report on im

7/31/2019 Report on IM

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A Comparative Study

on Inventory

management

Practice(A study on garments

industry)


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A Comparative Study onInventory management Practice

Prepared By

BLACK

Imran Islam Anik : 09 03 12

Falguni Chisty : 09 03 22

Sk. Farhan Uddin : 09 03 23

Md. Zahidul Islam : 09 03 50

Shovon Saha : 09 03 51

Prepared For

Md. Reaz Uddin

Lecturer

Course Title: Productions & Operations Management

Course No: BA-3215

3rd Year, 2nd Term

Business Administration Discipline

Khulna University,

Khulna

Date of submission: 6th March, 2012


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6th March, 2012

Md. Reaz Uddin

Lecturer

Business Administration Discipline

Khulna University, Khulna

Dear Sir

Here is the report on A Comparative Study on Inventory

Management Practice. You asked us to prepare this report as a

course requirement.

It was a great opportunity for us to be acquainted with such

organizations. We have tried our level best to gather what we believe

to be the most important information available. We believe that the

knowledge and experience we have gathered during our survey

period will immensely help in our future professional life.

We, therefore, would like to request you to accept our report and

thanks a lot for giving us this opportunity. We truly enjoyed it. We will

be happy to get such report further. If you need any additional

information regarding this report, we will be right before you for

necessary interpretation and defense.

Unconditional thank to you

Sincerely

Imran Islam Anik : 090312

Falguni Chisty : 090322

Sk. Farhan Uddin : 090323

Md. Zahidul Islam : 090350

Shovon Saha : 090351


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Table of Content

Contents Page No

Executive Summery vi

Chapter 1: Introduction 07

1.1. Introduction to the Study 081.2. Origin of the Report 09

1.3. Purpose of the Report 10

1.4. Scope of the Report 11

1.5. Limitation of the Report 12

1.6. Rationale to the Study 13

1.7. Objective of the Report 14

1.8. Methodology 15

Chapter 2: Literature Review 16

2.1. Historical Background of GarmentsIndustries in Bangladesh

17

2.2. Inventory Management 18

2.3. Lead Time 19

2.4. Carrying Costs of Inventory 19

2.5. Asset Management 19

2.6. Inventory Valuation 20

2.7. Physical Inventory 20

2.8. Quality Management 20

2.9. Demand Forecasting 21

2.10. EOQ 21

2.11. Total Cost 21

2.12. Reorder Level 21

2.13. Length of Order Cycle 22

2.14. Necessary Formulas 22

Chapter 3: Analysis 23

3.1. Analysis 24

3.2. Inventory Profile of the chosenGarments Companies

25

3.2.1. A ONE DRESSMAKERS LTD. 253.2.2. A.B. FASHION WEAR LTD. 26

3.2.3. A.B.C. ATTIRE LTD. 27

3.3. ANOVA Analysis 28

Chapter 4: Conclusion 35

4.1. Conclusion 36

4.2. Bibliography 37


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EXECUTIVE SUMMARY

Inventory to many small business owners is one of the more visible

and tangible aspects of doing business. Raw materials, goods in

process and finished goods all represent various forms of inventory.

Each type represents money tied up until the inventory leaves the


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Chapter 1

Introduction

1.1. Introduction to the Study


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08

1.2. Origin of the Report

This report on A Comparative Study on Inventory Management

Practice has been assigned to us by our respected course

instructor Md. Reaz Uddin. This report was assigned to us to find

out the practices of inventory management in a respective industry.

We were approved by our course instructor to study on garments

industries of Bangladesh for this purpose. To conduct this report we

took A ONE DRESSMAKERS LTD.; A.B. FASHION WEAR LTD. &

A.B.C. ATTIRE LTD. as sample.


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09

1.3.Purpose of the Report

Our course instructor Md. Reaz Uddin has assigned us to conduct a

comparative report on Comparative Study on Inventory

Management Practice as a part of the course.

The purposes of the report are:

To have practical knowledge of surveying any organization.

