Business Decision Making Assignment 1

It consists of 4 main tasks as follows:  Task 1: Forecasting Techniques By using 3 methods namely least square, additive model and proportional model, CMIcan forecast sales in next year

BANKING ACADEMY OF VIETNAM

BTEC HND IN BUSINESS (ACCOUNTING)

ASSIGNMENT COVER SHEET

NAME OF STUDENT Nguyễn Thị Kiều Anh - Snow - F05A

ASSIGNMENT TITLE Forecasting Techniques and Business Decisions

SUBMISSION DEADLINE June 13th, 2013

I, Nguyễn Thị Kiều Anh hereby confirm that this assignment is my own work and not copied

or plagiarized from any source I have referenced the sources from which information is obtained by me for this assignment.

_13 th June, 2013 Signature Date

-FOR OFFICIAL USE

Assignment Received By: Date:

Unit Outcomes

Learning

Outcomes Evidence for the criteria Feedback

Assessor’s decision Internal

3.1

Create trend lines in spreadsheet graphs to assist in forecasting for specified business information

3.2

Prepare a business presentation using suitable software and techniques to disseminate information effectively

4.1

Prepare a project plan for an activity and determine the critical path

4.2

Use financial tools for

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Outcomes Evidence for the criteria Feedback

Assessor’s decision

Internal Verification

Assignment

( ) Well-structured; Reference is done properly / should be done (if any)

Overall, you’ve

Areas for improvement:

ASSESSOR SIGNATURE DATE / /

NAME:

(Oral feedback was also provided)

STUDENT SIGNATURE DATE / /

FORECASTING TECHNIQUES AND BUSINESS DECISIONS

Prepared for:

Mr Jubred Ada Peñano (Lecturer)Unit 6: Business Decision MakingBanking Academy, HanoiBTEC HND in Business (Finance)

Prepared by:

Nguyễn Thị Kiều Anh – Snow - F05ARegistration No: ITP F05-014Submission date: 13th June 2013

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TABLE OF CONTENTS

EXECUTIVE SUMMARY 6

INTRODUCTION 7

Task 1: Forecasting Techniques 8

1 Additive model 8

2 Proportional model 10

3 Least squares regression method 14

REPORT ON FORECASTING SALES FOR YEAR 2012 17

Task 2: Management Information System (MIS) 19

Task 3: Project Plan 25

Task 4: Investment appraisal techniques 31

1 Net Present Value (NPV) method 31

2 Internal Rate of Return (IRR) method 34

CONCLUSION 40

APPENDIX 41

REFERENCES 43

EXECUTIVE SUMMARY

This assignment contains basic knowledge about business decision making as well as helplearner understand way to use techniques and method for calculating and forecasting as well

as giving decision in specific situations It consists of 4 main tasks as follows:

 Task 1: Forecasting Techniques

By using 3 methods namely least square, additive model and proportional model, CMIcan forecast sales in next year and make best decision in order to maximizeprofitability

 Task 2: Management Information System (MIS)

Based on 3 levels of management including strategic management, tacticalmanagement and operational management, CMI can select information system whichsuitable for each department of CMI to improve the company performance and runbusiness effectively

 Task 3: Project plan

Help learner understand the operational processes and techniques associated withproject management and know way to plan a project and draw up a work breakdownstructure

 Task 4: Investment Appraisal Techniques

By using financial tools such as NPV and IRR to evaluate the potential project andproposed investment, the company can make decision whether should choose whichproject to invest brings more benefit for company

INTRODUCTION

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CMI is a world-renowned manufacturer of private label, custom-made formulations andproducts, including skin care, hair care, body care, colour cosmetics, pharmaceuticals.Company’s state-of-the-art facilities and highly motivated staff of professionals set us apartfrom their competitors in the cosmetics industry CMI offer many specialize servicesincluding: Clinical and scientific studies, comprehensive research and development, highproduction capacity, bulk products…[ CITATION CMInd \l 1033 ]

This report will show the methods, techniques such as forecasting methods, project plan, andinvestment appraisal techniques to forecast sales, identify suitable project for investment…Forecasting method will help company forecast actual sales in year 2012 There are 3methods: least square, additive model and proportional model to help company understandadvantage and disadvantage of each method for using appropriate methods Investmentappraisal techniques support company making decision in investment, analysis based on NPVand IRR Moreover, CMI will be suggested for using MIS instead of paper-driven system toimprove company performance

