a research report

A RESEARCH REPORT

ON

A STUDY ON FREIGHT CHARGES VARIABILITY & FLEET AVAILABILITY IN ROAD TRANSPORT WITH SPECIAL REFERENCE TO CEMENT INDUSTRY

Submitted in partial fulfilment for the award of the degree

Master of Business Administration

Chhattisgarh Swami Vivekanand Technical University, Bhilai

Submitted by,

Sunita-Burman

MBA – Semester 3

(Session-2014-2015)

Approved by- Guided by-Mr. Pankaj Joge Sir Ratnesh Shukla ( RLH-CG) & Asst. Proff, FMS Rakesh Singh Chandel, Zonal Rail Co-ordinator

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DECLARATION

I the undersigned solemnly declare that the report of the research work entitled report A

STUDY ON FREIGHT CHARGES VARIABILITY & FLEET AVAILABILITY IN ROAD TRANSPORT WITH

SPECIAL REFERENCE TO CEMENT INDUSTRY is based on my own work carried out during the

course of my study under the supervision of Mr. Ratnesh Shukla, RLH-CG and Mr. Rakesh Singh

Chandel, Zonal Rail Co-ordinator-CG.

I assert that the statements made and conclusions drawn are an outcome of my

research work. I further declare that to the best of my knowledge and belief the report does

not contain any part of any work which has been submitted for the award of MBA degree in this

University or any other University of India or abroad.

_________________Sunita Burman

Enrolment No. AM5624

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CERTIFICATE FROM THE SUPERVISOR

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ACKNOWLEDGEMENTS

I would like to express my deepest appreciation to all those who provided me the possibility to complete this report. A special gratitude I give to my mentors Mr. Ratnesh Shukla, RLH-CG and Mr. Rakesh Singh Chandel, Zonal Rail -coordinator-CG whose contribution in stimulating suggestions and encouragement helped me to coordinate my project especially in writing this report.

Furthermore I would also like to acknowledge with much appreciation the crucial role of the faculties of Faculty of Management Studies who gave the permission to use all required resources and the necessary materials to complete the research work on A STUDY ON FREIGHT CHARGES VARIABILITY & FLEET AVAILABILITY IN ROAD TRANSPORT WITH SPECIAL REFERENCE TO CEMENT INDUSTRY. Last but not least, many thanks go to my friends, who have invested their full effort in guiding me in achieving the goal. I have to appreciate the guidance given by other faculties as well as the panels especially in our presentation that has improved our presentation skills thanks to their comment and advices.

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TABLE OF CONTENTS

Declaration by the Student …………………………………………………………………………………… Certificate from the Company ………………………………………………………………………………. Acknowledgments ……………………………………………………………………………………………….. Chapter 1. Introduction to the study

a. Research Background Chapter 2. Industry Profile

a. About the Company Chapter 3. Literature ReviewChapter 4. Research Methodology

a. Objectives b. Research Planc. Sampling Pland. Data Collection

Chapter 5. Data Tabulation, Analysis and Resultsa. Type of Analysis used and Whyb. Results of the Analysis

Chapter 6. Findings of the studyChapter 7. Recommendations

Conclusions

References …………………………………………………………………………………………………………… Appendices ………………………………………………………………………………………………………….. a. Questionnaire/ Instrument used………………………………………………………………………..

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CHAPTER-1

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INTRODUCTION

The cost which a party (the consumer or business providing goods for transport) or consignee (the person or company to whom commodities are transported) is charged for the transportation of goods is determined by a number of factors. The main factors in determining the freight rate are: mode of transportation, weight, size, distance, points of pickup and delivery, and the actual goods being transported. All of these factors play their own independent role in determining the price or rate at which the freight will be transported but they are also all interconnected. When determining which mode of transportation will be used to deliver the freight to its destination there are many things which need to be taken into consideration which will all have an effect on the freight rate.

During the last decade, road freight has grown at a compounded growth rate of 11.9% compared to 1.4% on rail. Share of road in freight likely to stabilize around 85%. In recent years, freight movement by road has not kept up with capacity – leading to lower capacity utilisation. The utilisation has gone down from nearly 70% in the early 1990s to less than 60% in 2001-02. This has affected profitability of operators though freight rates have gone up, fuel costs have trebled. According to GOI (1966) – about 89% of road transport operators owned one vehicle each .The proportion owning 5 vehicles or less was 98%. UN mission (1993) claimed 95% of vehicles belonged to operators who had less than 5 vehicles.

CIRT study (1998) -77% of fleet under operators who owned 5 trucks or less, 10% belonged to those with 6 to 10 trucks, 4% belonged to those with 11 to 15 trucks, 3% belonged to those with 16 to 20, 6% belonged to those with more than 20.This ownership pattern continues (Deloitte Study 2003). The unique ownership profile has resulted in middle men – booking agents and brokers. With Fleet Operators shifting to a non-asset based model, dependence of Small Road Transport Operators (SRTO) on middle men is increasing. Conceptually the presence of a large number of operators would lead us to infer that market is highly competitive. This indeed seems to be true in regard to general goods transportation – market forces determine freight rates. NCAER (1979) observed that due to intense competition, profitability was rather low in the case of SRTOs. In fact, GOI (1996) had been concerned with viability of operators especially from the financial point of view. GOI (1980), Sriraman (GOI 1998) had contested this since exit was an option. On the other hand, supply of services had, in reality, increased.

