MGT 314 Final Project on Lafarge Surma Cement

8/10/2019 MGT 314 Final Project on Lafarge Surma Cement

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TABLE OF CONTENTS

INTRODUCTION ............................................................................................................................................................. 2

VISION ............................................................................................................................................................................ 3

COMMITMENTS ............................................................................................................................................................. 3

ORGANIZATIONAL HIERARCHY ...................................................................................................................................... 4

COMPETITOR ANALYSIS ................................................................................................................................................. 5

MARKETING STRATEGY .................................................................................................................................................. 5

PRODUCT ................................................................................................................................................................... 5

PRICE .......................................................................................................................................................................... 6

PLACE ......................................................................................................................................................................... 6

PROMOTION .............................................................................................................................................................. 6

COLLECTION OF DATA ................................................................................................................................................... 7

FORECASTING TECHNIQUES AND CALCULATIONS......................................................................................................... 9

Moving Average (MA) ................................................................................................................................................ 9

A. 3-period moving average ............................................................................................................................. 9

B. Sixth-Period Moving Average .......................................................................................................................... 10

C. Ninth-Period Moving Average ......................................................................................................................... 12

Weighted Moving Average ...................................................................................................................................... 14

Three-period Weighted Moving Average ............................................................................................................ 14

B. Four-period Weighted Moving Average .......................................................................................................... 15

Exponential Smoothing ................................................................................................................................................ 18

Linear Regression Analysis ........................................................................................................................................... 20

Mean Absolute Deviation (MAD) ................................................................................................................................. 24

Mean Squared Error (MSE) .......................................................................................................................................... 25

Mean Absolute Percentage Error (MAPE) ................................................................................................................... 27

Conclusion ................................................................................................................................................................... 29


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INTRODUCTION

Lafarge Surma Cement Ltd. is a sister concern of Lafarge and Cementos Mollins. It was

incorporated on 11 November 1997 as a private limited company in Bangladesh under the

Companies Act 1994 having its registered office in Dhaka. On 20 January 2003 Lafarge Surma

Cement Ltd. was made into a public limited company. The Company is listed in Dhaka and

Chittagong Stock Exchange. Today, Lafarge Surma Cement Ltd. has more than 20,000

shareholders.

In November 2000, the two Governments of India and Bangladesh signed a historic agreement

through exchange of letters in order to support this unique cross border commercial venture and

till date it is the only cross border industrial venture between the two countries. Since

Bangladesh does not have any commercial deposit of limestone, the agreement provides for

uninterrupted supply of limestone to the cement plant at Chhatak in Bangladesh by a 17 km long

belt conveyor from the quarry located in the state of Meghalaya. The company in Bangladesh,

Lafarge Surma Cement Ltd. wholly owns a subsidiary company Lafarge Umiam Mining Private

Ltd. (LUMPL) being registered in India, which operates its quarry at Nongtrai in Meghalaya.

This commercial venture with an investment of USD 280 million, which is one of the largest

foreign investments in Bangladesh, has been financed by Lafarge of France, world leader in

building materials, Cementos Molins of Spain, leading Bangladeshi business houses together

with International Finance Corporation (IFCThe World Bank Group), the Asian Development

Bank (ADB), German Development Bank (DEG), European Investment Bank (EIB), and the

Netherlands Development Finance Company (FMO).

Lafarge Group, with 176 years of experience, holds worlds top-ranking position in Cement,Aggregates, Concrete and Gypsum. It operates in 64 countries with around 68,000 employees.

Lafarge is named as one of the 100 Most Sustainable Companies in the World.

Now, after three years of production operations, they are producing world class clinker and

cement which is a demonstration of the sophisticated and state-of-the-art machineries and


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processes of our plant at Chhatak. The Company is already meeting about 8% of the total market

need for cement and 10% of total clinker requirements of Bangladesh market whereas we

continue to enjoy strong growth rates. By supplying clinker to other cement producers in the

market, we contribute some USD 50~60 million per annum worth of foreign currency savings

for the country. We contribute around BDT 1 (one) billion per annum as government revenue to

the national exchequer of Bangladesh. About 5,000 people depend on our business directly or

indirectly for their livelihood.