To have practical knowledge about the application of inventory

management.

To compare the position of organizations of same industry.


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12

1.6. Rationale to the Study

However, evidences also suggest that there are cases of inventory

management of Garment industry. Inventory management of

Garment industry is influenced by many factors. This paper looks for

identifying the factors of inventory management such as calculating

EOQ, order limit, order duration, order size, holding period, total cost

for ordering. From the study, it has been identified that, total

inventory management of the garments factory, how a garment

factory successfully manage their inventory to minimize the cost and

run their firm toward profitability. This will turn the industrial

development of the country thus will contribute to the economic

development of the country.


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13

1.7.Objective of the Report

Main objective:

To identify the overall inventory management of the three garments

factories named A ONE DRESSMAKERS LTD.; A.B. FASHIONWEAR LTD. & A.B.C. ATTIRE LTD.

Sub objectives:

To find the EOQ,

To find the Order size,

To find the Order duration,

To find the Total cost,

To find duration of holding period.


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14

1.8. Methodology

Chapters of the study:

This study is divided into four sections; the first section is on

introduction to the study. Section two is on literature review.

Section three is on the contents of this research and finally

section four is for reference.

The methodology set for the study is as

follows:

Sources of information: Primary and secondary sources of

information is used for completing the inventory management

report. Primary information are collected from the survey and

the interview of the entrepreneurs and employees of the

garment factories and secondary information are collected from

periodicals, business magazines, and website of the garment

factories


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15

Chapter 2

Literature Review


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2.1. Historical Background of Garments

Industry in Bangladesh

Large-scale production of readymade garments (RMG) in organized

factories is a relatively new phenomenon in Bangladesh. Until early

sixties, individual tailors made garments as per specifications

provided by individual customers who supplied the fabrics. The

domestic market for readymade garment, excepting children wearsand men's knit underwear was virtually non-existent in Bangladesh

until the sixties.

Since the late 1970s, the RMG industry started developing in

Bangladesh primarily as an export-oriented industry although; the

domestic market for RMG has been increasing fast due to increase in

personal disposable income and change in life style. The sector

rapidly attained high importance in terms of employment, foreign

exchange earnings and its contribution to GDP. In 1999, the industry

employed directly more than 1.4 million workers, about 80% of whom

were female.

The hundred percent export-oriented RMG industry experienced

phenomenal growth during the last 15 or so years. In 1978, there

were only 9 export-oriented garment manufacturing units, which

generated export earnings of hardly one million dollar. Some of these


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units were very small and produced garments for both domestic and

export markets. Four such small and old units were Reaz Garments,

Paris Garments, Jewel Garments and Baishakhi Garments. Reaz

Garments, the pioneer, was established in 1960 as a small tailoringoutfit, named Reaz Store in Dhaka. It served only domestic markets

for about 15 years. In 1973 it changed its name to M/s Reaz

Garments Ltd. and expanded its operations into export market by

selling 10,000 pieces of men's shirts worth French Franc 13 million to

a Paris-based firm in 1978. It was the first direct exporter of garments

from Bangladesh. Desh Garments Ltd, the first non-equity joint-

venture in the garment industry was established in 1979. Desh had

technical and marketing collaboration with Daewoo Corporation of

South Korea. It was also the first hundred percent export-oriented

company. It had about 120 operators including 3 women trained in

South Korea, and with these trained workers it started its production

in early 1980. Another South Korean Firm, Youngones Corporation

formed the first equity joint-venture garment

17

factory with a Bangladeshi firm, Trexim Ltd. in 1980. Bangladeshi

partners contributed 51% of the equity of the new firm, named

Youngones Bangladesh. It exported its first consignment of padded

and non-padded jackets to Sweden in December 1980.

