Integrated Master Program
Industrial and Financial Economy
Gothenburg University
2003/11/20
Risk Management and Economics of Information
Case 2 : Arla
Supervisor: Prof. Göran Bergendahl
Authors:
Aijun Hou
Aránzazu Muñoz-Luengo
Emma Aer
GuangBin Zhao
Muna Girgis
TABLE OF CONTENTS
1.0 INTRODUCTION
1
2.0 OBJECTIVES
1
3.0 PROBLEM SITUATION DESCRIPTION
1
3.1 MINIMIZE COST OF CREAM / BUTTER PRODUCTION (IN GÖTENE)
3.2 MINIMIZE COST FOR LIQUID MILK PRODUCTION
3.3 MINIMIZE COST FOR CHEESE PRODUCTION
3.4 MINIMIZE TOTAL COST
2
2
3
3
4.0 PROPOSED METHOD
5
4.1 LINEAR PROGRAMMING
4.2 SIMPLE MULTI-ATTRIBUTE RATING TECHNIQUE (SMART)
4.3 HEURISTIC METHOD
5
8
9
5.0 SWOT ANALYSIS
10
5.1 STRENGTHS AND OPPORTUNITIES
5.2 THREATS AND WEAKNESSES
10
11
6.0 CONCLUSION
13
7.0 REFERENCE
15
1.0 Introduction
Arla is one of the largest Swedish milk producers, and it was built up on 26th April, 1915 in
Stockholm. In the beginning of 1970’s , Arla got a new name-Mjölkcentral Arla, which is a
name from the merger between Mjölkcentralen, lantbrukamas Mjölkcentral, Sydöstmejerier,
and örebro-Ortens Mejerförening. Nowadays, bedsides milk, Arla also produces other
products like butter, cheese, yoghurt, however, liquid milk, cheese, and butter are there
main product.
Now, Arla is planning to expend market to Skaraborg, there are 27 farms to produce milk
and 10 potential locations for dairies to produce Cheese, Cream/butter, and Liquid milk.
While butter/cream only could be produced in Götene, Liquid milk is to be produced at one
single site only, cream and skim milk will be the by- products during production process.
Cheese could be produced at any of the 10 dairies. Skim milk and whey will be the byproduct. The cream produced at cheese and liquid dairies will be transported to Götene as
the input for butter production.
2.0 Objectives
Under this situation, we have there main tasks:
1. Propose one or several objectives to determine :
 The best location for the cheese production
 The best single location for the production of liquid milk.
2. Propose a method to find out the best size, location and time-phasing for the
Investment in dairies
3. Analyze which effect that would come from :
 A substantial annual growth (2%) of the supply of the raw milk
 A substantial expansion (3%) in the sales of milk products like certain high
quality cheeses or an assortment of yogurt products (“high quality liquid
milk products)
3.0 Problem Situation Description
Our objective is to minimize total cost to determine the best location for cheese production,
the best single location for the production of liquid milk. In order to minimize total cost, we
have to minimize the cost from cheese, liquid milk, and butter/cream dairies.
1
3.1 Minimize Cost of Cream / Butter Production (in Götene)
Our objective here is to minimize total cost of cream/butter production. As the butter
production is produced only in Götene, we only need to consider the transportation cost of
raw milk.
Cost (butter/cream) = Transportation cost of raw milk ----(A)
As we know, from the liquid milk dairy and cheese dairy would also ship cream to Götene.
The amount of cream from cheese making and liquid milk dairies is equal to the demand for
raw milk at each dairies multiply the input-output coefficient for the cream (Table 7.3).The
demand raw milk we can divide the demand for each production by coefficient factor from
table 7.3
FROM
SUMMER WINTER
Cheese production
3,492
4056.94
Liquid milk production
325.19
612.8
TOTAL
3817
4670
Table1. Transported cream to Götene
TOTAL
7549
938
8487
Transported amount of cream summer time
= (60,000/0,756)*0,044+(11,500/0,7780)*0,022
=79,365*0,044+14,781*0,022
=3,492+325
=3817
Transported amount of cream winter time
=(60,000/0,7025) *0,0475+ 16,100/0,7225)*0,027
=85,409*0,0475+22283,74*0,027
=4056,94+612,80
=4670
This amount of cream will lower then the need for the total amount of raw milk for cream/
butter production, by getting cream from cheese and liquid dairies, will reduced the
transport cost of raw milk from farmers to dairy.
