In 1989, John Sterman has explained in the famous beer game the bullwhip effect in downstream supply chain and how structure influences behavior of supply chain members. In this article, we try to find answers to the following questions: Does structure influence performance in downstream supply chain? Can be identified a network configuration that performs better than other configurations? In finding answers to the research questions, we considered the axiom ‘what it is not measured, it cannot be managed’. In the study, we took SCOR metrics at first level as performance metrics and best practices to express each SCOR dimension. 30 top executives from World Class Manufacturers were surveyed to rate the importance of the metrics and best practices in measuring performance in downstream supply chain. The second step was to develop a multiple attribute utility model (MAUT) to select from the seven configurations identified the one that has the highest performance.downstream supply chain network; multiple attribute utility model; SCOR; performance metrics; best practices.

Horatiu CIRTITA

Liviu ILIES

English

Research Papers in Economics

Does Structure Influence Performance in Downstream 
Supply Chain?
Horaţiu CIRTITA
Università Degli Studi di Padova, Italy
Babeş-Bolyai University, Cluj-Napoca, Romania
E-mail: horatiu.cirtita@unipd.it; hcirtita@ollusa.edu
Tel.: +39 (348)237.9415
Liviu ILIEŞ
Babeş-Bolyai University, Cluj-Napoca, Romania
E-mail: lilies@staff.ubbcluj.ro
Tel.: +40 (0264) 418652
Keywords: downstream supply chain network, multiple attribute utility model, 
SCOR, performance metrics, best practices
1 Introduction
Human behavior plays an important role in bullwhip effect, defined as an 
increase of fluctuations in demand as it moves up the supply chain from retailers to 
wholesalers to manufacturers to suppliers. Research of Forrester (1958) and 
Sterman (1989) showed that members’ perceptions about inventory and demand 
information, and mistrust in downstream supply chain generate this negative 
phenomenon. 
The bullwhip effect distorts demand information and the supply chain 
members have a very different estimate of what demand looks like. The effect of 
      Volume 10, Issue 1, March  2009              Review of International Comparative Management 106
Abstract
In 1989, John Sterman has explained in the famous beer game the bullwhip 
effect in downstream supply chain and how structure influences behavior of supply 
chain members. In this article, we try to find answers to the following questions: Does 
structure influence performance in downstream supply chain? Can be identified a 
network configuration that performs better than other configurations?
In finding answers to the research questions, we considered the axiom ‘what it 
is not measured, it cannot be managed’. In the study, we took SCOR metrics at first 
level as performance metrics and best practices to express each SCOR dimension. 30 
top executives from World Class Manufacturers were surveyed to rate the importance 
of the metrics and best practices in measuring performance in downstream supply 
chain.  The second step was to develop a multiple attribute utility model (MAUT) to 
select   from   the   seven   configurations   identified   the   one   that   has   the   highest 
performance.the lack of coordination creates increase in variability of performance metrics in 
the downstream supply chain. Since bullwhip effect is a cause of human behavior 
within a given downstream supply chain, we put the questions: Does structure 
influence performance in supply chain? Could be avoided this effect reengineering 
the downstream structure? What type of downstream supply chain networks are 
and how can be selected the best network to achieve best performance?
Downstream supply chain refers to the movement and storage activities 
required to transport a product from the supplier to a customer in the supply chain 
(Chopra, 2003). It represents the ‘face of the company’ to customers with a strong 
bearing on customer satisfaction. Customers expect quality service defined as 
reliable product deliveries of the right amount, at the right time with no damage to 
product and at a low cost. The company,  however, must balance customer 
satisfaction with the need for profitability. The balance of customer satisfaction is 
determined by the proper mix of downstream supply chain process strategies.  
The process strategies can be figured in many ways using a complex 
distribution network of internal and external providers or they can be configured as 
a direct model, such as used by Dell. It has succeeded to eliminate important 
downstream echelons as distributors and retailers, improving the visibility by 
creating a direct link with the end-customers. Recently, Dell has announced that 
they will renounce to use exclusively their famous direct sales approach and they 
will start to use retailers for some PC models. 
The   dilemma   faced   by   companies   concerns   the   choice   of   the   best 
downstream supply chain that balances customer satisfaction and profitability. To 
solve this dilemma, companies need to have strategic supply chain planning tools 
to asses the performance along the supply chain.
One example is provided by SCOR model developed by Supply Chain 