Within a short period, Bangladeshi entrepreneurs got familiar with the

world apparel markets and marketing. They acquired the expertise ofmobilizing resources to export- Foreign buyers found Bangladesh an

increasingly attractive sourcing place. To take advantage of this

cheap source, foreign buyers extended, in many cases, suppliers'

credit under special arrangements. In some cases, local banks

provided part of the equity capital. The problem of working capital

was greatly solved with the introduction of back-to-back letter of

credit, which also facilitated import of quality fabric, the basic raw


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The government assigned high priority to the development of RMG

industry.

Over the last fifteen years or so the garments industries have

emerged as the largest source of earning foreign currency. About half

of the foreign currency from the ready-made garments is earned from

European Union and the U.S.A. Besides, Canada, Japan, Australia,

New Zealand; Russia etc. also are other garments importing

countries. At present about 20 countries of the world are importers of

our garments. Its market is being expanded in the Middle East,

Russia, Japan, Australia and many other countries.

2.2. Inventory Management

Inventory management is primarily about specifying the shape and

percentage of stocked goods. It is required at different locations

within a facility or within many locations of a supply network to

precede the regular and planned course of production and stock of

materials.

The scope of inventory management concerns the fine lines between

replenishment lead time, carrying costs of inventory, asset

management, inventory forecasting, inventory valuation, inventory

visibility, future inventory price forecasting, physical

18

inventory, available physical space for inventory, quality

management, replenishment, returns and defective goods, and

demand forecasting. Balancing these competing requirements leads

to optimal inventory levels, which is an on-going process as the

business needs shift and react to the wider environment.

Inventory management involves a retailer seeking to acquire and

maintain a proper merchandise assortment while ordering, shipping,

handling, and related costs are kept in check. It also involves systemsand processes that identify inventory requirements, set targets,


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provide replenishment techniques, report actual and projected

inventory status and handle all functions related to the tracking and

management of material. This would include the monitoring of

material moved into and out of stockroom locations and thereconciling of the inventory balances. It also may include ABC

analysis, lot tracking, cycle counting support, etc. Management of the

inventories, with the primary objective of determining/controlling

stock levels within the physical distribution system, functions to

balance the need for product availability against the need for

minimizing stock holding and handling costs.

2.3. Lead TimeA lead time is the latency (delay) between the initiation and

execution of a process. For example, the lead time between the

placement of an order and delivery of a new car from a manufacturer

may be anywhere from 2 weeks to 6 months. In industry, lead time

reduction is an important part oflean manufacturing.

2.4. Carrying Costs of Inventory Carrying cost refers to the total cost of holding inventory. This

includes warehousing costs such as rent, utilities and salaries,

financial costs such as opportunity cost, and inventory costs related

to perish ability, shrinkage and insurance.

2.5. Asset ManagementAsset management, broadly defined, refers to any system whereby

things that are of value to an entity or group are monitored and

maintained. It may apply to both tangible assets and to intangible

concepts such as intellectual property and goodwill.

19

Asset management is a systematic process of operating, maintaining,

and upgrading assets cost-effectively. Alternative views of asset

management in the engineering environment are: The practice of

managing assets so that the greatest return is achieved; and the
http://en.wikipedia.org/wiki/ABC_analysishttp://en.wikipedia.org/wiki/ABC_analysishttp://en.wikipedia.org/wiki/Latencyhttp://en.wikipedia.org/wiki/Lean_manufacturinghttp://en.wikipedia.org/wiki/Opportunity_costhttp://en.wikipedia.org/wiki/Intellectual_propertyhttp://en.wikipedia.org/wiki/Goodwill_(accounting)http://en.wikipedia.org/wiki/Latencyhttp://en.wikipedia.org/wiki/Lean_manufacturinghttp://en.wikipedia.org/wiki/Opportunity_costhttp://en.wikipedia.org/wiki/Intellectual_propertyhttp://en.wikipedia.org/wiki/Goodwill_(accounting)http://en.wikipedia.org/wiki/ABC_analysishttp://en.wikipedia.org/wiki/ABC_analysis


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process by which built systems of facilities are monitored and

maintained, with the objective of providing the best possible service

to users.