They believe that cement is an essential material that addresses vital needs of the construction

sector. They are optimistic to meet the growing needs for housing and infrastructure in the

construction sector of Bangladesh.

VISION

To be the undisputed leader in building materials in Bangladesh through

Excellence in all areas of operations in world class standards

Harnessing our strength as the only cement producer in Bangladesh

Sustainable growth that respects the environment and the community

COMMITMENTS

Offering highest quality of product and services that exceeds our customers

expectations.

Giving our people an enabling environment that nurtures their talent and opportunities to

give the best for the organization.

Contributing to build a better world for our communities.

Delivering the value creation that our shareholders expect.


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ORGANIZATIONAL HIERARCHY

Mr. Tarek Samir

Ahmed Elba

CEO

Board of

Directors

Mr. Masud Khan

Finance Director

Ms. Sayeda Tahya

Hossain

Hr & CorporateAffairs Director

Mr. Kazi Khalid

Mahmood

Commercial

Director

Mr. Asim

Chottopadyay

Senior Vice

President Operation

Mr. Mohammed

Arif Bhuiyan

Suppy Chain

Director

Mr. Narayan

Prasad Sharma

Vice President

and Director,LUMPL

Mr. Kazi Mizanur

Rahman

Comapany Secretary


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COMPETITOR ANALYSIS

There is only one type of competitor and that is Direct Competitors. There are no indirect

competitors because there are no substitutes for cement. There are many competitors of Lafarge

Surma Cement Ltd. and some them are the established local brands and also some multinational

brands. The companies which are the competitors of Lafarge Surma Cement Ltd are mainly the

multinational companies which are on the radical rating scale of Lafarge Surma Cement Ltd like

Heidelberg Cement Bangladesh Ltd, Cemex, Holcim, etc. be it in terms of brand image, product

quality and sales, each of the companies have their own unique features for which they are sited

in the higher ranks. However, Lafarge Surma Cement Ltd sees each of these companies as their

competitors and they are continuously fighting with these companies to reach their preferred

position in the market. Moreover, the cement companies which are ranked below the company

such as Shah Cement and Crown Cement are also viewed as competitors and they keep a look

out for these companies activities. Hence, Lafarge Surma Cement Bangladesh Ltd faces intense

competition in the market place.

MARKETING STRATEGY

PRODUCT

The products of Lafarge Surma Cement Ltd are Gray Cement and Clinker Cement. They are the

most preferred brands for long lasting constructions. They have the following features:

Consistent proven quality.


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Long age strength ensuring continuous strength gain.

Higher durability- sustainable from generation to generation.

Higher workability.

Low heat of hydration.

Better resistance to sulphate and other chemical attack.

PRICE

The price of the products of Lafarge Surma Cement Ltd is a little higher than the other cements

in the market. This is due to their high quality of cement. However, commissions are given to

their customers especially the retailers based on the volume taken.

PLACE

Lafarge Surma Cement Ltd has only one factory of their own which is located at Chattak under

Sunamgonj district. Lafarge Surma Cement Ltd. will extract and procss the basic raw materials

like limestone and shale from its from its own quarry in Meghalaya, India. A 17 km crossborder

belt conveyor will be installed to link the quarry with the cement plant for transportation of raw

materials.

PROMOTION

Lafarge Surma Cement Ltd uses a combination of promotional activities to advertise their

products. The most widely used instrument is Newspapers. They keep on updating about their

products to the local customers through both English and Bangla newspapers like Daily Star,

Financial Express, Amader Shomoy, Prothom Alo, Bangladesh Protidin etc. Moreover, they also


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go for Billboard advertisement to attract the customers. They also use brochures and posters to

inform their customers.