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Given the segmentation of both in terms of market supply and demand (players) – the emerging feeling is that there are some dominant elements especially in the case of specialized traffic where shippers are likely to dominate. At the next level, the fleet operators, and other market players like the middle men could be exerting a certain influence.

RESEARCH BACKGROUND-

Currently, brokers play a large role in obtaining contracts or business for truck operators, and they therefore play a large role in determining freight rates. This is due in large part to information asymmetries in the trucking industry. Small truck operators lack information on shipment of consignments and thus depend on brokers as intermediaries. However, information technology can bridge this information gap in supply and demand and reduce the role of brokers in obtaining business and deciding freight rates. Glacial change in railway infrastructure is likely to push the increase in freight on to roads and the share of road transport may rise to 85 per cent (from 70 per cent). Unless road infrastructure is improved, average speed of road transit may decline and worsen aggregate diesel use efficiency.

However, there is some variation in minimum freight rates in the trucking industry. Freight rates are mostly a function of demand due to the excessive supply of trucks. Fixing minimum rates can further reduce demand during periods of recession. Also, as there is large diversity in the nature of goods carried, fixing one rate is difficult and less effective, but fixing separate rates for each type of good is cumbersome. Similarly, the quality of roads differs between states and between hilly areas and plains. Therefore, setting a minimum per-km rate could have a negative impact on trucks with national permits. Also, fixed rates that may be higher than market prices would further increase supply and thereby increase idle time.

Despite these negative impacts, truck operators consider that fixing minimum freight rates, or at least issuing guidelines for fixing freight rates, would likely be an effective measure to reduce their vulnerability to increasing diesel prices and help in maintaining profit margins.

The present study is an endeavour to analyse the freight charges variability for three industries i.e. steel industry, cement industry & food grains from Raipur depot to various destinations in Raipur. The study also reveals the prominent factors that have a direct impact on freight rates. It attempts to identify those factors that are responsible for fleet availability. The data is collected from primary source through a self-administered questionnaire survey.

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CHAPTER-2

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ABOUT CEMENT INDUSTRY -

Introduction

The Indian cement industry is the 2nd largest market after China accounting for about 8% of the total global production. It had a total capacity of about 347 m tonnes (MT) as of financial year ended 2012-13. Cement production (weight: 2.41%) Increased by 13.6 % in June, 2014 over June, 2013. Its cumulative growth during April to June, 2014 -15 was 9.5 % over the corresponding period of previous year. Cement is a cyclical commodity with a high correlation with GDP. The housing sector is the biggest demand driver of cement, accounting for about 67% of the total consumption. The other major consumers of cement include infrastructure (13%), commercial construction (11%) and industrial construction (9%).The Indian cement industry grew at a commendable rate in the last decade, registering a compounded growth of about 8%. However, the growth has slowed down in recent years owing to the sluggishness in the economy. Moreover, the per capita consumption of cement in India still remains substantially poor when compared with the world average. This underlines the tremendous scope for growth in the Indian cement industry in the long term. Indian cement producers continue to face rising input costs. Freight costs have significantly increased over the past two years, as a result of a rise in freight rates by railways, diesel prices and dependence on expensive road transport (due to a shortage of railway wagons), ICRA reported. The rise in domestic coal prices has resulted in an increase in the cost of power and fuel. Prices of raw materials such as limestone and gypsum have also increased.

There are different varieties of cement based on different compositions according to specific end uses, namely, Ordinary Portland Cement, Portland Pozzolana Cement, White Cement, Portland Blast Furnace Slag Cement and Specialised Cement. The basic difference lies in the percentage of clinker used.

Ordinary Portland cement (OPC): OPC, popularly known as grey cement, has 95 per cent clinker and 5 per cent gypsum and other materials. It accounts for 70 per cent of the total consumption.

Portland Pozzolana Cement (PPC): PPC has 80 per cent clinker, 15 per cent pozolona and 5 per cent gypsum and accounts for 18 per cent of the total cement consumption. It is manufactured because it uses fly ash/burnt clay/coal waste as the main ingredient.

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White Cement: White cement is basically OPC -clinker using fuel oil (instead of coal) with iron oxide content below 0.4 per cent to ensure whiteness. A special cooling technique is used in its production. It is used to enhance aesthetic value in tiles and flooring. White cement is much more expensive than grey cement.

Portland Blast Furnace Slag Cement (PBFSC): PBFSC consists of 45 percent clinker, 50 per cent blast furnace slag and 5 per cent gypsum and accounts for 10 per cent of the total cement consumed. It has a heat of hydration even lower than PPC and is generally used in the construction of dams and similar massive constructions.

Specialised Cement: Oil Well Cement is made from clinker with special additives to prevent any porosity.

Rapid Hardening Portland cement: Rapid Hardening Portland Cement is similar to OPC, except that it is ground much finer, so that on casting, the compressible strength increases rapidly.

Water Proof Cement: Water Proof Cement is similar to OPC, with a small portion of calcium stearate or non- saponifibale oil to impart waterproofing properties.

Market Size

India is among the best cement markets in Asia, according to Switzerland-based cement major Holcim.The Indian cement sector is expected to witness positive growth in coming years, with demand set to increase at a CAGR of more than 8 per cent during 2013–14 to 2015–16, according to the latest RNCOS report titled, ‘Indian Cement Industry Outlook 2016’. The report further observed, after analysing the regional trend of cement consumption, that the Southern region is creating maximum demand, which is expected to increase in future.

