1

Analysis on

Forecasting Sales Turnover of Aluminium Profiles

In Nikki Thai Aluminium Industries Limited, Bangladesh

Submitted to

Professor Abdullahil Azeem, PhD

Faculty of MBA Program

North South University

Submitted by

Name ID

Syeda Shotorupa Zafar 151 2772 660

Khalad Md. Masum 142 1252 060

Muhibul Haque 131 0492 060

Shahadat Ullah 141 1571 060

Mrinmoy Kumar Sikder 143 0879 660

Spring 2016

BUS 650: Operation Management (Section: 3)

Date of Submission: April 15, 2016

EXECUTIVE SUMMARY

This term paper provides an analysis and study of various forecasting techniques using the

monthly and yearly sales turnover data of Nikki Thai Aluminium Industry; a manufacturer of

different types thai aluminium sections. Monthly sales turnover data of the year 2011 to 2015

were taken and monthly forecasts were made for the year 2014 and 2015 ; using the monthly

sales turnover data of the year 2011, 2012 and 2013.

Three kind of forecasting techniques are used and short interpretations are added. The

forecasting techniques includes-

1) Simple Moving Average

2) Single exponential Smoothing

3) Simple Linear Regression

Each type of forecast has been individually analyzed and the comparison between the original

sales data and forecasted sales data has been shown.

A comprehensive comparison of real data and forecasted data using the three different

forecasting techniques was also portrayed.

Accuracy test was done to find the accuracy of these 3 forecasts. Following techniques were

used-

# Mean absolute deviation (MAD)

# Mean squared error (MSE)

It was found that; these three different forecasts have different degree of accuracy. The

forecast derived from the simple moving average had the least error or least deviation.

Though all these forecasts were quantitative forecasting, but the company has been mostly

using qualitative forecasting derived from the experiences and skills of its owners and

managers and has been quite successful in that way. This considers many qualitative factors

and does not have a concrete formula.

But we suggest that the company should use and consider both qualitative and quantitative

system to bring more accuracy and to create a more structured framework of forecasting

process.

Table of Contents Chapter One: Introduction ...................................................................................................................... 1

1.1. Company Overview: ............................................................................................................... 1

1.2. Objective: ................................................................................................................................ 1

1.3. Methodology: .......................................................................................................................... 1

a) Source of Data: .......................................................................................................................... 1

b) Nature of Data: ........................................................................................................................... 1

c) Methods of Data Analysis: ......................................................................................................... 2

1.4. Forecasting: ......................................................................................................................... 2

Chapter Two: Literature Review............................................................................................................. 3

2.1. Forecasting: ............................................................................................................................. 3

2.2. Types of Forecasting: .............................................................................................................. 3

1) Simple Moving Average ..................................................................................................... 4

2) Single Exponential Smoothing ............................................................................................ 4

3) Simple Linear Regression ................................................................................................... 4

2.3. Accuracy Test: ............................................................................................................................. 5

Chapter Three: Analysis ......................................................................................................................... 6

Actuary Test .......................................................................................................................................... 15

Findings and Conclusion ....................................................................................................................... 15

Page | 1

Chapter One: Introduction

1.1. Company Overview:

Nikki Thai Aluminium Ind. Ltd. was incorporated as a Private Limited Company. It started

production of Aluminium Section/Profile in 2010. Nikki Thai Aluminium Industries Ltd. is

pursuing the superiority aiming at high quality extruded aluminum products such as Door &

Window section, Curtain walling system, Bazar Section, Automotive section, Decoration

section etc. In a short span of time it has grown to be a leading Aluminium Extrusion

Company in Bangladesh.

1.2. Objective:

We have prepared this report as a partial fulfillment of the course BUS 650. There are few

objectives for preparing this report. Those objectives are,

a) How Nikki Thai Aluminium Ind. Limited forecasts its future product demand

b) Compare between the forecasting techniques used by the company and the discussed

technique in our course.

c) Find out the Gap (Accuracy of different kind of forecasting methods that the company

uses and the effectiveness of those forecasts to predict the sales turnover of the

company)

d) Provide Suggestions

1.3. Methodology:

a) Source of Data:

There are two sources of data, one is primary and other is secondary. We used both sources to

our data. Main sources are,

Company’s Business Unit

Personal references on condition of proper secrecy

b) Nature of Data:

We used both the qualitative and quantitative data to prepare this report.