2.6. Inventory ValuationAn inventory valuation allows a company to provide a monetary

value for items that make up their inventory. Inventories are usually

the largest current asset of a business, and proper measurement of

them is necessary to assure accurate financial statements. If

inventory is not properly measured, expenses and revenues cannot

be properly matched and a company could make poor business

decisions.

2.7. Physical InventoryPhysical inventory is a process where a business physically counts

its entire inventory. A physical inventory may be mandated by

financial accounting rules or the tax regulations to place an accurate

value on the inventory, or the business may need to count inventory

so component parts or raw materials can be restocked. Businesses

may use several different tactics to minimize the disruption caused

by physical inventory.

2.8. Quality managementThe term quality management has a specific meaning within many

business sectors. This specific definition, which does not aim to

assure 'good quality' by the more general definition, but rather toensure that an organization or product is consistent, can be

considered to have four main components: quality planning, quality

control, quality assurance and quality improvement. Quality

management is focused not only on product/service quality, but also

the means to achieve it. Quality management therefore uses quality

assurance and control of processes as well as products to achieve

more consistent quality.
http://en.wikipedia.org/wiki/Monetary_valuehttp://en.wikipedia.org/wiki/Monetary_valuehttp://en.wikipedia.org/wiki/Inventoryhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Financial_statementhttp://en.wikipedia.org/wiki/Expenseshttp://en.wikipedia.org/wiki/Revenuehttp://en.wikipedia.org/wiki/Financial_accountinghttp://en.wikipedia.org/wiki/Taxhttp://en.wikipedia.org/wiki/Raw_materialshttp://en.wikipedia.org/wiki/Quality_controlhttp://en.wikipedia.org/wiki/Quality_controlhttp://en.wikipedia.org/wiki/Quality_assurancehttp://en.wikipedia.org/wiki/Service_qualityhttp://en.wikipedia.org/wiki/Monetary_valuehttp://en.wikipedia.org/wiki/Monetary_valuehttp://en.wikipedia.org/wiki/Inventoryhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Financial_statementhttp://en.wikipedia.org/wiki/Expenseshttp://en.wikipedia.org/wiki/Revenuehttp://en.wikipedia.org/wiki/Financial_accountinghttp://en.wikipedia.org/wiki/Taxhttp://en.wikipedia.org/wiki/Raw_materialshttp://en.wikipedia.org/wiki/Quality_controlhttp://en.wikipedia.org/wiki/Quality_controlhttp://en.wikipedia.org/wiki/Quality_assurancehttp://en.wikipedia.org/wiki/Service_quality


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20

2.9. Demand ForecastingDemand forecasting is the activity of estimating the quantity of a

product or service that consumers will purchase. Demand forecasting

involves techniques including both informal methods, such as

educated guesses, and quantitative methods, such as the use of

historical sales data or current data from test markets. Demand

forecasting may be used in making pricing decisions, in assessing

future capacity requirements, or in making decisions on whether to

enter a new market.

2.10. EOQEconomic Order Quantity is the order quantity that minimizes total

inventory holding costs and ordering costs. It is one of the oldest

classical production scheduling models. The framework used to

determine this order quantity is also known as Wilson EOQ Model or

Wilson Formula. The model was developed by Ford W. Harris in 1913,

but R. H. Wilson, a consultant who applied it extensively, is given

credit for his in-depth analysis.

2.11. Total CostIn economics, and cost accounting, total cost (TC) describes the

total economic cost of production and is made up of variable costs,

which vary according to the quantity of a good produced and include

inputs such as labor and raw materials, plus fixed costs, which are

independent of the quantity of a good produced and include inputs

(capital) that cannot be varied in the short term, such as buildings

and machinery. Total cost in economics includes the total opportunity

cost of each factor of production as part of its fixed or variable costs.