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Domestic cement consumption- The domestic cement consumption is expected to increase at a CAGR of 10.2 per cent during FY11-17 and reach 398 million tonnes.

Major cement demand drivers-

Housing sector accounts for 64 per cent of the total cement demand in India.

.

Production of cement- Cement production increased at a CAGR of 9.7 per cent to 272 million tonnes over FY06–13.

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Investments

The cement industry has been expanding on the back of increasing infrastructure activities and demand from the housing sector over the past many years. According to data released by the Department of Industrial Policy and Promotion (DIPP), cement and gypsum products attracted foreign direct investment (FDI) worth Rs 13,370.32 crore (US$ 2.24 billion) between April 2000 and February 2014.

Latest Developments

The Indian Cement Industry with Modernization and technology up-gradation has become a continuous process for industry. At present international standards and benchmarks in the quality of cement and building materials produced are met in India and is able to compete international markets. Substantial technological improvements have been bought in the industry for which we can legitimately be proud of its state-of-the-art technology and processes incorporated in most of its cement plants. This particular technology up gradation is resulting in increased capacity, reduction in cost of production of cement.

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ABOUT ULTRA TECH CEMENT

Introduction-

UltraTech Cement, India's leading manufacturer of cement and amongst the top cement producers globally, one of India's largest producers of RMC and the nation's largest producer of white cement has been instrumental in India’s rapid infrastructural growth. Its state-of-the-art manufacturing facilities produce products and services that have aided growth not only in urban areas but also in the rural interiors of the country. UltraTech as a brand is an embodiment of ‘strength’ and ‘reliability’. UltraTech Cement is part of the US $40 billion Aditya Birla Group. The company has an installed capacity of 62 Million Tonnes Per Annum (MTPA). UltraTech Cement provides a range of products that cater to all the needs from laying the foundation to delivering the final touches. The range includes Ordinary Portland Cement, Portland Blast Furnace Slag Cement, Portland Pozzalana Cement, White Cement, Ready Mix Concrete, building products and a host of other building solutions. White cement is manufactured under the brand name of ‘Birla White’, ready mix concretes under the name of ‘UltraTech Concrete’ and new age building products under the name of ‘UltraTech Building Products Division’. The retail outlets of UltraTech operate under the name of ‘UltraTech Building Solutions’.

UltraTech’s subsidiaries are- Dakshin Cements Limited, Harish Cement Limited, Gotan Limestone Khauj Udyog Private Limited, Bhagwati Limestone Company Private Limited, UltraTech Cement Lanka (Pvt.) (Ltd.), UltraTech Cement Middle East Investments Limited, PT UltraTech Mining Indonesia and PT UltraTech Investments Indonesia. UltraTech is the top cement producers in the world making it a significant global player. It has grinding units, jetties, bulk terminals and integrated plants all across the world. UltraTech Cement is the country's largest exporter of cement, catering to export markets in countries across the Indian Ocean and the Middle East. Such diverse presence across countries has helped UltraTech leverage economies of scale and enabled it to become a name to reckon with in the international market.

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Plant Locations in Chhattisgarh-

Hirmi Cement WorksVillage & Post: Hirmi,

Taluka: Simga,District: Raipur,

Chhattisgarh

Rawan Cement WorksGrasim Vihar,

Village P.O., RawanTehsil: Sigma,

Dist. Raipur (C.G.)

RAWAN CEMENT WORKS-

Rawan Cement Works was set up in 1995. Today, the product mix from plant includes 95 per cent blended cements for fly ash and 53 per cent for slag consistently throughout the year without compromising the quality norms. With the guidance and experience of the business’s Technology and Research Centre, the unit adopted technologies and processes using chemical additives to aid in faster assimilation and greater saturation of fly ash and slag. The unit also increased the tricalcium silicate (an important mineral in portland cement) content and completely eliminated the use of iron ore – again saving a finite natural resource. It also altered the grinding technology to ensure maximum slag use.

Today, the average content of recycled materials in each bag of cement going out from the plant is around 38.3 per cent. And it also saves on natural resources, power consumption and ground and air pollution through industrial wastes.

HIRMI CEMENT WORKS-

The cement manufacturing unit at Hirmi is the second cement unit of UltraTech after Awarpur Cement Works and has a capacity of 2.75 Million Tonne Per Annum (MTPA) of Clinker. Hirmi is located about 58 Km from Raipur and actually is a small village on Raipur-Baloda Bazar road. The cement plant has a township equipped with requisite civic facilities including a school and hospital.

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The construction started in February 1992 and was completed on 31 st March 1994. Plant is having Captive power of DG of 30 MW, 6MW set under commissioning. Balance power requirement is met by the Chhattisgarh State Electricity Board Grid power. Unit major raw material requirement is met through its 4.2 MTPA Captive Limestone Mine. Clinker from this plant is sent to our two grinding unit one located at Jharsuguda (Orissa) and other at Durgapur (West Bengal). This Plant of UltraTech caters to cement requirement of Eastern India covering Chhattisgarh, Madhya Pradesh, Orissa, Jharkhand, West Bengal and North East states.

