors, batch ordering, promotions, and stockpiling during shortages. Companies can reduce bullwhip through strategies like quick response, everyday low pricing, lead time reduction, vendor-managed inventory, and risk pooling across locations.
![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)](https://image.slidesharecdn.com/bullwhipeffectch3-150529201341-lva1-app6891/85/Bullwhip-effect-ch3-13-320.jpg)
Bullwhip effect ch3
- 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
- 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