PERHITUNGAN BULLWHIP EFFECT. Consider a two-stage supply chain with a retailer and manufacturer Customer demand seen by the retailer: where |  |<1 and.

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Presentation transcript:

PERHITUNGAN BULLWHIP EFFECT

Consider a two-stage supply chain with a retailer and manufacturer Customer demand seen by the retailer: where |  |<1 and  t ~ (0,  )  is the correlation parameter Retailer receives the order after a fixed lead-time L Quantifying the Bullwhip Effect:

The retailer must estimate the mean and standard deviation of demand based on observed customer demand Suppose the retailer uses Moving Average forcasts: Let p > 1 (an integer),

Let Q be the quantity ordered by the retailer Increase in variability is a function of –L –p – 

L=5 L=3 L=1 Var(Q)/Var(D) p increases

Suppose p=5, L=1,  =0. Variance of the orders placed by the retailer is 40 % higher than the variance of the actual demand !!

Now suppose that the retailer uses Exponential Smoothing forecasts:

Increase in variability is an increasing function of  –The more weight placed on the most recent observation the higher the variability in retailer’s orders (ignoring history) As  increases the variability of retailer’s orders decreases The longer the lead times, the higher the variability

The Impact of Centralized Information: A number of stages (stage 1=retailer, stage 2=wholesaler, stage 3=distributor,…) Centralized information: the retailer (1 st stage) observes the customer demand, forecasts the mean demand using a moving average with p observations, places an order with the wholesaler All other stages (stage 2,3,…) receives the order of the lower stage along with retailer’s forecast mean demand Each stage uses retailer’s forecast to determine the target inventory level (information passes along the chain)

Where L i is the lead-time between stage i and Stage i+1

In the decentralized information case the retailer does not make its forecast mean demand available to the reminder of the supply chain. Suppose each stage of the chain uses a moving average forecast based on p observations. Wholesaler’s demand is the retailer’s order,…

K=5, decentralized K=5, centralized K=3 K=1 p Var(Q)/Var(D) L=1 for each stage