İsmail Koca
98034570
1.
Introduction
Supply chain management has become very important for the companies seeking the ways of taking competitive advantages in their markets. It has a broader perspective that thinks the efficiency of the whole supply chain rather than managing the members separately. It thinks that members as links of a chain and involves the entire flow of the product from the raw material to the purchase of the final consumer.
The goal of the supply chain management is to meet the needs of the final consumer by supplying the right product at the right place, time and price. SCM tries to meet these goals while achieving the competitive advantages for the companies in the market.
And demand is the main driver of the activities in supply chain. Thus the accuracy and efficiency of the demand information in a supply chain is very important in achieving this goal.
Forecasting is a process in which historical data are used to estimate the expected demand for the future periods. Therefore forecasting is a key factor which effects the accuracy and efficiency of demand information in a SC. As a result forecasting is very important for SCM as it directly influences the SCM in its way to achieve its goal. However companies are not aware of the importance of it. The results of a recent study of 1,380 manufacturing and retail companies, done by KPMG Peat Marwick, said “many companies worldwide don’t know the exact amount of goods they need to manufacture, transport and store, yet they are very sluggish to adopt the very techniques and technologies that would solve the problem” (
Chain
Store Age , 1998, p.97). This shows that the companies do not know the importance of the forecasting or even they do not know what is forecasting. They may simply “guess” their production, sales, etc. This increases the uncertainty in the activities of the companies. And as they know that their forecasts are not inaccurate and do not know what to do about it they try to compensate this uncertainty by keeping excess inventory as safety stocks to keep the service level in a certain amount. This means they replace the lack of information and lack of confidence in the forecasts with excess inventory. When we think of the whole chain having such excess inventories, it is easily seen that the sum of the excess inventory in the chain is really an important amount.
Another issue is the accuracy of the demand forecasting. When retailers forecast demand using different models, the accuracy of the forecasts are significantly different depending on the market environment and how much the retailers try to do accurate forecasts. Retailer forecasting accuracy can significantly influence the quality of the orders that they make. Then these orders will influence the production and transportation decisions made by the supplier and, as a result, the performance of the entire supply chain. The accuracy of their forecasts can also influence the benefits of sharing information. As forecasts made by the retailers are not so good, inventory decisions made by them also become less reliable. Therefore, the value of information that retailers share with the supplier can also decrease.
As a result we see that forecasting is therefore a key factor in the successful implementation of a supply chain management strategy.

2.
Review of the Literature
An important concept in supply chain management is bullwhip effect which is the name of the increasing variability of the demand when one moves up a supply chain. Lee at al. (1997) analyzed the variability amplification along the supply chain and named this amplification as bullwhip effect and he showed four reasons of the bullwhip effect. One of them is demand forecasting in the supply chain. In a multilevel supply chain members having order-up-to level inventory policy, as the retailer faces demand in each period, updates its forecasts based on this demand and then use this updated forecast to update its target inventory level, this will increase the variability of the orders of the retailer. Then this variability will be amplified in an upper level of the supply chain as it will use the orders of the retailer to update its own forecasts. This will continue through the upper level of the supply chain. Also Chen at al.
(Tayur, Sridher R.,Quantitative Models for SCM. 420-421) it is explained that all forecasting techniques can cause the bullwhip effect. Because in most standard forecasting methods forecasts are each time a new demand is observed. Lee at al. (1997) mathematically proved that the demand variation amplifies when one moves up to supply chain but he did not quantify the impact of the bullwhip effect on the costs of the supply chain and did not discuss how to reduce the impact bullwhip effect on the performance of bullwhip effect.
Chen at al. (2000) quantified the bullwhip effect for a simple, two stage supply chain, consisting of a retailer and a manufacturer. The inventory policy is order-up-to inventory policy where the target inventory level in period t, y t
, equals to y t
  t
L
 z

t
L
In the formula
 ˆ t
L
is an estimate of the mean lead time demand,
 ˆ t
L
is an estimate of the standard deviation of the forecast errors over the lead time, and parameter z is chosen to meet a desired service level. To determine the target inventory level, both retailer and the manufacturer forecast both the expected demand and the standard deviation of the demand.
Also they face a fixed lead time L.
If the used forecasting technique is the moving average with p demand observations, and if the demand process satisfies
D t
   
D t

1
  t
D :customer demand in time t t

: correlation parameter with

<1
 t
: i.i.d. with sym. distr. with mean 0 and variance σ 2 then the variance of the orders q MA , placed by the retailer to the manufacturer, satisfies
Var ( q
MA
)
Var ( D )

1



2 L p

2 L
2 p
2



1
  p
 where Var(D) is the variance of customer demand.
The above inequality quantifies the increase in variability from the retailer to the manufacturer, which is quantifying the bullwhip effect.
The increase in variability is an increasing function of the lead time, L. This is because the retailer updates her target inventory level in each period by demand forecasting. Thus the longer lead times lead to larger changes in the target inventory level leading more variability in the orders placed by the retailer.

