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A Project Report on
“Product Classification for Finished Goods and Raw Materials”
Undertaken At
Croda India Company Pvt. Ltd.
Mumbai
In Partial Fulfillment of Summer Internship of
Post Graduate Diploma in Industrial Engineering (PGDIE)
By
Sumedh Shirgaonkar
(Roll No. 92)
PGDIE-43
Under the Guidance of
Industry Guide
Ms. Mallika Nair
Manager-Customer Service
Croda India
National Institute of Industrial Engineering,
Mumbai -400087
Faculty Guide
Prof. Sachin Kamble
Asst. ProfessorNITIE, Mumbai
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Certificate of Project Completion
This is to certify that Mr. Sumedh Shirgaonkar, a student of the Post Graduate Diploma
in Industrial Engineering (PGDIE), 43rd Batch of the National Institute of Industrial
Engineering (NITIE), Mumbai has successfully completed the summer project in Supply
Chain Management titled,
“Product Classification for Finished Goods and Raw Materials”
Under my guidance. Based on the professional work done by him, this report is being
submitted for the partial fulfillment of Post Graduate Diploma in Industrial Engineering
(PGDIE), at NITIE, Mumbai.
Faculty Guide,
Prof. Sachin Kamble
Asst. Professor
NITIE, Mumbai
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Certificate of Project Completion
This is to certify that Mr. Sumedh Shirgaonkar a student of Post Graduate Diploma in
Industrial Engineering, 43rd Batch of National Institute of Industrial Engineering
(NITIE), Mumbai has completed the summer project titled,
“Product Classification for Finished Goods and Raw Materials”
At Croda India, Navi Mumbai under my guidance from 1st April, 2014 to 31st May 2014.
His hard work is deeply appreciated. I wish him all the best in future life.
Industry Guide
Ms. Mallika Nair
Manager-Customer Service
Croda India
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Acknowledgement
I take this opportunity to extend my sincere thanks to Croda India for offering a
unique platform to earn exposure and garner knowledge in the field of Materials
Management aspect of supply chain.
I wish to extend my sincere and heartfelt gratitude to my guide Ms. Mallika
Nair (Manager-Customer Service) who guided, supported and encouraged me during
the entire tenure of the project. I also thank Mr. Jaideo Upasani (Manager-Supply
Chain) and Mr. Chetan Verma (Manager-Procurement) at Croda India Company Pvt.
Ltd, for their co-operation and valuable advice throughout the course of my project.
Their constant support helped me in accomplishing the objectives of the project. I am
able to say with conviction that I have immensely benefited from auspicious and
prestigious association as a summer intern with Croda India.
I also thank Prof. Sachin Kamble my faculty guide, who inspired me and showed
me the right course to pursue.
I would like to express my sincere gratitude to each and every employee of the
organization who has contributed for the successful completion of the project.
Sumedh Shirgaonkar
PGDIE-43,NITIE, Mumbai
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Executive Summary
Croda is a global leader in specialty chemicals, sold to a wide range of markets- from
Personal Care to Health Care; from Crop Care to Coatings and Polymers. Croda is a truly
international company with approximately 3400 employees working at 43 sites
in 34 countries. Croda’s products form vital ingredients in many ‘household name’
products and every day, every one of us does use a Croda product in some shape or form.
Croda uses a variety of technologies to manufacture a uniquely broad portfolio of
oleochemicals and specialty products. These products provide enhanced functionality
when used as ingredients, additives, or processing aids within a wide cross section of
industries.
Croda India is a demand driven organization, this increases the probability of uncertainty
in demand. The project is focused at reducing these uncertainties by classification of
products and recommending the inventory norms for finished goods as well as for the raw
materials.
The project is carried into three phases:
1) Product Matrix for Finished Goods
2) Product Matrix for Raw Materials
3) Recommendations for Inventory Norms
For finished goods product matrix the products are classified into four quadrants viz,
MTO, MTS, Predictable, Forecastable on a graph of “Gross Margin on Y-axis and
Forecast Accuracy on X-axis” also various other factors were taken into consideration
for the classification such as Regularity, Sales demand, S lob potential, Deviation in the
sales, etc.
For Raw materials Matrix the raw materials are classified into three types viz, MTO,
MTS, Forward Planning. The Graph plotted for raw materials is for Buying Demand on
Y-axis and Business Importance on X-axis. The X-axis is linked with the finished goods
to achieve high reliability on stock norms for planning of materials.
The End part of the project is to recommend Inventory norms, based on the Product
matrix for finished goods and raw materials. The Inventory norms are generally
developed for each quadrant of the matrix, but also recommending for some crucial
materials separately
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INDEX
I About the Company 6
II Objective of the
Project
11
III Methodology 12
IV Literature Survey 13
V Modeling & Analysis 19
VI Scope &Limitations 34
VII Academic
Contributions
35
VIII Bibliography 36
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I
About the Company
1.1 Company History
Croda was formed in Yorkshire, England in 1925 to make lanolin. Initially, trading was
difficult, but a report from the National Physical Laboratory showed that lanolin was
effective rust preventive. This opened up new markets, particularly during the war years
which led to collaboration with the government to produce specialties such as camouflage
creams, insect repellent and gun cleaning oils. Post war, many of these marketsdisappeared so the company had to diversify into new areas.