2.12. Reorder LevelReorder level is the quantity of the inventory that must be kept

reserved at the time of placing an order in order to continuing thebusiness and not to lose the market falling in short of inventory.
http://en.wikipedia.org/wiki/Pricinghttp://en.wikipedia.org/wiki/Market_entryhttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Cost_accountinghttp://en.wikipedia.org/wiki/Economic_costhttp://en.wikipedia.org/wiki/Variable_costhttp://en.wikipedia.org/wiki/Fixed_costhttp://en.wikipedia.org/wiki/Capital_(economics)http://en.wikipedia.org/wiki/Opportunity_costhttp://en.wikipedia.org/wiki/Opportunity_costhttp://en.wikipedia.org/wiki/Pricinghttp://en.wikipedia.org/wiki/Market_entryhttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Cost_accountinghttp://en.wikipedia.org/wiki/Economic_costhttp://en.wikipedia.org/wiki/Variable_costhttp://en.wikipedia.org/wiki/Fixed_costhttp://en.wikipedia.org/wiki/Capital_(economics)http://en.wikipedia.org/wiki/Opportunity_costhttp://en.wikipedia.org/wiki/Opportunity_cost


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Chapter 3

Analysis

3.1. ANALYSIS


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In this part, we are going to show the concurrent practice of inventory

management system in Garments Industry of Bangladesh from

different dimensions of costs and inventory level as well as the other

ingredients. For this purpose we have already collected related datafrom three companies of this sector and now we are going for detail

analysis of the collected data from the companies named - . For

analyzing and interpreting the collected data we are using one way

Analysis of Variance method or ANOVA

24

3.2. Inventory Profile of the Chosen Garments

Companies

We have chosen three garments companies named A ONE

DRESSMAKERS LTD., A.B. FASHION WEAR LTD. and A.B.C. ATTIRELTD. for our study. And for this purpose we also collected data


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regarding inventory management and these data are presented

bellow.

3.2.1. A ONE DRESSMAKERS LTD.

Title 2007 2008 2009 2010 2011

Demand (D) 230840 224382 220620 215780 208577

Unit Cost (C) 93 117 136 142 159

Per Order

Cost (S)

18000 19500 22250 23460 24580

Per unit

Carrying cost

(H)

6 6.5 8 9.5 11

Daily

Demand (d)

642 624 613 600 577

Lead Time (L) 25 22 21 19 15

From the above Information we can obtain some ingredients of

inventory decisions that we have calculated with the help of the

formulas presented in the literature part.

Ingredients

2007 2008 2009 2010 2011

EOQ 37216 36692 35031 32645 30531

Total Cost 216914

17

26491191 30284271 30950892 3065528

5

Reorderlevel

16050 13728 12873 11400 8655

Number of

order

6 6 6 7 7

Length ofOrderCycle

51 50 50 52 52

Averageinventory

16266 15323 15304 15482 14695

AveragedailyDemand

642 624 613 600 577

25


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3.2.2. A.B. FASHION WEAR LTD

Title 2007 2008 2009 2010 2011

Demand (D) 228830 222420 218360 213280 205322

Unit Cost (C) 94 118 134 140 155Per Order

Cost (S)

18500 19000 20380 23600 25240

Per unit

Carrying cost

(H)

8 9 9.5 10.5 12

Daily

Demand (d)

636 618 607 519 576

Lead Time (L) 22 20 22 21 18




Ingredient

s

2007 2008 2009 2010 2011

EOQ 32532 30645 30608 30964 29389Total Cost 2177027

7

26521364 29551021 3018431

8

3216288

5

Reorder

level

12550 10311 8920 9504 7434

Number of

order.

7 7 7 7 7

Length of

OrderCycle

58 59 57 54 53

Average

inventory

18608 18346 17516 16323 15266

Average

daily

Demand

636 618 607 519 576


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26

3.2.3. A.B.C. ATTIRE LTD.Title 2007 2008 2009 2010 2011

Demand (D) 180658 176498 160540 155272 148360

Unit Cost (C) 95 122 138 146 162

Per Order

Cost (S)

19000 19800 22840 24660 26790

Per unit

Carrying cost

(H)

5 4.2 6.8 7.5 9.6

Daily

Demand (d)

502 491 446 432 413

Lead Time (L) 25 21 20 22 18




Ingredien

ts

2007 2008 2009 2010 2011

EOQ 37054 40794 32839 31954 28776

Total Cost 1734778

0

21704089 22377830 2290936

8

2431056

6

Reorder

level

12550 10311 8920 9504 7434

Number

of order.

