Cost and Profit Distribution in Value Chains
Altendorfer Klaus1, Atzlinger Gregor, Jodlbauer Herbert, Reitner Sonja
Upper Austrian University of Applied Sciences, Steyr, Austria
Wehrgrabengasse 1-3, 4400 Steyr, Austria, Tel.: +43 (0)7252 884-3150, Fax.: -3199
Abstract
The following two issues concerning Value Chain networks are addressed in
this paper; firstly, the cost structure is studied and the cost factors leading to the
highest overall cost improvement for the network are identified, secondly, the influence of an increasing number of production stages with lower value added at
each stage on the network profit is studied. This is an interesting question since
looking at the past two decades research shows that from a company’s point of
view the proportion of personnel costs and in-house production depth decreases
whereby the costs of purchased parts increase. Therefore, looking at single companies within a Value Chain shows that the importance of personnel costs in comparison to material costs decreases. However, in this paper we develop a cost
model including a whole Value Chain network which shows that within the network the most important cost factor is personnel.
Keywords: value chain costs, cost and profit distribution, network analysis
1. Introduction
In the last two decades looking at the European automobile industry shows that
from a company’s point of view the proportion of personnel costs and in-house
production depth decreases while at the same time the value of purchased parts increases (see Sinn (2006), Krcal (2008), and Reeves et al. (2010)). The implications of this development are either discussed from an outsourcing point of view
(see Bryce and Useem (1998), Benson and Littler (2002), and Reeves et al.
(2010)) or from an economic point of view (see Krcal (2008) and Sinn (2006)).
Also the impact of this outsourcing or production depth decrease on the company’s performance is discussed in literature (see Jiang et al. (2006)). In a manufacturing context, the outsourcing decision which leads to decreased personnel
costs, is, in most contributions, evaluated based on single company perspectives.
1
Corresponding author. E-mail address: [email protected]
2
The objective is usually to identify whether a product or process should be outsourced. According to transaction cost theory (see e.g. Hobbs (1996) and Williamson (2008)) the outsourcing decision is not only based on a higher efficiency the
specialized supplier may have, but also on the transaction costs which stem from
the fact that transactions between two companies do not occur without friction.
Even though there is a lot of literature available concerning when to outsource and
showing that this outsourcing has been widely used for a lot of materials in the last
two decades (at least in the automotive industry) there is still a lack of models discussing what is the influence of outsourcing on an entire Value Chain. Therefore,
the questions addressed in this paper are:

firstly, what does the cost structure within a Value Chain network look
like and what are the cost factors leading to the highest overall cost improvement, and