During the 1990s, the company focused increasingly on its important specialty chemicals
business. In 2006, Croda acquired Uniqema from ICI. Following a successful integration
programme, Croda is firmly established as a global leader in natural based specialty
chemicals and well placed to meet the challenges of the twenty first century.
1.2 Presence:
Croda is a truly international company with approximately 3400 employees working at 43
sites, 34 countries.
Croda’s corporate headquarters are at Cowick Hall in East Yorkshire, England. Croda has
technical centers and manufacturing plants throughout the UK, France, Germany, the
Netherlands, Italy, Spain, USA, Brazil, India, Australia, Singapore, South Korea, Indonesia
and Japan.
1.3 Products
Croda uses a variety of technologies to manufacture a uniquely broad portfolio of
oleochemical and specialty products. These products provide enhanced functionality when
used as ingredients, additives, or processing aids within a wide cross section of industries,
including many of the following:
1.3.1Consumer Care
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Croda Personal Care is one of the world’s leading global suppliers of specialty raw
materials for the personal care industry, working with their customers to meet
consumer needs.
Croda Health Care is a world leading supplier of high purity ingredients suitable
for use across the pharmaceutical, dermatological, animal health, nutraceutical and
functional food markets. Ingredients range from Super Refined™ excipients to
ultra-pure medical grade lanolins and omega 3 lipid concentrates.
Croda Crop Care offers formulation aids and adjuvants under respected brand
names such as Atlox™, Crovol™ and Atplus™.
1.3.2 Performance Technologies
Croda Lubricant Additives offers a unique global range of specialty products
designed to deliver superior performance to formulators in the automotive and
industrial lubricant markets.
Croda Coatings and Polymers offers environmental solutions to the resin
manufacturers, formulators and additive producers through its range of natural,
high performance oleochemicals and specialty surfactants.
Croda Geo Technologies encompasses the business sectors of oilfield, mining and
water treatment
Croda Polymer Additives is a world leader providing effects to a wide range of
polymers used in today's plastics & packaging market
Croda Home Care is a world leader providing natural specialty ingredients for
home care and tissue, car care, and industrial and institutional (I&I) applications.
1.3.3 Industrial Chemicals
Croda’s Industrial Chemicals business serves a variety of important industrial
markets with ingredients, additives, and processing aids. They are applied into:
Emulsion technology
Technical and industrial fiber chemicals
Advanced materials
Ceramic ink-jet ink additives
Bitumen additives
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Leather auxiliaries
Paper chemicals
Candles and waxes
1.4 Research and Technology
R&D has played a key role in the Croda success story since the company pioneered the use
of lanolin based rust preventives in the 1930s. Innovation has always been at the heart of
this research, whether creating new products for existing markets, or new markets for
existing products.
Since the 1980s, a key area of research has been lipid technology, in particular the dietary
management of certain diseases using lipids. For example, a recent trial focusing on women
demonstrated the cardioprotective benefits of Croda’s high purity combined Omega
3/Omega 6 concentrates. Such research has stimulated strong global demand for our
marine and plant lipid concentrates.
A global Enterprise Technology function has been created specifically to develop and
acquire new technologies which are consistent with sustainable development. This has
resulted in a number of academic and commercial partnerships with recognized experts in
‘green chemistry’ and biotechnology.
Other areas of research include new natural based specialties for skin and hair care, sun
care, and for many industrial applications such as crop care, home care, lubricants,
polymers and coatings.
1.5 Manufacturing
Croda employs a wide range of technologies to transform basic natural oils and fats into
specialty chemicals for a diverse range of markets.
These processes are carried out using the most technologically advanced facilities available
anywhere in the world. Croda’s production capabilities have been strengthened by the
acquisition of Uniqema in 2006. Croda now has manufacturing facilities throughout the UK
and mainland Europe, North and South America, India, Singapore, South Korea, Indonesia
and Japan.
In manufacturing as with all aspects of our business, there is a constant focus on
sustainability, process safety and efficiency. Areas under constant review include
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minimizing health and safety risks, whilst developing simpler and low energy routes to
existing and new chemical processes.
Sites are regularly audited to ensure compliance with our rigorous SHE (Safety, Health and
Environment) policies. All Croda manufacturing sites have attained the international
quality standard ISO 19001. Some, particularly those associated with the personal care and
health care/pharmaceutical industries, operate to the principles of GMP (Good
Manufacturing Practice).
All Croda manufacturing sites have the objective of certification to BS EN ISO 14001
(environmental management) and BS OHSAS 18001 (occupational health and safety
management) standards by 2010.
1.6 Safety, Health and the Environment (SHE)
The management of SHE has a high priority throughout the Croda group. At all times, the
company operates its business in a manner which actively seeks to prevent or minimize the
possibility of its operations causing harm to people, animals or plants.
All SHE activities are co-ordinated by the Group SHE department whose role includes
setting company safety standards, offering advice on how to achieve these standards,
auditing Croda activities to ensure standards are met, collating and publishing keyperformance indicators to see how we are doing, and sharing best ideas in Croda with all
sites.