3.2 Minimize Cost for Liquid Milk Production
In order to minimize cost for liquid milk production, we have to minimize:
Total cost for liquid milk production = Transportation cost of the raw milk +
Delivery cost of cream to Götene +
Distribution cost of liquid milk. --------- (B)
As we know from case, the liquid milk production will generate two by product: cream and
feed. Cream will be delivered to Götene. The amount of cream to be delivered is equal to
demand for raw milk at this dairy times coefficient factor of, which equal to 11500/0,778
*0,022 + 16100/0,7225 *0,0275 = 938 (see table 1) . The total cost will also depends upon
2
where the dairy is located and the distribution cost from dairy to market, which is also
depend on where the dairy is located.
3.3 Minimize Cost for Cheese Production
We would minimize the cost for cheese production, hence we will minimize:
Total cost for cheese production = Transportation cost of the raw milk +
Delivery cost of cream to Götene +
Annual net investment cost. ----- (C)
As for the liquid milk production, the cheese production also generates cream during the
process, which will be transported to Götene. The transportation cost will depend on where
the dairy is located and the demand for raw milk, which equals 60,000/0,756) *0,044
+(60,000/0,7025) *0,0475=7549 (see table 1) besides this, total cost also depends on the
distribution cost to market.
The important thing here is that from table 7.6, we know that economic of scale could be
done by producing more and hence reducing cost.
Total Cost 100SEK/Year
2500
2000
1500
1000
500
0
0
40000
80000 120000
Milk for Cheese Tons/year
Figure 1. Milk for cheese tons/year
3.4 Minimize Total Cost
Our main task now is to minimize the total cost for the company. In order to minimize total
cost we have to minimize total cost of A+B+C.
It is really crucial to know the demand of raw milk at each dairy. From case, we know the
average annual (in tons) demand for different product during summer time and winter time,
and then we can calculate the total demand for raw milk at each dairy.
3
PRODUCT
Cheese
Cream
Liquid milk
TOTAL
SUMMER
79365
34530
14781.491
128676.574
WINTER
85409
20900
22283.737
128592.9897
TOTAL
164774.3
55430
37065.23
257269.6
Table 2. Demand for raw milk
We now calculate the demand for raw milk by dividing the demand of each product by
input and output coefficient in table 7.3. :
Demand for raw milk at cheese production
= 60000/0,7560=79 365 tons
Demand for raw milk at winter
=60000/0,7025=85 409tons
Total demand for raw milk at cheese production= 79365+85409=164 774
However, for the demand of raw milk at cream dairy, we deduct the amount transported
from cheese and liquid dairies.
Summer time demand=(7270-3817)/0.10=34530 tons
Winter time demand =(6760-4670)/0,10=20900tons
Total demand for raw milk at Götene=34530+20900=55430 tons
Demand for raw milk at liquid milk production
Summer time= 11500/0,7780=14,781 tons
Winter time = 16,100/0,7225=22,283tons
Total demand for raw milk at liquid milk production= 14781+22283=37065 tons
Total demand for raw milk at the dairies =257,269 ton / year
When we calculate total cost, we didn’t take distribution cost of butter and cheese into
account, the reason behind this, is that we didn’t get any information about this, hence we
assume that Arla has different marketing strategy regarding about this. The marketing
strategy might be is that cheese and butter will be sold to other area or focus on in Sweden
besides Skaraborg because of that cheese butter could be stored for long, while liquid milk
could not be fresh for long, hence Arla’s distribution channel must be very short. Now we
know the total demand for raw milk at each dairy during summer and winter time. Hence,
we have to decide the best location for each production.
4
4.0 Proposed method
In order to solve this transportation problem, we could choose from different methods
which range from more mathematical application based to more descriptive foundation. We
will present and discuss two methods of great convenience for these kinds of problems,
which are Linear Programming and SMART.
4.1 Linear Programming
In the previous section we explained the basic problem to be solved which will help us
understanding the concepts to take into account to implement this method. We considered
that this specific case is of great complexity and therefore we decided to deeply describe the
steps of its implementation. This sort of problem could be solved by different software such
as Solver, SAS or SPSS.
We first start specifying the objective function in words. The objective is to minimize the
total transportation costs compounded by the costs of the raw milk to the available dairies,
the delivery costs of cream to the butter factory in Götene from the liquid milk factory and
the cheese factory, and finally the distribution costs of the liquid milk to the markets.