Logistics-

UltraTech Cement has more than 200 sales offices across the country, which handles a combined load of around 14,000 orders per day. They do so through their efficient logistics department. UltraTech uses the latest technology to ensure that all stakeholders can track the delivery status of their orders in real time. Vehicle-based GPS technology is also being used to increase the efficiency of the fleet. To handle the complex nature of operations, the logistics operation is being handled at UltraTech through a multi-tiered structure which involves logistics teams at Plant, Region and Zonal levels. Beside this, there is a central logistics team who set the overall policy guidelines, monitor logistics performance and ensure segmental priorities as well as service requirements are met.

Logistics processes are empowered by best in class SCM processes using technology as the enabler with focus on:

Network Optimization

Web Based Order Management system with real time visibility of order status

Customer Service level measurement on real time basis

GPS based Vehicle Tracking System for dedicated fleet.

Automation at secondary service points like Railheads and Godowns.

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Financial Results for year ended 31st March, 2014

UltraTech Cement, an Aditya Birla Group company today announced its financial results for the year ended 31st March, 2014.

( ₹ in crores)

Quarter ended Year ended

31.03.14 31.03.13 31.03.14 31.03.13

Net Sales 5,832 5,391 20,078 20,023

PBIDT 1,329 1,383 4,147 4,980

PAT 838 726 2,144 2,655

EXPORTS-

UltraTech, the largest cement manufacturing company in India, is India's largest exporter of cement clinker spanning export markets in countries across the Indian Ocean, Africa, Europe and the Middle East. The company exports over 2.5 million tonnes per annum, which is about 30 per cent of the country's total exports. UltraTech Cement is the ultimate 360º building materials destination, providing an array of products ranging from grey cement to white cement, from building products to building solutions and an assortment of ready mix concrete catering to varied needs and applications.

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CHAPTER- 3

LITERATURE REVIEW-

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From a review of literature at the international level, it has been observed that the effects of deregulation have depended on the extent to which the industry was regulated earlier. However, broadly the effects have been as follows:

The capacity available for common use has increased significantly with increasing dominance by highly competitive small operators.

Rates have fallen considerably as a result of more capacity and introduction of better technological features.

Falling rates have benefited customers but with costs not reducing to such an extent, profit levels have fallen though operators offering higher levels of service have achieved higher profit levels.

The evidence that has emerged over quite some period of time is that the middlemen/intermediaries, who include the booking agents and the brokers, are the dominant players’ in the market and they in fact are the real ―makers‖ of the market. Given this feature, the issue is: who and / or what determines the freight rates? NCAER (1979) indicated that the booking agents, besides other functions, also had a role in fixing freight rates i.e. the rate charged to the user and the rate given to operators.

Given that economies of scale are low, there are virtually no sunk costs and that there are hardly problems of coordination, the road goods transport industry comes closest to lacking any structural barrier to competition or being virtually contestable (Kessides, 1993).

National Council of Applied Economic Research (NCAER, 1979). The study observed that the contribution of road transport to the process of economic development could be greatly enhanced by the ability of this industry to provide superior quality service and reduce total distribution costs through reductions in freight charges. The key elements, which could make this reduction in cost and superior service possible, are of two types, namely, external and internal. Among the significant external factors are high levels of taxation, road conditions, detention at check posts and problems related to inadequate and high cost of finance. The internal element was mainly in the form of organisation of the structure of the industry.

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CHAPTER-4

RESEARCH METHODOLOGY

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RESEARCH OBJECTIVES:

To study the variation in freight charges in road transport for various industries in Raipur.

To determine the factors influencing fleet availability in transport industry.

To determine the factors that has a direct impact on freight charges in Raipur.

RESEARCH PLAN

Research Design: Descriptive

Research Method Used survey

Research Technique Used Questionnaire

Data Collection (location) Raipur

Sampling type Probability sampling

Sampling plan Simple random sampling

Sample size 10

In this study we used primary data; it is collected from various transporters through a self-administered questionnaire. For calculation of variation and factors affecting freight charges in Raipur correlations, regression, factor analysis and ANOVA test is applied. For calculation of freight charges variation, we surveyed 10 potential transporters chosen randomly from Raipur city and collected information regarding freight charges from Raipur depot- destinations of various industries.

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CHAPTER- 5

22

DATA TABULATION, ANALYSIS & RESULTS

ANALYSIS-1

transporter

market demand

lot size competition

destination point

delivery period

trade relation

1 10 10 10 10 8 102 7 4 6 5 5 73 5 1 10 1 10 104 6 3 9 7 5 105 10 2 8 2 6 96 8 5 7 3 10 87 10 9 6 7 9 68 9 3 9 8 7 99 8 5 9 10 6 910 9 10 10 9 5 10

10 potential transporters are asked to rate the market conditions that have a direct impact on freight rates. where

1- Indicates low impact on freight rates.

10 - Indicates high impact on freight rates

FACTOR ANALYSIS-

KMO and Bartlett's TestKaiser-Meyer-Olkin Measure of Sampling Adequacy.

.410

Bartlett's Test of Sphericity Approx. Chi-Square

28.263

df 15

Sig. .020

a. Kaiser-Meyer-Olkin Measure of Sampling Adequacy - This measure varies between 0 and 1, and values closer to 1 are better.

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b. Bartlett's Test of Sphericity - This tests the null hypothesis that the correlation matrix is an identity matrix. An identity matrix is matrix in which all of the diagonal elements are 1 and all off diagonal elements are 0. You want to reject this null hypothesis.

Taken together, these tests provide a minimum standard which should be passed before a factor analysis (or a principal components analysis) should be conducted.