COLLECTION OF DATA

We have collected 20 months sales data from Lafarge Surma Cement Ltd. We have collected the

data to make the forecast and the compare between the forecast and the actual data by using

different forecasting and comparing techniques. Our purpose was to use these data for

forecasting and to compare between the forecast and the actual data of the sales of from Lafarge

Surma Cement Ltd. by using following forecasting and comparing techniques:

1.

We used the data to find out the 3-period moving average to forecast the production forthe 4

thperiod21

stperiods.

2. We used the data to find out the 6-period moving average to forecast the production for

the 4th

period21stperiods.

3. We used the data to find out the 9-period moving average to forecast the production for

the 4th

period21stperiods.

4.

We used the data to find out the 3 and 4- period weighted average to forecast the

production for the 4th

period21stperiods.

5. We used the data to find out Exponential Smoothing with =.05, =.1 and = 0.4 to

forecast the sales for the 4th

period21stperiods.

6. We used the data to plot the data to see whether the data follow a trend. Determine the

linear trend line for production. Use the trend equation to predict the production for the

5th

period30th

periods. Use Excel to do this experiment.

7. We used the data to compare the performance of the five methods by using the mean

absolute deviation (MAD) as the performance criterion.

8.

We used the data compare the performance of the five methods by using the Mean

Squared Error (MSE) as the performance criterion.

9. We used the data to compare the performance of the five methods by using the mean

absolute percentage error (MAPE) as the performance criterion.


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Here is the raw data for the analysis that is collected from the Lafarge Surma Cement Ltd. at

Gulshan on Cement sells figure in BDT for 20 months.

**Sales in BDT (Crore).

Sales

(Taka Crore)

2010 JAN 72.6

FEB 72.1

MAR 73

APR 64

MAY 65.4

JUN 27

JUL 29

AUG 26

SEP 26

OCT 37

NOV 39

DEC 34.4

2011 JAN 39

FEB 41

MAR 36.1

APR 79

MAY 80

JUN 76.5

JUL 42

AUG 45


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FORECASTING TECHNIQUES AND CALCULATIONS

MOVING AVERAGE (MA)

A. 3-PERIOD MOVING AVERAGE

Technique that averages a number of recent actual data values, updated as new values become

available is known as moving average forecast. The moving average forecast can be computedusing the following formula.

Formula

n

A

MAF

n

i

it

nt

1

Where,

tF

= Forecast for time period t

i = an index that corresponds to time period

n = No. of period in the moving average

iA = Actual value in period t-i

MA= Moving average


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Working Procedure of 3-Period Moving Average:

STEP 1: Get actual data for Period 1 to 3 from the month of January.

Period 1 to Period 3 shows the actual data.

STEP 2: Calculate the sum of last three months data (actual or forecast whatever we have).

We could find out the sum of last three months by using the formula-

=SUM (Period1:Period3) or

= (Period 1+Period 2+Period3)

STEP 3: Calculate the average by dividing the sum of last three months actual data by the no.

of correspondent time period.

To find out the average of last three periods by using the Formula-

SUM (Period1:Period3)/3"or, =(Period 1+ Period 2+ Period 3)/3

STEP 4: To find out the next months forecasted data we have to simply calculate last three

months data (actual or forecasted whatever we have) and divide that by the no of correspondent

time period.

B. SIXTH-PERIOD MOVING AVERAGE


available is known as moving average forecast. The moving average forecast can be computed

using the following formula.


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Formula

n

A

MAF

n

i

it

nt

1

Where,

tF





MA= Moving average

Working Procedure of Six-Period Moving Average:

STEP 1: Get actual data for Period 1 to 6.


STEP 2: Calculate the sum of last six months data (actual or forecast whatever we have).

We could find out the sum of last six months by using the formula

=SUM (Period1:Period6).

STEP 3: Calculate the average by dividing the sum of last six months actual data by the no of

correspondent time period.