secondly, what is the influence of an increasing number of production
stages implied by an increasing proportion of outsourced parts which in
turn means more specialized Value Chain partners.
These two questions are not addressed from an outsourcing or a transaction cost
perspective but a Value Chain model based solely on cost proportions at each
stage is developed to discuss which cost factor evolves to the most important one
from a holistic Value Chain perspective.
The main contribution of this paper is a model for the distribution of personnel,
material, machinery costs and profit for a Value Chain consisting of an OEM and
its suppliers of materials and machinery. Additionally, a numerical example is
studied which applies input data for the cost and profit distribution of single companies from a data base.
The remainder of the paper is organized as follows: Section 2 discusses related research. In Section 3 a common and a varying parameter model for describing the
cost and profit distribution is presented. Next, Section 4 outlines how the data
from the data base was generated and Section 5 discusses the numerical example.
The conclusions are stated in the final section.
2. Literature review
The topic of outsourcing materials or production processes in manufacturing companies is a frequently discussed topic in research (see Hobbs (1996), Bryce and
Useem (1998), Benson and Littler (2002), Jiang et al. (2006), Williamson (2008),
and Reeves et al. (2010)). Jiang and Qureshi (2006) provide a review of outsourcing literature which is therein divided into decision-oriented, process-oriented and
result-oriented approaches. According to Jiang et al. (2006), especially the outsourcing results have to be focused by further research. That such outsourcing de-
3
cisions lead to a lower production depth and therefore to lower personnel costs in
comparison to material costs is clear from the transaction cost theory (see Hobbs
(1996) and Williamson (2008)). An increase in outsourcing is reported for the last
two decades in Kumar et al. (2007) for India. For the German automobile industry
this reduction of personnel cost proportion in the last few decades is empirically
shown in Sinn (2006) and Krcal (2008). For the Australian industry the same is
reported in Benson and Littler (2002). Also Bryce and Useem (1998) discuss the
fact that outsourcing has increased.
Concerning the performance of outsourcing, Jiang et al. (2006) find that it leads to
an advantage in cost efficiency in their study, however, they cannot prove that
outsourcing leads to differences in the Return on Assets or the Net Profit Margin.
A study by Penfield (2007) identifies the main cost drivers in a supply chain and
he finds that material costs are one of the main drivers to reduce supply chain
costs. This finding is based on the single company based approach showing that a
high proportion of costs are material costs and also measures like negotiation are
proposed to reduce supply chain costs.
The literature on supply chain performance measurement as for example reviewed
in Lee et al. (2007), shows that the cost part in this supply chain performance
measurement is usually mostly linked to inventory costs, asset costs and transaction costs. Gunasekaran et al. (2001) provides an overview of financial and nonfinancial supply chain performance measures. Even though a lot of measures
linked to the logistical performance of the supply chain partners are covered, the
personnel costs are not directly mentioned. The same holds for Gunasekaran et al.
(2004). The overall supply chain costs as performance measure are explicitly discussed in Beamon (1998), Beamon (1999) and Chan (2003). However, these papers do not distinguish between materials and personnel costs since costs are
mostly assigned to functional aspects which are manufacturing costs, distribution
costs, inventory costs or warehousing costs. A good overview of supply chain
models is provided in Beamon (1998) where also the number of stages within a
supply chain is discussed as decision variable. With the performance measures
discussed in van Hoek (1998), the objective of strategic improvement is focused
on whereby a link between costs, market and integration is discussed. All the
supply chain performance measures discussed in this review are linked to supply
chain improvement, however, none of them directly discusses the influence of
personnel costs, and especially of more supply chain stages, on the overall supply
chain performance measured as overall profit created.
3. Model development
Reduction of in-house production depth, increased value of purchased material
and decrease of the personnel costs might imply that personnel costs are not important for a company’s competitive position. In addition to a decreasing person-
4
nel cost proportion you can observe an increasing material cost proportion and a
stagnating proportion of machinery costs (see Hobbs (1996), Sinn (2006) and Williamson (2008)).
Considering a supply chain with an OEM and multiple tiers changes this relationship. For an OEM a decreasing personnel cost proportion and increasing material
cost proportion might be true, but material costs of an OEM match with the revenue of the component suppliers. Again, the revenue of the suppliers can basically
consist of personnel, material, machinery costs and profit. Here machinery costs
are defined as the depreciation of machines, equipment, facilities or the like. Material costs comprise all costs for purchased consumables, auxiliary materials, energy and so on. Miscellaneous costs and financing costs are not discussed in this
paper.
3.1. Modelling frame
In our model not only the OEM and his material suppliers (= OEM supply chain)
are considered, but the machinery suppliers and their component suppliers as an