Task 1: Forecasting Techniques

1 Additive model

The formula for additive model for time series analysis is Y = T + S + R

Where Y is value of the changing valuable which is the actual volume of sale

T is trend

S is seasonal variation

R is residual component

Assuming the residual component is 0 the seasonal component is S = Y - T

The season variation for 6 year 2006, 2007, 2008, 2009, 2010 and 2011 is as follows:

ADDITIVE MODEL

of sales (Y)

Moving total of

4 quarters’

sales

Moving average of 8 quarters’ sales

Trend (T)

Seasonal variation (Y-T) 2006

Table 1.1.1: Applying additive model on the sales of CMI’s skin care cream from 2006 to 2011

Table 1.1.2: The summary and average of seasonal variations

The average seasonal estimates must therefore be corrected so that they add up to zero

Average seasonal variations (in £000’s) from Quarter 1 to Quarter 4 are +131.25 (Quarter1), -48.75 (Quarter 2), -141.25 (Quarter 3) and +58.75 (Quarter 4)

The trend line indicates an increase of about 10.5263 per quarter This can be confirmed bycalculating the average quarterly increase in trend line values between the third quarter ofYear 2006 (468.75) and the second quarter of Year 2011 (368.75)

The average rise is: 568.75 – 368.75

19 =¿10.5263Taking as 10.5263 as the quarterly increase in the trend, the forecast of sales for Year 2012,before seasonal adjustment (the trend line forecast) would be as follows:

Yea

r

Quarte r

Trend line 2011

Table 1.1.3: The trend line forecast in year 2012

Seasonal variation should now be incorporated to obtain the final forecast:

r

Trend line forecast

Adjustment average variation

Forecast actual of sale (in £’000s) 2012

Table 1.1.4: The forecast of actual sale of CMI for year 2012

With an average variation for Quarter 1 of +131.25, the prediction for first quarter of year

2012 would have been 600.3289 + (+131.25) = 731.5789 Calculate the same for remainingquarter of year 2012

 By using the additive method, the sales in year 2012 is forecasted about £2,464,470

We have chart for sales from year 2006 to year 2012:

0 100 200 300 400 500 600 700 800

f(x) = 12.05 x + 263.24 R² = 0.37

Time serious for sale of skin care cream

Sales Linear (Sales)

Quarters

Chart 1.1: The line graph of additive method for 7 years selling skin care cream

Explanation: The method is to use the differences between the trend (T) and actual data (Y).

This forecasting method basically relies on the time series analysis The model for time series

as Y = T + S + R (with stands for the seasonal variation; R stands for the residualcomponent) Extrapolation forecasting techniques is used by extending a trend line outsidethe range of known data to forecast the future from a trend line which based on historicaldata Therefore, firstly, we have to calculate a trend line using moving averages, and thenvalues by the average seasonal variation applicable to the future period Because this methodonly simply adds absolute and unchanging seasonal variations to the trend figures; thereforeusing the additive model to forecast sales is less accurate  this effects significant on makingbusiness decision of the company

2 Proportional model

The formula for proportional model for time series analysis is Y = T × S × R

Where Y is value of the changing valuable which is the actual volume of sale

T is trend

S is seasonal variation

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R is residual component

Assuming the residual component is 0 the seasonal component is S =Y/T

The season variation for 6 year 2006, 2007, 2008, 2009, 2010 and 2011 is as follows

PROPORTIONAL MODEL

of sales (Y)

Moving total of

4 quarters’

sales

Moving average of 8 quarters’ sales

Trend (T)

Seasonal percentage (Y/T) 2006

Quarter 2 (%)

Quarter 3 (%)

Quarter 4 (%)

total variation to 4

Final estimate of average

Table 1.2.2: The summary and average of seasonal variations

Instead of summing to zero, as with this approach, these should sum (in this case) to 4 Theyactually sum to 3.9782 so 0.00545 has to be deducted from each one

 Average seasonal variations (in £000’s) from Quarter 1 to Quarter 4 are 1.33875 (Quarter1), 0.88405 (Quarter 2), 0.62665 (Quarter 3) and 1.15055 (Quarter 4)

The trend line indicates an increase of about 10.5263 per quarter This can be confirmed bycalculating the average quarterly increase in trend line values between the third quarter ofYear 2006 (468.75) and the second quarter of Year 2011 (368.75)

The average rise is: 568.75 – 368.75

19 =¿10.5263Taking as 10.5263 as the quarterly increase in the trend, the forecast of sales for Year 2012,before seasonal adjustment (the trend line forecast) would be as follows:

Table 1.2.3: The trend line forecast in year 2012

Seasonal variation should now be incorporated to obtain the final forecast:

With an average variation for Quarter 1 of 1.33875, the prediction for first quarter of year

2012 would have been 600.3289 × 1.33875 = 803.6930 Calculate the same for remainingquarter of year 2012

 By using the proportional/multiplicative model method, the sales in year 2012 is

forecasted about £2,460,147.

