VIETNAM NATIONAL UNIVERSITY- HOCHIMINH CITY
INTERNATIONAL UNIVERSITY
SCHOOL OF BUSINESS ADMINISTRATION
DETERMINING SAFETY STOCK FOR AN
ADIDAS APPAREL FACTORY APPLYING
PROBABILISTIC DEMAND MODEL
In Partial Fulfillment of the Requirements of the Degree of
BACHELOR OF ARTS in BUSINESS ADMINISTRATION
Student’s name: NGUYEN VAN TUAN (BAIU09448)
Advisor: NGUYEN NHU TUNG, MBA
Ho Chi Minh city, Vietnam
2013
DETERMINING SAFETY STOCK FOR AN
ADIDAS APPAREL FACTORY APPLYING
PROBABILISTIC DEMAND MODEL
APPROVED BY: Advisor
_________________________
NGUYEN NHU TUNG, MBA
APPROVED BY: Committee,
_____________________________
Ph.D. HO NHUT QUANG
_____________________________
VO TUONG HUAN
_____________________________
ASSOC.PROF. HO THANH PHONG
_____________________________
NGUYEN NHU TUNG, MBA
THESIS COMMITTEE
i
ACKNOWLEDGMENTS
I gratefully acknowledge my large debt to Mr. Nguyen Nhu Tung who have advised and
inspired me during the time I was completing this thesis. Without his enthusiasm, I could
not overcome challenges I have faced when conducting this research. He help me to
realized my goal and give me the right direction to achieve it.
This paper would not be possible without the generous support of Mr. Tran Thanh Tung,
planning manager at adidas sourcing limited. He provides me a lot of useful information
and data related to inventory and production management.
I also want to give special thanks to all other staffs in adidas sourcing limited including
Ms. Oanh, Ms. Chuong, and Ms. Dung, Ms. Hong, Ms. Nga, etc. who give me a chance
for the interview and also provide me a lot of data to complete this thesis.
In a number of ways, I would like to thanks all professors and lecturers at International
University who taught and shared with me their insight, experiences and perspectives.
And last, but never least, many thanks to my friends and family, the most dedicated,
patient and encouraging group, who never stopped believing in me through my journey.
NGUYEN VAN TUAN
ii
TABLE OF CONTENTS
LIST OF TABLES ....................................................................................................... vii
LIST OF FIGURES ....................................................................................................... ix
ABSTRACT ....................................................................................................................x
CHAPTER 1: INTRODUCTION ....................................................................................1
1.1.
Research background ............................................................................................1
1.2.
Problem statement .................................................................................................1
1.3.
Research Objectives ..............................................................................................2
1.4.
Research Questions ...............................................................................................2
1.5.
Delimitation of the study .......................................................................................3
1.6.
Structure of the research ........................................................................................3
CHAPTER 2: LITERATURE REVIEW ..........................................................................5
2.1.
Operational Definitions: ....................................................................................5
2.1.1.
Supply chain management ..........................................................................5
2.1.2.
Inventory and its functions .........................................................................6
2.1.3.
Types of inventory......................................................................................7
2.1.4.
Relevant cost ..............................................................................................8
2.1.5.
Safety stock and Probabilistic model ..........................................................8
2.1.6.
Lead time ...................................................................................................9
2.2.
Theoretical Framework .................................................................................... 10
2.2.1.
Methods of Calculating Safety Stock ........................................................ 10
iii
2.2.2.
Methods to reduce safety stock ................................................................. 13
2.3.
Previous studies ............................................................................................... 13
2.4.
Conceptual framework..................................................................................... 16
CHAPTER 3: AN INTRODUCTION ABOUT ADIDAS SOURCING LIMITED
COMPANY ................................................................................................................... 18
3.1.
Company Name and a brief history .................................................................. 18
3.2.
Adidas Sourcing .............................................................................................. 18
3.3.
Office Layout and Extension ........................................................................... 22
3.4.
Production planning and control process ............ Error! Bookmark not defined.
CHAPTER 4: METHODOLOGY.................................................................................. 24
4.1.
Research methods and data collection: ............................................................. 24
4.2.
Research Design .............................................................................................. 25
4.3.
Data analysis technique ................................................................................... 26
4.3.1.
Performance Efficiency ............................................................................ 26
4.3.2.
Long time delivery ................................................................................... 26
4.3.3.
Material lead time uncertainty .................................................................. 26
4.3.4.
Service level ............................................................................................. 27
4.3.5.
Safety stock .............................................................................................. 27
4.4.
Summary .........................................................................................................28
CHAPTER 5: DATA ANALYSIS AND RESULTS ...................................................... 30
5.1.
Adidas Operational Model ............................................................................... 30
5.1.1.
Current operational process ...................................................................... 30
iv
5.1.2.
5.2.
Seasonal Operation ................................................................................... 30
Evaluating some major issues in company supply chain...................................31
5.2.1.
Long lead time delivery ............................................................................ 31
5.2.2.
Order delay............................................................................................... 33
5.2.3.
Over capacity ........................................................................................... 35
5.3.
Reasons for long lead time ............................................................................... 37
5.3.1.
Cause and Effect Diagram ........................................................................ 37
5.3.2.
Material and Production complexity reasons (product focus) .................... 38
5.3.3.
Process Efficiency (Process focus) ........................................................... 40
5.3.4.
Capacity planning (Strategy focus) ........................................................... 42
5.4.
Problem analysis ............................................................................................. 42
5.5.
Safety stock analysis ........................................................................................ 43
5.5.1.
Supply lead time and its uncertainty ......................................................... 44
5.5.2.
Demand Uncertainty................................................................................. 46
5.5.3.
Service Level............................................................................................ 47
5.5.4.
Safety stock determination........................................................................ 48
5.5.5.
Experimental analysis ............................................................................... 49
CHAPTER 6: DISCUSSION AND RECOMMENDATIONS........................................52
6.1.
Conclusion ...................................................................................................... 52
6.2.
Recommendation ............................................................................................. 53
6.2.1.
Material lead time ..................................................................................... 53
6.2.2.
Production complexity .............................................................................. 55
v
6.2.3.
Process efficiency ..................................................................................... 55
6.2.4.
Capacity planning ..................................................................................... 55
6.2.5.
Suggestion for further studies ................................................................... 55
LIST OF REFERENCES ............................................................................................... 57
APPENDIX ................................................................................................................... 59
vi
LIST OF TABLES
Table 2.1: Demand fluctuation and supplier responsiveness scores ................................ 15
Table 2.2: Internal Criticality score ................................................................................ 15
Table 2.3: Determining an appropriate service level ....................................................... 16
Table 4.2: Purchase order follow- up .............................................................................26
Table 4.3: Research instrument summary....................................................................... 28
Table 5.1: Lead time of adidas factories in Spring Summer 2013 (SS13) ....................... 31
Table 5.2: Garment lead time (in days), 2004 ................................................................. 32
Table 5.3: SDP .............................................................................................................. 34
Table 5.4: Capacity and customer forecast SS2013 ........................................................ 36
Table 5.5: Survey result for long lead time (product focus) ............................................38
Table 5.6: Lead time measurement ................................................................................ 41
Table 5.7: Conflicted schedule ....................................................................................... 43
Table 5.8: material lead time in two weeks period.......................................................... 44
Table 5.9: Mean and standard deviation for fabric lead time .......................................... 45
Table 5.10: Forecast demand of ARWP2275 and ARWP2280 .......................................46
Table 5.11: Mean and standard deviation for demand .................................................... 47
Table 5.12: Risk priority numbers (RPN) calculation ..................................................... 48
Table 5.13: Safety stock determination .......................................................................... 48
Table 5.14: Scenario for running simulation .................................................................. 49
Table 6.1: Actual Forecast demand of ARWP2275 and ARWP2280 .............................. 53
Table 6.2: New demand after pull forward ..................................................................... 54
vii
Table 6.3: new standard deviation .................................................................................. 54
Table 6.4: Safety stock for new demand standard deviation ........................................... 54
viii
LIST OF FIGURES
Figure 2.1: Activities in a supply chain ............................................................................6
Figure 2.2: The Impact of high, low safety stock..............................................................9
Figure 2.3: Inventory level in a fixed- period system ..................................................... 11
Figure 2.4: Safety stock with probabilistic demand ........................................................ 11
Figure 3.1: International Sourcing Volume .................................................................... 19
Figure 3.2: LO Vietnam Layout ..................................................................................... 23
Figure 4.1: Define variables for Crystal ball................................................................... 28
Figure 5.1: Current Operation Model .............................................................................30
Figure 5.2: Lead time in percentage ............................................................................... 32
Figure 5.3: lead time benchmarking ............................................................................... 33
Figure 5.4: SDP in 2011 ................................................................................................ 34
Figure 5.5: SS13 Customer Forecast Analysis ................................................................ 36
Figure 5.6: Cause and effect diagram .............................................................................37
Figure 5.7: Parato chart for different causes of long lead time ........................................39
Figure 5.8: Production process ....................................................................................... 40
Figure 5.9: Control chart for ARWP2275 ...................................................................... 45
Figure 5.10: Control chart for ARWP2280..................................................................... 46
Figure 5.11: Sensitivity chart for ARWP2275 ................................................................ 51
Figure 5.12: Sensitivity chart for ARWP2280 ................................................................ 51
ix
ABSTRACT
The research on ―determining safety stock for an adidas apparel factory
applying probabilistic demand model‖ finds out the causes and effects of long order lead
time in case of adidas sourcing limited in order to find a way to improve lead time and
reduce delay. Safety stock determination is the main method to achieve this objective.
The research was started by reviewing literatures, related theories, and other
sources of data to identify the model of this study. Then, observation and lead time
measurement is taken in a factory of adidas. The interview with key people of adidas
office and factories in Vietnam has conducted to support for the study purpose.
This research used both quantitative and qualitative method in analyzing
material lead time, capacity, production complexity and efficiency as the causes of long
order lead time. The result of this research indicated that material lead time has highest
influence on total lead time and delivery. Using the forecast demand of season Spring/
Summer 2014 and historical lead time in 2012, safety stocks were determined for two
products, ARWP2275 and ARWP2280. Different service levels were used and generated
different amount of safety stock. However, the investment for these stocks is quite high
so some recommendations to improve the inventory investment without affecting service
level are illustrated in the final part.