independent supply chain (= machinery supply chain) are also included. The combination of both the OEM and the machinery supply chain is referred to as the
Value Chain. Fig. 1 shows the entire Value Chain where the OEM supply chain in
this example consists of an OEM and two tiers of component suppliers. The machinery supply chain includes three levels (MS0, MS1, MS2). MS0 symbolises the
suppliers of the complete machinery in the Value Chain. MS1 combines all suppliers for MS0 and MS2 denotes the suppliers of MS1. Whenever machinery is
bought (also at the levels MS0, MS1 and MS2) this machinery comes from MS0.
Generally a Value Chain consisting of an OEM supply chain with n+1 levels
(numbered with 0, 1, ..., n) and a machinery supply chain with k  1 levels (numbered with 0, 1. ..., k) is considered. Level 0 of the OEM supply chain refers to the
OEM, level 1 to the 1st tier and so on. In the machinery supply chain, level 0 refers to MS0, level 1 to MS1 etc.
For every member of the Value Chain, the revenue, personnel costs, machinery
costs, material costs and the profit are analyzed. These values are not all completely independent from each other. The following relationships are assumed:
(A1)
(A2)
The material costs of the OEM match with the revenue of the component
suppliers of the 1st tier. The material costs of the 1st tier match with the
revenue of the 2nd tier and so on. The same relation holds for the machinery supply chain. (This relationship is illustrated with solid arrows in
Fig.1).
The revenue of MS0, which is the supplier of all machinery in the network, can be calculated as the sum of all machinery costs in the Value
Chain. (This relationship is illustrated with dashed arrows in Fig.1).
5
(A3)
The last tier of the OEM supply chain and the machinery supply chain are
the only members in the Value Chain which purchase materials outside of
the Value Chain.
OEM supply chain
OEM
Machinery supply chain
MS 0
r  0
Revenue
Personnel costs
Machinery costs
Material costs
Profit
C p  0
Cd  0 
Cm  0 
P 0
1st Tier
Revenue
r 0
Personnel costs
C p 0
Machinery costs
Cd  0 
Material costs
Cm  0 
Profit
P 0
MS 1
r 1
Revenue
Revenue
r 1
Personnel costs
C p 1
Personnel costs
C p 1
Machinery costs
Cd 1
Machinery costs
Cd 1
Material costs
Cm 1
Material costs
Cm 1
Profit
P 1
Profit
P 1
2nd Tier
MS 2
Revenue
r  2
Revenue
r  2
Personnel costs
C p  2
Personnel costs
C p  2
Machinery costs
Cd  2 
Machinery costs
Cd  2 
Material costs
Cm  2 
Material costs
Cm  2 
Profit
P  2
Profit
P  2
Material flow (purchased parts)
Machine capacity
Fig.1. Value chain model
The following notation is introduced and used in Fig.1 (Variables x in the OEM
supply chain are denoted as x in the machinery supply chain.):
…..
index which refers to the level i of
i
the OEM supply chain ( i  0, ..., n )
…..
index which refers to the level j of
j
the machinery supply chain ( j  0, ..., k )
r i  , r  j 
…..
revenue [MU] (monetary unit)
6
c p  i  , cp  j 
…..
personnel cost proportion of the revenue [%]
cd  i  , cd  j 
…..
machinery cost proportion of the revenue [%]
cm  i  , cm  j 
…..
material cost proportion of the revenue [%]
p i  , p  j 
…..
profit proportion of the revenue [%]
C p  i  , C p  j  …..
personnel costs [MU]
Cd  i  , Cd  j 
…..
machinery costs [MU]
Cm  i  , Cm  j 
…..
material costs [MU]
P i  , P  j 
…..
profit [MU]
The aim of the paper is the calculation of the total personnel costs, machinery
costs, material costs and the profit in the Value Chain subject to the number of
tiers in the OEM and machinery supply chain. For this reason two mathematical
models are developed. In the common parameter model we assume that the percentage distribution of personnel costs, machinery costs, material costs and profit
is the same for all members of the respective supply chain. In the varying parameter model three different values for the cost and profit distribution – dependent on
the level within the supply chain – are introduced. The section concludes with the
limit calculations of the cumulated costs and profit for both models.
3.2. Common parameter model
In this model, the revenue of the OEM and all proportions of the cost and profit
distribution are input parameters, the other variables can be computed with the
equations developed below.
Personnel costs, machinery costs, material costs and profit of each Value Chain
member are determined by the proportion values of the revenue of the respective
level of the considered supply chain.
(1)
C x  i   cx  i  r  i 
C x  j   cx  j  r  j 
 x  p, d , m 
P i   p i  r i 
P  j  p  jr  j
for i  0,..., n
for j  0,..., k
For every member of the Value Chain, the sum of cost and profit proportions are
100%:
(2)
c p  i   cd  i   cm  i   p  i   100%
for i  0,..., n
c p  j   cd  j   cm  j   p  j   100%
for j  0,..., k
Assumption (A1) yields the following recurrence relationship:
7
r  i  1  Cm  i  = cm  i  r  i 
r  j  1  Cm  j   cm  j  r  j 
for i  0,..., n  1
for j  0,..., k  1
(3)
For the common parameter model, constant proportions are assumed for all tiers in
the OEM supply chain as well as in the machinery supply chain.
(4)
c x  i   cx
c x  j   cx
 x  p, d , m 
p i   p
p  j  p
for i  0,..., n
for j  0,..., k
Using r  0   r0 , r  0   r0 and combining (1), (3) and (4) leads to
r  i   r0 cm i
r  j   r0 cm j
Cx  i   r0 cx cm i
Cx  j   r0 cx cm j
P  i   r0 pcm
i
P  j   r0 pcm
for i  0,..., n
for j  0,..., k
(5)
 x  p, d , m 
j
For the calculation of revenue, costs and profit in the machinery supply chain, the
revenue of MS0 is needed. r0 is implicitly defined by summing up all machinery
costs in the Value Chain (see also assumption (A2)). The implicit equation can be
solved easily because it is linear in r0 and the summation signs can be eliminated
by using the total sum for finite geometric series.
n
k
n
k
(6)
r0   Cd  i    Cd  j   r0 cd  cm i  r0 cd  cm j
i 0
j 0
i 0
1  cm
1  cm