To find out the average of last six periods by using the formula-

=SUM (Period1:Period6)/6"


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STEP 4: To find out the next months forecasted data we have to simply calculate last six

months data (actual or forecasted whatever we have) and divide that by the no of


C. NINTH-PERIOD MOVING AVERAGE


available is known as moving average forecast. The moving average forecast can be computed

using the following formula.

Formula

n

A

MAF

n

i

it

nt

1

Where,

tF= Forecast for time period t




MA= Moving average


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Working Procedure of Ninth-Period Moving Average:



STEP 2: Calculate the sum of last nine months data (actual or forecast whatever we have).

We could find out the sum of last nine months by using the formula

=SUM (Period1:Period9).

STEP 3: Calculate the average by dividing the sum of last nine months actual data by the no

of correspondent time period.

To find out the average of last nine periods by using the formula-

=SUM (Period1:Period9)/9"

STEP 4: To find out the next months forecasted data we have to simply calculate last nine

months data (actual or forecasted whatever we have) and divide that by the no of


Sales(Taka Crore) 3-Month

MA

6-Month

MA

9-Month

MA

2010 JAN 72.6FEB 72.1

MAR 73

APR 64 72.6

MAY 65.4 69.7

JUN 27 67.5

JUL 29 52.1 62.4

AUG 26 40.5 55.1

SEP 26 27.3 47.4

OCT 37 27 39.6 50.6

NOV 39 29.7 35.1 46.6

DEC 34.4 34 30.7 42.92011 JAN 39 36.8 31.9 38.6

FEB 41 37.5 33.6 35.9

MAR 36.1 38.1 36.1 33.2

APR 79 38.7 37.8 34.2

MAY 80 52 44.8 39.7

JUN 76.5 65 51.6 45.7

JUL 42 78.5 58.6 51.3


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WEIGHTED MOVING AVERAGE

THREE-PERIOD WEIGHTED MOVING AVERAGE

Weighted moving average is similar to the moving average, except that it assigns more weight tothe most recent values in a time series. For example, the most recent value might be assigned a

value of 0.4, the next most recent value weight of 0.3, the next after that a weight of 0.2, the next

after that a weight of 0.1 where the total sum of the weights is 1. The weighted moving average

can be computed by the following formula.

Formula

1122)2(2)1(1 ...... ttntnntnntnt AwAwAwAwAwF

Where,



nw

= Weight of the Actual Value

tA = Actual value in period t

AUG 45 66.2 59.1 51.9

SEP 54.5 59.8 52.6


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Working Procedure of Weighted Moving Average:



STEP 2: Select the weight, what we are going to assign for the weighted moving average.

We have selected weight as 0.5, 0.3, 0.2, where-

0.5 represents the most recent periods weight.

0.3 represents the next recent periods weight.


STEP 3: Multiply the data with their correspondent weight.

(Most recent periods * 0.5)

(Next recent periods * 0.3)


STEP 4: Calculate the sum of multiplied the data.

(Most recent periods * 0.5) + (next recent periods * 0.3) + (next recent periods * 0.2)

STEP 5: Step 4s calculated data will be the forecasted value for the next period. To find out

the next forecasted data we have to simply calculate last three periods data (actual or

forecasted whatever we have) and assign correspondent weights.

B. FOUR-PERIOD WEIGHTED MOVING AVERAGE

Weighted moving average is similar to the moving average, except that it assigns more weight

to the most recent values in a time series. The most recent value might be assigned a value of

0.4, the next most recent value weight of 0.3, the next after that a weight of 0.2, the next after

that a weight of 0.1 where the total sum of the weights is 1. The weighted moving average can

be computed by the following formula.


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Formula

1122)2(2)1(1 ...... ttntnntnntnt AwAwAwAwAwF

Where,



nw = Weight of the Actual Value

tA = Actual value in period t

Working Procedure of Weighted Moving Average:

STEP 1 : Get actual data for Period 1 to 4.


STEP 2: Select the weight, what we are going to assign for the weighted moving average.