Communalities

InitialExtractio

nmarket demand

1.000 .664

lot size 1.000 .783

competition

1.000 .906

destination point

1.000 .815

delivery period

1.000 .196

trade relation

1.000 .943

a. Communalities - This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables.

b. Initial - With principal factor axis factoring, the initial values on the diagonal of the correlation matrix are determined by the squared multiple correlation of the variable with the other variables.

c. Extraction - The values in this column indicate the proportion of each variable's variance that can be explained by the retained factors. Variables with high values are well represented in the common factor space, while variables with low values are not well represented. (In this example, we don't have any particularly low values.) They are the reproduced variances from the factors that you have extracted.

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Total Variance Explained

Component

Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total

% of Varianc

eCumulativ

e % Total

% of Varianc

eCumulativ

e % Total

% of Varianc

eCumulativ

e %1 2.189 36.478 36.478 2.189 36.478 36.478 2.181 36.344 36.3442 2.118 35.308 71.786 2.118 35.308 71.786 2.127 35.442 71.786

3 .999 16.652 88.438

4 .472 7.867 96.306

5 .198 3.301 99.607

6 .024 .393 100.000

Interpretations-

It shows all the factors extractable from the analysis along with their eigenvalues, the percent of variance attributable to each factor, and the cumulative variance of the factor and the previous factors. Notice that the first factor accounts for 36.478% of the variance, the second 35.308%. All the remaining factors are not significant.

a.Factor - The initial number of factors is the same as the number of variables used in the factor analysis. However, not all 6 factors will be retained. In this example, only the first two factors will be retained.

b. Initial Eigenvalues - Eigenvalues are the variances of the factors. Because we conducted our factor analysis on the correlation matrix, the variables are standardized.

c. Total - This column contains the eigenvalues. The first factor will always account for the most variance (and hence have the highest eigenvalue), and the next factor will account for as much of the left over variance as it can, and so on. Hence, each successive factor will account for less and less variance.

d. % of Variance - This column contains the percent of total variance accounted for by each factor.

e. Cumulative % - This column contains the cumulative percentage of variance accounted for by the current and all preceding factors

f. Extraction Sums of Squared Loadings - The number of rows in this panel of the table correspond to the number of factors retained

g. Rotation Sums of Squared Loadings - The values in this panel of the table represent the distribution of the variance after the varimax rotation. Varimax rotation tries to maximize the variance of each of the factors, so the total amount of variance accounted for is redistributed over the three extracted factors.

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Component Matrixa

Component

1 2market demand

.648

lot size .820

competition .895

destination point

.902

delivery period

trade relation . .953

Two component/factor is extracted out of six variables. The table below shows the loadings of the six variables on the two factors extracted. The higher the absolute value of the loading, the more the factor contributes to the variable. The gap on the table represent loadings that are less than 0.5, this makes reading the table easier. We suppressed all loadings less than 0.5.

Rotated Component Matrixa

Component

1 2market demand

.777

lot size .884

competition

.952

destination point

.833

delivery periodtrade relation

.960

Interpretations-

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The idea of rotation is to reduce the number factors on which the variables under investigation have high loadings. Rotation does not actually change anything but makes the interpretation of the analysis easier.

Looking at the table below, we can see that-

Market demand, lot size, destination point are substantially loaded on Factor 1, which I call product mix factors.

Competition, trade relations are loaded on Factor 2. Which I call relationship factors.

These factors can be used as variables for further analysis i.e physical factors contribute more to freight rates.

ANALYSIS-2

We surveyed about the availability of fleet/transport vehicles in company. 7 point Likert scale, is used where point 4 is neutral, 1 indicates high level of disagreement and 7 indicates high level of agreement.

Communalities

Initial ExtractionX11 1.000 .894X12 1.000 .895

X21 1.000 .896

X22 1.000 .771

X31 1.000 .728

X32 1.000 .969

X41 1.000 .948

X42 1.000 .867

X51 1.000 .977

X52 1.000 .912

X61 1.000 .860

X62 1.000 .901

Extraction Method: Principal Component Analysis.

a. Communalities - This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables.

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b. Initial - With principal factor axis factoring, the initial values on the diagonal of the correlation matrix are determined by the squared multiple correlation of the variable with the other variables.

c. Extraction - The values in this column indicate the proportion of each variable's variance that can be explained by the retained factors. Variables with high values are well represented in the common factor space, while variables with low values are not well represented. (In this example, we don't have any particularly low values.) They are the reproduced variances from the factors that you have extracted.

Total Variance Explained

Component

Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total% of

VarianceCumulative

% Total% of

VarianceCumulativ

e % Total

% of Varianc

eCumulativ

e %1 3.665 30.545 30.545 3.66

530.545 30.545 2.860 23.837 23.837

2 2.645 22.042 52.587 2.645

22.042 52.587 2.807 23.395 47.232

3 1.726 14.380 66.966 1.726

14.380 66.966 1.881 15.678 62.910

4 1.361 11.340 78.306 1.361

11.340 78.306 1.737 14.474 77.384

5 1.222 10.183 88.489 1.222

10.183 88.489 1.333 11.104 88.489

6 .652 5.436 93.924

7 .482 4.014 97.938

8 .173 1.443 99.381

9 .074 .619 100.000

10 2.266E-16

1.888E-15 100.000

11 9.358E-17

7.799E-16 100.000

12 -1.774E-16

-1.479E-15 100.000

Interpretations-

It shows all the factors extractable from the analysis along with their eigenvalues, the percent of variance attributable to each factor, and the cumulative variance of the factor and the previous factors. Notice that the first factor accounts for 30.546% of the variance, the second 22.042%, third factor accounts for 14.380%, and fourth accounts for11.340%. All the remaining factors are not significant.

