Madura coats : Supply chain
optimisation through order frequency
Presented by –
Saurabh Srivastava (16)
Coats India is the threads division of Madura Coats
It’s an Indian subsidiary of Coats Ltd.
It operates out 65 nations.
World largest manufacturer of Industrial sewing
threads & consumer needlecraft products.
Coats India manufactures & markets a complete
range of cotton, synthetic & corespun threads for the
Indian & export markets.
The threads division is the largest business of
Madura Coats, contributing to 91% of the turnover.
Obstacle & recovery
The enhanced quantities of stocks that used
to the fast moving once are dormant now.
Previously, Coats India had a SKU range of
over 30,000 covering regular colors & customer
Additionally, 10 new colors were added
everyday to this range.
Madura Coats decided to take up the SCM
project, which it called “supply Chain Optimization
through Order Frequency Analysis”.
The Order Frequency Analysis (OFA) project
resulted in the following benefits to the
organization within a year of complete
Reduction of 14% in finished goods.
Reduction of 15% in dormant stocks.
Increase from 51% to 77% in the percentage of
orders completed in 48 hours.
•Business span in four segments:-
• a) Consumer Threads.
• b) Industrial Threads.
• c) Exports.
• d) Other accessories.
•Direct and indirect customers.
•Different types of customers.
SCM- THE NEED
Quality of the color.
Large number of global buyers.
Change in fashion trends.
Reduction in volume of garment production.
Demand for thread.
High cost of production.
Judgmental and inaccurate process.
THE PROJECT ARCHITECTURE
1- SALES DATA FROM EACH LOCATION WAS
COMPILED, VALIDATED, AND CLEANSED, TO ISOLATE THE
PARAMETERS OF VOLUME, NUMBERO OF
CUSTOMERS, AND TIME OF THE ORDER.
2- THE DATA WAS FILTERED USING USING THE
ACCOUNTS CODE TO ACCURATELY CAPTURE THE
3- REGIONS ARE DEFINED ON THE BASIS OF STOCKING
LOCATIONS IN THAT GEOGRAPHICAL AREA.
4 - Simulation exercise was done for each stocking
locationson the basis of three different criteria –
iii. Customer spread.
Determination of stock range is done on the basis
of these three criteria for each location.
5 – Items which met 2 out of the 3 criteria are
studied separately to see if they could be
included in the stock range. Items sold to dealers
& low risk are identified separately.
6 – A set of inclusions & deletion list is generated to
determine the added & deleted stock from the
earlier stock range.
7 – A stock report was prepared for each location in
the following categories –
i. The regional stock range.
ii. The dormant.
iii. Slow moving stock range.
iv. Customer specials.
An action plan is drawn for each category.
8 – The sales data is used to predict future sales
for the SKU and based on this, a replenishment
quantity (ROQ) and a replenishment point
(ROP) were set up for the stock replenishment
For efficiency a codification is used to codify the
stock range at depot level & regional level.
Sales based replenishment system was set into
ROP/ROQ model was developed.
Past sales data was used to establish the
estimated level of sale for each SKU.
Current actual stock was used with ROP/ROQ
model to trigger replenishment.
OFA run was implemented from both the ends of
the supply chain- the manufacturing end and the
‘buy in from the marketing team’.
Contribution of manufacturing end &
buy in from the marketing team.
1. Steps are taken to ensure that the
production would match the ordered
quantity to the extent possible.
2. Developing a solution to the identified
dormant stocks by reprocessing them into
other saleable products.
3. Stock ranges were implemented with the
consultation of the front end sales personnel
so that there was a full buy in to the
THE SUPPLY CHAIN PROJECT
Basic tenet is to optimize stocks.
Keep production cost low.
Deliver best customer service.
Objective of ORDER FREQUENCY ANALYSIS –
Improve availability & service.
Reduce inventory & associated costs.
To reduce risk of dormancy.
To manufacture cost effectively.
To expand capacity at the right place in the right
KEY POINTS IN OFA
Using past order data to determine SKUs.
Usage of simplistic volume criteria.
Frequency of sale and consumer spread as risk
criteria to determine stock range.
Stock holding levels were based on frequency of
Categorization of items into stock & MTO.
ABC classification to define stock level.
Replenishment is triggered by the sales & was
tracked on a daily basis.
CAPACITY RESOURCE ALLOCATION
Matched existing capacity with order
Suggested the key areas where capacity
addition was required.
Gives scientific basis to a high value
Guides the investment decisions
BENEFITS OF SCM PROJECT
FINISHED GOODS STOCK –
oReduced 27% from April 2002 to December 2002.
o14 % reduction in 2003.
o6% reduction in 2004.
DORMANT STOCK –
oReduced 62% from April 2002 to December 2002.
o51% reduction in 2003.
o8% reduction in 2004.
More accurate capacity addition.
Yielded better services.
oIncreased by 50% from April 2002 to mid 2004.
oOrders completing in 48 hours increased from
51% to 77%.
98% orders were completed within 6 days.
After OFA project –
1) finished goods stock reduced 14%.
2) Dormant stock reduced 51%.
3) Percentage of orders completed in 48 hours
increased from 51% to 77%
IMPACT ON THE ORGANISATION
Growing trend of factory gate invoicing to
Factory warehouses were established to stock
and invoice finished goods from there.
Reducing stocks at depots considerably.
Increased stock range.
Improved manufacturing efficiency to a great
1- SALES DATA FROM EACH LOCATION WAS COMPILED, VALIDATED, AND CLEANSED, TO ISOLATE THE PARAMETERS OF VOLUME, NUMBERO OF CUSTOMERS, AND TIME OF THE ORDER. 2- THE DATA WAS FILTERED USING USING THE ACCOUNTS CODE TO ACCURATELY CAPTURE THE CUSTOMER SPREAD.3- REGIONS ARE DEFINED ON THE BASIS OF STOCKING LOCATIONS IN THAT GEOGRAPHICAL AREA.