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Value of Information.

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Chapter  5

Value of Information

         is a very slippery concept as information per se does not have any universal value. Its value is related to the person who uses it, when he uses it and for what he uses it. Any assessment of the value of information is therefore related to the value of the decision-making supported by such information.

Types of information includes inventory levels, orders, production and delivery status. The more information, the more it helps reduce variability in the supply chain, it enables the coordination of manufacturing and distribution systems and strategies and enables lead time reduction, etc,.

Bullwhip effect supply chain phenomenon describing how small fluctuations in demand at the retail level can cause progressively larger fluctuations in deman at the wholesale, distributor, manufacturer and raw material levels. This is also a concept for explaining inventory fluctuations or inefficient asset allocation

Quantifying the Bullwhip effect

  • Consider a two-stage supply chain:
  • Retailer who observes customer demand
  • Retailer places an order to a manufacturer.
  • Retailer faces a fixed lead time
  • order placed at the end of period t
  • Order received at the start of period t+L.
  • Retailer follows a simple periodic review policy
  • retailer reviews inventory every period
  • places an order to bring its inventory level up to a target level.
  • the review period is one
  • Base-Stock Level = L x AVG + z x STD x √L[pic 1]
  • Order up-to point =
  • If the retailer uses a moving average technique,[pic 2]
  •  [pic 3][pic 4]

[pic 5]

  • Var(D), variance of the customer demand seen by the retailer
  • Var(Q), variance of the orders placed by that retailer to the manufacturer[pic 6]
  • When p is large and L is small, the bullwhip effect is negligible.
  • Effect is magnified as we increase the lead time and decrease p.

Bullwhip Effect – as an occurrence detected by the supply chain where the orders sent to the manufacturer and supplier create a larger variance than the sales to the end customer. This variance can interrupt the smoothness of the supply chain process as each link in the supply chain will over or underestimate the product demand resulting in exaggerated fluctuations.

The bullwhip effect is a major concern for many manufacturers, distributors and retailers because the increased variability in the order process (i) requires each facility to increase its safety stock in order to maintain a given service level, (ii) leads to increased costs due to overstocking throughout the system, and (iii) can lead to an inefficient use of resources, such as labor and transportation, due to the fact that it is not clear whether resources should be planned based on the average order received by the facility or based on the maximum order.


If demand information is centralized, each stage of the supply chain can use the actual customer demand data to create more accurate (less variable) forecasts, rather than relying on the orders received from the previous stage, which can be significantly more variable than the actual customer demand.

Centralized Demand Information

Consider a multi-stage supply chain in which the first stage (i.e., the retailer) shares all demand information with each of the subsequent stages. In other words, in each period the retailer provides every stage of the supply chain with complete information on customer demand. Assume that all stages of the supply chain use a moving average forecast with p observations to estimate the mean demand, and that the demands seen by the retailer are i.i.d.. Therefore, since each stage has complete information on customer demand, each stage will use the same estimate of the mean demand.

In this centralized supply chain, each stage of the supply chain receives the retailer`s forecast mean demand and follows a base-stock inventory policy based on this demand. Therefore, in this case, we have centralized the demand information, the forecasting technique, and the inventory policy.

The variance of the orders placed by the kth stage of the supply chain , relative to the variance of the customer demand , is just:[pic 7][pic 8]

+[pic 9][pic 10]

where Li is the lead time between stage i and stage i+1. That is, the lead time Li implies that an order placed by facility i at the end of period t arrives at that facility at the beginning of period t + Li. Notice that this expression is quite similar to the single stage bound presented in (14.8),

but with the single stage lead time, L, replaced by the echelon lead time.

Notice that the formula demonstrates that even when (i) all demand information is centralized, (ii) every stage of the supply chain uses the same forecasting technique, and (iii) every stage of the supply chain uses an echelon inventory policy, we will still see an increase in variability at each stage of the supply chain. In other words, by centralizing customer demand information and coordinating inventory control we have not completely eliminated the bullwhip effect.

Decentralized Demand Information

Consider a supply chain similar to the one just analyzed, but without centralized customer demand information. In this case, the retailer does not provide the upstream stages with any customer demand information. Therefore, each stage determines its forecast demand based on the orders placed by the previous stage, not based on actual customer demand.

In this case, we have the following lower bound on the variance of the orders placed by each stage of the supply chain:



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