Application of Operations Management techniques in HP Bangladesh

8/13/2019 Application of Operations Management techniques in HP Bangladesh

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1. Introduction of Operations Management

Operations management is an area of management concerned with overseeing, designing, and

controlling the process of production and redesigning business operations in the production of

goods or services. It involves the responsibility of ensuring that business operations are

efficient in terms of using as few resources as needed, and effective in terms of meeting

customer requirements. It is concerned with managing the process that converts inputs in the

forms of materials, labor, and energy into outputs in the form of goods and or services.

2. Role of Operations Management in organizations

Operations management provides versatile activities of a particular organization. OperationsManagement Implies of an efficient forecasting techniques to predict the future demand of

the produced final products & as well as taking the corrective decisions regarding the

production planning and inventory management. It could be denoted as one of the major tasks

of the operation management team of that firm. Different forecasting methods are being used

in every aspect of today‟s modern business.

3. Forecasting Method

Through intensive study, it has been found that, forecasting study works best while

recognized models are used together. There are various types of forecasting methods such as:

Qualitative study, Time series analysis, Causal method etc. For this particular assignment, we

have used some methods of Time series analysis like Simple Moving Average, Single

Exponential Smoothing, and Regression Analysis etc. Various models, mostly quantitative

time series models have been used to determine the forecasted future monthly sales quantity

of Laptops for the month of November 2013. For the simplicity of the work, actual monthly

sales data have been taken from the November month of the year 2012 till to the second last

month of the year 2013 (12Months). There are various methods of measuring errors like

MAD (Mean absolute deviation), TS (Tracking signal), MSE (Mean Squared Error), MSD

(Mean Squared Deviation) and MAPE (Mean Absolute Percentage) etc. But we have used

only MAD (Mean Absolute Deviation) to measure errors. The more methods we will use to

find the forecasting quantity of sold Asus Laptops the more accurate our forecast will be.

Although in our assignment we have used limited methods to forecast, but still, it is our beliefthat the accuracy level of this particular assignment is satisfactory.

http://en.wikipedia.org/wiki/Management

http://en.wikipedia.org/wiki/Production_%28economics%29

http://en.wikipedia.org/wiki/Business_operations

http://en.wikipedia.org/wiki/Good_%28economics%29

http://en.wikipedia.org/wiki/Service_%28economics%29

http://en.wikipedia.org/wiki/Business

http://en.wikipedia.org/wiki/Efficiency

http://en.wikipedia.org/wiki/Effective

http://en.wikipedia.org/wiki/Materials

http://en.wikipedia.org/wiki/Manual_labour

http://en.wikipedia.org/wiki/Energy

http://en.wikipedia.org/wiki/Energy

http://en.wikipedia.org/wiki/Manual_labour

http://en.wikipedia.org/wiki/Materials

http://en.wikipedia.org/wiki/Effective

http://en.wikipedia.org/wiki/Efficiency

http://en.wikipedia.org/wiki/Business

http://en.wikipedia.org/wiki/Service_%28economics%29

http://en.wikipedia.org/wiki/Good_%28economics%29

http://en.wikipedia.org/wiki/Business_operations

http://en.wikipedia.org/wiki/Production_%28economics%29

http://en.wikipedia.org/wiki/Management



AsusTek Computer Inc.

1.1 Origin of the report

This production research report study is all about an appropriate forecasting model for a

specific company AsusTek Computer Inc. Asia Pacific- Bangladesh, in predicting future

quantity for production or order. The origin of the data used in the method was taken

from forecast and the actual sales, for the period November 2012 to November 2013.

Finally the forecast is calculated for the period of December 2013.

AsusTek Computer Inc. pursues technological and aesthetic perfection through continuous

innovation. We place ourselves in our customers‟ shoes to develop a deep understanding

of, and genuine empathy for, their needs — enabling us to create user experiences that

transcend the norm.