This research also had some limitations in the assumption as well as in the
scope of the study. Therefore, some suggestions for further study were recommended at
the end of this research.
x
CHAPTER 1: INTRODUCTION
1.1. Research background
Today, customer responsiveness is a main focus of many companies in global
market. It becomes more competitive for those who can satisfy customers best. On- time
delivery is one of the most important factors in deciding customer satisfaction as it comes
second after the product attributes (We love your product, but where is it?, Kumar and
Sharman, 1992). For nearly any business, an important aspect of customer service is the
ability to responsively deliver products to customers (Ghiana, Laporte and Mussamo,
2004). In fact, company may lose sale opportunity or suffer the impact of cancelled
orders because of late delivery. Product availability is a necessary element to ensure on
time delivery since company cannot deliver products when inventory is out. Therefore,
inventory management gain consideration from managers among company. ―A Modern
View of Inventory‖ in the July 2004 issue of Strategic Finance outlined the various roles
of inventory. From one perspective, inventory allows organizations to reduce total costs
through achieving wide-scale operational efficiencies and economies of scale. From
another perspective, safety or safety stock inventory acts as insurance by improving
product availability and buffering against the everyday uncertainty the organization faces.
On the one hand, if safety stock is too low, the probability of stock-out would be high. On
the other hand, high level of safety stock increases holding cost and thus reduces profit of
company. Therefore, in today dynamic global economy, with fluctuation in demand,
uncertainty in supply and global procurement, determining an optimal level of safety
stock has become a challenge task which gain attention from inventory managers.
1.2. Problem statement
Adidas is the world’s second biggest sporting goods maker focusing on three
1
sectors consist of footwear, apparel and accessory (Bloomberg, 2008). Material
procurement and manufacturing these products require a lot of time since they are using
global procurement network to ensure the quality of the product. According to Ms. Oanh,
Merchandise senior of Adidas Sourcing Limited- Vietnam, about 80% of material has
been ordered oversea, so lead time for material is quite long and that’s a reason for long
time delivery. Currently, adidas factories only order material when they receive purchase
orders from customers. From the order quantity, they forecast some defect may happen
during production to calculate the optimal amount for material. Thus, it takes them over
one month for raw material to be available in the warehouse and at least 90 days to
deliver an order to customer. Because the normal mode of input delivery is lengthy and
variable, purchase orders sometimes have to be delayed because of stock-out. It’s not
only affect the bottom line but also customer satisfaction. This research is, therefore,
conducted to help company to improve their key performance indicator (KPI) through
reducing delay in order delivery. It also ensures the balance in production without idle
time due to stockouts. Moreover, it’s expected to reduce the cost of stockouts when safety
stock is determined for the factory.
1.3. Research Objectives
In order to maintain enough stock for production and ensure on-time delivery,
this study is conducted to analyze the causes to the problems of long/delayed delivery
causing customer dissatisfaction. Moreover, it aims to evaluate material lead time and the
effect of material lead time uncertainty on safety stock. The final purpose is to give
recommendation for optimizing safety stock or to determine an optimal level of safety
stock for company.
1.4. Research Questions
This research studies models of inventory management in uncertainty
conditions and aims to answer the following questions:
2
a. What are the causes of long/delayed delivery?
b. How variable are material lead time and forecast demand?
c. What is the optimal safety stock level for company? What should company do?
1.5. Delimitation of the study
Since limitation of time, this research focuses only on two products in a
factory of adidas in Binh Duong province. Moreover, forecasted demand using in this
paper is assumed to follows a probabilistic demand model or normal probability distribution. A normal distribution is characterized by two parameters — mean and variance.
Since there is an equal likelihood that predicted demand is greater or less than actual
demand, the mean of the error distribution is assumed to be zero. In the language of
statistics, it is equivalent to saying that there is no bias in forecasting demand. The
assumption of normal distribution represent the demand simplifies the safety stock
calculation and moreover it’s widely used and accepted in many industries (Bhonsle,
Rossitti, & Robinson, 2005).
Safety stock level is only set for the main material, fabric, since more and
more sub-components of adidas products are purchased in Vietnam which can get with
short lead time so it does not affect the production too much.
1.6. Structure of the research
The research is divided into six main parts placed in six chapters.
Chapter 1: Introduction
This chapter provides a brief description about research background and the
problem statement for conducting this study. The objectives and research questions are
also included in this chapter. The next part will discuss about the scope and limitation of
the research. Finally, this chapter outlines the overall structure of the research.
3
Chapter 2: Literature Review
This chapter reviews the key concepts and definitions used in the research as
well as former researches related to the topic, present the theoretical basis and the model
will be applied in this study.
Chapter 3: An introduction to adidas sourcing limited
This chapter provides a brief introduction about company name and history. It
also presents the layout of the organization as well as the production process of the
factory from tier 2 suppliers to delivery.
Chapter 4: Methodology
This chapter shows the introduction to the methods applied in the research,
explain the process from building, adjusting the scales to collecting data from various
approaches.
Chapter 5: Data analysis and results
This chapter presents the process of data analysis, come up with the findings
and the discussion of them.
Chapter 6: Conclusion and recommendation
This chapter presents the results of the study, discuss the limitations and
provide recommendations for further research.
4
CHAPTER 2: LITERATURE REVIEW
2.1. Operational Definitions:
2.1.1. Supply chain management
Supply chain management is the integration of the activities that procure
materials and services, transform them into intermediate goods and final products, and
deliver them to customer (Jay Heizer and Barry Render, 2008). Supply chain
management emphasizes on procurement and supplier relationships management to
maximize final customer’s value.
The structure of a supply chain contains upstream and downstream activities.
It’s separated into tiers of suppliers for upstream and tiers of customers for downstream.
The first tier supplier directly sends its products to the organization. Similarly, the one
send materials to the first tier supplier called the second tier supplier. This logic applies
for other tiers in upstream activities until the original sources. In downstream activities,
we also have the first, second, third and final customers as shown in the figure below.
5
Figure 2.1: Activities in a supply chain
The number of tiers depends on the operation of an organization. Some supply
chains have many tiers of suppliers and customers while others have fewer. The supply
chain becomes longer with higher number of tiers of suppliers and customers and so the
flow of materials and goods become more complex and convoluted in such supply chain.
Organization often considers many factors before designing their appropriate supply
chain. These factors include the product’s value, bulk, perish-ability, availability,
profitability, type of customer demand, economic climate, availability of logistics
services, culture, rate of innovation, competition, market, financial arrangements and also
pursued strategy (Donald Waters, 2003).
2.1.2. Inventory and its functions
According to Donald Waters (2003), inventory is a list of things held in stock.
He also defined stocks as supplies of goods and materials that are held by an
organization. They are counted when the input or output become available but not be
used by the organization. Many organization tend to eliminate or keep as low as stock as
6
possible because they look at stock as a wastes of resource. However, the role of
inventory is very important in enhancing the flexibility for the firm’s operations. In
Operation Management book, Jay Heizer and Barry Render mention four important
functions of inventory:
1. To separate various parts of the production process. For instance, extra stock
helps company separate the production process from the fluctuation of suppliers
2. To decouple the firm from fluctuations in demand and provide a stock of goods
that will provide a selection for customers.
3. To take advantage of quantity discounts.
4. To hedge against inflation and upward price changes.
In this paper, we mainly discuss about the first two functions of inventory in
case of adidas.
2.1.3. Types of inventory
There are four types of inventory which are:
1. Raw material inventory. This is the inventory that has not been processed.
Manufacturing companies may keep this type of inventory to ensure the smooth
operation and separate the production process from supplier variability in term
of quantity, quality and lead time or delivery time. For example, fabric color
may have a slight difference if company order in different time, so it cannot be
combined together in a shirt or pant. Therefore, raw material inventory help
reducing this issue.
2. Work- in- process inventory is the inventory in the production process which
haven’t competed as a final product. This kind of inventory is also known as
panel inventory in some garment companies like adidas.
3. Maintain/ repair/operating inventory is material necessary to keep machinery
7
and processes productive. It’s hold to ensure to the supply while equipment is
under maintenance.
4. Finished-goods inventory is the finished products in the warehouse waiting for
shipment.
2.1.4. Relevant cost
Basically there are three costs relevant for consideration in developing an
inventory model.
1. Ordering cost: paper work cost, follow- up cost, inspection and checking cost
and also labor cost to purchase department.
2. Holding cost: interest on capital, insurance and tax charges, storage cost,
spoilage and obsolescence cost.
3. Under-stocking cost (Stock-out cost): lost production and lost sale cost
2.1.5. Safety stock and Probabilistic model
The safety stock is an additional quantity of an item held in inventory in order
to reduce the risk that the item will be out of stock. Safety stock determinations are not
intended to eliminate all stockouts—just the majority of them (Peter L. King, 2011). The
amount of safety stock maintained help company achieved a desired service level.
Service level is the complement of the probability of a stockout (Jay Heizer and Barry
Render, 2008). The service level, for instance, is 0.98 with the probability of stockouts of
0.02. There are two basic types of service level defined as type1 and type 2. Type 1
service is the probability of not stocking out in the lead time and is represented by the
symbol α. Type 2 service is the proportion of demands that are filled from stock (also
known as the fill rate) and is represented by the symbol β (Nahmias, 2009). In this paper,
we focus on type 1 service level.
Manager always pursues a level of service level which minimizes the
8
probability of stockouts and carrying cost (order cost). Both high and low safety stocks
have a negative impact to the company’s benefits:
Figure 2.2: The Impact of high, low safety stock
Source: cognizant, 2011
Under uncertainty of demand, it’s often assumed that demand follows
probabilistic model. Probabilistic model is a statistical model applicable when product
demand or any other variable is not known but can be specified by means of a probability
distribution (Operation management, Jay Heizer and Barry Render, 2008).
2.1.6. Lead time
This paper uses three terms of lead time which are procurement (material)
lead time, manufacturing (processing) lead time and order (delivery lead time). In order
to make it clear, we now distinguish them with each other.
The term procurement (material) lead time refers to the time between
procurement request and material availability at the next supply chain stage
(downstream) (BIBO YANG and JOSEPH GEUNES, 2006). This time interval is very
important in safety stock analysis. Long procurement lead times lead to high safety stock
requirements in order to meet customer service level expectations under uncertain
demand.
9
Processing Lead Time is the time required to make/manufacture the item. It’s
together with procurement lead time determining order lead time.
The order lead time, on the other hand, is the quoted time between customer
order and corresponding product receipt by the customer (BIBO YANG and JOSEPH
GEUNES, 2006). This lead time may influence demand since it determines customer
satisfaction.