We have chart for sales from 2006 to 2012:

0 200 400 600

800

f(x) = 11.98 x + 264.2 R² = 0.34

Time serious for sale of skin care cream

Sales Linear (Sales)

Quarters

Chart 1.2: The line graph of multiplicative method for 7 years selling skin care cream

Explanation: The model for time series as Y = T × S × R so similarly as the additive model

in calculating the actual result (Y) and the moving average (T), however, in this case wecalculate S = Y/T for the proportional model instead of S = Y - T In order word, withadditive model, add (or subtract for negative variations) the variation while with themultiplicative model, multiply the trend value by the variation proportion By using theproportional/multiplicative model method, the sales in year 2012 is forecasted about

£2,460,147 The multiplicative model, by multiplying increasing or decreasing trend values

by a constant seasonal variation factor, takes account of changing seasonal variations;therefore, using the additive model to forecast sales is better and more exact than additivemodel  This method have effect positively on making business decision of company

3 Least squares regression method

LEAST SQUARES Year

s

Quarter s

Table 1.3.1: Applying least square method on the sales of skin care cream of CMI

We have x = 0 is in quarter 1 of 2006, x = 1 is in quarter 2 of 2006 and so on to x = 23

n is the number of pairs data so n = 24

The least squares line uses a straight line which has the form: y = a + bx

With y: sales of skin care cream (in £1,000s)

x: time

a: the intercept of the line on the vertical axis

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b: the gradient of the line

n: the number of pairs of data

Table 1.3.2: The forecast actual sales in year 2012 of CMI

 By using least square method, the sales forecast for the year 2012 will be about

£2,225,384.

We have chart for sales from year 2006 to year 2012:

0 100 200 300 400 500 600 700 800

f(x) = 10.57 x + 276.24 R² = 0.34

Time serious for sale of skin care cream

Sales Linear (Sales)

Explanation: Least square regression technique investigates the association between

dependent and independent variables It determines the line of “best fit” for a set ofobservations by minimizing the sum of the squares of the vertical deviations between actualpoints and the regression line This method can be used to determine the fixed and variableportions of mixed cost It is likely to provide a more reliable estimate than any othertechnique of producing a straight line of best fit However, the value of one variable, y, can

be predict or estimate from the value of one other variable, x In reality, the value of y mightdepend on several other variables, not just on x This makes this method become less reliablethan multipliable and additive model method

4 Recommendation

After calculating with three methods, it is easy to recognize that there is a big differencebetween the least squares method and the others (additive method and proportional method)

Least squares method is method which is less reliable  CMI should not rely on this

method to forecast sales

Based on figure above, it is obvious that the results of sales in 2012 is predicted by

proportional model are similar to additive model However, the multiplicative/proportional

model is better than the additive model for forecasting because the additive model simply

adds absolute and unchanging seasonal variations to the trend figures whereas theproportional model, by multiplying increasing or decreasing trend values by a constantseasonal variation factor, takes account of changing seasonal variation [ CITATION BPP102 \l

1033 ]

 Forecasting method which CMI should use is proportional model method From the result

of sales forecasting by using proportional model, director of department may have rightstrategies and decision in business

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REPORT ON FORECASTING SALES ON SKIN CARE CREAM FOR YEAR 2012

CMI (COSMETIC MANUFACTURERS INC.)