5 4 5 5 5

Length of

Order

Cycle

74 83 74 74 70

Average

inventory

18527 20397 16420 15977 14388

Average

daily

Demand

502 491 446 432 413


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27

3.3. ANOVA Analysis

Now we are going to show the relation or correlation among the

companies different ingredients and it will say about the correlation

that will disclose the interdependency of the ingredients of these

companies.

Here our null Hypothesis will be one ingredient does not

differ from the same ingredient of other companies of

the industry

Null Hypothesis: Annual demand will not differ among thesecompanies.

ANOVA

Annual

Demand

Sum of Squares df Mean Square F Sig.

Between

Groups9.943E9 2 4.971E9 43.519 .000

Within Groups 1.371E9 12 1.142E8

Total 1.131E10 14

Annual Demand: After calculating ANOVA we found that the

computed value is 43.619. But from the F value table the table value

ofF for (2, 12) degree of freedom and at 5% level of significance is

3.89. Since the computed value ofF = 43.619 is greater than the

table value of F = 3.89, therefore we cannot accept our null

hypothesis. Hence the difference is significant and we can infer that

the storage cost of different companies differs.


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Null Hypothesis: Unit cost will not differ among thesecompanies.

ANOVA

Unit Cost

Sum of

Squares df Mean Square F Sig.

Between

Groups51.733 2 25.867 .042 .959

Within Groups 7321.200 12 610.100

Total 7372.933 14

28

Unit Cost: After calculating ANOVA we found that the computed

value is 0.042. But from the F value table the table value ofF for (2,

12) degree of freedom and at 5% level of significance is 3.89. Since

the computed value ofF = 0.042 issmaller than the table value of

F = 3.89, therefore we accept our null hypothesis. Hence the

difference is not significant and we can infer that the unit cost of

different companies does not differ.

Null Hypothesis: Per order cost will not differ among thesecompanies.

ANOVA

Per order cost

Sum of


BetweenGroups

4654120.000 2 2327060.000 .260 .776

Within Groups 1.0768 12 8963306.667

Total 1.1228 14

Per order cost: After calculating ANOVA we found that the

computed value is 0.260 But from the F value table the table value


3.89. Since the computed value ofF = 0.260is smaller than thetable value of F = 3.89, therefore we accept our null hypothesis.


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ANOVA

Daily Demand

Sum of


Between Groups 69828.933 2 34914.467 24.601 .000

Within Groups 17030.800 12 1419.233

Total 86859.733 14

Daily Demand: After calculating ANOVA we found that the computed

value is 24.601. But from the F value table the table value ofF for

(2, 12) degree of freedom and at 5% level of significance is 3.89.

Since the computed value ofF = 24.601 is greater than the tablevalue ofF = 3.89, therefore we cannot accept our null hypothesis.

Hence the difference is significant and we can infer that the Daily

demand of different companies differs.

30

Null Hypothesis: Lead time will not differ among thesecompanies.

ANOVA

Lead Time

Sum of


Between Groups 2.800 2 1.400 .179 .838

Within Groups 93.600 12 7.800

Total 96.400 14

Lead Time: After calculating ANOVA we found that the computed

value is 0.179

But from the F value table the table value ofF for (2, 12) degree of

freedom and at 5% level of significance is 3.89. Since the computed

value of F = 0.179 is smaller than the table value of F = 3.89,

therefore we accept our null hypothesis. Hence the difference is not

significant and we can infer that the per order cost of differentcompanies does not differ.


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Null Hypothesis: EOQ will not differ among these companies.