1  cm k 1
1  cd
1  cm
n
 r0 
j 0
n 1
r0 cd  cm i
r0 cd
i0
k
1  cd  cm j
j 0
Now all necessary input for calculating the total costs and profit in the Value
Chain is available. Cumulated personnel costs for the Value Chain with n  1
OEM supply chain levels and k  1 machinery supply chain levels are determined
by
n
k
(7)
SC p  n, k   r0 c p  cm i  r0 c p  cm j 
i 0
 r0
j 0
n 1
1  cm
1  cm

c p cd 1  cm k 1  
 cp 


1  cm  cd 1  cm k 1  

These cumulated personnel costs can be interpreted as the contribution for employees without considering taxes and other profit-dependent or wage-dependent
charges.
8
Cumulated machinery costs for the Value Chain are
n
k
r0 cd 1  cm n 1  1  cm 
SCd  n, k    Cd  i    Cd  j   r0 
i 0
j 0
1  cm  1  cm  cd 1  cm k 1 

(8)

External material costs only occur at the last levels of both supply chains (see assumption (A3)) and so cumulated external material costs are defined by
(9)
SC  n, k   Cm  n   Cm  k   r0 cm n 1  r0 cm k 1 
m
 r0 cm n 1  r0 cm k 1
cd 1  cm n 1  1  cm 
1  cm  1  cd 1  cm k 1  
Cumulated profit for the Value Chain can be interpreted as contribution for employers (or investors) and is calculated by
n
k
(10)
S P  n, k   r0 p  cm i  r0 p  cm j 
i 0
 r0
j 0
n 1
1  cm
1  cm

pcd 1  cm k 1  
p


1  cm  cd 1  cm k 1  

3.3. Varying parameter model
In this varying parameter model the proportion values do not need to be constant
over all supply chain levels as it was assumed in the common parameter model
(see equation (4)). In both supply chains three different values for the first level,
all intermediate levels and the last level are allowed, i.e. the OEM can have a different proportion of material costs than a material supplier on another tier:
(11)
cx  0   cx 0 , p  0   p0
cx  0   cx 0 , p  0   p0
cx  i   cx1 , p  i   p1
cx  j   cx1 , p  j   p1
for i  1,..., n  1
for j  1,..., k  1
cx  n   cxn , p  n   pn
cx  k   cxk , p  k   pk
 x  p, d , m 
From (1), (3) and (11) explicit formulas for revenue, costs and profit can be derived.
(12)
r  0   r0 ,
r  0   r0 ,
r  i   r0 cm 0 cm1i 1  for i  1,..., n 
r  j   r0 cm 0 cm1 j 1  for j  1,..., k 
9
Cx  0   r0 cx 0
C x  0   r0 cx 0
Cx  i   r0 cx1cm 0 cm1i 1
C x  j   r0 cx1cm 0 cm1 j 1
for i  1,..., n  1
for j  1,..., k  1
Cx  n   r0 cxn cm 0 cm1
(13)
C x  k   r0 cxn cm 0 cm1n 1
n 1
 x  p, d , m 
P  0   r0 p0
P  0   r0 p0
P  i   r0 p c c
P  j   r0 p1cm 0 cm1
 for i  1,..., n  1
P  n   r0 pn cm 0 cm1n 1
 for j  1,..., k  1
P  k   r0 pn cm 0 cm1n 1
i 1
1 m 0 m1
(14)
j 1
As in the common parameter model, the revenue of MS0 can be derived by summing up all machinery costs in the Value Chain.
n
k
n 1
(15)
r0   Cd  i    Cd  j   r0 cd 0  r0 cd 1cm 0  cm1i 1  r0 cdn cm 0 cm1n 1 
i 0
j 0
i 1
k 1
 r0 cd 0  r0 cd 1cm 0  cm1 j 1  r0 cdn cm 0 cm1k 1
j 1
1  cm1n 1
 cdn cm 0 cm1n 1
1  cm1
 r0  r0
1  cm1k 1
1  cd 0  cd 1cm 0
 cdn cm 0 cm1k 1
1  cm1
cd 0  cd 1cm 0
In the varying parameter model the cumulated values for personnel costs, machinery costs, external material costs and profit are:
n
k
(16)
SC p  n, k    C p  i    C p  j  
i 0
j 0


1  cm1n 1
 r0  c p 0  c p1cm 0
 c pn cm 0 cm1n 1  
1  cm1




1  cm1k 1
 r0  c p 0  c p1cm 0
 c pn cm 0 cm1k 1 
1  cm1


n
k
i 0
j 0
SCd  n, k    Cd  i    Cd  j   r0
(17)
SCm  n, k   Cm  n   Cm  k   r0 cmn cm 0 cm1n 1  r0 cmn cm 0 cm1k 1
(18)
10
n
k
i 0
j 0
S P  n, k    P  i    P  j  
(19)


1  cm1n 1
 r0  p0  p1cm 0
 pn cm 0 cm1n 1  
1  cm1




1  cm1k 1
 r0  p0  p1cm 0
 pn cm 0 cm1k 1 
1  cm1


Proposition 1:
The revenue of the OEM equals the sum of cumulated personnel costs, cumulated
external material costs and cumulated profit of the Value Chain:
(20)
r0  SC  n, k   SC  n, k   S P  n, k 
p
m
Proof – See Appendix.
3.4. Model with infinite tiers
For Value Chains modelling an entire economic region with multiple tiers a limit
calculation of the developed formulas can be of interest. Assuming that the number of tiers of the OEM supply chain and the machinery supply chain tends to infinity, the limit of cumulated personnel costs for the common parameter model is
(21)
c p cd 
r 
SC p  lim SC p  n, k   0  c p 

n , k 
1  cm 
1  cm  cd 
The limit of cumulated machinery costs for the Value Chain is
r0 cd 1  cm 
SCd  lim SCd  n, k  
n , k 
1  cm 1  cm  cd 
(22)
Total external material costs vanish by calculating the limit.
S Cm  lim S Cm  n, k   0
(23)
Calculating the limit of cumulated profit for the Value Chain results in

r 
pcd
S P  lim S P  n, k   0  p 

n , k 
1  cm 
1  cm  cd 
(24)
n , k 
Applying the formulas of the varying parameter model the following limits can be
calculated:
11
cd 1cm 0
1  cm1
R0  lim r0  r0
n , k 
c c
1  cd 0  d 1 m 0
1  cm1
(25)
c p1cm 0 
c p1cm 0 


