Optimizing Supply Chain of Zara
Guided By – Prof. Noor E Alam
Submitted By:
Arthik Alexander
Nivedita Todkar
Anmol Agarwal
Tarun Raj Jeripeti
Akhil Vardhuru
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ABSTRACT
 The Fashion Industry is proliferating in terms of Production and Demand.
Their sales and number are projected to rise even more. Therefore it is prudent
for Big Fashion Brands to invest in Supply Chain Management to achieve
continued success in business and among competitors. Common obstacles to this
could include high initial cost setup, considerable transportation distance and
unpredictability in distribution and sales.
 This project aims to achieve an analysis of Zara’s international
supply network and create a Mathematical Linear Programming model to
estimate optimal distribution from production to market across its global network.
All the data was gathered from public sources, and the data was used to formulate
a Linear Programming Model algebraically; the Problem was modelled and
solved using Python. The Result was evaluated, and sensitivity analysis was
performed.
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ACKNOWLEDGEMENT
We would like to sincerely thank our esteemed Prof. Noor E Alam for his invaluable
supervision and support during the entire course of this project. Additionally, we would like to
express our gratitude to Mr XXX for his constant support and help, which was influential in
concluding our project.
Lastly, we would like to thank all those who put in their valuable inputs, which helped us
improve our project.
Table of Contents
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INTRODUCTION
4
Project Statement
4
Scope of the Project
5
Background
6
PYTHON MODEL FILE
11
RESULTS
13
SENSITIVITY ANALYSIS
14
CONCLUSION
16
REFERENCES
17
INTRODUCTION
Project Statement
The purpose of this project is to do a high-level analysis of Zara’s international
supply network and create a Mathematical Linear Programming model to
estimate optimal distribution from production to market across its global network.
The report analyses the publicly available data and the inferred data to
minimise the cost of operations
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The model also calculates the quantity of material shipped from manufacturing
facilities to key market locations across the globe.
A Situational analysis has also been conducted to estimate costs and
distribution when certain constraints are affected by global phenomena (Example:
halting of operations in China when COVID first hit)
Scope of the Project
We are looking to create a generic LP model to estimate approximate costs of
the high-level global supply network.
As we are only looking at the high-level supply network and estimating
APPROXIMATE costs, we are NOT considering internal transport costs within
the country from international ports to the retail locations, as these costs are
considerably lesser than the costs of transporting from the government to the
country.
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Background
Established in 1975, Zara is a Spanish company considered one of the world’s
largest companies operating in the fashion industry. The company is engaged in
the designing, manufacturing, distribution, and selling clothing for men, women,
and children across the world.
‘Give customers what they want and get it to them faster than anyone else.’
This is the mission statement that Zara STRICTLY adhere to, and this is
reflected in the way they do business and the systems they have set in place to
enable their business operations.
The mission statement tells us that the company is working day in day out to
provide value to the customers through their products. It also highlights the point
that customer satisfaction is their top-most priority
.
Setting up Demand
Zara changes their clothing designs every 2-3 weeks instead of competitors
who change every three months. They produce 12,000 new designs every year
instead of the 2000-4000 items that competitors have. This massive variety and
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range in designs entice the customers and makes them visit the store more
regularly to check out the latest innovations. Higher traffic inflow to the stores is
directly proportional to sales.
Zara has also been very mindful in determining locations for factories,
distribution centres and stores. Stores are set up in prime locations across
prominent cities globally. Accordingly, it produces as per the geographical area
needs, customer group, and customer type across all ages, and of course, the
regional area based on its operational needs. It customises and improvises
accordingly, which acts as a further add-on to customer satisfaction. This also
means that this flexibility is also Zara’s secret to success. It has the edge over its
competitors, who are rigid or inflexible in their ways.
So, the well-kept secret is that the company deliberately brings in only a few
pieces of stock into their stores, each different with different nuances periodically,
so that the supply is low. This creates a sense of urgency among customers to buy
their clothes as soon as possible, as specific designs may never be available again.
These methods change consumer behaviour, generating huge
demand for their products. Low supply and high demand allow the company to
set their selling price well above their cost price, thus increasing profit margins.
Their Production & Supply Chain –
The company has highly flexible and agile business operations, particularly in
the Production and Supply Chain departments.
Their factories are equipped to quickly increase or decrease production rates,
allowing them to respond rapidly to trends in real-time. The short production runs
are based on short term forecasts, and shorter-term forecasts are more accurate.
This result in less excess inventory. Thus they save the working capital, which
would have been otherwise used to finance that extra inventory, which can instead
be used to maximise value to the customer. This is particularly useful in a hard to
predict industry like the fashion industry. Thus, Zara does not need to make
significant, risky bets on Yearly trends. They make many small, more accurate
bets on a short-term basis, which is a far more sustainable approach to the fashion
industry.
They have also implemented several lean concepts like the Just-in-time system,
and thus they only manufacture 50-60% of their products in advance, as opposed
to the industry average of 80-90%. The fact that they produce a limited inventory
for each design ensures that they spend very little on maintaining inventory and
thus don’t need to give significant markdowns on their items as they usually get
sold out. Also, the short term focussed order cycles makes forecasts a lot more
accurate than for competitors who have to carry out long term forecasts, which
are mathematically less precise than short term forecasts.
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The HEART of their operations, the heart of their supply chain is “The Cube”,
which is a highly automated distribution network. Zara buys vast quantities of
just a few types of garments; the large amounts give them massive leverage in
supplier negotiations, further reducing their “per unit” production costs. The
garments are usually purchased from suppliers within Europe, Spain, Portugal,
and Morocco. “The Cube” is connected to its supply network in Europe by
underground tunnels with high-speed monorails to move resources within its
network. Zara primarily uses rail and truck to transport material within Europe
and air freight to other markets. Their highly efficient operational costs allow
them the capital to transport by air, which further reinforces their mission
statement –
‘Give customers what they want and get it to them faster than anyone else.’