The increase in variability is a decreasing function of p , the number of observations used in the moving average. This is because when p is large the estimate of the mean will change slower than when p is small. Thus larger p values leads to smoother forecasts which decreases the variability in order quantities of the retailer.
Also it is a decreasing function of

, the correlation parameter between demands of the consequtive periods. So that negatively correlated demands lead to higher variability than positively correlated demands.
A conclusion in this paper is that a retailer faced with long lead times which increases the variability, must use smoother demand forecasts in order to prevent a further increase in variability. That is it must use more demand data in her forecasts in order to reduce the bullwhip effect.
Chen at al. (2000) compares exponential smoothing and moving average forecasting models in a two stage supply chain, one retailer and a manufacturer. In this paper he first uses the model in Chen at al. (1997) that he used for moving average forecast model and quantifies the increase in variability from the retailer to the manufacturer with the exponential smoothing forecast model, having parameter α, 0<α≤1. Then the variance of the orders q
EX
, placed by the retailer to the manufacturer, satisfies
Var ( q
EX
)
Var ( D )

1


 2 L
 
2 L
2

2
2
 




1
1

 


 where

=1- α and Var(D) is the variance of customer demand.
The increase in variability is an increasing function of the lead time, L. The increase in variability for the exponential smoothing forecasts is an increasing function of α, the smoothing parameter used in the exponential smoothing. As α is the weight placed on the most recent observation of demand in the exponential smoothing forecast, the more weight we place on a single observation, the greater the increase in variability. Thus small values of α produce smoother forecast, which reduces the variability in order quantities of the retailer.
Also it is a decreasing function of

, the correlation parameter between demands of the consequtive periods. So that if demand corralation increases, the increase in variability decraeses.
A conclusion in this paper is that the smoother the demand forecasts the lower the bullwhip effect. That is it must use more demand data in her forecasts in order to reduce the bullwhip effect.
For the above results the assumption was that the demand is constant over time. When demand has an increasing or decreasing trend, fluctuation over time or seasonality the demand is like
D t
   bt
  t where b denotes a linear trend factor. This demand pattern implies that the D t
are independent with mean
µ+bt and variance σ
2
. In this case for both moving average and exponential smoothing forecasts the variation of the retailers order will be higher than the i.i.d demand case. It is concluded conclude that the increase in variability is not due to the magnitude of the linear trend, but to the need to estimate an additional parameter, b , when forecasting a demand process with a linear trend.

Chen at al also compares the two forecast models. For this he choses the parameters α and ρ such that both forecasts have the same variance of the forecast errors. Then he finds that for i.i.d. demands or linear trend demands exponential smoothing forecasts lead to larger increase in variability than moving average forecasts.
Zhao at al. (2002) examined how the selection of forecasting models influence the performance of the supply chain and the value of information in a two level supply chain consisting of one supplier and four retailers. The results are taken by simulation of different situations such as five forecasting models and four different demand distribution cases and comparing them.
The results are 1. The selection of forecasting model is significant in influencing total costs and service levels for both the supplier and the retailers, and the total costs for the entire supply chain. 2. Higher forecasting accuracy improves the performance of the supply chain and enhances the benefits of information sharing. 3. Improvement in forecasting accuracy significantly improves the performance in the supply chain only when information is shared.
Thus more accurate forecasts may not help to improve the performance of the supply chain significantly when the retailers do not share information with the supplier. 4. The supplier can usually improve its total costs and service level dramatically through information sharing under all conditions. However, the total costs and service level for retailers may even worsen when they share information with the supplier under some demand conditions when capacity tightness is low. Therefore, the supplier must provide some incentives to the retailers under these conditions, or retailers may not be willing to participate in an information-sharing project. 5. Appropriate forecasting model will decrease the negative impact of forecasting errors.
As a last conclusion it can be said that both selecting the information sharing and the right forecasting model are important for improving the supply chain performance.
Zhao and Xie (2002) examined the impact of forecasting error on the information sharing in supply chain. In this paper the model in Zhao at al. (2002) is used and again the results are taken with simulating the model. It is assumed that the retailer’s forecasting errors follow normal distributions. The forecasts that the retailers make will be equal to the demand plus the forecasting errors. The forecasting errors distributions are characterized by three parameters: the mean (bias) of forecasting errors (EB), the initial standard deviation (ED) that measures forecast variability and the rate of increase (IR) of forecast deviation over time.
The results are 1. Higher standard deviation always results in a higher total cost for the supplier, the retailers and the entire supply chain ss the higher variation in the forecast will produce higher backorder costs. Therefore reducing the variability in the forecasting errors will help to improve the performance of all members in the supply chain. 2. An unbiased forecast (EB=0) will result in the lowest total cost for the supplier, the retailer and the entire supply chain. When there is a bias in the forecast, the costs for the retailers, suppliers and the entire supply chain will go up. 3.
When the bias is low (EB = -50, 0 and 50), a higher standard deviation in the error always results in a higher cost for both the supplier and the retailer.
However when the bias is high (EB = 100), a higher standard deviation can even result in slightly better performance of the supply chain. This result indicates that a positive bias can be used in the forecast as a buffer to protect against uncertainty and thus reduce the negative impact of that uncertainty. The positive bias is more effective in reducing the supplier’s total cost than the retailer’s cost. 4. EB has greater impact on supply chain performance when retailers share planned order information with the supplier, as the bias in the forecast will be