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a. Factor - The initial number of factors is the same as the number of variables used in the factor analysis. However, not all 12 factors will be retained. In this example, only the first five factors will be retained.

b. Initial Eigenvalues - Eigenvalues are the variances of the factors. Because we conducted our factor analysis on the correlation matrix, the variables are standardized.

c. Total - This column contains the eigenvalues. The first factor will always account for the most variance (and hence have the highest eigenvalue), and the next factor will account for as much of the left over variance as it can, and so on. Hence, each successive factor will account for less and less variance.

d. % of Variance - This column contains the percent of total variance accounted for by each factor.

e. Cumulative % - This column contains the cumulative percentage of variance accounted for by the current and all preceding factors

f. Extraction Sums of Squared Loadings - The number of rows in this panel of the table correspond to the number of factors retained

g. Rotation Sums of Squared Loadings - The values in this panel of the table represent the distribution of the variance after the varimax rotation. Varimax rotation tries to maximize the variance of each of the factors, so the total amount of variance accounted for is redistributed over the three extracted factors.

Component Matrixa

Component

1 2 3 4 5X11 .724X12 .680

X21 .649

X22 .838

X31 .503 .562

X32 .594 .568

X41 .712

X42

X51 .824

X52 .502

X61

X62 .792

Extraction Method: Principal Component Analysis

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a. 5 components extracted.

Five component/factor is extracted out of 12 variables. The table below shows the loadings of the six variables on the two factors extracted. The higher the absolute value of the loading, the more the factor contributes to the variable. The gap on the table represent loadings that are less than 0.5, this makes reading the table easier. We suppressed all loadings less than 0.5

Rotated Component Matrixa

Component

1 2 3 4 5X11X12 .678

X21 .774

X22 .616

X31 .851

X32 .708 .513

X41

X42

X51 .971

X52 .949

X61 .883

X62 .897

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 7 iterations.

Interpretations-

The idea of rotation is to reduce the number factors on which the variables under investigation have high loadings. Rotation does not actually change anything but makes the interpretation of the analysis easier.

Looking at the table below, we can see that-

Seasonal variation in supplies is substantially loaded on Factor 1, which I call production factors.

30

Trade relations and market demand of products are loaded on Factor 2, which I call external factor.

High profits from clients are fairly loaded on Factor 3, which I call financial factor.

Distance of destination point, trade relations, market demand are substantially loaded on Factor 4, which I call customer service factor.

Seasonal variation in supplies is fairly loaded on Factor 5

These factors can be used as variables for further analysis i.e. service factors, contribute more to fleet availability.

ANALYSIS-3

Variation in freight rates from Raipur depot-destinations-for steel, food grain and cement industries in Raipur for Taurus (10 wheeler/12 wheeler).

INTERPRETATION-

Null Hypothesis- There is no significant difference in the mean freight charges of 3 industries.

Alternate hypothesis- There is a significant difference in the mean freight charges of 3 industries.

FROM RAIPUR DEPOT-MAHASAMUND

ANOVA-single factor

SUMMARYGroups Count Sum Average Variancecement 10 4850 485 5583.333

steel 10 5550 555 4694.444food grain 10 4700 470 3444.444

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ANOVASource of Variation SS df MS F P-value F crit

Between Groups 41166.6667 2 20583.333 4.5 0.020574 3.354130829Within Groups 123500 27 4574.0741

Total 164666.667 29

As we can see, the mean level of freight charges of steel 555 is higher than that of either cement (485) or food grain (470). But are these differences statistically significant? According to the test result F = 4.5. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is greater than the critical value, we reject the null hypothesis. On conducting scheffe’s test. We obtain that-

Pair of treatments Difference in sample mean

Critical difference Conclusion at 5% level

Cement &steel 70 78.33 Differ insignificantlySteel &food grain 85 78.33 Differ significantly

Food grain &cement 15 78.33 Differ insignificantly

From RAIPUR-BASNA-

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

cement 10 6450 645 1361.111steel 10 7150 715 1694.444food grain 10 7250 725 1250


Between Groups 38000 2 19000 13.23871 9.84E-05 3.354131

32

Within Groups 38750 27 1435.18519

Total 76750 29

As we can see, the mean level of freight charges of Food grain 725 is higher than that of either cement (645) or steel (715). But are these differences statistically significant? According to the test result F = 13.23871. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is greater than the critical value, we reject the null hypothesis. On conducting scheffe’s test. We obtain that-



Cement &steel 70 43.88 Differ significantlySteel &food grain 10 43.88 Differ insignificantly

Food grain &cement 80 43.88 Differ significantly

FROM RAIPUR DEPOT-ABHANPUR-


cement 10 3800 3802333.33

3

steel 10 4750 4754027.77

8

food grain 10 4720 4729173.33

3


Between Groups58326.666

7 229163.33

35.63200

10.00903

13.35413082

9

Within Groups 139810 275178.148

1

Total 198136.66 29

33

7






FROM RAIPUR DEPOT-DEOBHOG-

SUMMARY

Groups Count Sum AverageVarianc

e

cement 10 5550 55526916.6

7

steel 10 6550 65521916.6

7

food grain 10 6400 64033777.7

8



7 229083.333

31.05615

30.36172

33.35413

1Within Groups 743500 27 27537.037

Total801666.

