“ In modern supply chains, information replaces inventory” (True or False Why?)
Helps reduce variability
Helps improve forecasts
Enables coordination of systems and strategies
Improves customer service
Facilitates lead time reductions
Enables firms to react more quickly to changing market conditions.
The Bullwhip Effect and its Impact on the Supply Chain
Consider the order pattern of a color television model sold by a large electronics manufacturer to one of its accounts, a national retailer.
Fig 1. Order Stream
The Bullwhip Effect and its Impact on the Supply Chain Fig 2. Point-of-sales Data-Original Figure 3. POS Data After Removing Promotions
The Bullwhip Effect and its Impact on the Supply Chain Figure 4. POS Data After Removing Promotion & Trend
Higher Variability in Orders Placed by Computer Retailer to Manufacturer Than Actual Sales
Increasing Variability of Orders Up the Supply Chain
We Conclude ….
Order variability is amplified up the supply chain; upstream echelons face higher variability.
Increased safety stock
Reduced service level
Inefficient allocation of resources
Increased transportation costs
Problems with quality
Increased raw material costs
Cause of BW: 1.Demand Forecasting
One day, the manager of a retailer observed a larger demand (sales) than expected.
He increased the inventory level because he expected more demand in the future (forecasting).
The manager of his wholesaler observed more demand (some of which are not actual demand) than usual and increased his inventory.
This caused more (non-real) demand to his maker; the manager of the maker increased his inventory, and so on. This is the basic reason of the bull whip effect.
Cause of BW: ２． Lead time
With longer lead times, a small change in the estimate of demand variability implies a significant change in safety stock, reorder level, and thus in order quantities.
Thus a longer lead time leads to an increase in variability and the bull whip effect.
Cause of BW: ３． Order Batching
When using a min-max inventory policy, then the wholesaler will observe a large order, followed by several periods of no orders, followed by another large order, and so on.
The wholesaler sees a distorted and highly variable pattern of orders.
Thus, batch ordering increases the bull whip effect.
Takes care of promotional aspects also.
Cause of BW: Variability of Price/Forward Buying
Retailers (or wholesalers or makers) offer promotions and discounts at certain times or for certain quantities.
Retailers (or customers) often attempt to stock up when prices are lower.
It increases the variability of demands and the bull whip effect.
Cause of BW: ５． Rationing & Shortage Gaming
When retailers suspect that a product will be in short supply, and therefore anticipate receiving supply proportional to the amount ordered (supply allocation).
When the period of shortage is over, the retailer goes back to its standard orders, leading to all kinds of distortions and variations
Supply Chain in Equilibrium
Customer demand forecast = 10 units
Suppliers Producers Distributors Retailers Products & Services Products & Services Products & Services Information Cash Key: = Inventory Levels 10 Units 10 Units 10 Units 10 Units 10 Units 10 Units
Retailers are selling product at a constant rate and price. Firms along the supply chain are able to set their inventory to meet demand.
Supply Chain Disrupted
Customer Demand forecast = 20 units
Suppliers Producers Distributors Retailers Products & Services Products & Services Products & Services Information Flow Cash Flow Key: = Inventory Levels 160 Units 80 Units 40 Units 80 Units 40 Units 20 Units
As demand increases, the distributor decides to accommodate the forecasted demand and increase inventory to buffer against unforeseen problems in demand. Each step along the supply chain increases their inventory (double in this example) to accommodate demand fluctuations. The top of the supply chain receives the harshest impact of the whip effect.
Consider a simple supply chain…
Single retailer, single manufacturer.
Retailer observes customer demand, Dt.
Retailer orders q t from manufacturer.
Retailer Manufacturer D t q t L
Quantifying the Bullwhip Effect
Suppose a P period moving average is used.
If the variance of the customer demand seen by the retailer is Var(D), then the variance of the orders placed by that retailer to the manufacturer, Var(Q), relative to the variance of customer demand satisfies:
Var(q)/Var(D): For Various Lead Times L=5 L=3 L=1 0 2 4 6 8 10 12 14 0 5 10 15 20 25 30 L=5 L=3 L=1 P A lower bound on the increase in variability given as a function of p
Figure shows the lower bound on the increase in variability as a function of p for various values of the lead time,L. When p is large, and L is small, the bullwhip effect due to forecasting error is negligible.
The bullwhip effect is magnified as we increase the lead time and decrease p.
Assume p=5, L=1
The variance of the orders placed by the retailer to the manufacturer will be at least 40 percent larger than the variance of the customer demand.
Multi stage SC systems
Multi-Stage Supply Chains
Consider a multi-stage supply chain:
Stage i places order q i to stage i+1 .
L i is lead time between stage i and i+1 .
Retailer Stage 1 Manufacturer Stage 2 Supplier Stage 3 q o =D q 1 q 2 L 1 L 2
SC with centralized Demand Information
Centralized: each stage bases orders on retailer’s forecast demand.