Another optimization problem is the cheese production based on the decision regarding the
number and size of cheese dairies, transportation costs and delivery costs.
In the mathematical formulation, as we specified in the previous section we do not
contemplate the delivery costs for cheese and butter as we are not provided with these costs
in the case.
Now we formulate the mathematical problem for solving the optimal dairies and the
amount of flow from the farms to the optimal dairies:
m = number of lorries (m = 27)
n = number of potential dairies (n = 10)
x ij = amount of raw milk collected in region i to dairy j
c ij = cost of transporting one unit from i to j
for 1  i  m , 1  j  n
m
Total cost of all transportations is:
n
 c
i 1 j 1
ij
xij
The objective function is results:
Min. 3.96x0101+9.57x0102 +9.08x0103 +10.4x0104 +16.83 x0105…9.572708 +13.04x2709
+5.78x2710
5
At this point we should define the constraints, the purpose of the constraints are to
guarantee that the total amount of raw material, in this case the milk that is shipped to each
factory equals the transportation capacity of each lorry. Also the constraints will define that
the number of products shipped to a dairy coincides with the number of the required
products.
We will find a number of constraints, n + m, besides the nonnegative constraints.
The variables are:
ai = total amount to be transported from lorry i
bj = total amount to be transported to dairy j.
The constraints are as follows:
n
x
j 1
ij
 ai ,
for 1  i  m
ij
 bj ,
for 1  j  n
m
x
i 1
xij  0 ,
for 1  i  m and 1  j  n .
If we apply the constraints to our case: (We are supposed to obtain some of the x, decision
variables equal to zero since not all the routes are optimal).
Transportation from lorries
x0101 + x0102 + x0103 + …+ x0110 = 9200
x0201 + x0202 + x0203 + … + x0210 = 8900
(Transportation out of Hova)
(Transportation out of
Toreboda)
………..
x2701 + x2702 + x2703 + … + x2710 = 9000
(Transportation out of
Sandhem)
Where Xn is the total amount to be shipped from each farm. We can calculate Xn from table
7.1 by adding up the forecasted production of milk at winter and summer time for each farm.
Transportation to dairies
x0101 + x0201 + x0301 + …+ x2701 = M1
x0102 + x0202 + x0302 + …+ x2702 = M2
………..
(Transportation into Toreboda)
(Transportation into Tibro)
x0110 + x0210 + x0310 + …+ x2710 = M10
(Transportation into Stenstorp)
6
Where Mn is the total amount to be shipped to each dairy or the input capacity for each
facility. Although we calculated the demand for raw milk for cheese, butter and liquid milk
we still do not know the allocation of raw milk for the different dairies.
Next, we continue by formulating the problem for the butter production in the site of
Götene, following the same reasoning, the constraint function is, (from table 7.2, the third
column which corresponds to the specified dairy number for Götene that is number 3):
x0103 + x0203 + x0303 + …+ x2703  0
(Transportation into Götene)
We also should minimize the delivery costs for cream to Götene in the linear
programming method, and then the constraint would be (data from table 7.4):
Min.
12.5Q1 + 13Q2 + 10.5Q4 +…4.75Q8 + 5.5Q9 + 11.5Q10
Where Qn is the cream deliveries in dairy n.
We now proceed to analyze the best location for the liquid milk production, to do so we
gather the information of forecasted cost for liquid milk distribution (data from table 7.5)
and should also be taken into account the lowest distribution costs for the optimal liquid
milk dairy.
Then, the objective function for this constraint is:
Min.
608299M1, 484589M2 .… 430939M10,
Subsequently, we should try to decide the number and capacity of cheese factories that
could be constructed.
Using the available information in the case (table 7.6), we can calculate the annual net
investment per ton of cheese making, and by looking at the results we observe that the
smallest annual costs is for the facility that produces more tons of milk for cheese, so Arla
can take advantage of so called economies of scale, which are defined as primary advantage
of expanding or installing a big facility, that is to say the marginal cost decreases by
producing one more unit.