SC p  lim SC p  n, k   r0  c p 0 
  R0  c p 0 

n , k 
1  cm1 
1  cm1 


(26)
S Cd  lim SCd  n, k   R0
(27)
S Cm  lim S Cm  n, k   0
(28)


pc 
pc 
S P  lim S P  n, k   r0  p0  1 m 0   R0  p0  1 m 0 
n , k 
1
1
c

 cm1 
m1 


(29)
cd 0 
n , k 
n , k 
The result of this limit calculation already leads to an interesting proposition.
Proposition 2:
For any Value Chain with an infinite number of members no external material
costs occur and therefore the cumulated profit only depends on cumulated revenue
and cumulated personnel costs.
Proof: Proposition 2 follows directly from SCm  lim SCm  n, k   0 and Proposin , k 
tion 1. Note that cost relation factors are not equal to the cumulated costs.
A detailed numerical study of the influence of the number of supply chain levels
in the respective OEM and machinery supply chain as well as of the influence of
cost proportions is provided in Section 5.
4. Data generation
In the common parameters model and the varying parameters model, the revenue
of a supplier level always matches the material costs of the subsequent customer
level and the sum of the overall machinery costs (at all levels) matches the revenue of the machinery supplier MS0. This assumption implies a closed system
where no Value Chain member except the OEM sells goods to customers outside
the Value Chain. Even though this is a limiting assumption which is not fulfilled
in real world Value Chains, the data generated from this empirical data set, which
only provides the cost and profit proportions, can be applied for the numerical example.
12
The database used to gather empirical data is AMADEUS, which includes the annual reports of more than 11 million European enterprises whereby the data of
more than 35 local information-providers is collected. Since especially the automotive industry is widely studied in outsourcing literature, only the data of European automotive OEM’s and suppliers is considered in this study. To provide the
specific cost proportions necessary to calculate the varying parameter model, the
following classifications are applied:
O (A)
OEM automotive: Producers of cars and trucks (e.g. VW, BMW, Mercedes Benz, MAN, FIAT, Peugeot, etc.).
T1 (A) Tier 1…n-1 supplier automotive: All suppliers that deliver their materials
directly or indirectly to OEM automotive companies. This classification
includes all automotive supply chain levels, except OEMs and raw material suppliers.
O (M) OEM machinery: Producers of machines and components that are directly
used in plant construction (steel construction, machine tools, electrical
systems).
T1 (M) Tier 1…n-1 supplier machinery: All suppliers that deliver their materials
directly or indirectly to OEM machinery companies. This classification
includes all machinery supply chain levels, except OEMs and raw material suppliers.
R
Raw material supplier: Enterprises that produce raw materials like iron
ore, coal, oil etc.
The empirical data is gathered by the extraction of a large scale sample of annual
reports from the database. To ensure the quality, consistency and comparability of
the data, only enterprises with values in all relevant fields were included in the
analysis. This results in a sample of n=6,507 enterprises which is assumed to be
valid for this purpose. The classification of the companies is based on the industry
identifier listed in the AMADEUS-database according to the Statistical classification of economic activities in the European Community (NACE Rev. 2) from Eurostat (2008). For companies that could be assigned on the basis of their product
range to several industries, the classification is conducted according to the highest
revenue product group. Tab. 1 shows the NACE codes and their classification.
The following average cost and profit percentages for the years 2000 to 2008 have
been extracted from the database: operating revenues, material costs, costs of employees, depreciation and EBIT (earnings before interest and tax). The cost and
profit proportions gained, calculated as proportions of the operating revenues, are
shown in Tab. 2 and Tab. 3 (already corrected by outliers). The proportions shown
in Tab. 2 and Tab. 3 include a position titled other costs, which represents the remaining percentage of operating revenues not comprised of material costs, costs of
employees, depreciation and EBIT. These remaining other costs can be costs of
external services, leasing and so on. These costs are neglected in the remainder