The retailer observes customer demand, forecasts the mean demand using a moving average with p demand observations, finds his target inventory level based on the forecast mean demand, and places an order to the wholesaler.
The wholesaler receives order along with the retailer’s forecast mean demand, uses this forecast to determine his target inventory level, and place an order to the distributor.
Similarly, the distributor
places order to the factory.
SC with centralized Demand Information (cont’)
In this centralized SC, each stage of the SC receives the retailer’s forecast mean demand and follows and order-up-to inventory policy based on this mean demand.
The variance of the orders placed by the kth stage of the SC, Var(Q k ), relative to the variance of the customer demand, Var(D), is just:
SC with centralized Demand Information (cont’)
For example, if the lead time from the retailer to the wholesaler is two periods, then L1=2. Similarly, if the lead time from the wholesaler to the distributor is two periods, then L2=2, and if the lead time from the distributor to the factory is also two periods, then L3=2.
The total lead time from the retailer to the factory is L1+L2+L3=6
This expression for the variance of the orders placed by the kth stage is very similar to the expression in the previous section, with the single stage lead time.
Decentralized Demand information
Decentralized: each stage bases orders on previous stage’s demand.
The retailer does not make its forecast mean demand available to the remainder of the SC. Instead, the wholesaler must estimate the mean demand based on the orders received from the retailer.
The variance of the orders placed by the k th stage of the SC, Var(Q k ),relative to the variance of the customer demand, Var(D) satisfies:
The variance increases multiplicatively at each stage of the SC.
Increase in variability for centralized and decentralized system
Effect of Information Sharing
It is now clear that by sharing demand information with each stage of the SC, we can significantly reduce the bullwhip effect.
When demand information is centralized, each stage of the SC can use the actual customer demand data to estimate the average demand.
When demand information is not shared, each stage must use the orders placed by the previous stage to estimate the average demand. These orders are more variable than the actual customer demand data, thus, the forecasts created using these orders are more variable, leading to more variable orders.
The Bullwhip Effect: Managerial Insights
Exists, in part, due to the retailer’s need to estimate the mean and variance of demand.
The increase in variability is an increasing function of the lead time .
Centralized demand information can significantly reduce the bullwhip effect, but will not eliminate it.
Coping with the BW Effect １． Demand uncertainty
Adjust the forecasting parameters , e.g., larger p for the moving average method.
Centralizing demand information ; by providing each stage of the supply chain with complete information on actual customer demand ( POS: Point-Of-Sales data ）
VMI （ Vender Managed Inventory: VMI ）
Coping with the BW Effect ２． Lead time
Lead time reduction
Information lead time can be reduced using EDI （ Electric Data Interchange ） or CAO （ Computer Assisted Ordering ）
Coping with the BW ３． Order Batching
Reduction of fixed ordering cost using EDI and CAO
3PL （ Third Party Logistics ）
Shipping in LTL sizes by combining shipments
The supplier—usually the manufacturer but sometimes a reseller or distributor—makes the main inventory replenishment decisions for the consuming organization.
The supplier monitors the buyer’s inventory levels (physically or via electronic messaging) and makes periodic resupply decisions regarding order quantities, shipping, and timing.
Transactions customarily initiated by the buyer (like purchase orders) are initiated by the supplier instead.
The purchase order acknowledgment from the supplier may be the first indication that a transaction is taking place; an advance shipping notice informs the buyer of materials in transit.
The VMI Partnership
The manufacturer is responsible for both its own inventory and the inventory stored at is customers’ distribution centers.
Coping with the BW Effect ４． Variability of Price
Eliminate promotions & variation in prices
Limit quantity purchased during a promotion
Coping with the BW Effect ５． Rationing & Shortage Gaming
Allocate the lacking demand due to sales volume and/or market share instead of order volume. （ General Motors ， Saturn, Hewlett-Packard ）
Share the inventory and production information of makers with retailers and wholesalers. （ Hewlett-Packard ， Motorola ）
Reducing BW effect in your firm
Are prices in your supply chain stable?
Is information between firms along the supply chain accurate and timely?
Is sales being forecasted on projected data?
Are you forecasting sales using data from EDI or Point of Sale computer systems.
Are incentives for sales representatives along the supply chain at minimum?
Are orders being placed in small increments?
Are batch orders reduced to minimum levels?
Reducing BW effect in your firm
If you answered no to any of the previous questions regarding your firm and the bullwhip effect, then you may have an opportunity to reduce costs to your individual firm.
Information for Effective Forecasts
Pricing, promotion, new products
Different parties have this information
Retailers may set pricing or promotion without telling distributor
Distributor/Manufacturer might have new product or availability information
Collaborative Forecasting addresses these issues.
Information for Coordination of Systems
Information is required to move from local to global optimization
Who will optimize?
How will savings be split?
Information is needed :
Production status and costs
Transportation availability and costs
Locating Desired Products
How can demand be met if products are not in inventory?