Total investment costs
(SEK)
(1)
Maximum quantity of
milk for cheese (ton)
(2)
Annual net investment
costs per ton (SEK/ton)
(3)
580 000
10
58 000,00
580 000
13 400
43,28
1 123 000
40 000
28,08
1 754 000
80 000
21,93
2 202 000
120 000
18,35
Table 3. Annual net investment costs per ton
7
Next we calculated the raw milk requirements for every kind of available facility to
facilitate the understanding of our proposal, and then we will compare these figures with the
previously calculated amounts of raw milk needed in order to fulfill the annual demand of
cheese.
Total investment
costs (SEK)
(1)
580000
580000
1123000
1754000
2202000
Maximum quantity of
milk for cheese (ton)
(2)
10
13400
40000
80000
120000
Demand of cheese in
summer (ton)
(3)
Demand of cheese in
winter (ton)
(4)
5
6700
20000
40000
60000
5
6700
20000
40000
60000
Quantity of raw milk
for cheese (ton)
(3)/0,756+(4)/0,7025
13,73
18399,80
54924,78
109849,55
164774,33
Table 4. Quantity of raw milk for cheese for each facility size
Looking at table 2, we observe that the annual demand for raw milk to produce cheese is
164 774,3 tons which is the same capacity of raw milk for the largest facility. The facility
with the larger capacity then is able to produce the forecasted annual demand for cheese.
It is also possible to draw this conclusion just by looking at the annual demand for cheese
(120 000 tons) and the production capacity of the largest facility for cheese which is also
120 000 tons. So we could say that this investment could be a good option for Arla.
On the other hand, we should take into account that if there is any variation in the demand
for cheese or an increase in the production of raw milk, the facility probably will not be
ready to cope with more input than 164 774,3 tons.
In this sense Arla should be aware that any variation of this kind could affect the future
sales of Arla (not fulfil the demand), also in the case the production of raw milk increases,
the farmers could find themselves with an excess of raw milk, in order to face any of this
situation they should think of whether enlarge the capacity of facility or sell the milk to
other producers.
Concluding we could say that according to our calculations one of the optimal solutions is
to build three facilities, one for cheese, one for liquid milk and one for butter. By first
impression it could be thought that also the cheese facility could be place in Götene, but on
the other hand the transportations costs for delivery could highly increase.
Of course this is a hypothesis based on no specific calculation, so we should wait to obtain
the results from the implementation of the linear programming approach.
4.2 Simple Multi-Attribute Rating Technique (SMART)
The SMART method is considered a more feasible method to solve these problems.
Additionally, the method could be applied at a relatively high speed. But this sort of method
does not capture all the complexities of the present case. We will show the different steps in
order to solve the problem:
8
Step 1: Identify the decision maker: Arla
Step 2: Identify the alternative courses of action:
Select one dairy for liquid milk production, one dairy for butter production, and one or
several dairies for cheese production.
Step 3: Identify the attributes which are relevant to the decision problem:
Minimize the transportation costs of raw milk, the delivery cost of cream to Götene from
the other dairies, and the distribution costs of liquid milk.
Step 4: For each attribute, assign values to measure the performance of the alternatives on
that attributes:
The values depend on how well the location of potential dairies corresponds to the
constraint of minimizing costs.
Step 5: Determine a weight for each attribute:
The weights depend on the demand for each product and the transportation costs and
delivery costs of each demanded quantity.
Step 6: For each alternative, take a weighted average of the values assigned to that
alternative.
Step 7: Make a provisional decision.
Step 8: Perform sensitivity analysis in order to see how robust the decision is to changes in
the information provided by the decision maker. In order to design the sensibility analysis
the decision maker should take into account the risks involved in the milk industry.
4.3 Heuristic Method
The simple heuristic approach may be a good method to apply to gain some intuition about
the structure of the problem, and how flows are assigned in networks and how one could
obtain reasonable solutions quickly. On the other hand, this method will often result in
solutions that are suboptimal. In case of having more available information this method
would perform better results.
For the performance of this method, the necessary information is production of raw milk in
the different farms, transportation costs for raw milk, delivery costs of cream to the butter
facility and distribution costs for the liquid milk.
In order to determine the best locations for the cheese and the liquid milk dairy/dairies the
following steps could be followed:
9
1. Determine the optimal production size for the cheese dairy/dairies, since we already
know the production size (from the case) for the liquid milk and the butter facilities
as there is only one of each these two kinds of facilities.
2. Determine the lowest transportation costs from the central points to the potential
dairies, which satisfy the production capacity constraint.