since they are not relevant for the model created in this paper. For the numerical
example, the remaining costs and profit are scaled to account for 100% again.
13
T1 (A)
O (A)
O (M)
T1 (M)
R
2910
2016 2434 2562 2510 2894 2016 2434 2550 2813
510
The code 2910
cannot be
allocated
directly to
O(A)
according to
the definition
above. Hence,
2910 coded
companies are
classified
manually.
2017 2440 2572 2511 2895 2017 2440 2560 2813
520
2220 2442 2593 2529 2896 2221 2442 2561 2814
610
2221 2443 2594 2530 2899 2311 2443 2562 2815
620
2311 2444 2720 2821
2312 2444 2572 2820
700
2312 2450 2813 2822
2320 2450 2593 2825
710
2400 2451 2900 2840
2400 2451 2594
729
2410 2452 2910 2841
2410 2452 2711
2420 2453 2910 2849
2420 2453 2712
2430 2454 2920 2890
2430 2454 2730
2431 2550 2930 2891
2431 2500 2732
2432 2560 2931 2892
2432 2520 2733
2433 2561 2932 2893
2433 2521 2812
Tab. 1. Classification according to NACE codes.
OEM supply chain
OEM
st
th
Raw material
supplier
OEM supply chain
average
16.3%
17.8%
Personnel
11.3%
1 to (n-1)
tier suppliers
18.0%
Depreciation
3.3%
4.0%
6.3%
4.2%
Material
70.1%
53.6%
49.5%
53.4%
Profit
2.6%
3.8%
3.2%
3.7%
Other Costs
12.7%
20.6%
24.7%
20.9%
Tab. 2. Cost proportions OEM supply chain; average from 2000 to 2008
Machinery supply chain
Machinery
supplier
st
th
Raw material
supplier
Machinery supply
chain average
16.3%
18.9%
Personnel
20.3%
1 to (n-1)
tier suppliers
18.4%
Depreciation
3.0%
3.8%
6.3%
3.7%
Material
49.7%
52.2%
49.5%
51.3%
Profit
4.0%
4.2%
3.2%
4.1%
Other Costs
23.0%
21.4%
24.7%
22.0%
Tab. 3. Cost proportions Machinery supply chain; average from 2000 to 2008
The detailed cost proportions for the years 2000 to 2008 can be found in the Appendix.
14
5. Numerical study
The purpose of this numerical study is to identify the relationship between the
number of supply chain levels and the cost and profit proportions within the Value
Chain according to the model created in this paper. Therefore, the cost and profit
proportions identified from the dataset in Section 4 are firstly applied to the common parameter model and secondly to the varying parameter model. Additionally,
a sensitivity analysis showing the influence of the respective parameters on the results is conducted. For simplicity reasons the number of supply chain levels in the
OEM and the machinery supply chain is assumed to be equal in all the numerical
examples calculated in this section. Note that the number of supply chain levels is
equal to n+1 if the nth tier supplier is the raw material supplier.
For the common parameter model, Fig. 2 shows the cumulated cost and profit
proportions with respect to the number of supply chain levels. The Value Chain
revenue is normalized to 100 and the OEM and machinery supply chain average
values from Tab. 2 and Tab. 3 are applied.
100
90
Cumulated values
80
70
60
50
40
30
20
10
0
0
5
10
15
20
Number of supply chain levels
cumulated personnel costs
cumulated deprecia tion
cumulated material costs
cumulated profit
Fig.2. Cumulated cost and profit proportions – common parameter model
For the varying parameter model, Fig. 3 shows a similar result. Based on these
two Figures applying real company data, the following observations can be stated.
Observation 1:
For any Value Chain (as modelled in this paper) the importance of personnel
costs increases with increasing number of levels in the included supply chains.
This observation holds since the cumulated personnel costs in Fig. 2 and Fig. 3 increase with the number of supply chain levels. Even though the material costs
within the single companies account for between 60% and 80% of the overall
15
revenues from the data set, assuming supply chains with only 4 levels already
shows that these material costs from outside the Value Chain reduce to below
30%. When 10 levels are assumed Fig. 2 and Fig. 3 show that cumulated material
costs are already below 5%. In the same extent, cumulated personnel costs increase from between 20% and 30% for a single company perspective to a cumulated amount of more than 60% for 4 supply chain levels and more than 80% for a
10 level supply chain.
100
90
Cumulated values
80
70
60
50
40
30
20
10
0
0
5
10
15
20
Number of supply chain levels
cumulated personnel costs
cumulated deprecia tion
cumulated material costs
cumulated profit
Fig.3. Cumulated cost and profit proportions – varying parameter model
Observation 2:
Whenever a holistic view of the Value Chain involved in producing certain products is applied (which means all suppliers up to the basic raw material supplier