passed to the supplier, which will change its production decisions. 5.
When EB increases further from 50, information sharing is not good. Because information sharing will help to reduce the backorder cost and increase the inventory holding costs, but because of the bias there is a buffer that reduces the backorder costs, thus sharing information will not reduce the backorder costs, but will increase the inventory holding costs.
Collaborative forecasting is the case where the SC members jointly maintain and update a single forecast in the system and it has become popular in industry in recent years.
To understand what is collaborative forecasting Marilyn at al. (2000) has a good work. It examined the collaborative forecasting, its benefits, the problems it faces and the future of collaborative forecasting.
One benefit of the collaborative forecasting is that it reduces a company’s reliance on historical data. Historical data is valuable information but the developing technology the lifecycles of the products, the promotions, etc decrease the value of it as it cannot show the demand pattern of the future successfully at all. After giving examples of success stories the paper concludes that through internal and external efforts the companies have been able to reduce time, cost and slack from their supply chains and now better able to coordinate inventory levels with changing demand. Thus the company-wide combination of these benefits will result in improved customer service, lower supply chain costs, and reduced risk for the company. And the supply chain cost savings come in the form of lower inventory levels, lower production costs, lower incidence of transshipment (which occur when product is originally shipped to one location and demanded at another), and lower incidence of product obsolescence (because the shelf-life expired). According to the paper collaborative forecasting applies supply chain management concepts to the forecasting function and uses available information and technology to force a shift from independent, forecasted demand to dependent, known demand. Eventually, the future of forecasting may evolve to the point where forecasting is not even necessary. Demand information will be supplied completely by supply chain partners and the need to predict demand will be eliminated.
Yet there is not enough numerical studies on this topic and Aviv (2001)’s study will serve as building blocks for future work in this area. Aviv (2001) did a numerical study simple, using two stage supply chain, consisting of a retailer and a manufacturer to examine the impact of collaborative forecasting on supply chain performance. He uses simulation and regression to reach the results. He uses two models local forecasting (LF) and collaborative forecasting
(CF). LF is the model where future demand forecasts are updated periodically and integrated back into the replenishment process. The inventory and forecast information is not shared between the retailer and the manufacturer. CF is the model where the supply chain members jointly maintain and update a single forecast in the system. An important assumption is that demand is i.i.d and normally distributed with known mean μ and standard deviation σ. Then demand forecasts have mean μ and error term and adjustment part. Thus forecasts do not depend on the actual demand observations. As a result Aviv found that collaborative forecasting improved significantly the performance of the SC. Also observed that collaborative forecasting performs better when the lead times are shorter and the members of the supply chain have varying forecasting capabilities.
3.
Research Questions to be Analyzed

Is collaborative forecasting or information sharing with an appropriate forecasting model improves more the performance of the supply chain?

Zhao at al. (2002) examined how the selection of forecasting models influence the performance of the supply chain and the value of information in a two level supply chain consisting of one supplier and four retailers and Aviv (2001) did a numerical study simple, using two stage supply chain, consisting of a retailer and a manufacturer to examine the impact of collaborative forecasting on supply chain performance. These two studies showed that supply chain performance is improved in both collaborative forecasting and information sharing with an appropriate forecasting model. However there is not a study comparing the better one giving the better performance tan other. If this study can be achieved it will also be another study that quantifies the effect of collaborative forecasting on the performance of the supply chain. Because as I can found only one study, that is Aviv (2001) which does a numerical study on collaborative study.