3. Determine the delivery costs of cream to Götene from the potential dairies.
4. Determine the lowest distribution costs for the liquid milk from the chosen dairies.
We can assume that the investment costs in the cheese dairy will have a greater impact on
the location than the transportation costs of raw milk.
After reviewing these methods to come up with the optimal solution, we believe that the
best approach is the linear programming method. It simultaneously and mathematically
evaluates all the constraints and in this way the risk of sub-optimization is lower although is
the most complex method to carry out.
5.0 SWOT Analysis
Potential Resource Strengths and Competitive
Capabilities
A widely recognized market leader and an attractive
customer base, quality differentiation strategy
Good skill and expertise in key areas
Strong brand name
Ability to achieve economies of scale
Wide geographic coverage and distribution capacity
Potential Resource Weaknesses and
Competitive Deficiencies
Limited supplier capacity
Lack of distribution flexibility
Potential strains on financial resources
Potential opportunities
Expanding new product line
Integrating forward and backward
Rapidly grow in market demand
Acquisition, joint venture, or strategic alliance
extend market to other region
Sources: see reference 1.
Potential external threats
Loss of sales due to substitutes products
Slowdowns in market growth
Potential new regulatory
A shift in customer's buying behavior
Table 4. SWOT analysis
5.1 Strengths and opportunities
In order to understand Arla’s situation, A SWOT analysis offer us a better perspective.
From our analysis, we know that ARLA is widely recognized market leader in the region of
Skaraborg. ARLA is almost in the monopoly situation from Kågeröd to Uppsala. A good
10
skill and expertise in the key areas offers Arla a remarkable economy of scale. A highly
backward integrated distribution line -owned farmer- gives Arla a strong monopoly position
in the milk supplier chain. It is extremely hard for other competitors to find raw milk
supplier within Skaraborg.
Meanwhile, the competition in this region is not high, the only one competitor—
Falkköpings is definitely a market follower of Arla. As the highly investment outlay in
production facility—high entry barrier, the threats from new entry to the market is very low.
Moreover, Arla will face a huge potential opportunities in the market development. Arla has
the potential opportunity to expand market to other region, facing a huge market perspective
for new products; Arla could develop several new products to catch potential niche market,
e.g. Low lactose milk.
5.2 Threats and weaknesses
However, there are still weaknesses and threats that Arla must take into account for the
future development. First of all, the company needs to make a long term market
development strategy, namely, Arla has to consider the external threats and internal
weaknesses.
The most serious threat comes from the capacity of raw milk production—supply of raw
milk, as we know that the total average demand of raw milk in this region is 257 269 tons,
which are nearly the same quantities produced at 27 farmer 257 400 tons. We know that
Arla is owned by the farmers, we should not worry about the switching of supplier, but what
if the market demand increased? If the demand couldn’t be fulfilled, customer will switch to
competitor quickly, as milk is not a differentiated product. Under this threat, Arla may
consider building up more farmer facility, and the cost of facility will be a financial
consideration.
From the market perspective, the highly developed new technology and new fashion affect
customer behavior. It is very possible that there will be a trend where people tend to
consume more high quality cheeses or yogurt product. Hence, the sales for some milk
product like yogurt will increase dramatically, (e.g. 3%), however, if Arla’s raw milk
supply grows at comparatively lower rate (e.g. 2%), the significant different 1% will
affect company’s profitability.
11
Supply Grows at 3%
Supply Grows at 2%
Figure 2. Supply in time (X axis = Time, Y axis = Supply)
What will come if this scenario happens?
First, Arla needs to extend supply chain by either investing more within this area, or finding
or establishing a joint venture with new supplier in the neighborhood. If Arla’s goal remains
the same: in order to minimize costs, company has to find best location, size, and quantities
for these extra suppliers from another region. Arla has to balance the trade off between
transportation cost and investment cost, which means that Arla has to consider whether to
build up another cheese lot near that area. We have to use the method we suggested above
to process again. The different is that we have to carefully consider total cost from cheese
production if we choose to build up other cheese dairies. However, the total cost will be
changed.
Net
investment
cost
Transportation
cost of raw
milk
Cheese production1
Liquid milk
Butter/cream
w1
x*A
37065*D
55430*C
N1*B
938*E
***'******
27600*F
Cheese production2
w2
(169717-x)*A
(7775-N1)*1,03
**********
Location
Transportation
cost of cream to
Götene
Distribution
cost
Table 5. Transportation costs Götene
Where, x = raw milk produced at cheese production 1, A is transportation cost.