AsusTek Computer Inc. places great importance on employee virtues. Five characteristics

have been identified as key to the smooth development of processes and relationships. The

five ASUS virtues are: Humility, Integrity, Diligence, Agility and Courage.

AsusTek Computer Inc. , a technology-oriented company with a global staff of more than

ten thousand and blessed with one of the world's top R&D teams, is renowned for high-

quality products and cutting-edge innovation. As a leading company in the new digital era,

ASUS offers a complete product portfolio to compete in the new millennium.



1.2 Objective of the report

The purposes of this report are:

1. To develop a suitable and accurate forecasting technique to predict future monthly

Laptops sales quantity for the month of December, 2013 and to find out an

appropriate forecasting model to use in predicting future quantity for production or

order of the year 2013 (December) of Asus.

2. To partially fulfill the requirements of the course named Operation Management.

2.0 Products of Asus

Asus offers a wide range of products and services.

The products line:

Computers & Accessories

Laptop

Desktop

Tablets

Customize Desktop ( Commercial)

2.1 History of AsusTek Computer Inc.

AsusTek Computer Inc. was founded in Taipei in 1989 by T.H. Tung, Ted Hsu, Wayne Hsieh

and M.T. Liao, all four having previously worked at Acer as computer engineers. At this

time, Taiwan had yet to establish a leading position in the computer-hardware business. Intel

Corporation would supply any new processors to more established companies like IBM first,

and Taiwanese companies would have to wait for approximately six months after IBM

received their engineering prototypes. By 2009, AsusTek Computer Inc. was receiving Intel

engineering sample ahead of its competitor.



2.5 Distribution Process of AsusTek Computer Inc.

Asus is an international brand worldwide. They have their unique distribution channel for

Bangladesh. They market their product through a domestic renowned company. Asus importtheir product from China and the product come to the Chittagong port and then it send to the

warehouse, from where the product delivered to the different showroom located in the

country. Lastly the showroom or the direct sales force can sell to the end user.

Head Office (Dhaka) Branch Office (Dhaka)

AsusTek Computer Inc. AsusTek Computer Inc.

19/2, West Panthapath, BCS Computer City,Salim Center (3rd~5th & 7th floor), IDB Bhaban (1st floor),

Dhanmondi, Dhaka-1205. Shop no. SR-123/6,

Tel.: (88-02) 8123273~5, Agargaon, Dhaka.

8123283~4, Phone: (88-02) 8123281,

Fax: (88-02) 9145687, 9120045 9133776.

Mailto: [email protected]

ASUS NOTEBOOK SERVICE CENTER

AsusTek Computer Inc.

19/2, West Panthapath,

Salim Center (7th floor),

Dhanmondi, Dhaka-1205.

Phone: (88-02) 8123273~5,

Fax: (88-02) 9145687, 9120045

Hotline: 01977476485 / 01973257935 / 01713257935

Mailto: [email protected]

Distributor

Global Brand Pvt. Ltd.

19/2, West Panthapath (3rd~5th & 7th floor)

Dhanmondi, Dhaka-1205, Bangladesh

E-mail: [email protected],

[email protected],

[email protected]

mailto:[email protected]
















3.0 DATA DESCRIPTION

A time series is a time ordered sequence of observations of a variable.

Time series analysis uses only the time series history of the variable being forecasted in order

to develop a model for predicting future values. Analysis of time series that are requires the

analyst to identify the underlyingbehavior of the series. This can often be accomplishing by

merely plottingthe data and visually examining the plot.

ASUS is one of the world‟s famous Companies in producing and supplying digital goods

like laptops, pc technology, server, tablet and other products. The main operation of ASUS is

“Transforming ASUS” into a top-level innovative and diversified electric/electronic

manufacturer with strong competitive power. ASUS aims to become an even stronger global

contender by unleashing our powers of imagination to anticipate, ahead of others, and

capitalize on the coming trends in the world business environment.

Here we use monthly sold quantity or number of pieces sold of Laptops in ASUS from

November 2012 to November 2013 which generates a sample size of 13 observations.