In collaborative forecasting analysis Aviv (2001) did not consider the retailer and the supplier separately. Retailer or supplier always gains in all conditions?

In collaborative forecasting analysis Aviv (2001) worked on a specific case, simple two level supply chain consisting of one retailer and one manufacturer, retailer facing i.i.d. demand pattern having normal distribution, and with small variation, Martingale model of forecasting evolution, etc.. Does collaborative forecasting improves the performance of the supply chain in all cases, such as models having multilevel, products that have smaller shelf-lives, etc.?
In the study of Zhao at al. (2002) we see that in some situations the improvement in the whole supply chain may lead a loss for the retailer. Because the improvement for the supplier dominates to the loss of the retailer and it is seen that there is an improvement in the performance of the supply chain. Because of this in the studies that are considering the whole we cannot say about the members’ gains or losses. In Aviv (2001) the collaborative forecasting improves the performance of the supply chain but it is also beneficial for the retailer? Moreover Aviv studies on a specific case and if the assumptions are changed, what should be the case. The supply chain performance will still be improved, or when it is improved will all members gain from it? These questions can be considered in new studies. In my opinion for the gain of the retailers it cannot be said anything but in a very big amount of the models the collaborative forecasting will improve the performance of the supply chain, because it increases the information amount of the future (decreasing the uncertainty) demand and increase the forecasting quality o the demand forecasting. Therefore the uncertainty will leave its place a bit more to the real information about demand and this will decrease the risk of the companies, the variation of the forecasts, etc.

Aviv (2001) assumed that the actual demand observations provide no information about the future demands. If we do not do this assumption how will the performance of the supply chain be affected?
As a further examination Aviv (2001) says that his model has a demand pattern assumption of an i.i.d. and thus the actual demand observations provide no information about the future demands. If the demand pattern is not i.i.d and the forecasts are done based on the previous demand, or demand has a decreasing or increasing trend, what should be the results?

4.
Solution Methodology:
My resarch question is The first question above: Is collaborative forecasting or information sharing with an appropriate forecasting model improves more the performance of the supply chain?
My hypothesis is that “Collaborative forecasting performs better than the information sharing with an appropriate forecasting model.”
To find the answer for the question we should first test the hypothesis above. To achieve this, firstly we should find a model with acceptable assumptions that will best fit to study on the quantification of the cost reduction of collaborative forecasting in supply chain and quantification of the cost reduction of information sharing with an appropriate forecasting model in the supply chain. This may be a simple two level inventory model with a retailer and a manufacturer. Because a model with acceptable assumptions that fits for both two conditions may be the simplest one. Than after developing the model some numerical results might be driven from it. Or by developing a simulation model for both cases the simulation outputs might be compared and can be reached some results. If the model developed is a model that we can change the demand patterns we can compare the two cases for different demand structures and this might be really a good study. However if we cannot develop such a model we should try to develop a few models such as retailers having demand with a small variation such as the Aviv (2001)’s, or a high variation. Because Aviv (2001) studied with a demand having small variation and get a good result, but we now that information sharing performs better when variability is high. This might be really a good comparison. Or in the other case we do not know how performs the collaborative forecasting with a demand having high variation. This might also be a good comparison.
References:
Aviv,Y., 2001. The Effect of Collaborative Forecasting on Supply Chain Performance.
Management Science 47(10), 1326-1343
Chen, F., Z. Drezner. J. Ryan, and D. Simchi-Levi, 2000. Quantifying the bullwhip effect in a
simple supply chain: The impact of forecasting , lead times, and information. Management
Science 46(3), 436-443
Chen, F., Ryan, J.K, Simchi-Levi, D., 2000. The impact of exponential smoothing forecasts
on the bullwhip effect. Naval Research Logistics 47, 269-286
Helms, M.M., Ettkin, L.P., Chapman, S., 2000. Supply chain forecasting, collaborative
forecasting supports supply chain management. 6(5), 392-407
Lee, H., Padmanbhan, V., Whang, S., 1997. Information distortion in a supply chain: The
bullwhip effect. Management Science 43(4), 546-558.
Zhao, X., Xie, J., Leung, J., 2002. The impact of forecasting model selection on the value of
information sharing in a supply chain. European Journal of Operational Research 142,
321-344
Zhao, X., Xie, J., 2002. Forecasting errors and the value of information sharing in a supply
chain. International Journal of Production Research. 40(2), 311-335