The management of each site is responsible for its own SHE performance, working closely
with the Group SHE department in respect of the above.
Each year, group objectives and targets are set, and the group SHE performance statistics
are reported in detail, together with news articles on SHE issues.
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II
Need for the project
The consumer today have myriad of choices which makes it essential for the manufacturers
to come up with more and more innovative ways to remain competitive. To streamline the
supply chain is also crucial for sustainability of an organization in such a competitive
environment.
To streamline the supply chain it is important for any organization to have solid inventory
norms at various echelons of the chain. So the project directly addresses the level of
inventory at various levels. The inventory levels should be optimized to reduce the on hand
inventory while at the same time the customer service level should be maintained which is
highly critical for the organization.
The chemical industry is characterized by high degree of variations due to number of factors
like seasonality, trends, competitor moves etc. Here there is a paradoxical decision to
establish tradeoff between the supply chain responsiveness, the customer service level, the
on hand inventory and write-offs.
Croda India company Pvt. Ltd is operating in India since 2006. The company being in
growth phase is trying to set inventory norms for its 1083 SKUs. Inventory Turns and
Product Availability are two big issues in Croda’s supply chain. Achieving the target
customer service level and making the product available at right place, at right time and in
right quantity is challenge that company is facing.
Also, the stock out opportunity cost is extremely high. So it is very essential to maintain a
high service level which can be achieved by having optimum inventory in hand which is
possible only by having correct product classification and concrete inventory norms
1051.9
82.7
382.8
586.4
1077
96.7
387.1
593.2
0
200
400600
800
1000
1200
operations industrial chemicals performance tech consumer care
V a
l u e
Segment
Growth
2012 2013
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III
Objectives of the project
3.1 Objectives: To classify the finished goods and raw materials into product matrix.
To recommend Inventory norms for raw materials and for finished goods
To smoothen the Planning process of the organization
3.2 Deliverables:
Increase in the customer service level
Minimization in inventory investment
Reduction in product stock outs
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IV
Methodology
The steps followed to work on the project are shown in the chart below.
Understanding the Project Objectives , Scope andDeliverables
AS-IS Analysis of the Existing Systems used in thecompany
Data Collection and Analysis for Finished Goods
Identifying the Matrix Factors for Finished Goods
Product Matrix for Finished Goods
Data Collection and Analysis for Raw Materials
Identifying Matrix Factors For Raw Materials
Product Matrix for Raw Materials
Linkage of Raw Materials Matrix to Finished GoodsMatrix
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V
Literature Survey
5.1 Inventory Positions in the Supply Chain:
5.2 Approaches used for classification of the products:
5.2.1 ABC Analysis (Always Better Control):-
Classify the items on the basis of importance and the technique of grouping is called as ABC
analysis. To provide maximum overall protection against the stock outs for a given
investment in safety stock. This analysis prepared and checked weekly or monthly.
5.2.2 VED ANALYSIS (Vital, Essential, Desirable):
This classification is applicable only for spare parts. It based on the price, availability etc.
For V items, a reasonable large volume of stocks might be necessary, while for items, no
Stocks are, perhaps, required be kept. For V items of A classification a close control should
be kept on stock levels, but if it is a C items, than large quantities mat be stored. Essential
(E) A spare part will be considered essential if, due to its non-availability, moderate loss is
Raw
Materials
Work in
Process
Finished
Goods
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incurred.
5.2.3 FSN (Fast, Slow, Nonmoving):
FSN Classification of materials can be done on average stay in the inventory, consumption
rate.
5.2.4 HML ANALYSIS (High, Medium, Low):
Only the difference from the former is being that it is the unit value and not the annual
consumption value.
H Unit value > 1000 (Sanctioned by higher officials)
M Unit value 100 to 1000
L Unit value < 100
5.3 Components of Inventory:
The four basis components of inventory can be identified as being:-
Replenishment Stock:
This is the stock resulting from the ordering policies and is determined
by the frequency of ordering and the quantity ordered.
Safety or buffer stock :
This is the stock held for protection against the uncertainty of demand and,
where applicable also of supply.
Anticipation or investment stock This is the stock procured in advance of requirement.
E.g. Schedules; planned requirements such as, product launches, promotions;
seasonal demands; purchases to take advantage of market exploitation.
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Movement or transit stock:
This is stock which is in transit between suppliers and customers and
can be separately identified.
5.4 Classification of demand:
Random/predictive demand:
This initial item classification is usually carried out by firstly identifying any item
which has a predictive demand. By process of elimination the remainder of the item
range will be classified as having a random demand. It is necessary to set precise
rules for the identification of predictive demand items.
In this context predictive relates only to those items which have a quantity and time
commitment for when they will be required.
These would include:-
V items which are called off on a schedule basis by a customer with no
deviation in quantity or time against the original forecast.
V items provided for a sales campaign or promotion which will cease on the sale of
the initial supply and not generate any further demand.
V items provided in advance of a new product launch, sales campaign or
promotion, but note that these items may later become random demand items.
It can be seen that these do not include any items with any uncertainty in the
demand - this is the criterion. Any item, therefore, where there is uncertainty in the
demand over time, will be classified as random. Having completed the initial review
and classification the remaining sections apply largely to random demand items.