7 29

34

As we can see, the mean level of freight charges of steel 655 is higher than that of either cement (555) or food grain (640). But are these differences statistically significant? According to the test result F = 1.0561. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is smaller than the critical value, we accept the null hypothesis. There is no significant difference in the means of freight charges of steel, cement & food grain industry.

From Raipur depot-SARAIPALI-


cement 10 6850 6852805.55

6

steel 10 7800 7802888.88

9

food grain 10 7400 7405444.44

4


Between Groups 45500 2 227506.12718

20.00639

6 3.354130829Within Groups 100250 27 3712.963

Total 145750 29


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Food grain &cement 55 70.57 Differ insignificantly

From RAIPUR DEPOT-CHHURA-

SUMMARY


e

cement 10 5450 5453027.77

8

steel 10 6450 6451361.11

1

food grain 10 6050 6051361.11

1



7 225333.333

313.2173

99.95E-

053.35413

1

Within Groups 51750 271916.6666

7

Total102416.

7 29

36






FROM RAIPUR DEPOT-DANTEWARA-


cement 10 12160 121620226.6

7

steel 10 12870 12876556.66

7

food grain 10 12750 12752361.11

1



7 214443.3

31.48673

30.24404048

33.35413

1

Within Groups 262300 279714.81

5

Total 291186.6 29

37

7

As we can see, the mean level of freight charges of steel 1287 is higher than that of either cement (1216) or food grain (1275). But are these differences statistically significant? According to the test result F = 1.4867. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is smaller than the critical value, we accept the null hypothesis. There is no significant difference in the means of freight charges of steel, cement & food grain industry.

FROM RAIPUR DEPOT-KANKER-

SUMMARY


e

cement 10 8245 824.513724.7

2

steel 10 8295 829.54591.38

9

food grain 10 8700 8702888.88

9



7 26225.8333

30.88080

60.42602

23.35413

1

Within Groups 190845 277068.3333

3

Total203296.

7 29

As we can see, the mean level of freight charges of food grain 870 is higher than that of either cement (824.5) or steel (829.5). But are these differences statistically significant? According to the test result F = 0.880806. With a critical value of .05, the critical F = 3.354. Therefore, since

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the F statistic is smaller than the critical value, we accept the null hypothesis. There is no significant difference in the means of freight charges of steel, cement & food grain industry.

FROM RAIPUR DEPOT-BIJAPUR-


cement 10 13740 13742865.55

6

steel 10 15030 15035417.77

8

food grain 10 15770 15775401.11

1



7 2105543.

323.1379

5 1.40099E-063.35413

1


1

Total334246.6

7 29

As we can see, the mean level of freight charges of food grain 1577 is higher than that of either cement (1374) or steel (1503). But are these differences statistically significant? According to the test result F =23.13795. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is greater than the critical value, we reject the null hypothesis. On conducting scheffe’s test. We obtain that-



Cement &steel 129 78.22 Differ significantly

39

Steel &food grain 74 78.22 Differ insignificantlyFood grain &cement 203 78.22 Differ significantly

FROM RAIPUR DEPOT-JAGDALPUR-

SUMMARY


e

cement 10 9310 9314387.77

8

steel 10 11260 11268315.55

6

food grain 10 11000 11006666.66

7


Between Groups224206.

7 2112103.33

317.3624

21.42E-

053.35413

1

Within Groups 174330 276456.6666

7

Total398536.

7 29


40





FROM RAIPUR DEPOT-BASTAR-


cement 10 9710 971 25410

steel 10 11920 119215395.5

6

food grain 10 12400 124014888.8

9



7 2205843.

311.0878

20.00030534

13.35413

1


1

Total912936.6

7 29

As we can see, the mean level of freight charges of food grain 1240 is higher than that of either cement (971) or steel (1192). But are these differences statistically significant? According to the test result F =11.08782. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is greater than the critical value, we reject the null hypothesis. On conducting scheffe’s test. We obtain that-

41





FROM RAIPUR DEPOT-SUKMA-


cement 10 11350 1135 10444.44steel 10 13625 1362.5 1562.5food grain 10 13450 1345 3027.778


Between Groups 320541.7 2 160270.833 31.980147.57E-

08 3.354131Within Groups 135312.5 27 5011.57407

Total 455854.2 29

As we can see, the mean level of freight charges of steel 1362.5 is higher than that of either cement (1135) or food grain (1345). But are these differences statistically significant? According to the test result F = 31.98014. With a critical value of .05, the critical F = 3.354. Therefore, since the F statistic is greater than the critical value, we reject the null hypothesis. On conducting, scheffe’s test. We obtain that-



Cement &steel 227.5 84.25 Differ significantlySteel &food grain 17.5 84.25 Differ insignificantly


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CHAPTER-6

FINDINGS OF THE STUDY-

43

1) From factor analysis, we found that product mix factors contribute more to setting freight rate of road transport steel, cement and food grain industry.

2) We found that client-service factors influences the fleet availability of road transport in Raipur, C.G.

3) On conducting one-way anova test on different freight rates of 3 industries from Raipur depot-various destinations, we analysed that-

From Raipur depot- Mahasamund, the freight rate of steel and food grain differs significantly.