Forecasting data from January 2011 to December 2015.

Page | 2

Yearly and monthly sales turnover data of general types of aluminium

profile/sections.

c) Methods of Data Analysis:

1.4. Forecasting:

Forecasting is a statement about the future based on something that can be qualitative or

quantitative.

Quantitative Method:

For doing analysis we will use 3 types of quantitative forecasting methods:

Name of

Method Explanation of Method Formula

Simple Moving

Average

Method

Uses a number of the recent actual data values in

generating a forecast.

Ft =∑ At−i

nt=1

n

Single

Exponential

Smoothing

Each new forecast is based on the previous

forecast plus a percentage of the difference

between that forecast and the actual value of the

series at that point.

Ft = Ft−1 + α(At−1 − Ft−1)

Simple Linear

Regression

Obtain an equation of a straight line that

minimizes the sum of squared vertical deviations

of data points from the line.

y = a + bx

Page | 3

Chapter Two: Literature Review

2.1.Forecasting:

Forecasting is the process of making statements about events whose actual outcomes

(typically) have not yet been observed. A commonplace example might be estimation for

some variable of interest at some specified future date. Prediction is a similar, but more

general term. Forecasting is an important tool for the future of demand condition. This is a

prediction of future events used for planning purposes. Forecasting is needed to aid in

determining what resources are needed, scheduling existing resources and acquiring

additional resources.

2.2.Types of Forecasting:

Mainly, the forecasting can be classified as:

Qualitative methods

Quantitative methods

Qualitative Methods:

Qualitative forecasting techniques generally employ the judgment of experts in the

appropriate field to generate forecasts. A key advantage of these procedures is that they can

be applied in situations where historical data are simply not available. Moreover, even when

historical data are available, significant changes in environmental conditions affecting the

relevant time series may make the use of past data irrelevant and questionable in forecasting

future values of the time series.

Quantitative Methods:

Include causal methods and time-series analysis. Causal methods are historical data on

independent variables, such as promotional campaigns, economic conditions and

competitors’ actions to predict demand or supply. Time series analysis is a statistical

approach that relies heavily on historical demand data to project the future size of demand or

supply and recognizes trends and seasonal patterns.

To make our analysis we have followed the following techniques of forecasting.

1) Simple Moving Average

2) Single exponential Smoothing

3) Simple Linear Regression

Page | 4

1) Simple Moving Average

The simple moving average forecast uses a number of the recent actual data values in

generating a forecast. The moving average forecast can be computed using the following

equation:

𝐹𝑡 =∑ 𝐴𝑡−𝑖

𝑛𝑡=1

𝑛

=𝐴𝑡−𝑛 + ⋯ + 𝐴𝑡−2 + 𝐴𝑡−1

𝑛

Where, Ft= Forecast for the time period t

At−1 = Actual value in period 𝑡 − 1

MAt =n period moving average

N= number of periods (data points) in the moving average

2) Single Exponential Smoothing

Single Exponential Smoothing largely overcomes the limitations of moving average models.

Each new forecast is based on the previous forecast plus a percentage of the difference

between that forecast and the actual value of the series at that point.

𝐹𝑡 = 𝐹𝑡−1 + 𝛼(𝐴𝑡−1 − 𝐹𝑡−1)

Where, Ft = Forecast for period t

Ft−1= Forecast for previous period

= Smoothing constant

At−1 = Actual demand for previous period

3) Simple Linear Regression

This method involves linear relationship between two variables. The object in linear

regression is to obtain an equation of a straight line that minimizes the sum of squared

vertical deviations of data points from the line. The equation is:

𝑦 = 𝑎 + 𝑏𝑥

Where, a = constant and b = slope of the line

And,

𝑎 = �̅� − 𝑏�̅�

𝑏 =𝑛 ∑ 𝑥𝑦 − ∑ 𝑥 ∑ 𝑦

𝑛 ∑ 𝑥2 − (∑ 𝑥2)

Page | 5

2.3. Accuracy Test:

After forecasting, applying different techniques, we have found out the forecasting accuracy

and the appropriate techniques for forecasting the sales of the product. To calculate the

forecasting accuracy, we have used the following techniques:

Mean absolute deviation (MAD)