2.2. Theoretical Framework
2.2.1. Methods of Calculating Safety Stock
Different companies have different method to calculate safety stock. In the
journal ―A New Framework for Safety Stock Management‖ published in 2011 by
cognizant, these methods can be classified under three umbrellas which are fixed safety
stock, time-based calculation and statistical calculation. For fixed safety stock, company
set a fixed level of safety stock and it often lead to high inventory costs or stock-outs
because demand is not always constant. Time-based safety stock level is used to calculate
the stock required over a fixed period. With this method, various amounts are ordered at
regular time intervals based on the quantity necessary to bring inventory up to the target
quantity.
10
Figure 2.3: Inventory level in a fixed- period system
Source: Operations management, 9th edition, Jay Heizer and Barry Render
The statistical method of calculation uses the normal curve or bell curve.
Safety stock and reorder point can be determined under a certain service level.
Figure 2.4: Safety stock with probabilistic demand
Source: Operations management, 9th edition, Jay Heizer and Barry Render
11
There are three situations in this method of calculation:
1. Demand is variable and lead time is constant
Safety stock and reorder point can be calculated as:
Safety stock= Z�d Leadtime
Where �d = Standard deviation of demand per day. The standard deviation
of a set of observations is the (positive) square root of the variance of the set. The
calculation of standard deviation is:
Adopted from Business statistic 7th edition, Aczel−Sounderpandian
Z is the number of standard deviation under a certain service level. For service
level= 95%, Z= 1.65
Reorder point (ROP) = (average daily demand x lead time in days) + safety stock
2. Lead time is variable, and demand is constant
When only lead time is variable, the formula of safety stock becomes:
Safety stock= Z(Daily demand)x σLT
Where σLT= Standard deviation of lead time in days
ROP = (Daily demand x Average lead time in days) + Safety stock
12
3. Both demand and lead time are variable
The formula becomes more complex when both lead time and demand are
variable:
Safety stock= Z
average lead time x σ2d + (Average daily demand)2 x σ2LT
ROP = (average daily demand x average lead time) + Safety stock
Probabilistic model with variable demand and lead time is applied in this
paper.
2.2.2. Methods to reduce safety stock
A goal of any supply chain manager is to reduce the level of safety inventory
required in a way that does not adversely affect product availability. It can be seen clearly
in the formulas above that there are three ways to achieve this objective:
1. Reduce the supplier lead time
2. Reduce the supplier lead time variability
3.
Reduce the uncertainty of demand
Since company always tries to obtain order as highest as possible, reducing
safety stock by affecting average demand (D) variable is impossible.
2.3. Previous studies
Improve delivery performance is a critical factor in maintaining customer
loyalty. Many companies have realized that and invested more and more to shrink the
delivery time as customer expectation. Therefore, this issue gets attentions from many
researchers. Let’s review some papers about this issue which also support for the research
13
purpose.
In capacity planning and lead time management, W.H.M. Zijm, R. Buitenhek
(1996) pointed out the important of capacity planning to reduce the delivery lead time or
at least improve the on time delivery. Good capacity planning can simultaneously reduce
overload of machine at this time and improve the utilization of the machine at another
time. Therefore, the research shows that delivery lead time and capacity planning should
be managed together.
Reducing lead time by reduce cycle time also gain attention from researchers.
Hopp and Spearman (1996) constructed a set of the mathematical principles determining
lead time, based on queuing theory, which they referred to as ―factory physics.‖ Suri
(1998) simultaneously developed a manufacturing strategy (also based on queuing
theory) called Quick Response Manufacturing that addressed implementation of lead time
reduction principles in manufacturing environments. Factory physics and Quick
Response Manufacturing formalized the relationships of bottleneck utilization, lot sizes,
and variability to lead times. More recent research about lead time reduction at Fokker
Aerostructures B.V by H.G. (Henk) Esveld (2010) conclude that shortening lead time in
Fokker Aerostructures need to reduce the waiting time at the batching zone at the
chemical line and optimize the scheduling of orders. These researchers provide different
way to reduce lead time but they mainly focus on minimizing the time in production
floor. In the other words, if total lead time is defined as the time required to buy and
make an item (lead time in supply chain, Antonin, 2011), the studies above suggest
solutions to reduce time to make an item
In addition, the role of safety stock is also very important on lead time
reduction since it ensures the availability of material and smooth operation. Therefore,
many studies have been conducted to determine an appropriate service level and find a
method to improve it without affecting order fulfillment. Bhonsle, Rossitti, & Robinson,
(2005) considered multiple criteria in calculating safety stock. He concluded that
14
company can reduce inventory investment by calculating safety stock based on
differentiated service level developed through risk level. He developed a model for
determining appropriate service level for different products based on demand fluctuation,
lead time and internal criticality. For each variable, he score from 1 to 9 for different
range and then compute risk priority number (RPN) by multiplying the three score of
each item and get an appropriate service level as in the table he developed (Table 2.3).
An optimal safety stock is kept with this service level.
Table 2.1: Demand fluctuation and supplier responsiveness scores
Table 2.2: Internal Criticality score
15
Table 2.3: Determining an appropriate service level
Sunil Chopra, Gilles Reinhardt and Maqbool Dada (2004) study the effect of
lead time uncertainty on safety stock. The conclusion from this research draws for both
normal approximation and exact demand. For normal distribution and service level above
50%, a manager who wants to decrease inventories should focus on decreasing lead time
variability rather than lead times.
2.4. Conceptual framework
Adidas sourcing limited is under the pressure of delivery time since the
delivery lead time is quite long and sometimes company cannot meet the customer
request date that leads to delayed delivery. As discuss previously, material lead time is
always concerned first when questioning about long delivery time. The material lead time
in this case covers both the material production lead time and material transportation lead
time. Moreover, delivery lead time also depend on the nature of the style. Since some
styles are easier to construct than others. For instance, bonding, seam seal, and jacket
style are complicated and require more time in production process. The efficiency in the
production and factory capacity are also important in determining order lead time. This
research, therefore, considers all of these factors as the causes of long lead time. The
finding will divide these causes into three separate groups: product focus reasons, process
focus reasons and finally strategy focus reasons. Product focus reasons are the reasons
related to the design and characteristics of product including material usage and the
16
production complexity while process focus reasons are the efficiency in the production
including the cycle time and idle time. On the other hand, strategy focus reasons are the
ways company looking at the forecast and decide a strategy for capacity planning. All
these reasons will be discussed in details in chapter 5.
17
CHAPTER 3: AN INTRODUCTION ABOUT ADIDAS SOURCING LIMITED
COMPANY
3.1. Company Name and a brief history
Adidas – a name that stands for competence in all sectors of sport around the
globe. The vision of company founder Adolf (―Adi‖) Dassler has long become reality.
Adi Dassler’s aim was to provide every athlete with the best possible equipment. It all
began in 1920, when Adi Dassler made his first shoes using the few materials available
after the First World War. Today, the adidas product range extends from footwear and
apparel to accessories for all kinds of different sports. The key priorities are: running,
football, basketball and training.
In 1949 Adi Dassler first registered adidas in the commercial register in Fürth
(near Herzogenaurach). The official name of the company back then was ―Adolf Dassler
adidas Sportschuhfabrik‖. After a period spanning almost 70 years, the Dassler Family
withdrew from the company in 1989, and the enterprise was transformed into a
corporation. Robert Louis-Dreyfus was Chairman of the Executive Board from April
1993 to March 2001. He initiated adidas’ flotation on the stock market in November
1995. Since 2001, Herbert Hainer has been leading the Group
3.2. Adidas Sourcing
Sourcing is a department of Global Operations. Its functions include
Apparel/Footwear/Accessories & Gear Sourcing and Continuous Improvement. The
primary responsibility is to ensure adidas Group’s products are manufactured according
to sales and market needs. Sourcing people manage suppliers, as well as create and
implement sourcing strategies, to ensure manufacturing volume is being supported in a
timely, quality and cost-efficient manner. In addition, sourcing support Global Operations
and the Group on strategic initiatives aimed at building speed, agility and connectivity in
18
supply chain to differentiate themselves from competitors as well as enabling them to be
closest to every consumer.
Sourcing operates through a network of over 20 offices located on four
continents with 1,400 staffs working across different functions from operations, strategy,
systems to administration. In 2011, adidas sourced over 434 million units of shoes,
garments and accessories from approximately 300 factories in 41 countries—this is a
total sourcing value of USD 4.2 billion
250,000
200,000
Pieces
(x1000)
International Sourcing Volume
189,000
176,433
150,000
2008
2009
136,439
126,185
100,000
50,000
35,550
41,120
0
Apparel
FW
A&G
Figure 3.1: International Sourcing Volume
Apparel Sourcing (AS) is responsible for the adidas Group’s worldwide
apparel sourcing activities. AS primary mission is to develop a supply chain capability
that meets the needs of company brands in quality, price, innovation, as well as
19
consistency and reliability in service. The relationship AS form with the vendors is a
partnership - vendors are selected for their ability to deliver on service expectations and
for sharing the core values of social and environmental responsibility.
Company revised the structure of the apparel senior management team on 1st
January 2010. The objective is to become closest to every consumer. To enable this, AS
have created the role of Head of Strategy and Brand Sourcing. Under this role, it will
appoint a strategic sourcing team collocated in each creation centre to drive the sourcing
strategy to meet the needs of all of the brands.
Another new role is the Head of Sourcing Operations. This role will focus on
ensuring day to day sourcing activities meet the needs identified in our strategy. The
liaison offices report in to this role
The other positions in the senior team are the specialist functions of quality,
costing, manufacturing excellence who will work across the liaison offices to drive their
functional teams to support the strategic direction.
Strategy and Brand Sourcing: The Strategy team is focusing on creating the
Apparel Global Sourcing Strategy based on the needs of the Brands. In particular,
company aims to answer the question: What to source where and why? This is being
done by considering key strategy drivers like landed cost performance, delivery
performance, speed to market based on historical information as well as macro-economic
trends country specific risk and opportunities.
The Operations team drives process and performance excellence across LOs.
This is being done by closely monitoring our key performance indicators, addressing the
root cause of upcoming challenges and sharing best practices across our supply base.
Besides, the team focuses on supporting and implementing key strategic
projects like miadidas, Replenishment, Virtualization, RAGS and Fast & Lean.
Country Managers: Apparel Country Managers are responsible for the
20
overall management of sourcing within either a country or a region. They manage the
day-to-day operations of the liaison office which includes development, costing,
planning, quality and administration. Country Managers are responsible for setting the
sourcing strategy for their region to maximize cost benefits through a reliable and highperforming supply base. The Country Manager acts as the legal representative for the
adidas- group within their respective areas and also has the ownership for the supplier
relationships.