Stable, trend, seasonal demand:
A key element in the determination of the system to be employed for any item or
group of items is the expected general demand pattern.
These falls into three major groups:-
Stable demand:
A stable demand pattern is one where although the demand rate varies, it varies
about a constant average over time. As such, it will provide no evidence of an
increasing or decreasing trend.
Trend demand:
A trend demand pattern is one where the average demand rate varies over time
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showing the tendency to increase or decrease.
Seasonal demand:
A seasonal demand pattern is one which shows a variation in the average demand,
at different points in time throughout the planning cycle, and can generally be
related to market forces which influence the demand patterns.
5.5 Forecasting:
As part of the inventory system the ability to forecast future demand patterns is an
essential feature.
To be able to predict these changes, before they occur, in order to be able to
adjust the control parameters within the system, is the purpose of forecasting.
There are a wide variety of methods for short and long term forecasting of demand
varying from guesses or estimates, through simple, to extremely sophisticated
mathematical techniques.
In practice in inventory systems it is usual to make some quantified forecast and
modify or overlay this with known additional information, where appropriate.
Short term forecasting:
All short term forecasting systems are designed to establish, firstly, an estimate of
the demand in the current period i.e. an estimate for the latest period in which
the actual demand is known.
The second stage is then to use this estimate as a basis for predicting future
demands.
Simple average:
This method of predicting future demands is attractive due to the simplicity of
calculation.
E.g. 58 66 56 58 60 62
Avg. demand =(58+56+58+60+62)/6
=360/6
=60
Moving average
This method is also used where the demand pattern indicates a trend and
from the demand data a smoothing of the pattern of demand will help to establish
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both the trend and future demand.
e . g . of a simple equally weighted running mean for a n-day sample of closing price
is the mean of the previous n days' closing prices. If those prices
are then the formula is
5.6 Order point calculation
The components in the calculation to establish the point at which an order should be
placed are covered by the following:-
Lead time
The lead time is defined as the interval between deciding that an order needs to beplaced and the order being physically available for issue.
This should not be confused with supplier delivery time, which will cover a shorter
period, but does not include the administrative processes prior to and following the
delivery time as well as the physical activity of receiving and storing the stock. In
most inventory systems the lead time is set to a fixed time period.
Lead time variability
Although most inventory systems tend to use a fixed lead time the measurement oflead time variability can be included in the calculation.
As with demand variability it can be assumed that the spread and variability of
lead time will follow a normal distribution pattern. It is therefore possible
to calculate the standard and/or the mean absolute deviations to arrive at
a more 'correct' assessment of lead time.
Example
Lead time Std. deviation
= 21 days =5 days
95% lead time service level = 1.64 standard deviations
Therefore lead time = 21 + (5 x 1.64) = 21 + 8.2 = 29.2 days.
5.7 Economic order quantity
The purpose of calculating an economic order quantity is to balance the costs of
ordering and the costs of holding stock, such that the two costs are equal or that the sum
of the two costs is the minimum total cost .
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5.8 Reasons for Inventories
Improve customer service
Economies of purchasing
Economies of production
Transportation savings
Hedge against future
Unplanned shocks (labor strikes, natural disasters, surges in demand, etc.)
5.9 Functional Roles of Inventory
Transit
Buffer
Seasonal
Decoupling
Speculative
Lot Sizing or Cycle
Mistakes
5.10 Costs Associated with Inventory:
Procurement costs
Order processing
Shipping
Handling
Purchasing cost
Mfg. Cost
Carrying costs
Capital (opportunity) costs
Inventory risk costs
Space costs
Inventory service costs
Out-of-stock costs
Lost sales cost
Back-order cost
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VI
Modeling and Analysis
6.1 Existing System:
6.1.1 Inventory Norms for finished goods:
Initial study of the project included study of the current inventory management system
incorporated in Croda.
The finished goods were classified into three categories viz, A, B and N. Various Factors
were considered for this classification and by rating these factors the classification was
carried out. The classification factors are,
Raw material availability
Slob Potential of FG
Customer Importance
Scheduling Pattern
Seasonality
Stock Norms for the categories:
Category A: 80 percentile of the previous year’s total sale was calculated
1/3rd of the 80 percentile is produced twice a month
2/3rd of the 80 percentile is produced once a month
Category N: These product were considered as MTO, so there is no inventory for these
materials. Whenever the order is placed planning for these materials is taken
into consideration.
6.1.2 Inventory Norms for Raw Materials:
For Raw materials previous year’s consumption data is considered to categorize the
materials into MTO and MTS.
Stock Norms for the categories:
Category MTS: For these materials one month inventory is stocked depending on the size
of the storage container and infrastructure available.
e g RMT0001 RMT0011 RMT0013 RMT0015
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Category MTO: There is no inventory for these materials; whenever the order is placed
planning for these materials is taken into consideration.
e.g. RMT0026, RMT0027, RMT0029, RMT0030
6.2 Reasons for Revision of Stock Norms:
Main aspect of business i.e. Business Importance of the material is not taken into
consideration
The devised stock norms were developed few years back, hence to sustain in today’s
dynamic market
Increase in the volume of business, hence proper planning is necessary
Regularity of the product is not taken into consideration both for FG and RM
Lead time is not calculated to devise norms for RM
Forecasting Data is missing while devising the norms
6.3 Data Collection and Data Analysis of Finished Goods:
Data Analysis was carried out for all the SKU’s of the organization i.e. 1083 FG. To prepare
a product matrix various aspects of product were taken into account.
