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CASE STUDY ON FORECASTING AND TRACKING SIGNAL IN SUPPLY CHAIN

T.V.S. Raghavendra & D.V.A.Rama Sastry,

Assistant Professors,

Department of Industrial & Production Engineering,

K.L.E.F.University, Vijayawada.

ABSTRACT

There are some thumb rules for demand forecasting: first, forecasts are always

inaccurate. There is no process that will repeatedly match forecast to actual. Second, quantifying the

error and using this to adapt the forecasts is essential to the process. Third, forecasts that are made at

higher level (e.g. product families instead of SKUs) will always be more accurate than at the lower

levels. Creating forecasts at the lower levels and then grouping them accordingly for planning

purposes is vital to a healthy process. Fourth, the planning horizon should be kept relatively short,

since errors tend to be significantly fewer in the short term. And, finally, it must be remembered that

forecasts are only the starting point for the planning process forecasts help provide a basis for further

refinements and the selection of a most likely scenario for the future.

In most cases the forecasting and demand estimation is based on historical order or

delivery information, which might not reflect the actual demand. However, actual consumer demand

may be very different from the order stream. Each member of the supply chain observes the demand

patterns of its customers and in turn produces a set of demands on its suppliers. But the decisions made

in forecasting, setting inventory targets, lot sizing and purchasing act to transform (or distort) the

demand picture.

The further a company is upstream in the supply chain (that is, the further it is from the

consumer), the more distorted is the order stream relative to consumer demand. This phenomenon is

also known as the Forrester effect or bull-whip effect. It is important to see the meaning of the bull-

whip effect both in downstream and upstream of the value chain; the variability caused by the gap (or

unbalance) between companies speculation and postponement of business activities.

This leads to a demand curve with steeper and steeper peaks and downs and with less

and less reliability the further up the party is in the value chain. In the upper stream of the value chain

the parties are forced to take extreme actions to survive the peaks only to find out that the demand was

exaggerated. The total cost of the value chain is increased heavily and the reliability and timelines of

the deliveries has suffered.

The cause of the steep demand curve and the fluctuations is not necessarily related to

seasonality or economic trend variations. According to Lee et al there are four main causes for the

bullwhip effect; demand forecast updating, order batching, price fluctuations and rationing and

shortage gaming. The lack of trust for the supplier as well as for the companys internal planning

creates these disturbances. Also the fragmented organizations in companies have led to atomisticconsiderations, i.e. sub-optimization of business activities, which cause the bullwhip effect to occur

internally in the company. The multiplied effect of the intra-organizational and cross-enterprise sub-

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optimization and non-collaborative, non-synchronized, individual processes leads to the bullwhip

curve.

The traditional bull-whip definition starts from the basis that each company speculates

more in their incoming goods inventory than in their outgoing goods inventory. Svensson describes

a reverse bull-whip effect, where the starting point is the opposite; the company speculates more in the

outgoing inventory than in the incoming inventory. If there is a balance between the companys

inventory management in incoming and outgoing side, there is no bull-whip effect within that

company. In other words that means that the internal forecasting process is operating well, and the

company has a common plan or forecast at both ends.

The bullwhip effect can be (at least partially) eliminated through information sharing

with suppliers and customers including intra-company suppliers and customers. By sharing

information, a common understanding of the real demand can be achieved. Special attention should be

paid to finding the items of information causing the overreactions. The final aim is to have centralized

demand information one forecast. The four material flow principles, which can be used to reduce the

bullwhip effect, are control system, time compression, information transparency and echelon

elimination.

In this paper, considering a typical supply chain consisting of plant, warehouse,

distributors, retailers and customers, future demand using one of the forecasting techniques and

tracking signal at retailers and distributers end are calculated and are related. Further an attempt is

made to quantify bullwhip effect for this case.