Materials: The Materials team manages the supply planning, development,
costing, quality and delivery of materials and trims from the concept stage to Tier 1
manufacturing. It ensures seasonal materials and new suppliers go through the buy ready
process and are complied with strict requirements. Today, there are more than 10,000
active materials from which roughly 2,000 are contained in the material toolbox
seasonally – not including trims. Material Sourcing is also responsible for defining the
material supplier base according to our strategies which include enhanced supplier
performance and T1/T2 (Tier 1/Tier2) alignment, among others. It also trains our LO
staff on new materials and related processes, as well as resolves material issues with our
suppliers.
Costing: Apparel Costing sets the direction and strategy for each season’s
costing efforts. The focus of the team is to assess and improve costing tools, ensure
allocation is based on pricing success, implementation of appropriate costing standards,
and management of all LOP (labor overhead and profit) related activities across all
sourcing regions. The Costing function strives to create a profitable trade environment for
suppliers where they are able to invest in quality and improvement initiatives. It is also
responsible for sharing best practices in costing approaches, procedures and processes for
all brands.
Technical Services: Technical Services was formed five years ago to improve
quality and build technical expertise into the business, with the aim to improve quality,
21
reduce cost and standardize processes (e.g. systems, methods, documentation,
reporting…etc). The core functions include setting and maintaining group standards,
performing technical audits, conducting training, supporting LO (liaison office)
development teams and introduction of new technologies
3.3. Office Layout and Extension
22
Figure 3.2: LO Vietnam Layout
CHAPTER 4: METHODOLOGY
The roles of this research are to find out the current problems in adidas supply
chain and the causes of long time delivery. Safety stock is then determined as a method to
reduce total lead time of adidas. In order to achieve these objectives, this part provide the
detail methodology to conduct this study
4.1. Research methods and data collection:
The data using in this paper is secondary data collected from adidas factory
and adidas liaison office. These data support for quantitative method used in this paper.
Particularly, data for:
1. Lead time from WIP report
2. Target service level from KIP report
3. Expected demand from seasonal forecast report
4. Capacity and demand analysis from forecast analysis.
Besides that, the checklist for reasons of long lead time is delivered to
merchandise teams of four apparel factories of adidas sourcing limited in Vietnam. Ms.
Hong is responsible for DTVN factory while Ms. Diep Nga is responsible for Espinta,
QVE and QVT factories
Qualitative method would be collected from interviewing and emailing with
key people in planning team of adidas. Moreover observation in warehouse of two
factories during internship period also support for this method.
Recording is used to measure lead time of some purchase orders to check for
the cause of long lead time and delay. It also aims to test the efficiency of factory by
measure the actual idle time. Since the time limitation, the record is conducted only on
three purchase orders. Moreover, it’s difficult to follow up many purchase orders at the
same time since they are produced in different sub-plants and the schedule is often
changed by the planning manager of the factory, so we cannot keep track the actual
progress of many purchase orders at the same time. For these reasons, lead time is
recorded only for three orders and then it will be rechecked via the interview with
employees and management team.
This research also refers to secondary data from various sources including
books, articles, journals and previous research and thesis, etc.
4.2. Research Design
Checklist for the reason of long delivery is designed for 165 styles (models)
which have lead time from 90 days to 120 days and without possibility to reduce further.
The checklist will answer the following questions:
1. Does the style have long material production lead time?
2. Does the style have long material transportation lead time?
3. Does the style have complicated printing?
4. Does the style have Bonding?
5. Does the style have Seam seal?
6. Is the style jacket?
7. Does the style have complicated construction?
Lead time is measured for three purchase orders (PO) with 90 days lead time
from Esprinta factory. The quantity varies among purchase orders. The smallest quantity
is 624 pieces and the largest one is 1688 pieces. The detail of each PO is summarized in
the table below:
25
Table 4.1: Purchase order follow- up
Customer
Order
No
Quantity
105867175
548229
Esprinta
105866853
Esprinta
105866914
Style code
Factory
PO number
ARMW2019
Esprinta
ARMW1039
ARMW1039
Delivery date
Printing
1688
2012.06.30
Yes
548229
624
2012.06.30
Yes
548229
680
2012.06.30
Yes
4.3. Data analysis technique
4.3.1. Performance Efficiency
Performance efficiency is evaluated through observation in factory from June
to August. This is also based on the opinion of employees in the factories for a specific
problem encountered in the production process. The results of lead time measurement
will support for the evaluation of idle time in the production.
4.3.2. Long time delivery
Fish bone diagram, also known as cause and effect diagrams or Ishikawa
diagram, is used to analyze the reasons for this issue. It identifies many possible causes
for an effect or problem and sorts ideas into useful categories. After that, Parato chart will
be applied to display the result from the survey. Parato charts are a method of organizing
errors, problems, or defects to help focus on problem solving efforts.
4.3.3. Material lead time uncertainty
This study uses control chart to analyze the uncertainty in material lead time.
A control chart is a statistical tool used to distinguish between variation in a process
resulting from common causes and variation resulting from special causes. This chart is
26
used to evaluate the fluctuation of lead time.
4.3.4. Service level
This study applies the risk priority number method developed by Bhonsle,
Rossitti, & Robinson (2005) to calculate an appropriate service level for two products of
adidas. This service level will be compared with the primary level obtained from KPI
report of company.
4.3.5. Safety stock
With the certain service level company pursue, safety stock is calculated using
formula of probabilistic model with variable demand and lead time (Heizer & Render,
2008):
Safety stock = Z
average lead time x σ2d + (Average daily demand)2 x σ2LT
After that Crystal ball simulation will be run so the influence of each variable
to safety stock will be shown in the sensitivity chart. Based on this result, the
recommendation for reducing safety stock will be generated.
Crystal Ball lets us define three types of cells:
Assumption cells contain the values that we are unsure of: the uncertain
independent variables in the problem we are trying to solve. The assumption cells must
contain simple numeric values, not formulas or text.
Decision variable cells contain the values that are within our control to
change. The decision variable cells must contain simple numeric values, not formulas or
text.
27
Forecast cells (dependent variables) contain formulas that refer to one or more
assumption and decision variable cells. The forecast cells combine the values in the
assumption, decision variable, and other cells to calculate a result.
Assumption Variables
Average LT
Average Demand
LT SD
Demand SD
2.97
1347
0.44
1100
Assumption Variables
Safety stock ARWP2275
1688.266
Decision Variables
Forecast
variable
Service Level
0.85
Decision
variable
Decision Variables
Figure 4.1: Define variables for Crystal ball
4.4. Summary
The table below will summarize the sources of all factors used in this paper:
Table 4.2: Research instrument summary
Dimension
Description
Long time
delivery
Long lead time from
purchase order receipt to
delivery
Research instrument
& Data collection
- Secondary data
- Checklist for 165
styles
- Parato chart.
Source
BIBO YANG
and
JOSEPH
GEUNES, 2006
28
Delay
Percentage
order
of
unfilled
- Secondary data from Adidas Internal
KPI report
data
Capacity
planning
Strategy for production
scheduling
- Secondary data from Adidas Internal
forecast analysis
data
- interview
Supply
uncertainty
The
fluctuation
material lead time
- Historical lead time
- Control chart
Demand
Uncertainty
of
Heizer
Barry
2008
and
render,
The fluctuation of forecast
demand
- Secondary data from Heizer
forecast report
Barry
2008
and
render,
Service level
The complement of the
probability of a stockout
- Secondary data
- Calculated by RPN
method
Bhonsle,
Rossitti,
&
Robinson, 2005
Safety stock
an additional quantity of
an item held in inventory
in order to reduce the risk
that the item will be out of
stock
- Secondary data
- Experimental
analysis by Crystal
ball
Heizer
&
Render, 2008
29
CHAPTER 5: DATA ANALYSIS AND RESULTS
5.1. Adidas Operational Model
5.1.1. Current operational process
The current operation model takes a linear approach that provide a long lead
time between receipt of Purchase Order to delivery of finished goods from the factories
The material lead-time is included in the Purchase Order lead-time.
The process go through different stages from planning, place material order to
tier 2 supplier to material receipt, cutting, printing, sewing, packing and exporting. The
total lead time is often from 90 to 120 days for the whole process. This model provides
enough lead time for factory not to plan for material before the receipt of a purchase
order from customers.
Figure 5.1: Current Operation Model
5.1.2. Seasonal Operation
Adidas Sourcing Limited organizes its operation in two main seasons which
are spring summer (SS) and fall winter (FW). Spring summer starts from the beginning of
September this year to the end of February in the year after. On the other hand, FW
season starts from the beginning of March to the end of August. The demand of products
30
fluctuates with the seasons of the year. For example, T- shirt and Short have higher
demand for SS. In contrast, jacket and coat, especially down jacket, have higher demand
than other styles during FW.
5.2. Evaluating some major issues in company supply chain
5.2.1. Long lead time delivery
For different products, adidas offered different lead time depending on the
design and material used in each style. Recently, adidas sourcing limited Vietnam
manages 4 factories in Vietnam and one other in Cambodia. However, all of them offered
the delivery time quite long. A record over 413 apparel styles in 5 factories in the last
season shows that about 75 % of them required 90 days to complete an order of customer.
The remains need over 105 days from receiving the purchase order to delivering to
customer.
Table 5.1: Lead time of adidas factories in Spring Summer 2013 (SS13)
Fty Name
Fty. Code
Lead Time (Days)
105
120
90
DIN TSUN ENTERPRISE CO, LTD.
AV1001
36
1
QVE
A2N001
1
10
11
A2N004
10
5
15
ESPRINTA (VIET NAM) CO. , LTD
16U502
4
36
40
NAN KUANG (CAMBODIA)
A3D501
2
12
14
QVT
A2N503
Grand Total
263
Grand Total
33
53
49
300
33
311
413
31
13%
12%
105 days
120 days
75%
90 days
Figure 5.2: Lead time in percentage
The average lead time for many countries including Vietnam is under 90 days
(Rasiah, 2007). Lead times adidas offered are, however, above this average level.
Table 5.2: Garment lead time (in days), 2004
The chart below shows the benchmark of average lead time in garment industry between
some countries and adidas. The current average lead time of adidas is about 105 days
which is lower than the average lead time of most country including Vietnam. This urges
32
adidas to improve their delivery time.
120
105
105
105
95
Lead time (days)
100
75
80
75
75
75
75
60
60
50
40
20
0
Figure 5.3: lead time benchmarking
If company takes a lot of time to response to customer preference, it may lose
sale opportunity. According to Ms. Oanh, senior merchandiser, long lead time reduces
customer satisfaction since it requires customer to place the order for the far future
demand and it’s not always an easy task for them. For example, in order to have products
on hand for Christmas event, they have to forecast the demand and place the order at least
three months before Christmas. Obviously, the forecast like this often yield a big error
and thus customer always seek for a shorter lead time.