We have selected weight as 0.4, 0.3, 0.2, 0.1, where-

0.4 represents the most recent periods weight.




STEP 3: Multiply the data with their correspondent weight.

(Most recent periods * 0.4)




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STEP 4: Calculate the sum of multiplied the data.

(Most recent periods * 0.4) + (next recent periods * 0.3) + (next recent periods * 0.2) +(next recent periods * 0.1)

STEP 5: Step 4s calculated data will be the forecasted value for the next period. To find out

the next forecasted data we have to simply calculate last four periods data (actual or

forecasted whatever we have) and assign correspondent weights.

Sales(Taka Crore) 3-Month WMA 4-Month WMA

2010 JAN 72.6

FEB 72.1

MAR 73

APR 64 72.7

MAY 65.4 68.3 69.2

JUN 27 66.5 67.2

JUL 29 45.9 50.5

AUG 26 35.7 39.2

SEP 26 27.1 31

OCT 37 26.6 26.7

NOV 39 31.5 30.7

DEC 34.4 35.8 34.5

2011 JAN 39 36.3 35.5

FEB 41 37.6 37.4

MAR 36.1 39.1 38.9

APR 79 38.2 38

MAY 80 58.5 54.5

JUN 76.5 70.9 67

JUL 42 78.1 74AUG 45 60 63.7

SEP 50.4 53.9


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EXPONENTIAL SMOOTHING

Weighted averaging method based on previous forecast plus a percentage of the forecast error. It

is sophisticated weighted average method that is still relatively easy to use and understand.

Formula

Next forecast = Previous forecast + (actualprevious forecast)

111

tttt FAFF

Where,

1tF = Forecast for the previous time period

= The smoothing constant = % of the error

1tA

= Actual value in the previous period

tF


The value of commonly usedranges from 0.05 to 0.5. Low values are used when the average

tends to be stable. Higher values of are used when the average is not stable.

Working Procedure of Exponential Smoothing:

For = 0.05, 0.1 and 0.4

STEP 1: Get actual data.


STEP 2: Find out the difference between actual and forecasted data. For the 2nd

period, as we

dont have the forecasted value so we are going to take the period 1st actual value as

period 2nd

forecasted value.

STEP 3: Multiply the difference between actual and forecasted period with the error

constants.


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STEP 4: Add forecasted data and error constant considered difference of actual and

forecasted data.

STEP 5: Step4s calculated data will be the forecasted value for the next period. To find out

the next years forecasted data we have to simply use this formula for all the next period

[Forecasted+ (smoothing constant*(actual-forecasted)]

Sales(Taka Crore) =.05 =.1 =.4

2010 JAN 72.6

FEB 72.1 72.6 72.6 72.6

MAR 73 72.6 72.6 72.4

APR 64 72.62 72.6 72.6

MAY 65.4 72.2 71.7 69.2

JUN 27 71.9 71.1 67.7

JUL 29 69.8 66.7 51.4

AUG 26 67.8 62.9 42.4

SEP 26 65.7 59.2 35.8

OCT 37 63.7 55.9 31.9

NOV 39 62.4 54 33.9DEC 34.4 61.2 52.5 35.9

2011 JAN 39 59.9 50.7 35.3

FEB 41 58.9 49.5 36.8

MAR 36.1 58 48.7 38.5

APR 79 56.9 47.4 37.5

MAY 80 58 50.6 54.1

JUN 76.5 59.1 53.5 64.5

JUL 42 60 55.8 69.3

AUG 45 59.1 54.4 58.4

SEP 58.4 53.5 53


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a t

LINEAR REGRESSION ANALYSIS

Analysis of linear regression involves developing an equation that will suitably describe the

trend. It may be linear or may not be. Linear trends are fairly common.

Formula:

A linear trend equation has the formtbaFt

Where,

tF


t= Specified no. of time periods from t= 0

a = Value of tF

at t= 0

b = Slope of the line


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The coefficients of the line a and b can be computed from the historical data using the

formulas:

22

ttn

yttynb

&

tby

n

tbya

Where,

t is the period, n is the number of periods and y is the actual demand.