Council in 1996, to integrate business management and improvement practices, 
including a multi-tiered metric system to improve the plan, source, make, and 
deliver activities in supply chain.
Our study uses SCOR metrics and best practices in selecting the best 
alternative in downstream supply chain. It can serve as a tool for supply chain 
managers to plan supply chain networks based on a set of metrics and best 
practices to obtain a superior performance, continuous improvement and avoid 
negative phenomena as bullwhip effect. We propose a tool to define the best 
network alternative in downstream supply chain, based on Multiple Attribute 
Utility Theory (MAUT), creating a value function with scalable importance criteria 
coefficients. 
      Volume 10, Issue 1, March  2009              Review of International Comparative Management 1072 Criteria and sub-criteria based on performance metrics 
and best practices in downstream supply chain
In order to identify the most significant performance metrics and best 
practices that express the SCOR dimensions, we  surveyed 30 top executives from 
world   class   companies   in   different   industrial   sectors   such   as   machinery 
manufacturing;   computer   and   electronic   product   manufacturing;   electrical 
equipment,   appliance,   and   component   manufacturing;   and   transportation 
equipment manufacturing. The executives had key responsibilities in supply chain 
management. The individuals had positions from senior manager to president. A 
personalized   email   invitation   and   web   survey   instrument   were   used   as 
questionnaire   carrying   ways.   Questionnaire   has   comprised   three   sections: 
Company Profile, Network Design, and Performance Metrics.
2.1 Network Design 
The purpose of this section is to see how companies deliver the products 
on   their   downstream   supply   chains.   We   consider   the   manufacturer,   the 
intermediary distribution (distributors and retailers) and third party logistics as 
downstream supply chain echelons.
Based on these echelons, we have found seven network configurations, 
presented in table 2.
Network Configurations in Downstream Supply Chain
Table 2
No. Network 
Configuration Description
1. DS-3PL-B2B Manufacturer   sells   products   using   a   direct   sales 
approach and third party logistics. The customer is a 
business company.
2. DS-3PL-B2C Manufacturer   sells   products   using   a   direct   sales 
approach and third party logistics to end-customers.
3. DS-OF-3PL-
B2B
Manufacturer sells products using a direct sale approach, 
own fleet and third party logistics. The customer is a 
business company.
4. ID-3PL-B2B Manufacturer   sells   products   using   an   intermediary 
distribution   approach   and   third   party   logistics.   The 
customer is a business company.
5. DS-ID-3PL-B2B Manufacturer   sells   products   using   both   sales 
approaches, direct sales and intermediary distribution, 
and third party logistics. The customer is a business 
company.
Review of International Comparative Management              Volume 10, Issue 1, March  2009 108No. Network 
Configuration Description
6. DS-ID-3PL-B2C Manufacturer   sells   products   using   both   sales 
approaches, direct sales and intermediary distribution, 
own fleet, and third party logistics to end-customers.
7. DS-ID-OF-3PL-
B2C
Manufacturer   sells   products   using   both   sales 
approaches, direct sales and intermediary distribution, 
own fleet and third party logistics to end-customers.
Note. DS – Direct sales approach, ID-intermediary distribution, 3PL-third party logistics, 
OF-own fleet, B2B-business-to-business, B2C-business-to-consumer.
2.2 The model
In this section, we present a model of Multiple Attribute Utility Theory 
(MAUT) in the sense of (Keeney and Raiffa, 1976) that defined a preference 
representation function as a value function. 
In our model, we consider the value function to represent the best 
downstream supply chain network design under certainty, using a set of attributes 
as decision criteria.
The model assumptions are:
, m ,a , 1 s, a lternative nsider m a ves, we co  alternati A - set of ¼
ria, er n crite  we consid  criteria, C - set of
), ( j h v ciated wit tant) asso aling cons weight (sc   -criteria j w ×
id process decision a
the   stage of  at a given a weights  he criteri lues for t ions of va e combinat  acceptabl W - set of
1 w 0 and  w
aints  the constr t satisfy   of the se e elements  W. All th ctor w ed as a ve  represent ination is (each comb
n
1 j
j j = å ³
Î
=
We express the value function as:
(Dias and Climaco, 2000) showed that when the number of alternatives is 
relative small (in our case, seven alternatives) a good approach in selecting the best 
alternative from the set is pairwise comparisons and proposed three types of 
computations: (1) computation of a range of values for each network design 
alternative; (2) computation of the highest difference for each pair of alternatives, 
and (3) computation of the maximum regret associated with each alternative.
      Volume 10, Issue 1, March  2009              Review of International Comparative Management 109
. set      the of element  an    is   ) ,..., (    where ), ( ) , (
1