5.2.2. Order delay
The delay delivery of factory is evaluated in KPI report (Key performance
indicator) of adidas. In fact, adidas has totally 10 KPIs which measure different aspects of
supplier performance. However, the delay degree can be seen clearly in SDP indicator.
SDP means Supplier Delivery Performance. It measures the percentage of quantity
33
shipped in accordance to First Confirmed Customer date.
Table 5.3: SDP
Figure 5.4: SDP in 2011
SDP
Target
92%
Jan
82.2%
Feb
78.1%
Mar
62.0%
Apr
64.3%
May
42.0%
Jun
74.6%
Jul
47.2%
Aug
95.2%
Sep
93.9%
Oct
98.8%
Nov
95.6%
Dec
100.0%
Generally, the graph above show that the capability for on- time delivery in
this factory is still low. In the first 7 periods of 2011, the factory cannot achieve the target
of 92%. Particularly, SDPs for this factory on May and July are even lower than the
average of 50%, achieved only 42% and 47.2% on-time delivery over total order
quantity. From the on- time delivery percentage, the delay line is also sketched as in
figure 5.4. Obviously, this line and on- time delivery line is symmetric through the
average curve. The new line shows that delay is a problem at this factory since it’s quite
high during the year.
The strategy of adidas is sourcing which means their products are
manufactured by a third party called tier 1 suppliers. The role of adidas sourcing limited
34
is to follow and manage the progress and the efficiency of factory and requires factories
to perform as adidas standard. This strategy can help adidas reducing the cost of order
delay since factory have to pay part of delay cost for adidas liaison office if factory could
not meet the delivery date. However, the more important things than money are adidas
reputation, customer satisfaction and customer loyalty. These factors are critical for long
term business so limiting the problem of delay is concerned by adidas management.
5.2.3. Over capacity
Over capacity is another problem in some tier 1 factories of adidas. Let take a
look at a customer forecast analysis for season spring/ summer 2013 (SS13) to
understand more clearly about this problem.
Spring/ summer season includes six months from September 2012 to February
2013. Beside marketing forecast on March 2012, the customer forecast is released on
May for this season. In the chart below, we can see three customer forecast number in
every two weeks of May. Since the forecast in 31- May is the latest one for SS13 season,
we then analyzing the capacity of the factory with this forecast amount. In some months,
capacity offer by tier 1 factory can cover all the order of customer. In some period, order
receipt is quite low, so the utilization of machine in these months will be low also. For
instance, only 49% of the capacity will be utilized. However, in other months, the order
quantity exceeds the offered capacity. Particularly, in the customer forecast analysis,
factory capacity is below the order quantity on October, November and January. With the
high demand on January, 156% over the offered capacity, if company doesn’t have an
appropriate strategy, many orders will be cancelled
35
Figure 5.5: SS13 Customer Forecast Analysis
2000000
1710000
1500000
1710000
1710000
1600000
1590000
1570000
1575000
1575000
1370000
1000000
1000000
1000000
913000
500000
614474
68712
114228
130298
125607
OCT
NOV
DEC
38513
0
SEP
JAN
23-Mar
3-May
17-May
ROC
Most updated OC
Place Holder
FEB
31-May
Table 5.4: Capacity and customer forecast SS2013
Milestone
Offered Capacity
Lastest
Mktg
FC
Fill
rate
Customer Forecast
ROC
Most
updated
OC
23-Mar
3-May
17-May
31-May
1710000
1710000
1570000
1341112
1655542
1481848
1517759
OCT
1710000
1710000
1575000
1351560
1814808
1643428
1595892
114228 101%
NOV
1710000
1710000
1575000
1346640
1551496
1415251
1588929
130298 101%
DEC
1160000
1370000
1590000
1374438
1403707
1241571
1262566
125607
JAN
1470000
1000000
913000
1698893
1666139
1325821
1420232
614474 156%
FEB
1910000
1000000
1600000
470069
876398
789968
790490
38513
49%
SS13
9670000
8500000
8823000
7582712
8968090
7897887
8175868
1091832
93%
Initial
OC
SEP
Month
Place
Holder
68712
36
97%
79%
5.3. Reasons for long lead time
The long lead time has a huge effect to adidas business. In short term,
customer may accept their offer and adidas still enjoys profit. However, adidas may lose
the competiveness in dynamic market for long term business. The important thing here is
that adidas lead time is over the average time in the same industry. Therefore, it’s a gap
to looking for the reason influencing its operation and resulting a long lead time.
5.3.1. Cause and Effect Diagram
As mentioned in the conceptual framework, there are many factors that affect
total lead time or delivery lead time of adidas sourcing limited but the they can be
categorized into four main groups which are material, production complexity, process
efficiency and capacity planning strategy. These four groups and its sub-factors are
plotted in the cause and effect diagram before we go into details for each factor.
Production complexity
Material
Bonding
Jacket style
Transportation LT
Seam seal
Complicated construction
Material production LT
Complicated Printing
Long order
lead time
Required Capacity
Unnecessary movement
Bottle neck
Capacity planning Strategy
Idle time
Offered capacity
Process Efficiency
Capacity
Figure 5.6: Cause and effect diagram
37
5.3.2. Material and Production complexity reasons (product focus)
Since the material and production complexity are the issues related to the
nature of the products and it depends on the design of each product, it need the
investigation in every single model of adidas. Therefore, the survey is conducted in 165
models to support for the analysis of these reasons. In this survey, problems related to
material are divided into 2 sub-reasons including long transportation lead time and long
material production lead time while production complexity is divided into 5 sub- reasons
which are complexity construction, jacket, printing, bonding and seam seal .
The result of the survey is summarized in table 5.5:
Table 5.5: Survey result for long lead time (product focus)
Problem
Long Material Production LT
Long material transportation LT
Complicated construction
Jacket
Printing
Bonding
Seam seal
Frequency
105
99
53
31
11
11
1
Percent
Cumulative
33.76%
33.76%
31.83%
65.59%
17.04%
82.64%
9.97%
92.60%
3.54%
96.14%
3.54%
99.68%
0.32%
100.00%
The reason for long lead time with highest frequency is long material
production lead time. It means that materials like fabric and trim take long time in the
production before it come to warehouse of adidas factories. The next important reason is
long transportation lead time between tier 1 and tier 2 suppliers with the frequency of 99,
account for 31.83%. The cumulative percent of these two material reasons is over 65%.
The production complexity affects lead time with lower frequency which
accounts for about 35%. Particularly, complicated construction takes 17.04%, jacket
takes 9.97%, printing and bonding take 3.54%, and seam seal takes less than 1%.
38
120
100%
90%
80%
70%
80
60%
60
50%
40%
40
30%
cumulative percent
Frequency (number)
100
20%
20
10%
0%
0
Long
Long
Complicated
Material
material
construction
Production transportation
LT
LT
Jacket
Printing
Bonding
Seam seal
Figure 5.7: Parato chart for different causes of long lead time
In the analysis of the Cambodia's garment industry and catch‐up strategy,
Joosung J. Lee and Vathana TE Duong have pointed out the characteristics of the raw
materials in Vietnam and China which are the answer for why the majority of adidas
materials are purchased from China and Taiwan. The article says that although there are
supporting industries available in Vietnam, the raw material inputs are classified as
―medium‖. Vietnam cultivates cotton, but it is not internationally competitive in quality
and price (Knutsen, 2004). China is classified as ―very high‖ by having the world’s
largest production capabilities for cotton, man-made fibers, and silk, and by having
instant access to high-quality imported fabrics from South Korea, Taiwan, and Japan
(Minor, 2002). Almost all raw materials for the manufacturing of garments are produced
in China (Towers and Peng, 2006). In fact, adidas brand is not only famous for the design
but the quality. Adidas products are usually worn in sporting activities like football,
volleyball, and baseball, etc. so the quality of their products is the first requirement. Most
fabric produced in Vietnam cannot meet the requirement of company, and then it has to
be purchased oversea in China and Taiwan with longer lead time and high uncertainty.
39
5.3.3. Process Efficiency (Process focus)
From the above survey, the main reason of long lead time is related to
material lead time. In order to check for this, lead time measurement is conducted to find
out the actual time in the production when fabric and other material are available in
warehouse until final inspection. The measurement and observation from the production
floor also aim to evaluate the efficiency of the factory. The idle time between each stage
will be important for this evaluation. The inefficiency also rechecks by interviewing with
employees to find out the reasons for the issue and also the occurrence probability of the
problems.
Fabric in
warehouse
Cutting
Printing
Sewing
Inspection
Scan and
pack
Final
Inspection
Figure 5.8: Production process
The results of lead time measurement for three POs are recorded in table 5.6:
40
Table 5.6: Lead time measurement
In the table above, the blue line presents for PO 105867175, the red line for
PO 105866853 and the orange one for PO 105866914. Although the purchase orders have
a large quantity, the actual time required in the production process is not very long, under
30 days. The stage requires longest time, as we can see in the table, is printing stage of
the first PO which is 16 days for all quantity. After checking with line supervisor, this
style needs special printing method. This method cannot use drier after printing because
the characteristics of the ink and fabric require drying naturally. Hence, time spend for
printing is also longer than other styles.
When following these purchase orders in the production floor, many
unnecessary activities take place and increase production lead time. For instances, on 26
41
June, Line 16 (floor 1) sent 117 items (size L) of PO 105867175 to line 25 (floor 2), then
line 16 recognized that they can complete this size by themselves, so fabric was again
returned to line 16 on the day after. This activity took time for transportation between 2
lines which locate in different floors. Another example is that line B23 was first
responsible for PO 105866914 but then they transferred to line B07 and they started
sewing other orders. Transportation between lines increases the idle and thus the total
lead time although it’s not much.
5.3.4. Capacity planning (Strategy focus)
Capacity planning is mentioned in this session as a factor of lead time since if
company doesn’t have an optimal strategy when capacity is over, the purchase order
cannot proceed and it has to be delayed which lead to the total time of a purchase order
become longer.
When over capacity happens, company strategy is to pull forward the order
based on customer forecast. For instance, the forecasted orders (on May, 2012) for
January exceed the offered capacity about 56 %, but the order for the previous month is
only 79 % of the offered capacity. Therefore, company will plan to produce a portion of
product quantity of January on December. With this strategy, company can balance the
production line during the season.
However, if we look at the analysis in more details, the remained capacity on
December is not enough to cover the entire exceeded amount on January. In this situation
company can only pull forward partial of the total quantity and negotiate with customers
to change the delivery date to the future. If the agreement cannot reach, the final decision
is to cancel the order.