are included), the main driver to increase Value Chain profit (and therefore performance) is the reduction of personnel costs.
Observation 2 is clear from Fig. 2 and Fig. 3. This observation implies that the focus for performance improvement in Value Chains should be on personnel costs of
the single companies even though within the single companies this cost block is
not the biggest one.
In order to discuss the influence of outsourcing on the whole Value Chain performance, the common parameter model with its cost proportions is used as the
basic scenario and then the influence of an increase in outsourcing is evaluated.
Based on the findings about outsourcing in the literature (see Jiang et al. (2006),
Hobbs (1996) and Williamson (2008)) we assume that the personnel and material
cost proportions are changed whereby the depreciation and profit proportions stay
constant (Jiang et al. (2006) explicitly reports that outsourcing did not change the
return on sales in an empirical study). The data for the four scenarios compared
are provided in Tab. 4.
16
Basic scenario Outsourcing 1 Outsourcing 2 Outsourcing 3
Personnel
23.3%
18.3%
13.3%
8.3%
Depreciation
5.0%
5.0%
5.0%
5.0%
Material
66.7%
71.7%
76.7%
81.7%
Profit
5.0%
5.0%
5.0%
5.0%
Tab. 4. Cost proportions for outsourcing increase example
According to Tab. 4, the outsourcing intensity is increased in the Outsourcing 1 to
Outsourcing 3 scenario. To reflect this, the personnel costs are simply shifted to
material costs. For simplicity reasons the cost proportions for the machinery and
the OEM supply chain are kept equal (as an average of the values from the respective data sets). The following Fig. 4 shows the cumulated personnel costs, external
material costs and the cumulated profits for the same number of supply chain levels.
100
b)
90
80
70
60
50
40
30
20
10
80
70
60
50
40
30
20
10
0
0
0
5
10
15
20
Number of supply chain levels
c)
100
90
Cumulated material costs
Cumulated personnel costs
a)
0
5
10
15
20
Number of supply chain levels
Basic scenario
Outsourcing 1
Basic scenario
Outsourcing 1
Outsourcing 2
Outsourcing 3
Outsourcing 2
Outsourcing 3
100
90
Cumulated profit
80
70
60
50
40
30
20
10
0
0
5
10
15
20
Number of supply chain levels
Basic scenario
Outsourcing 1
Outsourcing 2
Outsourcing 3
Fig.4. Cumulated cost and profit proportions for different outsourcing intensities
The results from Fig. 4a and 4b show the intuitive result that with the same number of levels in the OEM and material supply chains, a higher outsourcing intensi-
17
ty leads to lower cumulated personnel and higher cumulated material costs in the
Value Chain. Fig 4c shows the rather interesting result that an increase in outsourcing intensity leads to an increase in cumulated Value Chain profit for the
same number of partners in the Value Chain.
Observation 3:
For any Value Chain (as modelled in this paper) an increase in outsourcing intensity, meaning a change from personnel costs to material costs, leads to an increase in cumulated Value Chain profit.
This interesting finding from Observation 3 provides one reason why outsourcing
has been performed so extensively in the last few decades.
6. Conclusion
In this paper a Value Chain cost proportion model has been developed which
shows that whenever the profit of a whole Value Chain network is to be optimized
the main focus should be on personnel costs rather than on material costs. This
finding is shown to be true even though for the single companies in the Value
Chain, the material costs might be the main cost factor. A numerical study using
real world data has been conducted which shows that with only a very low number
of Value Chain partners the material costs for materials purchased outside the
Value Chain become negligible and the personnel costs become dominant. Furthermore, it is observed that an increase in outsourcing intensity leads to an increase in Value Chain profit for the same number of Value Chain partners under
certain conditions. This supports the empirical finding that outsourcing has been
focussed on by industry in the last few decades.
In further research this model could be extended to identify optimal make-or-buy
decisions based on the overall Value Chain profit.
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Appendix
Proof of Proposition 1:
The sum of cumulated personnel, material costs and profit for whole the Value
Chain follows from (16), (18) and (19) as:
S C p  S Cm  S P 
(30)