Mean squared error (MSE)

MAD is the average absolute error, MSE is the average of squared error and MAPE is the

average percent error. The formulas used to compute MAD, MSE are given below:

MAD = ∑ |Actual − Forecast|

n

MSE = ∑(Actual − Forecast)2

n − 1

MSE is similar to the variance of a random sample; however, it is more sensitive to a few

large errors than MAD. Consequently, MAD, the average of the absolute discrepancies

between the actual and fitted values in a given time series is often preferred. If a model fits

the past time-series data perfectly, the MAD value would be zero. As the fit worsens, the

value of MAD increases. In other words, a small value of MAD is desirable. In addition,

when forecast errors are normally distributed, an estimate of the standard deviation of the

forecast error is given by 1.25 times MAD. We also considered MAPE test. The advantage of

this measure of accuracy is that MAPE is not dependent on the magnitude of the values of

demand.

Page | 6

Chapter Three: Analysis

Nikki Thai Aluminium Industries Limited basically manufacturing and selling different

types of thai aluminium items. Main products are categorized into two category Economy and

general category. Economy items are the 40% of total sales and 60% is the general category

items. Again all of these categories have four divisions, they are furniture, automotive, doors

and windows and others. We are working on the basis of main categories which are normal

and general items.

For the forecasting we are using different types of techniques and found out the forecasted

values. Finally we compare the actual data with the forecasted data.

Actual Demand:

ACTUAL DEMAND OF PRODUCTS

Amount

YEAR 2011 2012 2013 2014 2015

Month BDT '000 BDT '000 BDT '000 BDT '000 BDT '000

January 58,400.00 55,800.00 42,400.00 68,700.00 85,074.00

February 62,400.00 42,700.00 38,500.00 58,520.00 55,245.00

March 55,400.00 38,740.00 48,700.00 67,440.00 58,530.00

April 43,500.00 42,550.00 56,400.00 58,940.00 65,045.00

May 48,500.00 57,340.00 48,300.00 43,200.00 62,500.00

June 65,400.00 52,460.00 56,700.00 38,500.00 59,550.00

July 43,200.00 48,400.00 67,300.00 47,500.00 45,300.00

August 54,000.00 56,500.00 53,400.00 57,200.00 65,003.00

September 46,730.00 36,700.00 57,000.00 28,570.00 42,780.00

October 43,400.00 46,200.00 57,700.00 38,500.00 35,070.00

November 54,000.00 38,540.00 67,400.00 57,400.00 67,604.00

December 65,400.00 48,800.00 72,500.00 63,000.00 75,420.00

Page | 7

Interpretation:

From the chart and graph we can say that actual demand of products in Nikki Thai

Aluminium Industries Limited is fluctuated in every month. In January 2011 it was

58,400,000 BDT where as in December 2015 it was 65,400,000. Again in January 2014 it is

68,700,000 BDT and in December 2014 it is 63,000,000 BDT. But in 2015 the demand of

Aluminium items are higher than the previous years, but still it demand is fluctuated

tremendously.

Simple Moving Average:

The simple moving average forecast uses a number of the recent actual data values in

generating a forecast. Here we used the actual data of 2011, 2012 & 2013 and forecasted the

demand for 2014 & 2015 for each month. For this we summed the demand for previous 3

years and divided by the number of period “n” and found the demand for a particular month.

So, the forecasted demand for 2014 & 2015 are,

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

ACTUAL DEMAND

2011

2012

2013

2014

2015

Page | 8

Forecasted Demand for 2014 & 2015

Amount

YEAR 2011 2012 2013 2014 2015

Month BDT '000 BDT '000 BDT '000 BDT '000 BDT '000

January 58,400 55,800 42,400 52,200 50,133

February 62,400 42,700 38,500 47,867 43,022

March 55,400 38,740 48,700 47,613 45,018

April 43,500 42,550 56,400 47,483 48,811

May 48,500 57,340 48,300 51,380 52,340

June 65,400 52,460 56,700 58,187 55,782

July 43,200 48,400 67,300 52,967 56,222

August 54,000 56,500 53,400 54,633 54,844

September 46,730 36,700 57,000 46,810 46,837

October 43,400 46,200 57,700 49,100 51,000

November 54,000 38,540 67,400 53,313 53,084

December 65,400 48,800 72,500 62,233 61,178

Actual Demand VS Forecasted Demand by using Simple Moving Average:

Now we can see that there are differences between the actual demand and the forecasted

demand for the years 2014 & 2015. Comparisons are shown in the graph.