As we said earlier, the data are monthly sold quantity of laptops of ASUS from November2013 to November 2013. Thus a total of 13 observations are used in this analysis.

4.0 Process, Perceptionand Data Analysis:

The purpose of this report is to compare the results of several forecasting methods to

determine which model appears most appropriate for the given time series. The use of

historical data contains „hidden‟ information which may prove useful in our attempt to

forecast future number of Laptops sold quantity of per month. Our implicit assumption is that

the underlying variables which influence Laptops sold quantity in the past will continue to

influence in future. The computation method of different forecasting model is done with the

help Microsoft Excel. In this report we examine the forecasting accuracy of several models,

including Moving Average, Regression Analysis, Exponential Smoothing (single). We have

also used MAD as measures of accuracy.



4.1 SIMPLE MOVING AVERAGE

When demand of a product is neither growing, nor declining rapidly, and it does not have any

seasonal effect, then this method is applicable.

Formula is:

Ft = (At-1 + At-2 +At-3 +----------------------------------+ At-n)/n

Where

Ft = Forecast for period t.

N= No. of periods to be averaged.

At-1 = Actual occurrences in the past period.

At-2 = Actual occurrences in the two period ago.

At-n= Actual occurrences up to n period ago.

S/N Month Year Period Actual sell

(pcs)

3 Months MA

Forecast (pcs)

1 January 2012 1 245

2 February 2012 2 213

3 March 2012 3 158

4 April 2012 4 170 206

5 May 2012 5 194 1806 June 2012 6 169 174

7 July 2012 7 155 178

8 August 2012 8 254 173

9 September 2012 9 301 193

10 October 2012 10 271 237

11 November 2012 11 224 275

12 December 2012 12 253 265

13 January 2013 13



F13 Jan 2013= 253+224+271 = 250 pcs (When 3 Month MA Forecast)

3

Chart 1: Graphical representation of Simple Moving Average

4.2 SINGLE EXPONENTIAL SMOOTHING

Single exponential smoothing calculates data by computing exponentially weighted averages

and provides short-term forecasts. This procedure works best for data without a trend or

seasonal component. 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. That is:

Next forecast = Previous forecast + α ( Actual- Previous forecast)

Where (Actual- Previous forecast) represents the forecast error and αis a percentage of that

Error, then more concisely,

Ft = Ft-1 + α (At-1- Ft-1) or Ft = α At-1+ (1-α) Ft-1

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12 13 14

sell

Period



Where,

Ft = Forecast for the next period (week, month, quarter, year, etc.),

Ft-1 = Forecast for the previous period,

At-1 = Actual demand/sales for the previous period

A = Smoothing constant (0-1)

Let,

Smoothing Constant: Alpha ( α ) =0.6

Smoothing Constant: Alpha ( α ) = 0.4

Smoothing Constant: Alpha ( α ) =0.35

Ft = α At-1+ (1-α) Ft-1 = 0.6 (253) + (1-0.6) 265 = 257.8 or 258 pcs

= 0.4 (253) + (1-0.4) 265 =260.2 or 261 pcs

= 0.35(253) + (1-0.35) 265 = 260.8 or 261 pcs

4.3 REGRESSION ANALYSIS

Regression analysis is useful when the trend is increasing or decreasing. The forecast

followsthe basic regression formula which is Y= a + bx, where Y is the forecast for the

month foundby the value of a, b and period x.