5.4. Problem analysis
After investigating all reasons of long lead time, the main reason affecting the
total lead time of adidas garments is long material lead time. This time takes more than
42
half of the total lead time. Moreover, the uncertainty of lead time also creates barriers for
planning activities. Plannning manager had to change the schedule frequently because
raw material couldn’t arrive as schedule. Sometimes, it confused many teams in the
production process since planning manager change the date for one stage and forget to
change the schedule for other stages. As the result, conflicted schedule happens. In the
table 5.7, the schedule for cutting is later than that for sewing. Obviously, it’s impossible
to implement such schedule.
Table 5.7: Conflicted schedule
Inline date
Cutting date
According to Ms. P, a member of production team, such schedule often
happens because the schedule changes every day and it’s seem that the team is quite
familiar with this changing schedule. Also, the cutting team was not strange with this
issue, cutting leader said. They response to this issue by updating the schedule everyday
and report to production team to fixed the conflict.
In order to deal with the problem of high supply uncertainty, company need to
hold an appropriate level of inventory which will be discussed here as safety stock.
5.5. Safety stock analysis
The level of safety stock is influenced by many factors as its formula shown.
These factors include:
1. Service level:
2. Supply lead time and its uncertainty
43
3. Demand and its uncertainty
average lead time x σ2d + (Average daily demand)2 x σ2LT
Safety stock = Z
(Heizer & Render, 2008)
Each of these factors will be analyzed in details before determining safety
stock level for some products of adidas:
5.5.1. Supply lead time and its uncertainty
The historical data for material order last year in table A4 and A5 (see
appendix) show the actual lead time for products ARWP2275 and ARWP2280. Lead
times in these tables are measured in day. However, we will see in the next part that
demand is forecasted for period of two weeks so it is necessary to convert these lead
times in period of two weeks. In the other words, material lead time for ARWP2275 and
ARWP2280 will be divided by 14. The table below illustrates the processed data.
Table 5.8: material lead time in two weeks period
ARWP2275
2.43
3.57 3.43
2.64 2.50
2.50 3.21 3.14
3.57 2.57
2.86 3.21
ARWP2280
2.64
2.29 2.93
3.36 2.43
2.79 3.57 2.50
2.07 2.43
2.93
In the production lead time measurement described above, it needs less than
one month for a PO going through all stages in the production process. It means that it
needs about two periods to complete a PO from cutting to final inspection. This
production time is less than the material lead time in table above. In fact, material lead
time is greater than 2 periods and sometimes it nearly reached 4 periods which are about
2 months or two third of the total lead time. The table also shows the high uncertainty in
material lead time which fluctuated from 2 to 4 periods. These variable lead times will be
displayed in term of mean and standard deviation in order to calculate safety stock for
44
this supply uncertainty.
Table 5.9: Mean and standard deviation for fabric lead time
Product codes
Mean
Standard deviation
ARWP2275
2.97
0.44
ARWP2280
2.72
0.45
With the mean and standard deviation as in table 5.9, control chart for both products are
draw. The upper control limit and lower control limit are 3� away the mean. No values in
the chart exceed the control limit but we again see the fluctuation of these values.
5
UCL
4
CL
3
2
LCL
1
0
Figure 5.9: Control chart for ARWP2275
45
5
UCL
4
3
CL
2
LCL
1
0
Figure 5.10: Control chart for ARWP2280
5.5.2. Demand Uncertainty
The demand for each season is forecasted by adidas system. The forecast
demand releases before the season start 4 months. After that, every two week, a new
forecast will be updated with more accuracy. Therefore, the forecast reports are available
for SS on May and for FW on October. This study analyzes the demand of SS 2014 so we
get forecast data through report released on 3rd May, 2013.
The two products of Reebok brand with the highest demand during this season
are Shapewear Action Tank (ARWP2275) and ShapeWear Action Bootcut Pant
(ARWP2280). The detail amounts of these two products are shown in the table below:
Table 5.10: Forecast demand of ARWP2275 and ARWP2280
15/9
30/9
15/10
31/10
15/11
30/11
15/12
31/12
15/1
31/1
15/2
28/2
ARWP2275
964
2497
347
1521
221
3367
450
2992
360
1498
391
1560
ARWP2280
824
2970
350
4468
307
3529
667
3798
265
941
367
3039
46
The total demands are 16168 pieces for the first model and 21525 for the
second product in 6 months of SS 2014. Since customers often place the purchase order
with the request date at the end of the month so in this forecast, demand is also high at the
end of each month and quite low for mid of the month.
During this season, the demand for two products is fluctuated a lot. The
variation of demand is evaluated through mean and standard deviation.
Table 5.11: Mean and standard deviation for demand
Products
Mean
Standard deviation
Coefficient of variance
ARWP2275
1347
1100
0.82
ARWP2280
1794
1616
0.9
5.5.3. Service Level
Although adidas hasn’t set safety stock for its factories, a target service level
always set in order to achieve highest customer satisfaction. It is a target Supplier
Delivery Performance. It is an important indicator for all tier 1 suppliers to achieve it. In
KPI report in 2011, tier 1 suppliers need to achieve a service level of 92 %. When setting
safety stock, this service level may not appropriate every product. According to Bhonsle,
Rossitti, & Robinson (2005), in order to optimize safety stock and minimize inventory
investment, service level used to calculate safety stock should be determined based on
three factors which are demand uncertainty, supply lead time uncertainty and criticality
of the product. Let look at two products we examine in this paper and consider each
factor of the product. The classifications for two products ARWP2275 and ARWP2280
are demonstrated in the table below:
47
Table 5.12: Risk priority numbers (RPN) calculation
Item
CV of
number
demand
ARWP2275
0.82
6
2.97
3
ARWP2280
0.9
6
2.72
3
DFS
Item lead
time
SRS
Item
ICS
RPN
Very high
9
162
Very High
9
162
Criticality
Since two products chosen for this investigation are similar in term of
demand, material lead time as well as their criticality, risk priority numbers (RPN) and
other scores are equal for two products. With RPN of 162, we can easily look for the
appropriate service level based on table 2.3 in literature review. The table scores 80% for
RPN range from 151 to 162. This number is much less than the pursued service level of
adidas so we can save more cost for inventory investment which balances the cost of
stockout and the cost of holding safety stock. We will discuss more in safety stock
analysis.
5.5.4. Safety stock determination
All components of safety stock have been analyzed in previous sections. In this part, the
level of safety stock will be determined for two proposed products. The results can be solved
easily by excel spreadsheet:
Table 5.13: Safety stock determination
Item
number
ARWP2275
Unit
dollars
4.579
Service level
of 92%
2801
$12867
Service level
Investment
of 80%
1688
$7729
ARWP2280
8.175
3927
$32103
2367
Investment
$19350
With the service level of 92%, safety stocks of ARWP2275 and ARWP2280
are 2801 units and 3927 units with the investment of $12867 and $32103 respectively.
For service level of 80%, however, safety stocks reduce significantly which are over
1000 units. With this service level, factories can reduces $5138 of safety stock
48
investment for the first item and over $12753 for the second one.
By adjusting service level, company can benefits from safety stock reduction.
However, if company sees the risk at service level of 80% or they are expected to reduce
the investment further, the next sections will give the answer by looking at other
elements/ variables in safety stock formula.
5.5.5. Experimental analysis
The safety stock level calculated in previous section is quite high so
experimental analysis is used to evaluate the contribution of each factor to safety stock
level. The scenario of the simulation is summarized in table 5.14. The assumption for the
range of average demand and lead time are based on the minimum and maximum value
of the forecasted demand and historical lead time. The range of demand standard
deviation is set with the distance of 65% away the calculated value while the range of
lead time standard deviation is from 0.1 to 0.9. The decision variable is service level
because it is under the control of company. Again, service level of 80% and 92% are used
in this simulation with discrete distribution. These service levels need to be converted
into Z values which are 0.85 and 1.41 respectively.
The simulation is run by crystal ball for 1000 trials and generates the
sensitivity charts for two items.
Table 5.14: Scenario for running simulation
Characteristics
Distribution Item number
Average demand
Uniform
Average lead time
Range
Unit
ARWP2275
221- 3367
Units
ARWP2280
265- 4468
Units
ARWP2275
2.43- 3.57
Periods
ARWP2280
2.29- 3.57
Periods
Uniform
49
Demand standard
deviation
Uniform
Lead time standard
deviation
Uniform
Service level
Discrete
ARWP2275
385-1815
---
ARWP2280
550-2150
---
ARWP2275
0.1-0.9
---
ARWP2280
0.1-0.9
---
Both products
0.85
1.41
---
The result of the simulation is shown in figure 5.11 and 5.12. In the sensitivity
charts, demand standard deviation plays an important role in determining the safety stock
level. It contributes over 55% to the total level of safety stock. The second factor should
takes into consideration is supply lead time uncertainty. It has high impact to the stock
holding in the inventory. The contribution of this variable is over four times higher than
that of average lead time. Although the length of material lead time is quite high, its
contribution to safety stock is not much, under 5%.
The average demand is not discussed here because company never wants to
reduce the demand for their products. On the other hand, company can affect all other
factors including demand variability, lead time variability and the average lead time in
order to reduce the level of safety inventory required in a way that does not adversely
affect product availability.
50
Figure 5.11: Sensitivity chart for ARWP2275
Figure 5.12: Sensitivity chart for ARWP2280
51
CHAPTER 6: DISCUSSION AND RECOMMENDATIONS
6.1. Conclusion
The research ―determining safety stock for an adidas apparel factory applying
probabilistic demand model‖ was conducted to discover the main reasons that lead to the
long lead time which affect company bottom line. After that, it determined an appropriate
level of safety stock for company.
With the four factors in the conceptual framework, the result of this research
has shown that material lead time has the highest impact to the total lead time. The time
spends for material replenishment take more than half of the total customer order lead
time. Sometimes, this value even reaches two third of the order lead time.
Material/supply lead time is quite long because the goal of adidas is to provide their
customers the highest quality. Therefore, majority of material is purchase from China and
Taiwan. The global procurement took more time for an order to be completed. Moreover,
Tier 2 suppliers sometimes take too much time to response to an order in case of material
production complexity.
On the other hand, production lead time was acceptable because it took less
than one month to complete a purchase order. Some other factors in production process
such as unnecessary movement or unstable schedule also increase the lead time from
cutting to final inspection.
Capacity planning also played a very important role in
reducing order delay. Proactive planning can utilize factory capacity in low demand
months and reduce it in higher demand month and limit over capacity happen that lead to
delay or push back the purchase orders.