Data used:
Sales Data for last three 3 years
Customer Data for last 3 years
Forecast Data for last 3 years
Gross margin of FG (Qualitative term for maintaining secrecy)
Slob Data
Sales Data: (Sample)
Material PlantInvoiceDate Year Month
ShortCode Demand
AET0284/0200/TS08 IN01 3/15/2011 2011 3 AET0284 5600
AET0284/0210/TP49 IN01 12/17/2013 2013 12 AET0284 10500
AET0284/0210/TP49 IN01 6/12/2012 2012 6 AET0284 9870
AET0284/0210/TP49 IN01 2/14/2011 2011 2 AET0284 9240
AET0284/0210/TP49 IN01 8/31/2011 2011 8 AET0284 8820
AET0284/0210/TP49 IN01 5/31/2012 2012 5 AET0284 7350
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AET0284/0210/TP49 IN01 4/9/2013 2013 4 AET0284 7350
AET0284/0210/TP49 IN01 4/29/2013 2013 4 AET0284 7350
AET0284/0210/TP49 IN01 4/30/2013 2013 4 AET0284 7350
AET0284/0210/TP49 IN01 5/30/2013 2013 5 AET0284 6930
AET0284/0210/TP49 IN01 6/24/2011 2011 6 AET0284 6090
AET0284/0210/TP49 IN01 7/25/2011 2011 7 AET0284 5880
AET0284/0210/TP49 IN01 9/30/2013 2013 9 AET0284 5670
AET0284/0210/TP49 IN01 2/11/2013 2013 2 AET0284 5040
Customer Data: (Sample):
Ship-to Code Material Billed Quantity
111421 ETT1822/0180/TS08 1440
2340
121451 ETT0129/0050/TP18 200
250
121451 ETT0369/0025/TB76 50
121474 EM83354/0020/8C02 760
121474 ETT2059/0045/TP05 225
450
675
990
121474 ETT2075/0050/TP18 150
6.4 Data Analysis and modeling for Finished Goods:
Data Obtained from the company sources was analyzed on different fronts to design the
product matrix.
The factors to be considered for matrix:
Plant
Category Source
Material Code
R l it
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No. of customers in 2013
Sales data for 2013
No. of customers in 2011-13
Avg. Sales for 2011-13
Forecast accuracy
Forecast (Qualitative term)
Gross Margin (Qualitative term)
Slob potential 2012
Slob potential 2013
Sales Business Unit
6.4.1 Plant: To get an idea about the production of each plant, so that inventory at each
warehouse can be simplified products were arranged according to the plant code
e.g. IN01- Mfg. at Thane Plant
IN04- Imported Materials
(Every code has not been explained to maintain the secrecy)
6.4.2 Material Code: The products were analyzed on the material short code to avoid the
confusion and to obtain the real time data.
e.g. ETR1675/ 0020/ RB18
Chemical Pack Pack
Composition Quantity Container
6.4.3 Regularity: The product sales data was analyzed to obtain the sales pattern i.e. is it
regular in sale or is it non-regular. This gives a clear picture to place the material in its
appropriate quadrant in the matrix.
Condition for regularity:
1 Invoice per month and at least 12 Invoices per year
6.4.5 Seasonality: The Sales data was analyzed in graphical format to obtain the
seasonality of the product.
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e.g.
6.4.6 Forecast: To evaluate the forecast accuracy of the products, forecast data was
analyzed which makes the picture crystal clear to place the products on X- axis. To get an
exact idea for setting the stock norms based on the sales team forecast this factor was
placed on X- axis.The products having forecast accuracy less than 60% were termed as low
while those having more than 60% were termed as high.
e.g.