Working Procedure of Trend Equation:

STEP 1: Get all the actual data and plot the data in the graph and find out n.

Period 1 to Period 20 shows the actual data where n is 17.

STEP 2: Define, t and y and calculate the sum of t and the sum of y.

Here, period is expressed as t and volume nosas y. and

STEP 3: Multiply t and y and calculate the sum of ty.

STEP 4: Find out t2and calculate the sum of t

2.

STEP 5: Calculate the square of sum of t

STEP 6: Calculate b by using formula, 22

ttn

yttynb

STEP 7: Calculate a by using formula, tbyn

tbya

STEP 8: Find out the trend line by using this formula tbaF and plot that in the graph.

STEP 9: Calculate each years forecasted data by using the same

Formula

tbaF Where t is the time for each and every period.


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Here, b= 1.19, and a= 35.5

The regression equation is

C2 = 35.5 + 1.19 C1

Predictor Coef SE Coef T P

Constant 35.527 9.444 3.76 0.002

C1 1.1924 0.9216 1.29 0.215

S = 18.6153 R-Sq = 10.0% R-Sq(adj) = 4.0%

Month t y(At) t Ty

Apr 1 64 1 64

May 2 65.4 4 130.8

Jun 3 27 9 81

Jul 4 29 16 116

Aug 5 26 25 130

Sep 6 26 36 156

Oct 7 37 49 259

Nov 8 39 64 312

Dec 9 34.4 81 309.6

Jan 10 39 100 390

Feb 11 41 121 451

Mar 12 36.1 144 433.2

Apr 13 79 169 1027

May 14 80 196 1120

Jun 15 76.5 225 1147.5

Jul 16 42 256 672

Aug 17 45 289 765

153 786.4 1785 7564.1


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From the above equation we can see the R (coefficient of determination) is only 10%. This

means that only 10% variation in sales is explained by the changes in the number of periods.

181614121086420

80

70

60

50

40

30

20

C1

C2

Scatterplot of C2 vs C1

Trend Analysis

161412108642

80

70

60

50

40

30

20

Index

C2

MAPE 34.910

MAD 15.718

MSD 305.761

Accuracy Measures

Actual

Fits

Variable

Trend Analysis Plot for C2Linear Trend Model

Yt = 35.53 + 1.19*t


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MEAN ABSOLUTE DEVIATION (MAD)

It is a significant factor when deciding among forecasting alternatives. Accuracy is based on the

historical error performance of a forecast. It is very important to find the historical errorperformance of different techniques. So, we can easily find which the best method is. There are

different methods of measuring historical errors. Mean absolute deviation (MAD) is the first one.

Formula

Mean absolute deviation (MAD): Average absolute error.

MAD = n

FA tt

Where,


n = No. of period

tA

= Actual value in period t

Here, we have to take absolute value of the error.

Working Procedure of Mean Absolute Deviation (MAD):

STEP 1: Get actual data and all the forecasted data for every techniques

Here we have four techniques of- 30 days moving average, linear forecasting method, weighted

moving average and Exponential smoothing.

STEP 2: Calculate the difference between actual data and forecasted data for every

techniques by using formula, ( tt FA

).


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STEP 3: Take the absolute value of the differences of actual and forecasted data for every

techniques by using formula, tt FA

.

STEP 4: Calculate the sum of absolute value of the differences of actual and forecasted data

for every techniques. tt FA

STEP 5: Divide the sum of absolute value of the differences of actual and forecasted data by

the no of the period, n for every six technique Using formula, n

FA tt

.

STEP 6: Follow STEP 1 to STEP 5 for every techniques and find out the MAD.