From Raipur depot-Basna, the freight rate of cement &steel, food grain & cement differs significantly.

From Raipur depot-Abhanpur, the freight rate of cement &steel, food grain & cement differs significantly.

From Raipur depot-Deobhog, there is no significant difference in the freight charges of steel, cement & food grain industry.

From Raipur depot-Saraipali, the freight rate of cement and steel differs significantly. From Raipur depot- Chhura, the freight rate of cement &steel, food grain & cement

differs significantly. From Raipur depot-Dantewara, there is no significant difference in the freight rate

of steel, cement & food grain industry. From Raipur depot-Kanker, there is no significant difference in the freight charges of

steel, cement & food grain industry. From Raipur depot-Bijapur, the freight rate of cement &steel, food grain & cement

differs significantly. From Raipur depot-Jagdalpur, the freight rate of cement &steel, food grain & cement

differs significantly. From Raipur depot-Bastar, the freight rate of cement &steel, food grain & cement

differs significantly. From Raipur depot-Sukma, the freight rate of cement &steel, food grain & cement

differs significantly.

RECOMMENDATIONS-

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Keeping strong product mix factors like, branding, packaging, good quality, lot size etc. by companies will help to attract transporters will help to gain cost effectiveness and availability in road transport.

The difference in freight charges for different destinations will help to plan out an effective routing and scheduling of freight. It will be an effective measure to reduce their vulnerability to increasing diesel prices and help in maintaining profit margins.

Customer service plays a dominant role in getting the fleet all over the year from transporters. Trade relations influence the fleet movement of transporters to a great extent, so to overcome competition and meeting market demand these service factors should be taken care of.

The role of brokers in obtaining business and deciding freight rates especially in specialized traffic where shippers are likely to dominate also has an impact on variation in freight rates in road transport in Raipur. It is required to opt for those transporters that have less intermediator in profit sharing.

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CONCLUSION-

For determining the variation in freight rates in road transport for steel, cement and food grain industry, I employed primary data and, anova test is applied which indicates that there is a significant difference in the freight rates in three industries for difference destinations. Factor analysis was applied which indicates that product mix factors has a direct impact in setting freight rates for commodities. The study also revealed that customer service factors influences fleet availability in road transport. The null hypothesis is rejected and the alternate hypothesis is accepted in this study, H1: there is no significant difference in the mean freight rates of steel, cement and food grain industry.

APPENDICES-

SURVEY

46

Dear Sir/Madam:

As a MBA student of Shri Shankracharya technical college, Junwani, Bhiali. I am conducting this survey as part of academic curriculum. Please help us better understand your service by completing the survey below. Thank you for your time.

Sincerely,

Sunita Burman MBA/3rd sem.

Here are some statements about the availability of fleet/transport vehicles in your company. Please indicate your level of agreement with each statement in this 7 point Likert scale, where point 4 is neutral, 1 indicates high level of disagreement and 7 indicates high level of agreement.

Q-1

Strongly Highly Disagree Neutral Agree Highly StronglyDisagree disagree agree agree

a) I prefer fleet/vehicles that cover greater distances for clients.

1 2 3 4 5 6 7

b) The distance of the destination point is important to me.

1 2 3 4 5 6 7

Q-2


a) I prefer fleet/vehicles that incur high profit margin by clients.

1 2 3 4 5 6 7

b) The profit margin I get from vehicles is important to me.

1 2 3 4 5 6 7

Q-3

47


a) I prefer fleet/vehicles for clients having better trade relations with us.

1 2 3 4 5 6 7

b) The trade relations with clients are important to me.

1 2 3 4 5 6 7

Q-4


a) I prefer low movement of fleet/vehicles during holidays and festivals.

1 2 3 4 5 6 7

b) The occurrence of festivals and holidays during the year is important for me.

1 2 3 4 5 6 7

Q-5


c) I prefer providing fleet/vehicle services to clients having least seasonal variation in supplies.

1 2 3 4 5 6 7

d) The seasonal variation in the supplies of clients is important to me.

1 2 3 4 5 6 7

Q-6


a) I prefer providing fleet/vehicle services to clients having good market demand.

1 2 3 4 5 6 7

b) The existing demand of the products of our clients is important for me.

1 2 3 4 5 6 7

Here are some statements about the market conditions that have a direct impact on freight rates. Please assign points from 1-10, where

48

1- Indicates low impact on freight rates.

10 - Indicates high impact on freight rates

CONDITIONS Level of impact on freight rates

a) Market demand 1 2 3 4 5 6 7 8 9 10

b) Quantity or size of lot 1 2 3 4 5 6 7 8 9 10

c) Level of competition 1 2 3 4 5 6 7 8 9 10

d) Destination point 1 2 3 4 5 6 7 8 9 10

e) Delivery period 1 2 3 4 5 6 7 8 9 10

f) Trade relations 1 2 3 4 5 6 7 8 9 10

Please provide information regarding freight charges from various depots to destination in Chhatisgarh.

From Raipur Depot- Destinations

Commodity Mahasamund Basna Abhanpur Deobhog Saraipali Chhura

Cement-

ACC

Ultra Tech

Century

Ambuja

Food grain

Iron & steel

Commodity Dantewara Kanker Bijapur Jagdalpur Bastar Sukma

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Cement-

ACC

Ultra Tech

Century

Ambuja

Food grain

Iron & steel

Name of the transporter _________________________Contact-_________________________

Thank you very much for participating in this valuable survey!

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