- 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000

Forecasted Demand for 2014 & 2015

2011

2012

2013

2014

2015

Page | 9

Interpretation:

Here we can see, there is a huge difference between actual and forecasted demand in 2014. In

some month the actual demand is higher than the forecasted demand like January, February,

March and April. But in some month it’s the actual demand is lower and also same like June,

July, and December and so on.

Interpretation:

Here we can see, there is a huge difference between actual and forecasted demand in 2015. In

some month the actual demand is higher than the forecasted demand like January, February,

March and April, May. But in some month it’s the actual demand is lower and also same like

June, July, and October and so on.

- 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000

Year 2014 (Actual VS Forecasted)

Actual 2014 Forecasted 2014

-

20,000

40,000

60,000

80,000

100,000

Year 2015 (Actual VS Forecasted)

Actual 2015 Forecasted 2015

Page | 10

Single Exponential Smoothing:

Here for the single exponential analysis we used the previous forecasting data and actual data

to find out the next periods forecasted demand. For the simplicity our calculation we

calculated forecasted demand for the year 2013 with the base of 2011 & 2012. After that we

calculated forecasted demand for 2014 & 2015 on the basis of single exponential smoothing.

Our forecasted demands are given below,

Forecasted Demand for 2014 & 2015

Amount

YEAR 2011 2012 2013 2014 2015

Month BDT '000 BDT '000 BDT '000 BDT '000 BDT '000

January 58,400 55,800 57,100 55,630 55,287

February 62,400 42,700 52,550 51,145 50,817

March 55,400 38,740 47,070 47,233 47,271

April 43,500 42,550 43,025 44,363 44,675

May 48,500 57,340 52,920 52,458 52,350

June 65,400 52,460 58,930 58,707 58,655

July 43,200 48,400 45,800 47,950 48,452

August 54,000 56,500 55,250 55,065 55,022

September 46,730 36,700 41,715 43,244 43,600

October 43,400 46,200 44,800 46,090 46,391

November 54,000 38,540 46,270 48,383 48,876

December 65,400 48,800 57,100 58,640 58,999

Comparison Between Actual Vs Forecasted:

-

20,000

40,000

60,000

80,000Year 2014 (Actual VS Forecasted)

Actual 2014 Forecasted 2014

Page | 11

Interpretation:

In this graph it’s clear that, the actual demand and forecasted demand is almost same. So

there is less forecasting error.

Interpretation:

In this graph it’s clear that, the actual demand and forecasted demand is same. So there is less

forecasting error.

Simple Linear Regression:

For the forecasting demand of 2014, we assumed the months as “X” and Demand for 2013 as

“Y” and on the basis of linear equation y=a+bx, we found out the forecasted demand for 2014

& 2015. For this we found out the values of “a” and “b”.

For the forecasting of the year 2014, we found out the values of “a” is 40,000; “b” is 2,388

and so the equation is,

Y = 40,000+2,388*X

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Year 2015 (Actual VS Forecasted)

Actual 2015 Forecasted 2015

Page | 12

And then we find out the demand for the year 2014 on the basis of above equation and

the results are given below,

YEAR 2013 XY X Square 2014

X Y

Month BDT '000 BDT '000

1 January 42,400 42,400 1 42,388

2 February 38,500 77,000 4 44,777

3 March 48,700 146,100 9 47,165

4 April 56,400 225,600 16 49,554

5 May 48,300 241,500 25 51,942

6 June 56,700 340,200 36 54,331

7 July 67,300 471,100 49 56,719

8 August 53,400 427,200 64 59,108

9 September 57,000 513,000 81 61,496

10 October 57,700 577,000 100 63,885

11 November 67,400 741,400 121 66,273

12 December 72,500 870,000 144 68,662

78 666,300 4,672,500 650

Interpretation:

Here we can see, there is a huge difference between actual and forecasted demand in 2014. In

some month the actual demand is higher than the forecasted demand like January, February,

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Forecasting: Simpel Linear Regression

Actual 2014 Forecasted 2014

Page | 13

March and April. But in some month it’s the actual demand is lower and also same like May,

June, July, August, September, October, November and December and so on.

