92_Sumedh Shirgaonkar

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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,


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,


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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92_Sumedh Shirgaonkar

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