0
500
1000
1500
2000
2500
3000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
DC08311/0025/V07/9
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MaterialShort
Code
Actual
Sales
SalesForecas
tError
Absolut
e Error% Error
%
AccuracyTerm
AET0284/0210/TP4
9
AET028
4 7560 7560 8000 440 440
5.8201
1
94.17989
4 H
AET0284/0210/TP4
9
AET028
4
1617
0
1617
0 12000 -4170 4170
25.788
5
74.21150
3 H
AET0284/0210/TP4
9
AET028
4
2415
0
2415
0 14000
-
1015
0 10150 42.029
57.97101
4 L
AET0284/0210/TP4
9
AET028
4
1218
0
1218
0 7000 -5180 5180
42.528
7
57.47126
4 L
AET0284/0210/TP4
9
AET028
4
1071
0
1071
0 5000 -5710 5710
53.314
7
46.68534
1 L
AET0284/0210/TP4
9
AET028
4
1596
0
1596
0 1890
-
1407
0 14070
88.157
9
11.84210
5 L
AET0284/0210/TP4
9
AET028
4 0 0 10000
1000
0 10000 0 0 L
AET0284/0210/TP4
9
AET028
4 0 0 5000 5000 5000 0 0 L
AET0284/0210/TP4
9
AET028
4 3570 3570 0 -3570 3570 100 0 L
AET0284/0210/TP4
9
AET028
4 3360 3360 10000 6640 6640
197.61
9 0 L
AET0296/0200/TP4
9
AET029
6 3200 3200 3200 0 0 0 100 H
AET0296/0200/TP4
9
AET029
6 3200 3200 2600 -600 600 18.75 81.25 H
AET0296/0200/TP4
9
AET029
6 3200 3200 2400 -800 800 25 75 H
AET0296/0200/TP4
9
AET029
6 4000 4000 2600 -1400 1400 35 65 H
AET0296/0200/TP4
9
AET029
6 4000 4000 2400 -1600 1600 40 60 H
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6.4.7 Gross Margin: This factor is considered as one of the leading factor, as the ultimate
objective of any business is to earn profit by satisfying their customer. The Y- axis of the
matrix is gross margin which is measured in qualitative term and is not revealed as to
maintain the confidentiality of the company.
6.4.8 Product Matrix: The products are analyzed on all the identified factors. With the
defined conditions and rules as well as by human intelligence of the organization people
and the presenter the products are classified into their respective matrix quadrant. The
rules devised for classification were dynamic and used to vary product to product.
Sample Table:
On the above analyzed data we derived a product matrix in graphical format which will be
useful for the organization to know the potential materials as well as the lagged materials.
The product matrix consist of Four Quadrants viz,
Predictable
Forecastable
MTS
MTO
Plant Categor y Source Material Regular ity No o f custo mers 2013 Sales 2013 No o f custo mers 2011- 13 Avg. Sales 2011- 13 For ecast Accuracy Forecast (L/H) Gro ss Margin Slo b 2012 Slo b 2013 Priority SBUIN01 Mfg. AET0296 N 3 158000 3 264800 62.74278768 H L N N Fiber Finishes
IN01 Mfg. CPT0748 N 1 16000 1 5333.333333 100 H L N N Personal Care
IN01 Mfg. CPT0811 N 1 16000 1 5333.333333 100 H L N N Personal Care
IN01 Mfg. CPT2104 N 1 23000 1 34933.33333 71.73913043 H L N N Crop Care
IN01 Mfg. CPT2177 N 1 22000 1 23666.66667 61.0974611 H L N N Crop Care
IN01 Mfg. EST0464 N 1 21420 1 12600 76.75723944 H L N N Crop Care
IN01 Mfg. EST1921 N 1 18180 1 7380 93.62139918 H L N N Lubricant Additives
IN01 Mfg. ETT0105 N 1 350 1 116.6666667 85.71428571 H L N N Personal Care
IN01 Mfg. ETT0156 Y 4 507360 8 481250 74.08021102 H L Y N Textile Auxillaries
IN01 Mfg. ETT0186 Y 17 26450 30 30366.66667 60.16046381 H L N N Textile Auxillaries
IN01 Mfg. ETT0190 Y 9 33180 12 33180 67.5804684 H L Y Y Home Care
IN01 Mfg. ETT0498 N 2 3250 2 1250 100 H L Y N Coatings & Polymers
IN01 Mfg. ETT0698 N 3 49600 3 31200 90.39986781 H L N N Textile Auxillaries
IN01 Mfg. ETT0922 N 9 32530 12 28013.33333 82.99795512 H L N Y Personal Care
IN01 Mfg. ETT0935 Y 5 37170 5 25340 62.54005291 H L N N Coatings & Polymers
IN01 Mfg. ETT0981 N 2 34400 2 29466.66667 63.32833833 H L N Y Coatings & Polymers
IN01 Mfg. ETT1232 N 2 115200 2 111720 73.47513598 H L N N Crop Care
IN01 Mfg. ETT1534 Y 2 391400 3 218400 62.33567667 H L N N Personal Care
IN01 Mfg. INT0455 Y 30 224220 49 214160 75.5050616 H L Y N Textile Auxillaries
IN01 Mfg. INT0501 Y 10 78900 17 88526.66667 64.15339307 H L N N Textile Auxillaries
IN01 Mfg. SDT0936 Y 3 17350 3 10683.33333 70.04439434 H L N N Personal Care
IN01 Mfg. SPT0059 Y 3 102050 5 107450 69.70318153 H L N N Textile Auxillaries
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6.5 Predictable:
These are the high potential materials which have high gross margin as well as high
forecast accuracy along with high sales and are the regular sold materials of the
organization.
Recommendations:
These products should be planned 1 month prior
1 month inventory should be carried for these products
These products should be independent of Sales Dept. Forecast
Safety Stock= z *(Avg. Lead Time *(Std. Deviation of Demand)^2+(Avg.
Demand)^2*(Std. Dev. Of Lead time)^1/2
Here,
Z= Service level factor taken from normal distribution t ables.
SafetyStock
ServiceLevel
Lead Time
ActualDemand
ForecastedDemand
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6.6 Forecastable:
These are products which have low gross margin but are highly forecastable. They have
good customer demand and have moderate sale .They do show seasonality in their sales
pattern and are regular as well as non regular in demand.