S/N Month Year Period

(X)

Actual

sell

(Y)

XY X2

1 January 2012 1 245 245 1

2 February 2012 2 213 426 4

3 March 2012 3 158 474 94 April 2012 4 170 680 16

5 May 2012 5 194 970 25

6 June 2012 6 169 1014 36

7 July 2012 7 155 1085 49

8 August 2012 8 254 2032 64

9 September 2012 9 301 2709 81

10 October 2012 10 271 2710 100

11 November 2012 11 224 2464 121

12 December 2012 12 253 3036 14413 January 2013 13 169

14 ∑X=91 ∑Y=2547 ∑XY=17843 ∑X2 =819



Forecast for the next month would be:

– b = 2 -

2 = 17843-12(7.5)212.25 = - 8.75 or 9 (approximately)

819- 12(7.5) 2

- = 212.25- (-9) (7.5) = 144.75

So, Forecast for the next period, Y13 Jan 2013 = a + b (13) {As Y= a + bx}

Y13 Jan2011 = 144.75 + (9)13 = 261.75 or 262 pcs

Graph 2: Regression Analysis System

5.0 Measuring the Forecasting Error by - Mean Absolute Deviation (MAD)

There may be difference between actual sales and forecast amount. It is necessary not only to

forecast, but also to measure error for future adjustment.

Forecast Error is measured using Mean Absolute Deviation (MAD)

MAD= ∑ I At-Ft I / N

Where n= no. of periods.

0

50

100

150

200

250

300

350

Sell

Sell



At = Actual sales in period t.

Ft = Forecasted sales in period t

S/N Month Year Period Actual

sell

3 Months

MA

Forecast

I At-Ft

I

1 January 2010 1 245

2 February 2010 2 213

3 March 2010 3 158

4 April 2010 4 170 206 36

5 May 2010 5 194 180 14

6 June 2010 6 169 174 5

7 July 2010 7 155 178 23

8 August 2010 8 254 172 82

9 September 2010 9 301 193 108

10 October 2010 10 271 237 34

11 November 2010 11 224 275 51

12 December 2010 12 253 265 12

13 January 2011 13

When Moving Average Length is 3

So, MAD = 365/9 = 40.55 or 41 Pcs



Graph 3:Mean Absolute Deviation (MAD)

6.0 RESULTS AND DISCUSSION

The observed the results are combined and shown as follows:

Serial

no

Forecasting

methods

Results Error by MAD

01 Single

moving

average

F13 January 2011= 250

pcs

(When 3 Month MA

Forecast)

02 Single

exponential

smoothing

258 pcs (When α =

0.6)

261 pcs(When α =

0.4)

261 pcs(When α =

0.35)

03 Regressionanalysis

262 pcs

Period

0

50

100

150

200

250

300

350

1 2 3 4 56 7 8 9 10 11 12 13

Period

Sell

3 months Forecast

Forecast Error



The purpose of forecasting is to reduce the risk in decision-making. For our chosen company

ASUS, appropriate forecasting technique examining help to identify the peak order-

producing-delivering-customer service season, and help managers plan accordingly to handle

the peak load very effectively and efficiently. Forecasting also helps in scheduling staff and

resources and in financial planning. To forecast future quantity for producing or quantity for

order to sell of any home products or appliances; it must have a forecasting method that is

reliable and accurate. It must also minimize costs, of course, but lowest cost is not an

absolute priority, given the serious financial ramifications of forecast error. The forecast

should be cost-effective; the benefits should overweight the costs. Therefore it makes a great

deal of sense to experiment with various forecasting models to determine which model

generates the most reliable results.

To that end this report examines the forecasting accuracy of several models, including ,

Simple Moving Average, Exponential Smoothing, Regression Analysis, By examining these

models using MAD, as measures of accuracy, our evaluation shows that Regression analysis

is giving moderate result(rationally acceptable ) in terms of the original data and resulting

less error comparatively to other forecasting techniques .



7.0 Bibliography

Sources used in this report have followed theories and information from the following books

and corporate office:

1. Chase; Aquilano and Jacobs (2000), “ Production and Operations Management:

Manufacturing and services” (8th ed.), Irwin- McGraw Hill.

2. ASUS Bangladesh (2013), ASUS Products. Retrieved November 26, 2013, from

http://www.asus.com/bd/.

http://www.asus.com/bd/



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Application of Operations Management techniques in HP Bangladesh