Since material is considered as the big obstacle for adidas factories in
52
reducing purchase order lead time, safety stock is determined for two products with
highest demand. The role of safety stock is to cover the Tier 1 production demand during
tier 2 material replenishment lead time. Applying the method of Bhonsle, Rossitti, &
Robinson (2005), service level of 80% is determined for two products which reduce the
investment in safety stock.
6.2. Recommendation
6.2.1. Material lead time
Material lead time is considered as the most important factor that affecting the
total lead time of the factories. Therefore, safety stock is the recommendation for the
factories to separate the procurement lead time and customer order lead time.
Although safety stock is determined in this research, the investment for these
stocks is quite high. Therefore, company need to invest in reducing these stocks for their
products based on the experimental analysis result. The factor with the highest impact to
the company’s stock is demand standard deviation or demand fluctuation. For this reason,
planning team should look for a way to reduce this fluctuation. In this paper, pull forward
demand is suggested to reduce the fluctuation during the season. In customer forecast,
demand at the end of each month is always higher than demand at the middle of that
month. This reason created the high fluctuation demand. Thus, planning team can pull
forward demand at the end of each month to the middle of demand. For two products
above, we can pull forward as following:
Table 6.1: Actual Forecast demand of ARWP2275 and ARWP2280
15/9
30/9
15/10
31/10
15/11
30/11
15/12
31/12
15/1
31/1
15/2
28/2
ARWP2275
964
2497
347
1521
221
3367
450
2992
360
1498
391
1560
ARWP2280
824
2970
350
4468
307
3529
667
3798
265
941
367
3039
53
Table 6.2: New demand after pull forward
15/9
30/9
15/10
31/10
15/11
30/11
15/12
31/12
15/1
31/1
15/2
28/2
ARWP2275
1731
1731
934
934
1794
1794
1721
1721
929
929
976
976
ARWP2280
1897
1897
2409
2409
1918
1918
2233
2233
603
603
1703
1703
Table 6.2 balances demand of two periods in a month, so the fluctuation also
reduces significantly. In fact, item ARWP 2275 drops from 1100 to only 420 and item
ARWP2280 falls from 1616 to 607.
Table 6.3: new standard deviation
Products
Mean
Standard deviation
ARWP2275
1347
420
ARWP2280
1794
607
With new demand standard deviation, level of safety stock is recalculated and
improved considerably. For service level of 92%, the holding stock is just around the
average value of the demand while safety stock with service level of 80% is quite low
compared with the mean of demand during this season.
Table 6.4: Safety stock for new demand standard deviation
Item
number
Unit
dollars
Service level
of 92%
Investment
Service level
Investment
of 80%
ARWP2275
4.579
1319
$6040
795
$3640
ARWP2280
8.175
1813
$14821
1093
$8935
Another recommendation to reduce safety stock without affecting service
54
level is tier 1/ tier 2 alignments which can reduce supply uncertainty. It means that tier 1
supplier need to keep a rapport with tier 2 supplier so that tier 2 can understand the
situation of tier 1 and supply raw material at the lowest variation. The fluctuation of
material lead time also can reduce through transportation mode. Company need to choose
a reliable transporter to reduce the time fluctuation during transportation.
6.2.2. Production complexity
Although production complexity does not affect delivery lead time too much,
some suggestions for time reduction of production are necessary. In order to reduce
problems happened for these styles, development team need to work closely with
production team. Development team is the one who understand the design of the products
and also some problems can happened when produced these items. Thus, for complexity
styles, development team should announce the production team to get caution at which
stages and how to reduce problems. For example, for some styles like T2060234,
overcast fabric is needed before printing to prevent unraveling.
6.2.3. Process efficiency
In order to limit the idle time and reduce waiting time, factory should limit
unnecessary transportation in the production. One purchase order should be produced at
one production line to reduce transportation between lines. If the quantity of purchase
order is high, the schedule should allocate to two lines close to each other.
6.2.4. Capacity planning
Planning team should consider carefully before deciding to pull forward, transfer to other
factories or push back. Some time pull forward is good but it can cause low utilization
after.
6.2.5. Suggestion for further studies
The limitation of this research is the assumption of demand distribution which
55
is normal distribution. Therefore, researches on other demand distributions are
recommended. Moreover, this research calculated safety stock for only two products, so
it needs more researches to optimize safety stock for all items of adidas.
56
LIST OF REFERENCES
1. Bhonsle, A., Rossitti, M., & Robinson, D. (2005). Identifying and Setting Safety
Stocks Levels via Multiple Criteria. The Proceedings of the 2005 Industrial
Engineering Research Conference. Atlanta, GA: Institute of Electrical and
Electronics Engineers.
2. Chopra, S., & Meindl, P. (2009). Supply Chain Management (9th Edition ed.). New
Jersey: Pearson Prentice Hall.
3. Chopra, S., Reinhardt, G., & Dada, M. (2004). The Effect of Lead Time Uncertainty.
Decision Sciences, Volume 35 Number 1.
4. Cognizant. (2011, December). A New Framework for Safety stock. Retrieved March
2, 2013, from Cognizant.com: http://www.cognizant.com/InsightsWhitepapers/ANew-Framework-for-Safety-Stock-Management.pdf
5. Heizer, J., & Render, B. (2008). Operations Management (9th Edition ed.). Pearson
Education.
6. King, P. L. (2011, August). Crack the code, Understanding safety stock and
mastering its equations. Retrieved March 2, 2013, from The association for operation
management: http://media.apics.org/omnow/Crack%20the%20Code.pdf
7. Lee, J. J., & Duong, V. T. (2010). Analysis of the Cambodia's garment industry and
catch‐up strategy. Asian Journal of Technology Innovation , 97-123.
8. Rasiah, R. (2009). Garment manufacturing in Cambodia and Laos. Journal of the
Asia Pacific Economy , 150-161.
9. Shivsharan, C. T. (2012, May). Optimizing the Safety Stock Inventory Cost Under
Target Service Level Constraints. Amherst, MA, United States of America.
57
10. Waters, D. (2003). Logistics: An Introduction to Supply Chain Management. New
York: PALGRAVE MACMILLAN.
11. Yang, B., & Geunes, J. (2007). Inventory and lead time planning with lead-timesensitive demand. IIE Transactions , 439-452.
12. Rasiah, R., 2007a. State of technological capabilities in garment firms in the least
developed countries of Asia. Background paper prepared for UNCTAD, Geneva.
13. IDE, 2007. Survey of garment firms in Cambodia and Laos. Tokyo: Institute of
Developing Economies.
14. ADB, 2004. Cambodia’s garment industry: meeting the challenges of the post-quota
environment. Manila: Asian Development Bank.
15. Aczel−Sounderpandian (2008). Business Statistics (7th Edition ed.). The
McGraw−Hill Companies.
58
APPENDIX
Table A1: Checklist table
Working #
Fty
LT
Long material
transportation
LT
Long Material
Production
Lead time
Printing
bonding
Jacket
ARMKF13005
ESP
90
x
X
x
ARMKF13006
ESP
105
x
X
x
ARMKF13010
ESP
90
x
x
ARMKF13011
ESP
90
x
x
x
ARMKF13012
ESP
90
x
X
x
x
x
ARMKF13013
ESP
90
x
X
x
x
x
ARMKF13014
ESP
105
x
X
x
x
ARMKF13025
ESP
90
x
X
x
x
ARMKF13062
ESP
90
x
X
ARMKF13065
ESP
105
x
X
ARMKF13066
ESP
90
x
X
x
ARMKF13067
ESP
90
x
X
x
ARMP3038
ESP
90
x
X
x
ARMPF13020
ESP
90
x
X
x
ARMPF13021
ESP
90
x
X
x
ARMPF13022
ESP
90
x
X
x
ARMPF13023
ESP
90
x
X
x
ARMPF13024
ESP
90
x
X
x
ARMPF13026
ESP
90
x
X
ARMPF13063
ESP
90
x
X
x
ARMR3045
ESP
90
x
X
x
ARMRF13016
ESP
90
x
X
ARWKF13039
ESP
90
x
X
ARWKF13042
ESP
90
x
X
ARWKF13043
ESP
90
x
X
ARWKF13044
ESP
90
x
X
x
x
ARWKF13045
ESP
90
x
X
x
x
ARWKF13056
ESP
90
x
X
ARWKF13057
ESP
105
x
X
ARWKF13058
ESP
90
x
X
x
ARWKF13068
ESP
90
x
X
x
ARWKF13069
ESP
90
x
X
x
ARWKF13073
ESP
90
x
X
x
ARWKF13081
ESP
90
x
X
ARWPF13068
ESP
90
x
X
ARWPF13103
ESP
90
x
X
Seam
seal
Complicated
construction
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
59
ARWRF13017
ESP
90
x
X
ARWWF13044A
ESP
90
x
X
ARWWF13045A
ESP
90
x
X
F0121305
QVE
105
X
X
F1122502
QVE
105
X
X
F1302M107
QVE
120
X
X
F1302M108
QVE
120
X
X
F1302M109
QVE
120
X
X
F1302M203