For the forecasting of the year 2015, we found out the values of “a” is 60,840; “b” is 1,315

and so the equation is,

Y = 60,840+1,315 *X

And then we find out the demand for the year 2015 on the basis of above equation and

the results are given below,

YEAR 2014 XY X Square 2015

X Y

Month BDT '000 BDT '000

1 January 68,700 68,700 1 59,524

2 February 58,520 117,040 4 58,209

3 March 67,440 202,320 9 56,893

4 April 58,940 235,760 16 55,578

5 May 43,200 216,000 25 54,262

6 June 38,500 231,000 36 52,947

7 July 47,500 332,500 49 51,631

8 August 57,200 457,600 64 50,316

9 September 28,570 257,130 81 49,001

10 October 38,500 385,000 100 47,685

11 November 57,400 631,400 121 46,370

12 December 63,000 756,000 144 45,054

78 627,470 3,890,450 650

-

20,000

40,000

60,000

80,000

100,000

Forecasting: Simpel Linear Regression

Actual 2015 Forecasted 2015

Page | 14

Interpretation:

In this graph it’s clear that, the actual demand and forecasted demand is same. So there is less

forecasting error.

Comparison Among Methods

METHOD Actual Data

Simple

Moving

Average

Exponential

Smoothing

Simple Linear

Regression

YEAR Demand for the year 2014

Month BDT '000

January 68,700 52,200 55,630 42,388

February 58,520 47,867 51,145 44,777

March 67,440 47,613 47,233 47,165

April 58,940 47,483 44,363 49,554

May 43,200 51,380 52,458 51,942

June 38,500 58,187 58,707 54,331

July 47,500 52,967 47,950 56,719

August 57,200 54,633 55,065 59,108

September 28,570 46,810 43,244 61,496

October 38,500 49,100 46,090 63,885

November 57,400 53,313 48,383 66,273

December 63,000 62,233 58,640 68,662

METHOD Actual Data

Simple

Moving

Average

Exponential

Smoothing

Simple Linear

Regression

YEAR Demand for the year 2015

Month BDT '000

January

85,074

50,133 55,287 59,524

February

55,245

43,022 50,817 58,209

March

58,530

45,018 47,271 56,893

April

65,045

48,811 44,675 55,578

May

62,500

52,340 52,350 54,262

June

59,550

55,782 58,655 52,947

July

45,300

56,222 48,452 51,631

August

65,003

54,844 55,022 50,316

Page | 15

September

42,780

46,837 43,600 49,001

October

35,070

51,000 46,391 47,685

November

67,604

53,084 48,876 46,370

December

75,420

61,178 58,999 45,054

Actuary Test

We conducted the accuracy test for the forecasted demands and the values are presented in

the below,

Forecasting Method Test Method 2014 2015

Simple Moving Average MAD 307 8,237

MSE 169,399,193 258,196,232

Single Exponential

Smoothing

MAD 1,900 9,647

MSE 172,020,401 271,861,388

Simple Leaner Regression MAD (3,235.83) 7,470.92

MSE 330,481,696 244,723,102

Findings and Conclusion

After applying all three qualitative forecasting system and after deriving the result and

comparing with the real forecast we can say that – none of the forecast matched perfectly

with the actual data. Though there is no perfect winner but after the accuracy test we can say

there is a ranking of accuracy between these three forecast and among them the Simple

Moving Average method has been the closest and had the least error or deviation.

Consequently the Single Exponential Smoothing and Simple Leaner Regression came second

and last. The forecast for the year 2014 used by Single Exponential Smoothing shows similar

accuracy like the forecast used by Simple Moving Average system.

Though the company often use the Simple Moving Average system but most of the time it

relies on the qualitative forecasts made by the experienced owners and managers. Their

forecasts are often based on various macroeconomic, social, political and global economic

factors and sometimes based on intuitions also; these are mostly derived from years of

accumulated experiences.

Page | 16

In this case with such volatile and hardly predictable monthly sales turnover trend; it is hard

to bring accuracy with a qualitative forecasting technique. But combining or using both the

qualitative and quantitative technique to make forecasts and then deciding based on those

may be the best option for the company.

………………………………………….The End………………………………………….