Results

MAD of 3-period moving average 15.13



MAD of 3-period weighted period moving average 13.31

MAD of 4-period weighted period moving average 14.94

MAD of Exponential Smoothing = .05, 21.82

= .1, 18.93

= .4, 12.89

MAD of Linear Regression Analysis 15.72

Recommendation

We recommend MAD of exponential smoothing where = 0.4because the value of it is lowest,

12.89 compare to others.

MEAN SQUARED ERROR (MSE)


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Mean Squared Error is the second methods of measuring error or accuracy of the forecasted data.

Formula

Mean squared error (MSE): Average of squared errors.

MSE =

1

2

n

FA tt

Where,


n = No. of period

tA


Working Procedure of Mean Squared Error (MSE):


Here we have five techniques of- 30 days moving average, linear forecasting method, weightedmoving average and Exponential smoothing.



).

STEP 3: Do squares of the differences of actual and forecasted value for every techniques by

using formula,

2)(

tt FA

.

STEP 4: Calculate the sum of the squares of the differences of actual and forecasted data for

every techniques,

2

tt FA .


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STEP 5: Divide the sum of squares of the differences of actual and forecasted data by the n-

1, no of the period for every technique using formula,

1

2

n

FA tt

.

STEP 6: Follow STEP 1 to STEP 5 for every techniques and find out the MSE.

Result

MSE of 3-period moving average

438


507


506

MSE of 3-period weighted period moving average

369

MSE of 4-period weighted period moving average

420

MSE of Exponential Smoothing

= .05, 674.4

= .1, 549.6 = .4, 342.5

MSE of Linear Regression Analysis

305.76

Recommendation

We recommend MAD of exponential smoothing where = 0.4because the value of it is lowest,

342.5 compare to others.

MEAN ABSOLUTE PERCENTAGE ERROR (MAPE)

Mean Absolute Percentage Error is the third most common methods of measuring error or the

accuracy of the forecasted data.

Formula


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Mean absolute percent error (MAPE): Average absolute percent error.

MAPE = nA

FA

t

tt100

Where,


n = No. of period

tA


Working Procedure of Mean Absolute Percentage Error (MAPE):




).

STEP 3: Take the absolute value of the differences of actual and forecasted data for every

techniques by using formula, tt FA

STEP 4: Divide the absolute value of the differences of actual and forecasted data by the

actual value for every techniques. t

tt

A

FA

STEP 5: Multiply, the division of the absolute value of the differences of actual and

forecasted data by the actual value for every techniques.

100

t

tt

A

FA

STEP 6: Divide the division of the sum of absolute value of the differences of actual and

forecasted data by the no of the period, n for every five technique using formula,


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n

A

FA

t

tt100

.

STEP 7: Follow STEP 1 to STEP 6 for every techniques and find out the MAPE.

Results

MAPE of 3-period moving average

36.3


40.9


26.1

MAPE of 3-period weighted period movingaverage

32.0

MAPE of 4-period weighted period movingaverage

36.7

MAPE of Exponential Smoothing

= .05, 61.4

= .1, 51.3 = .4, 31.7

MAPE of Linear Regression Analysis

34.91

Recommendation

We recommend MAPE of 9-period moving average because the value is lowest, 26.1 compare to

others.

CONCLUSION

Forecasting is a useful method for predicting future demand in all sectors. It helps managers plan

the system and help them plan the use of the system. Forecasts play an important role in the

planning process because they enable a manager to anticipate the future so he/she can plan

accordingly. A wide variety of forecasting techniques are in use. In many respects, they are quite

different from each other. In our analysis we have used some techniques of forecasting and they

are moving average, weighted average, exponential smoothing and linear trend method. Each

one of them has a unique advantage, easy to understand, provide good forecast and are reliable.

We have also used MAD, MSE and MAPE to analyze the accuracy of the forecasts. At the end

we would like conclude that we have tried our best to give the best results of forecasting and

hope that it will help the organization in the future.

MGT 314 Final Project on Lafarge Surma Cement

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Transcript of MGT 314 Final Project on Lafarge Surma Cement