ANOVA

EOQ

Sum of


Between Groups 4.1047 2 2.0527 1.965 .183

Within Groups 1.2538 12 1.0447

Total 1.6638 14

EOQ (Economic Order Quantity): After calculating ANOVA wefound that the computed value is 1.985 But from the F value table

the table value ofF for (2, 12) degree of freedom and at 5% level of

significance is 3.89. Since the computed value of F = 1.985 is

smaller than the table value ofF = 3.89, therefore we accept our

null hypothesis. Hence the difference is not significant and we can

infer that the per EOQ of different companies does not differ.

31

Null Hypothesis: Total cost will not differ among thesecompanies.

ANOVA

Total cost

Sum of


Between

Groups 1.8199 2 9.096E8 .000 1.000

Within Groups 1.94014 12 1.61713

Total 1.94014 14

Total Cost: After calculating ANOVA we found that the computed

value is 0.00


freedom and at 5% level of significance is 3.89. Since the computedvalue ofF = 0.00 is far smaller than the table value ofF = 3.89,


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significant and we can infer that the Total cost of different companies

does not differ.

Null Hypothesis: Recover level will not differ among thesecompanies.

ANOVA

Recover level

Sum of


Between Groups 2.6087 2 1.304E7 2.663 .110

Within Groups 5.8777 12 4897562.700

Total 8.4867 14

Recover level: After calculating ANOVA we found that the computed

value is 2.663



value of F = 2.663 is smaller than the table value of F = 3.89,


significant and we can infer that the Recovery Level of Inventory ofdifferent companies does not differ.

32

Null Hypothesis: Number of order will not differ among thesecompanies.

ANOVA

Number of

Order

Sum of


Between

Groups12.933 2 6.467 38.800 .000

Within Groups 2.000 12 .167

Total 14.933 14


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Number of Order: After calculating ANOVA we found that the

computed value is 38.8.



value of F = 98.8 is greater than the table value of F = 3.89,

therefore we cannot accept our null hypothesis. Hence the

difference is significant and we can infer that the number of order per

year of different companies differs greatly.

Null Hypothesis: Length of order cycle will not differ amongthese companies.

ANOVA

Length of

Order Cycle

Sum of


Between Groups 1594.133 2 797.067 77.889 .000

Within Groups 122.800 12 10.233

Total 1716.933 14

Length of Order Cycle: After calculating ANOVA we found that the

computed value is 77.889 But from the F value table the table value


3.89. Since the computed value ofF = 77.889 is greater than the

table value of F = 3.89, therefore we cannot accept our null

hypothesis. Hence the difference is significant and we can infer that

the length of order cycle per year of different companies differs

greatly.

33

Null Hypothesis: Average Inventory will not differ among thesecompanies.


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ANOVA

Average

Inventory

Sum of


Between

Groups1.037E7 2 5183364.200 1.997 .178

Within Groups 3.114E7 12 2595147.633

Total 4.151E7 14

Average Inventory: After calculating ANOVA we found that the

computed value is 1.997 But from the F value table the table value ofF for (2, 12) degree of freedom and at 5% level of significance is

3.89. Since the computed value ofF = 1.997 issmaller than the

table value of F = 3.89, therefore we accept our null hypothesis.

Hence the difference is not significant and we can infer that the Level

of Average Inventory of different companies does not differ.

34


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Chapter 4

Conclusion


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3.2.Conclusion

During conducting this report, we learned how an organization

maintains its inventories. We also learned to use SPSS, specializedsoftware for calculating ANOVA and other necessary factors.

We have set some null hypothesis to measure the position of three

garments companies in different situation. Depending on the

information, we came to know which could be accepted and which

could not. But, main purpose of this report was to learn the inventory

practice in garments industry and using ANOVA test compare more

than two variables and we successfully and practically learned it. So,

we think purpose of assigning this report is served. We are grateful to

our course instructor to give us such opportunity to learn in a better

and practical environment.


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