1  cm1n 1
 r0  c p 0  p0   c p1  p1  cm 0
  c pn  pn  cmn  cm 0 cm1n 1 
1  cm1


k 1


1  cm1
 r0  c p 0  p0   c p1  p1  cm 0
  c pn  pn  cmn  cm 0 cm1k 1 
c

1
m1


19
applying equation (2) leads to:


1  cm1n 1
 r0  1  cd 0  cm 0  1  cd 1  cm1  cm 0
 1  cdn  cm 0 cm1n 1 
1
c

m1


k 1


1  cm1
 r0  1  cd 0  cm 0  1  cd 1  cm1  cm 0
 1  cdn  cm 0 cm1k 1 
1
c

m1


which can be simplified to:


1  cm1n 1
 r0  1  cd 0  cd 1cm 0
 cdn cm 0 cm1n 1 
1  cm1


(31)
(32)


1  cm1k 1
 r0  1  cd 0  cd 1cm 0
 cdn cm 0 cm1k 1 
1  cm1


and with equation (15) it follows
SC p  SCm  S P  r0
(33)
20
Cost proportions from database
OEM
Personnel
Depreciation
Material
Profit
Other Costs
st
2008
10.6%
2.9%
70.9%
3.6%
12.0%
2007
11.7%
2.9%
70.5%
4.8%
10.2%
2006
10.2%
3.1%
70.2%
4.4%
12.1%
2005
11.1%
3.3%
69.7%
3.9%
11.9%
2004
11.0%
3.4%
69.5%
3.6%
12.6%
2003
11.9%
3.4%
70.4%
1.5%
12.9%
2002
11.9%
3.4%
69.8%
0.9%
13.9%
2001
12.0%
3.4%
70.3%
-0.7%
14.9%
2000 average
11.3% 11.3%
3.4%
3.3%
69.9% 70.1%
1.8%
2.6%
13.6% 12.7%
th
1 to (n-1)
tier suppliers
Personnel
Depreciation
Material
Profit
Other Costs
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
17.2%
3.3%
55.8%
3.0%
20.6%
16.6%
3.2%
56.3%
4.2%
19.7%
17.4%
3.4%
55.5%
3.9%
19.8%
18.1%
3.8%
54.0%
3.7%
20.4%
18.3%
4.0%
53.4%
4.0%
20.3%
19.0%
4.6%
51.8%
3.1%
21.5%
18.8%
4.7%
51.4%
3.5%
21.5%
18.3%
4.7%
51.9%
3.9%
21.2%
17.9%
4.6%
52.3%
4.5%
20.6%
18.0%
4.0%
53.6%
3.8%
20.6%
Raw material
supplier
Personnel
Depreciation
Material
Profit
Other Costs
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
16.1%
5.3%
47.8%
5.2%
25.6%
16.5%
5.8%
47.8%
3.9%
26.0%
17.8%
6.2%
48.2%
2.9%
24.9%
17.7%
6.0%
46.9%
3.6%
25.8%
17.6%
6.6%
44.5%
3.6%
27.7%
17.4%
7.5%
52.3%
-2.0%
24.8%
16.5%
6.6%
52.3%
1.2%
23.4%
14.2%
6.4%
52.3%
5.2%
21.8%
12.8%
5.9%
53.1%
5.6%
22.6%
16.3%
6.3%
49.5%
3.2%
24.7%
OEM supply
chain average
Personnel
Depreciation
Material
Profit
Other Costs
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
17.1%
3.5%
55.4%
3.1%
20.9%
16.6%
3.4%
55.7%
4.2%
20.1%
17.3%
3.7%
55.1%
3.8%
20.1%
18.0%
3.9%
53.6%
3.7%
20.8%
18.1%
4.2%
52.8%
4.0%
20.8%
18.9%
4.8%
52.0%
2.7%
21.6%
18.6%
4.8%
51.7%
3.3%
21.6%
17.9%
4.8%
52.1%
3.9%
21.2%
17.5%
4.7%
52.5%
4.6%
20.7%
17.8%
4.2%
53.4%
3.7%
20.9%
Tab. 5. Yearly data for cost proportions of OEM supply chain
Machinery
supplier
Personnel
Depreciation
Material
Profit
Other Costs
st
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
19.2%
2.4%
51.3%
4.0%
23.1%
18.9%
2.4%
51.5%
4.7%
22.5%
20.0%
2.5%
51.1%
4.2%
22.2%
20.6%
2.7%
50.1%
4.0%
22.6%
20.9%
2.9%
49.5%
3.6%
23.0%
21.4%
3.3%
48.1%
3.0%
24.1%
21.4%
3.5%
47.9%
3.5%
23.6%
20.4%
3.4%
48.7%
4.5%
23.0%
20.1%
3.5%
49.3%
4.5%
22.6%
20.3%
3.0%
49.7%
4.0%
23.0%
th
1 to (n-1)
tier suppliers
Personnel
Depreciation
Material
Profit
Other Costs
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
17.4%
3.1%
54.3%
4.0%
21.2%
16.9%
2.9%
54.8%
5.0%
20.4%
17.7%
3.2%
54.3%
4.5%
20.4%
18.6%
3.5%
52.5%
4.2%
21.1%
18.8%
3.8%
51.9%
4.3%
21.2%
19.6%
4.4%
50.2%
3.4%
22.4%
19.4%
4.5%
50.1%
3.8%
22.2%
18.7%
4.5%
50.6%
4.2%
22.0%
18.5%
4.5%
51.1%
4.7%
21.4%
18.4%
3.8%
52.2%
4.2%
21.4%
Raw material
supplier
Personnel
Depreciation
Material
Profit
Other Costs
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
16.1%
5.3%
47.8%
5.2%
25.6%
16.5%
5.8%
47.8%
3.9%
26.0%
17.8%
6.2%
48.2%
2.9%
24.9%
17.7%
6.0%
46.9%
3.6%
25.8%
17.6%
6.6%
44.5%
3.6%
27.7%
17.4%
7.5%
52.3%
-2.0%
24.8%
16.5%
6.6%
52.3%
1.2%
23.4%
14.2%
6.4%
52.3%
5.2%
21.8%
12.8%
5.9%
53.1%
5.6%
22.6%
16.3%
6.3%
49.5%
3.2%
24.7%
2008
2007
2006
2005
2004
2003
2002
2001
2000
average
17.9%
3.0%
53.1%
4.0%
22.0%
17.5%
2.9%
53.5%
4.8%
21.3%
18.4%
3.1%
53.1%
4.3%
21.1%
19.2%
3.4%
51.5%
4.1%
21.8%
19.4%
3.7%
50.9%
4.1%
22.0%
20.1%
4.2%
49.7%
3.0%
23.0%
19.9%
4.3%
49.6%
3.6%
22.7%
19.0%
4.2%
50.1%
4.3%
22.3%
18.7%
4.2%
50.6%
4.7%
21.8%
18.9%
3.7%
51.3%
4.1%
22.0%
Machinery
supply chain
average
Personnel
Depreciation
Material
Profit
Other Costs
Tab. 6. Yearly data for cost proportions of machinery supply chain