QVE
120
X
X
F1302M204
QVE
120
X
X
F1302M205
QVE
120
X
X
F1302M308
QVE
120
X
X
F1302M602
QVE
120
X
X
F1302W103
QVE
120
X
X
F1302W104
QVE
120
X
X
F1302W105
QVE
120
X
X
F1302W203
QVE
120
X
X
F1302W204
QVE
120
X
X
F1302W205
QVE
120
X
X
F1302W409
QVE
120
X
X
F1302W602
QVE
120
X
X
F2022107
QVE
120
X
X
F1302W202
QVE
120
X
X
F1306GHIP014
QVE
105
X
F1315WJ402H
QVE
120
X
X
F1315WJ403H
QVE
120
X
X
F13PDMS03
QVE
105
x
X
F13PDMS05
QVE
105
x
X
F13PDWS02
QVE
105
x
X
F13PDWS03
QVE
105
x
X
F13PDWS04
QVE
105
x
X
F2150906H
QVE
105
X
F2150906HA
QVE
105
X
F2153513H
QVE
120
F3151806H
QVE
105
X
X
F3151806HA
QVE
120
X
X
F3153503H
QVE
120
X
X
F3153504H
QVE
120
X
X
F3153505H
QVE
120
X
X
F3153506H
QVE
120
X
X
F3153507H
QVE
120
X
X
x
X
60
F3153508H
QVE
120
X
AAMCF13011
QVT
120
X
AAMWF13001
QVT
120
X
AAMWF13002
QVT
120
X
AAMWF13006
QVT
120
X
AAWWF13008
QVT
120
X
AAWWF13009
QVT
120
X
ARMKF13070
QVT
120
X
ARMKF13079
QVT
120
X
ARMWF13004
QVT
120
ARMWF13005A
QVT
120
X
X
ARMWF13007
QVT
120
X
X
ARMWF13009
QVT
120
X
X
ARMWF13010
QVT
120
X
X
ARMWF13013
QVT
120
X
X
ARMWF13013A
QVT
120
X
X
ARMWF13026
QVT
120
X
X
ARMWF13027
QVT
120
X
X
ARMWF13028
QVT
120
X
X
ARMWF13032
QVT
120
X
X
ARMWF13033
QVT
120
X
X
ARMWF13036
QVT
120
X
X
ARMWF13037
QVT
120
X
X
ARMWF13041
QVT
120
X
X
ARWKF13072
QVT
120
X
X
ARWWF13046
QVT
120
X
X
ARWWF13050
QVT
120
X
X
ARWWF13051
QVT
120
X
X
ARWWF13051A
QVT
120
X
X
ARWWF13059
QVT
120
X
X
F1306CHAC006
DTVN
90
F1306CHAC009
DTVN
90
F1306CHCH009
DTVN
90
x
F1306CHCH013
DTVN
90
x
F1306CHSE002
DTVN
90
x
F1306CHSP001
DTVN
90
x
F1306CHSP003
DTVN
90
x
F1306GHIF005
DTVN
90
x
F1306GHIF006
DTVN
90
x
F1306GHIF008
DTVN
90
x
F1306GHIM001
DTVN
90
x
F1306GHIM002
DTVN
90
x
F1306GHIM003
DTVN
105
x
X
X
X
x
x
61
F1306GHIM005
DTVN
105
X
F1306GHIP001
DTVN
90
x
F1315MCB4
DTVN
105
x
F1315MCB7
DTVN
105
x
F1315MPR1
DTVN
90
x
F1315WCT130B
DTVN
90
x
F1315WCT201
DTVN
90
x
F1315WCT207
DTVN
105
x
F1315WCT208
DTVN
105
x
L2061230
DTVN
90
x
L2061230P
DTVN
90
x
L2061230PL
DTVN
90
x
L2061230PY
DTVN
90
x
L3000508
DTVN
90
x
L3000508P
DTVN
90
x
L3000508PL
DTVN
90
x
L3000508PLY
DTVN
90
x
L3000508W
DTVN
90
x
L3000508Y
DTVN
90
x
L3000510
DTVN
90
x
SUFB30001HSV04
DTVN
90
x
SUFB30004SKR09
DTVN
90
SUFB30006GDA01
DTVN
105
x
SUFB30006GDA02
DTVN
105
x
SUFB30006GDA03
SUFB30006GDA04
DTVN
DTVN
105
105
x
SUFB30006GDA05
SUFB30006GDA18
DTVN
DTVN
105
90
x
SUFB30008FCB10
SUFB30008FCB11
DTVN
DTVN
90
90
x
SUFB30008FCB12
SUFB30020LWA22
DTVN
DTVN
90
90
x
SUFB30020LWA23
SUFB30020LWA25
DTVN
DTVN
90
90
x
SUFB30024AY06
SUFB30025AST37
DTVN
DTVN
90
90
x
SUFB30025AST41
SUFB30026ZK13
DTVN
DTVN
90
90
x
SUFB30061FDK02
DTVN
90
x
SUFB30996WK16
DTVN
90
x
SUFB30999WPFC22
DTVN
90
x
SUFB30A1017MV23
DTVN
DTVN
90
105
x
DTVN
DTVN
90
105
x
SUFB30A1017MV24
SUFB30A998SFC22
SUFB30A998SFC32
x
x
x
x
x
x
x
x
x
x
62
Shapewear Action Tank
ShapeWear Action Bootcut Pant
Table A3: Customer forecast for season SS 2014
Product
code
Product name
Factory
code
Material
Number
15/9
30/9
15/10
31/10
15/11
30/11
15/12
31/12
15/1
31/1
15/2
28/2
ARWP2275
Shapewear
Action Tank
AGC508
12041075BB
964
2497
347
1521
221
3367
450
2992
360
1498
391
1560
ARWP2280
ShapeWear
Action Bootcut
Pant
AGC508
12041078BB
824
2970
350
4468
307
3529
667
3798
265
941
367
3039
Table A4: Historical material order lead time for ARWP2275- 2012
PO
number
Quantity
Tier 2
T1 Place
order
T2
Confirm
LT
Actual arrival
Actual
LT
105419084
2932
NEWWIDE
BRIMADON
GREAT
11/19/2011
1/3/2012
45
12/23/2011
34
105423381
1102
NEWWIDE
BRIMADON
GREAT
12/23/2011
2/6/2012
45
2/11/2012
50
105424697
3588
NEWWIDE
BRIMADON
GREAT
2/3/2012
3/19/2012
45
3/22/2012
48
105425387
2167
NEWWIDE
BRIMADON
GREAT
2/23/2012
4/8/2012
45
3/31/2012
37
105646968
2288
NEWWIDE
BRIMADON
GREAT
3/30/2012
5/14/2012
45
5/4/2012
35
105671670
2913
NEWWIDE
BRIMADON
GREAT
5/4/2012
6/17/2012
45
6/8/2012
35
105671698
2461
NEWWIDE
BRIMADON
GREAT
6/4/2012
7/19/2012
45
7/19/2012
45
105671778
1089
NEWWIDE
BRIMADON
GREAT
6/27/2012
8/11/2012
45
8/10/2012
44
105673177
3817
NEWWIDE
BRIMADON
GREAT
7/26/2012
9/9/2012
45
9/14/2012
50
105858566
1906
NEWWIDE
BRIMADON
GREAT
9/18/2012
11/2/2012
45
10/24/2012
36
106180411
3336
NEWWIDE
BRIMADON
GREAT
10/3/2012
11/17/2012
45
11/12/2012
40
106182158
2200
NEWWIDE
BRIMADON
GREAT
11/4/2012
12/19/2012
45
12/19/2012
45
Table A5: Historical material order lead time for ARWP2280 - 2012
PO
Quantity
T2
T1 Place
order
T2
Confirm
LT
Actual arrival
Actual
LT
105349519
4667
BEMIS-F
SUE'S-F
ECLAT-F
12/23/2011
1/27/2012
35
1/29/2012
37
105349566
3620
BEMIS-F
SUE'S-F
ECLAT-F
2/3/2012
3/9/2012
35
3/2/2012
28
105396330
3721
BEMIS-F
SUE'S-F
ECLAT-F
3/2/2012
4/5/2012
35
4/12/2012
41
105431318
3960
BEMIS-F
SUE'S-F
ECLAT-F
4/23/2012
5/28/2012
35
6/9/2012
47
105670274
3985
BEMIS-F
SUE'S-F
ECLAT-F
5/4/2012
35
6/7/2012
34
105670285
2217
BEMIS-F
SUE'S-F
ECLAT-F
6/24/2012
7/29/2012
35
8/2/2012
39
105716468
3589
BEMIS-F
SUE'S-F
ECLAT-F
8/2/2012
9/6/2012
35
9/21/2012
50
105717082
1288
BEMIS-F
SUE'S-F
ECLAT-F
9/3/2012
10/8/2012
35
10/8/2012
35
105759109
3538
BEMIS-F
SUE'S-F
ECLAT-F
9/17/2012
10/22/2012
35
10/16/2012
29
106115118
3519
BEMIS-F
SUE'S-F
ECLAT-F
10/18/2012
11/22/2012
35
11/21/2012
34
106176253
3185
BEMIS-F
SUE'S-F
ECLAT-F
11/11/2012
12/16/2012
35
12/22/2012
41
6/8/2012
65
Table A6: Interview list
NAME
POSITION
QUESTION AND ANSWER
Tung Tran
Planning
manager
Q: What is PO lead time?
A: PO lead time is measured from purchase order receipt
until delivery
Q: How many kinds of lead time does adidas offer?
A: The majority of adidas products are 90 days. For
complexity products like jackets and complicated styles,
lead time is often 105 days or 120 days. Adidas sourcing
limited are trying to reduce to 60 days and further.
Q: Which factors will adidas consider to reduce lead time
further?
A: We are looking at two groups of reasons lead to long
lead time which are material and non- material. Material
reasons include time to produce fabric and trim called
material production lead time and material transportation
lead time. For non material reasons, we mainly look at the
production complexity like printing, bonding, seam seal,
jacket or complicated construction.
Q: Do you have any plan to reduce lead time?
A: We are going to reduce lead time to 60days for products
without complexity by align with suppliers to ensure the
supply delivery. For complexity styles, it’s hard to reduce
to 60 days.
Q: How about reduce time in production?
A: Adidas always try to eliminate waste in production and
reduce WIP products.
Thanks for your time
Oanh
Nguyen
Merchandise
senior
Q: Why is adidas lead time too long, over 3 months?
A: Because majority of material is imported from other
countries like china
66
Q: How long does it take?
A: At least over two week
Q: Can you estimate how many percent of material is
imported?
A: Yes, it’s about 80%
Q: Why does adidas import these materials instead of use
local supply?
A: Because local supply cannot meet the requirement of
adidas. Adidas always look at the highest quality for the
products.
Q: Can you list some local supplier?
A: Some of them are Promax, Formosa or YKK.
Q: Is it good for lead time to reduce as much as possible?
Why?
A: Yes, because short lead time will improve customer
satisfaction. For example, in order to have products on
hand for Christmas event, customers have to forecast the
demand and place the order at least three months before
Christmas. Obviously, the forecast like this often yield a
big error and thus customer always seek for a shorter lead
time.
Thank you for your answer
Hong Phan
Development
team
Filling the checklist
Nga Diep
Development
team
Filling the checklist
Employee 1
Q: Why does the schedule conflict, sewing day is earlier
than cutting day?
67
A: It’s because of the production manager. He changes the
schedule for cutting but forget to change the date for
sewing
Q: How can you deal with that?
A: Go to ask him to change
Q: Does it happen often?
A: It happens every day.
Thank you a lot
Employee 2
Q: Why does the schedule conflict, sewing day is earlier
than cutting day?
A: The production schedule change very often, so
production manager also has mistake in prepare the
schedule
Q: How can you deal with that?
A: Update the schedule frequently and when the conflict
happen go to production team to report
Q: Does it happen often?
A: Yes, It happens for many purchase order.
Thank you for your answer
68
Figure A1: Material unit price for ARWP2275
Figure A2: Material unit price for ARWP2280
69
Figure A3: Order confirmation- Process overview
70