1 å
=
= =
n
j
n i j j i W w w w a v w w a VIn order to make these computations, we generate the following steps:
1) The data obtained from the survey are normalized an m x n pairwise 
confrontation table is analyzed, that is represented as M = (mij). 
2) The results produced by VIP (the free software created by Dias and 
delivered us by personal communication with the author) from a set  W  of 
acceptable combinations of values for the importance parameters and a set A of 
alternatives include the following:
· Computation of range of value for each alternative ai: the minimum 
value of ai given W can be computed by solving a linear program (LP) with the 
scaling weights W=(w1,w2,…,wn) as variables; or the maximum value of ai given W 
can be computed by  solving another LP.
· Computation of the highest difference of value for each ordered pair of 
alternatives: given an ordered pair of alternatives (ai,aj)Î A
2 and W, an LP may be 
solved to find the maximum possible advantage of the first alternative over the 
second one. If the maximum difference is negative or null
then
If the maximum difference does not exceed a tolerance parameter ε, then 
· Computation of the “maximum regret” associated with choosing each 
alternative: give an alternative aiÎ A, the set A\{ai}, and W, this amounts to find the 
maximum difference of value by which ai can lose to another alternative in A\{ai}. 
The scaling weights w= (w1,w2,…,wn) are considered as variables (rather than being 
fixed) to allow the regret to be as high as possible given A and w.
If                                    then we can say that the alternative ai is optimal. 
If                           then we can say that the alternative ai is “quasi-
optimal”, with tolerance ε.
In our model, we consider as decision criteria: perfect order defined as 
perfect deliveries per total deliveries (in percents), delivery performance defined as 
orders delivered on-time per total orders (in percents), order fillrate defined as 
orders filled complete per total orders ( in percents), total supply chain cost, 
supply chain leadtime as the time from order receipt to order deliver to the 
customer and, order accuracy as error-free orders per total orders (in percents). The 
scaling weights for criteria were determined based on the ratings made by the 
respondents   on   each   performance   metrics.   As   result,   perfect   order>delivery 
performance>order fillrate>total supply chain cost>leadtime>order accuracy. The 
sum of the scaling weights is equal with one.
Review of International Comparative Management              Volume 10, Issue 1, March  2009 110
} { W w w a V w a V m j i ij Î - = : ) , ( ) , ( max
W w w a V w a V j i Î " ³ ) , ( ) , (
W w w a V w a V j i Î " - ³ , ) , ( ) , ( e
}} max{ { max ) ( Regret
,.., 1
max W ,w):w V(a ,w) V(a a i j
m j W w
i Î - =
= Î
0 ) (a Regret i max =
e £ ) (a Regret i max3 The results
After performance the computations of the maximum regret (defined as the 
maximum disadvantage of an alternative when compared with another), we obtain 
that the network configuration ‘ID-3PL-B2B’ is the best structure (0-the lowest 
disadvantage),   being   absolute   dominant   respecting   other   five   network 
configuration and dominant respecting one 
On the confrontation table, we obtain the same  result, the network 
configuration ‘ID-3PL-B2B’ is the best structure (the minimum value of the 
corresponding function has higher values than the maximum value of any other 
network configuration – from 0,13 corresponding to the maximum value function 
for ‘DS-3PL-B2C’ up to 1,00 corresponding to the maximum value function for 
‘DS-ID-3PL-B2C).
Figure 1  Confrontation of values
4 Conclusions
In this paper, we present a multiple attribute utility theory model that can 
be used for the decision makers in supply chain to select the best downstream 
supply chain network configuration. The problem of selecting the best choice from 
a set of network designs can be approached using multiple criteria decision aid 
tools when the data collection process is available and reliable. The main 
advantages of this method are the avoidance of the issue of rank preservation and 
rank reversal when an alternative is added/deleted, and the selection of the best 
alternative is based on a combination between data and judgments (the respondents 
from the first wave of the survey were asked to rank the importance of the 
performance   metrics   on   a   seven   points   Likert   scale).   The   main   potential 
deficiencies of the method could be in the situation when the decision makers 
could not have access to data (to expensive or impossible to collect the data 
regarding the specific issue) and it uses just tangible criteria (quantitative criteria). 
When it does appear at least one of the two situations, an alternative approach of 
Analytic Hierarchy Process (AHP) could be explored.
      Volume 10, Issue 1, March  2009              Review of International Comparative Management 111References
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Review of International Comparative Management              Volume 10, Issue 1, March  2009 112

Does structure influence performance in downstream supply chain?

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