Recommendations:
Sales Dept. Forecast must strictly be followed for planning of activities
For seasonal products planning should be done in pre-S&OP
80% of inventory with respect to previous years sales should be kept as inventory
Slob potential should be considered while planning for these materials
Safety Stock= z* ơ (l/t)^1/2
Here:
Z= Service level factor taken from normal distribution t ables.
l= lead time in days
ơ=Std. Deviation in the demand
t= Forecast period in months
6.7 Make to stock:
These are the materials which have high gross margin but are low in forecast accuracy.
They have moderate sales demand and show irregularity in their sales pattern with few
exceptions. Most of them have seasonal demand.
Recommendations:
As per the seasonality of the products, they should be planned.
They can be stocked as they have less slob potential.
6.8 Make to order:
These are the products which are less in gross margin as well as have less forecast
accuracy. They have less demand and are irregular in sales. Also these products have less
No. of customers and are non-seasonal.
Recommendations:
No inventory should be kept for these products
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They should be planned as the order arises from the customer end.
Sample Matrix:
MTS PREDICTABLE
Gross Margin
MTO FORECASTABLE
Forecast Accuracy
6.9 Data Collection and Data Analysis of Raw Materials:
To prepare a product matrix various aspects of RM were taken into account.
Data used:
Consumption Data for last three 3 years
Slob Data
Lead Time Data
6.9.1 Consumption Data :
Sample Data
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6.10 Data Analysis and modeling for Raw Materials:
Data Obtained from the company sources was analyzed on different fronts to design the
product matrix
The factors to be considered for matrix:
Stock No.
Regularity
Classification
Lead Time
6.10.1 Regularity: The Raw materials Regularity was determined by imposing a condition
i.e.
Regularity= 6 Entries per year for consumption
2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2013 2013 2013 2013 2013
Material 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
RMR0213 -120
RMT0004 -8936 -18748 -29750 -30936 -23914 -12810 -10748 -20858 -5604 -7802 -17145 -22619 -63928 -58284 -54542 -33338 -52638
RMT0005 -83 -7 -95 -82 -58 -82 -164 -90 -82 -82 -82 -164 -164 -164 -164
RMT0006 -6 -0.5 -11 -0.5 -30 -14 -12 -1 -16
RMT0011 -57212 -38235 -39160 -29260 -820 -2020 -1526 -2708 -5680 -7160 -18480
RMT0013 -22414.6 -14863.4 -18157 -12055 -9321 -7111 -10412 -8364 -11321.4 -10287.4 -15651.2 -11464 -35828 -24064 -35205.2 -27390 -28336.4
RMT0015 -9000 -12000 -18000 -12000 -12000 -9000 -9000 -6000 -9000 -6000 -6000 -6000 -18000 -21000 -21000 -30000 -27000
RMT0016 -1020 -510 -510 -510 -510 -510 -1291 -1020 -495 -4420 -5980 -9436 -7708 -14664
RMT0017 -1916.5 -2380.5 -175 -4065 -44 -3600 -2258 -3866 -600 -1243 -648 -21912 -36332 -146 -222
RMT0020 -22660 -420 -1680
RMT0021 -13050 -5550 -14446 -10700 -11300 -11845 -7276 -14058 -11850 -4475 -13490 -4505 -13934 -9000 -17826 -27400 -17558
RMT0022 -32453 -38670 -24166 -32903 -40784 -34857 -24546 -30387 -17346 -26597 -19292 -33206 -65822 -35304 -52062 -70456 -62518
RMT0023 -218 -123 -149 -88 -167.7 -174 -67 -47.5 -112 -78.5 -145 -81 -331.4 -354 -265.4 -248.6 -180.6
RMT0024 -10322 -4753 -2151 -903 -2558 -2434 -13208 -2074 -7787 -7804 -717 -2620 -19478 -2270 -1526 -3054RMT0025 -1050
RMT0026 -31658 -33994 -30620 -24620 -35238.3 -43730.9 -43048.8 -40341 -38599 -32799 -34287 -31946 -74178 -66720 -89538 -68298 -73020
RMT0027 -10 -4568 -10620 -46 -12 -21160
RMT0029 -100
RMT0030 -10700 -20842 -28204 -22783 -27303 -31297 -30559 -16334 -25791 -22059 -7033 -32288 -31734 -35574 -63294 -49970 -77308
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Classification: ABC classification is the one very popular method of inventory
classification based on PARETO’s Principle. Pareto rule states that the major chunk of
the wealth of any nation is with small percentage of people.
Hence by using ABC classification based on Pareto’s principle we classify all the raw
materials.
The ABC analysis categories the inventory into 3 classes namely:
Category A: These are the materials which have 85% of buying influence
Category B: These are the materials which have 10% of buying influence
Category C: These are the materials which have 5% of buying influence
(Buying influence is a term used to maintain the confidentiality of the company)
6.10.2 Lead Time: Lead time is the time taken by the raw material to reach at the company
premises from the time of order to the supplier. This time has great influence to classify the
Raw materials in their respective matrix.