The amount of raw milk required at Cheese production 2; (164,774*1,03)=179717, A is the
transportation cost of raw milk per ton to cheese production 2. N1 is the amount cream
transport to Götene from Cheese production 1, then cream transport from Cheese
production 2; 7549*1,03 = 7775, B is the cost per ton.
12
K is the amount raw milk needed at liquid milk, when calculate this amount, we have to
deduct amount cream transported from cheese production 1 and 2. While cost at liquid milk
production remains the same.
Arla’s objective now is to minimize all of these cost regarding net investment cost,
transportation cost of raw milk, transportation cost of cream to Götene, and distribution cost.
By consideration cost, there is a financial constrain –company has to decide how to finance
the investment to build up facility. This involves a high liquidity risk. Leasing?
Outsourcing?, Buying?, this decision will change Arla’s capital structure; therefore,
decision must be carefully made. As we don’t know Arla’s current Debt and equity ratio,
we will not do further discussion. However, more sensitivity and scenario analysis are really
essential.
Another dilemma company faces is to decide when to invest. Now? or Later?. Hence, more
sensitivity and scenario analysis need to be done when we consider this problem. If we have
the confidence that demand will grow continually, demand for cheese is sufficient for the
largest dairy, Arla should invest now to achieve the economic of scale. If the demand is not
sufficient, Arla needs to outsource or invest in a small dairy now, and later invest another
dairy when the demand increases. For doing so, a NPV method can help us to compare
different NPV from different scenario; the basic rule is that we discount NPV back to the
same basic year. Our goal is to decide the highest value so to decide the most suitable
investment date, as NPV bigger than 0 is necessary, but not sufficient for investment.
For the future threats and opportunities, Arla need consider the PEST1 model. Political risk
refers to the political decision from government, e.g. more strict regulations regarding
different discharge or government’s decision concerning environmental protection could
affect farmer’s production so to increase cost of raw milk. Economic depression reduced
consumer’s income, so to affect total demand of product. Cultural and technology will
change customer’s consumer behavior, e.g. more young people will prefer low fat milk,
Arla has to change strategy to fit the change in demand, the total cost will increase due to
the market research or other factors.
6.0 Conclusion
Arla is one of the largest Swedish milk producers, and it was built up on 26th April, 1915 in
Stockholm. Nowadays, bedsides milk, Arla produces other products like butter, cheese,
yoghurt; however, liquid milk, cheese, and butter are there main product.
Now, Arla is planning to expand its market to Skaraborg, there are 27 farms to produce milk
and 10 potential locations for dairies to produce Cheese, Cream/butter, and Liquid milk.
While butter/cream only could be produced in Götene, Liquid milk is to be produced at one
single site only; Cheese could be produced at any of the 10 dairies. The cream produced at
cheese and liquid dairies will be transported to Götene as the input for butter production.
1
PEST, stands for Political, Economical, Socio-cultural and Technological analysis.
13
The set of objectives for Arla is to minimize total cost in terms of transportation cost of raw
milk, and transportation cost of cream to Götene from liquid milk and cheese production,
distribution cost of product to market.
The methods we suggested are liner programming, greedy heuristic, and SMART,
However, the most preferable method is Linear programming, we believe that this method
could perfectly cope up with the optimal solution because this method evaluate all the
constraints that we defined in case, therefore, we believe that risk and cost are minimized.
During our analysis, we highly recommend that Arla should carefully complete sensitivity
and scenario analysis for every decision. The SWOT and PEST model help us to analyze
Arla’s current situation. Thus, if demand grows at 3%, while supply grows at 2%, in order
to cope up with the difference, Arla needs to consider time -phasing- when to invest (now or
later), where to invest?, and by which way to finance new investment? The main objective
is still minimizing cost, NPV method will help Arla define the highest value project to
invest. Other risk Arla need to considered also include political, culture, technology and
economic factors.
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7.0 Reference
1. A. Thompson & A.J. Strickland, (1998), Strategic Management, 10th Edition,
chapter 4, Evaluating company resources and competitive capabilities, page 107,
Irwin/McGraw-Hill.
2. Goodwin & Wright, (1999), Analysis for Management Judgment, Wiley.
3. S. Nahmias, (2001), Production and Operations Analysis, 4th Edition, chapter 6,
Supply Chain Management, McGraw-Hill.
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