The company has 2200+ stores worldwide, and the establishment of this HUGE
figure of stores is only possible through huge profits. Zara very evidently gains
enormous profits on a periodical basis as it has adopted a cost-effective
production strategy and Supply Chain Network. Over the years, it has
successfully built a brand image that has very winningly taken its product to its
inelastic demand side.
The company’s supply chain management’s scalability and ability have grown to
be very flexible and helpful in the company’s operations. Their agile supply chain
is spread across a few countries according to the requirement of resources and
saving in cost purposes; the division of work and distribution through
intermediaries makes the whole process robust for the company. It helps cater to
its customer on time to ensure they don’t create a gap between the time of demand
and time of supply which could hinder their brand image and their sole motive of
a mission statement.
Zara has spent over 30 years building and optimising its supply chain processes
and training its people in the best manner possible to enable its operations.
These reassuring and redeeming qualities have made Zara what it is today.
Amancio Ortega’s Zara is the top brand of all the brands under its parent
company, ‘INDITEX GROUP.’ It is the most successful company among all its
subsidiaries, and a major substantial part of the parent company earns its revenues
from Zara alone. Hence, the less stock – no advertisement policy was a price they
were prepared to pay, and indeed, it worked out for the company. They relied on
word-of-mouth advertising based on product quality and service, which has
worked out to be a sustainable long-term strategy for the company.
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Mathematical Model
Linear Programming Formulation for Zara Transportation Problem –
1. Decision Variables –
a. xi,j = number of items sent from Supplier country ‘i’ to Demand country ‘j’
Where,
i = supplier country [countries: Spain, Portugal, Turkey, Morocco, India, Bangladesh, China, Vietnam]
j = demand country [countries: Spain, Portugal, Turkey, Morocco, India, Bangladesh, China, Vietnam]
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NOTE: for this model, we assume that the supplier and demand countries are the same and
distribute items amongst each other depending on the demand and supply constraints.
b. yi,k = binary variable (0 or 1) to determine if the ‘k’ (high or low) capacity plant for
country ‘i’ needs to opened or not (o = closed, 1 = open) , both the plant may also
need to be opened depending on the situation
Where,
i = supplier country [countries: Spain, Portugal, Turkey, Morocco, India, Bangladesh, China, Vietnam]
k = high or low-capacity plant for ‘i’ supplier country
2. Objective Function –
Minimize Z = Cost of Manufacture + Fixed Plant Cost + Transportation Costs
Therefore,
 Minimize Z = Σ (xi,j * MCi,j) + Σ (Fi,k * yi,k) + Σ (xi,j * TCi,j)
Where,
MCi,j = Manufacturing Cost per item for each suppler country
Fi,k = Fixed Cost of opening ‘k’ plant (high or low) in ‘i’ supplier country
TCi,j = Cost of freight transport per item (this varies depending on mode of transport and distance)
3. Subject to Constraints –
a. Supply Constraints
Σ xi,j <= si ∀ i ϵ 1 to m
Where,
si = Supply limit for Supplier country ‘i’
m = number of suppliers
b. Demand Constraints –
Σ xi,j >= dj ∀ i ϵ 1 to n
Where,
dj = Minimum demand requirement for Demand country ‘i’
n = number of suppliers
c. Non – Negativity Constraints –
xi,k >= 0
yi,k >= 0
d. Conservation of Flow Constraint –
Σ si = Σ dj
e. Linking Constraints (for binary variables)
Σ xi,j <= M * yi,k
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PYTHON MODEL FILE
PYTHON MODEL
Python is a high-level, general-purpose programming language that is
interpreted. The use of considerable indentation in its design philosophy
emphasises code readability. Its language elements and object-oriented approach
aim to assist programmers in writing clear, logical code for both small and largescale projects.
Considering the project’s aim, a python model was created, and all the
information was in the format of excel sheets and all the tables. These files would
contain the data, which was collected through quantitative research. Initially, the
model was tested on the sample data of various countries. Various scenarios were
considered for the demands of each country. After that, the model file was
examined for each procedure, and its robustness was determined. Further
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completing this analysis phase, the model was tested on the actual assumed set of
data presented.
The figure below is the benchmark model of python.
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RESULTS
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SENSITIVITY ANALYSIS
Situational Analysis
We simulated three different scenarios to explain the robustness of the python
model best.
Benchmark – Where the input data is closest to actual data, the cost of
operations is
7,861,877,329
Situational Analysis – The capacity of China is reduced by Half, which
increases the overall variable costs. So, the cost of operations rises to
8,583,346,289
Situational Analysis - The capacity of Vietnam is reduced by five times which
increases the overall variable costs. So, the cost of operations rises to
8,783,742,746
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Название диаграммы
9 000 000 000
8 800 000 000
8 600 000 000
8 400 000 000
8 200 000 000
8 000 000 000
7 800 000 000
7 600 000 000
7 400 000 000
438000
425500
418000
Ряд2
As we can see, by common sense, the cost should be less if we make less. Still, in an optimized global supply
chain model, that ideology can be catastrophic to the business, as just reducing capacity will increase cost
overruns, and fulfilling demand will start to reduce your net profit margins.
Limitations of the model.
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This model has the tendency to overload on the type of facility used, as it can
show 8.5 facilities used when there is only one facility if the demand is really
high.
It does not give insight into when the model status is anything but optimal.
CONCLUSION
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REFERENCES
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