To have better stock norms we decided to link the raw materials matrix to the finished
goods matrix.
6.10.3 Steps Followed to classify the Raw materials:
Forward
Planning
MTS
MTO
Initially it was decided to classify raw materials into 3 categories MTO, MTS, FP
Firstly,
o If the lead time for raw material is less than 7 days it will be MTO
o If the lead time for raw material is less than 15 days it will be MTS
o If the lead time for raw material is more than 90 days it will be FP
Then Check for regularity of the material if it is regular then move upward as in fig
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or else move down wards
If the material has slob potential then move it downwards as in fig.
By using above mentioned steps we classify the raw materials into 3 categories
Sample:
StockNo Classification Regularity 2013 Lead time Category
RMR0213 C N 25 MTO
RMT0004 A Y 90 FP
RMT0005 C Y 60 FP
RMT0006 C N 30 MTO
RMT0011 A Y 15 FP
RMT0013 A Y 30 FP
RMT0015 A Y 90 FP
RMT0016 A Y 0 MTO
RMT0017 A Y 90 FP
RMT0020 B N 15 MTO
RMT0021 A Y 45 FP
RMT0022 A Y 45 FP
RMT0023 B Y 45 FP
RMT0024 A Y 30 FP
RMT0025 C N 30 MTO
RMT0026 A Y 15 FP
6.11 Recommendations:
Forward Planning:
Materials should be planned early before as they have high lead time and also they
are regular in consumption
Inventory for these materials should be carried proportionately as most of them
falls in A classification
Make to stock:
Lead time of most of the materials is less 7 days, but as they are regular in nature we
have to plan them early before the order is placed depending upon the forecast data
Inventory for MTS should be carried depending upon the lead for each material
Make to order:
No inventory should be carried for these materials, except some which have lead
time more than 15 days
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As most of them falls in C classification they should be planned as the order is
placed.
6.12 Linking Raw Materials to Finished Goods:
To have a better matrix which can be just to the materials we linked raw materials matrix
to finished goods matrix.
As we have seen earlier raw materials were classified into 3 categories, In linking process
we classified them into 9 Quadrants by plotting a graph of RM classification based on
Pareto Vs. Finished goods importance.
This classification gives a clear picture to know the RM position.
Graph:
A LA MA HA
RM B LB MB HB
Classification
LC MC HC
C
LOW MEDIUM HIGH
FG IMPORTANCE
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6.13 Steps Followed for linking RM to FG:
Predictable FinishedGoods
RM Classification A
Forecastable Finished
Goods
Make to Stock
Finished Goods
Made to Order
Finished Goods
RM Classification C
RM Classification B
LC/LB
LB/MC
LA/LB/MB
RM Classification B
RM Classification A
RM Classification C
LB
LBRM Classification B
RM Classification A
RM Classification A
RM Classification B
RM Classification C HC
MA
MA/MB
MB/MC
HB
HA
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VII
Scope and Limitations
7.1 Scope of the Project:
The product classification and the inventory norms recommendations will give the senior
authorities a scenario to improve inventory norms and hence, customer service level. This
project is important for long term as it shows the products into their respective category
with importance to the business. It shows the effects of seasonality, regularity on product
sales, this can be useful to establish concrete stock norms.
Following are some suggestions to increase customer service in future:
Reduction in Sales Variation:
o Establishing a robust forecasting mechanism
o Strengthening the S&OP process
o Reclassification of the products timely according to sales
Transit Time:
o Identification of high lead time materials
o
Explore ways of reduction in Lead Times & possibility of alternate sources of
supply
o Explore reasons for variation in lead times
7.2 Limitations:
The Recommendations are based on the Past data Analysis which might fail in future
due to dynamism of the business
Definition of the Regularity might not fit for all materials both in RM and FG
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VIII
Academic Contribution
The project undertaken by me required the knowledge of work system, operation
related to inventory management, the problems faced in making an accurate practical
model. I also learned the operation of different departments and their interrelationship.
While completing the project I learnt a lot many things which are stated as
follows:
Business process of the company
Inventory processing
Data Analytics
Demand Planning
Safety stock calculation in high uncertainty scenario
Forecasting of Intermittent demand
The concepts learned at NITIE were of great use and helped in understanding things with
more depth. Following subjects were helpful during the project
System Efficiency and Improvement Techniques
Business Statistics
Materials Management
Operation Planning & Control
Supply Chain Management
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IX
Bibliography
Tony Arnold, Stephen Chapman & R V Ramakrishnan,“Introduction to materials management ”
David Simchi Levi, Philip kaminsky, and Edith Simchi Levi.
“Designing and Managing the Supply Chain: Concepts, Strategies, and Case
Studies.” Irwin McGrawHill, 2000.
Sunil Chopra, Peter Mendil, D V Kalra,“Supply Chain Management ”
G Hadley & T M Whititn,
“Analysis of Inventory systems”
Study of Mensurating Methods for Uncertain Factors Influencing Safety Goods
Stock in Supply Chain Managemento Ding Yongbo, Zhu Zhendong
o School of Business Administration
o Jilin University of Finance and Economics, Changchun, China
SCMOPS.com
Croda India Internal Documents
Wikipedia.org
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