To: Ms Carol Lee, Sales Director

From: Nguyen Thi Kieu Anh, Sales manager

Status: Confidential

Date: 26th May 2012

I INTRODUCTION AND TERM OF REFERENCE

This report shows the actual sales results of the last 6 year (from 2006 to 2011) of selling skincare cream of CMI and aim to present the results of sales forecast for year 2012 using each ofthree methods: least squares method, additive model and proportional model method Andcommenting on the reliability of your forecast sales figures for the year 2012, suggestingwhich method is likely to be more reliable

II METHOD

The report consists of 3 methods for forecasting

 The additive model method

 The proportional/multiplicative model method

 The least squares regression method

III FINDINGS

1 Forecast the sale for year 2012

By summarizing 3 methods - 3 different techniques, we can gain three results which is thesale forecast for 2012

Quarter 1 (£’000s)

Quarter 2 (£’000s)

Quarter 3 (£’000s)

Quarter 4 (£’000s)

Total (£’000s)

 Additive model method: By using additive model method, the forecast of sale for year

2012 is estimated approximately about £2,464,470 With the first quarter of the year is predicted about £731,5789 The sales of other quarters fluctuate significantly, from

£731,5789 in quarter 1 go down to £562,1052 in quarter 2 and just is £480,1315 in

quarter 3 Despite going down in quarter 2, and 3, the sale in quarter 4 is predicted to be

gone up to £690,6578.

 Proportional/Multiplicative method: Total forecast sale of this method is £2,460,147.

In quarter 1, the sale is forecasted to achieve £803,6903 It decreases significantly in quarter 2 with £540,0265 and quarter 3 with £389,3887 Finally, the quarter 4 of 2012 reaches £727,0415 which increase nearly 2 times than the last quarter.

 Least square regression method: By using least square method, the actual sales of

year 2012 is estimated about £2,225,384 in total Quarter 1 of year 2012 is £540,4983 After that, the sales continue increasing until quarter 4 which is £572,1939.

2 Evaluation and reliability

In the least square method, in order to identify the reliability, we need to calculater2, which iscoefficient of determination “r” is index of relationship between variation in sales and

variation in time Bases on the formula and table 1.3.1, r2 is calculated as follow:

The multiplicative/proportional model is better than the additive model for forecastingbecause the additive model simply adds absolute and unchanging seasonal variations to thetrend figures whereas the proportional model, by multiplying increasing or decreasing trendvalues by a constant seasonal variation factor, takes account of changing seasonal variation

[ CITATION BPP102 \l 1033 ]  Forecasting method which CMI should use is proportionalmodel method

IV CONCLUSION

After basing on figures in forecasting sales in 2012 and comparing and contrasting theadvantage and disadvantage of three methods carefully, as a sales manager of CMI, I draw a

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conclusion that proportional model method is reliable techniques more than least square

method and additive model From the result of sales forecasting by using proportional model,

CMI can give out right strategies and decision in business

Sales Manager

Nguyen Thi Kieu Anh

Task 2: Management Information System (MIS)

2.1 Introduction of CMI (Cosmetic Manufacturers Inc.)

CMI is a world-renowned manufacturer of private label, custom-made formulations and

products, including skin care, hair care, body care, colour cosmetics…

CMI has the various functional departments such as

 Production/Manufacture

 Marketing and Sales

 Finance and Personnel and so on

At present, company relies on paper-driven systems to share information among

departments However, because of the increased advancement in technology, CMI need to

use Management Information System to manage each department more efficiently and

effectively and quickly as well

2.2 Applying MIS on actual department of CMI Company

Sales

Management Level Management Support

System (MSS)

Inventory Trackers

Middle manager (Sales manager)

Marketing

Management Level systems

Decision Support System(DSS)

Vistaar software

Middle manager (Marketing manager)

Accounting

Operational Level Transaction Processing

Systems (TPS) Peachtree Operational manager

(Accounting manager )

Knowledge and data worker

Operational manager (HR manager)

Table 2.1: Applying MIS on actual department of CMI company

2.3.1 Sales Department

a Functions of the Sales Department

 Creating positive customer relations: Create a good relationship with the customer

by identify what the customer wants and show the customers that the company valuethem by listening These are achieved by carry out Market Research and improveexisting products and developing new ones to meet their tastes

 Communicate with customers at all times: To ensure that the relationship stays

strong

 Process and monitor customer order: Customer orders are dealt with quickly and to

a high standard to make sure that the customer is happy at all times [ CITATION Robnd \l 1033 ]

 Attract, retain customers as well as increase sales volume, reduce sale inventory,increase profitability

b Decision Level

Management Level: Help sales manager monitor activities of customer order and control

sale inventory

c Information types

Management Support System (MSS): It provides information to be used by or at least to

support managerial decision making in fields such as sales management and salesinventory

d Instrument

Inventory Tracker

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Inventory tracker allows users to quickly determine how much inventory the companyhas in stock on a daily basic, keep a list of all transaction records from the dailyinventory for later review [ CITATION mssnd \l 1033 ].

2.3.2 Marketing Department

a Functions of Marketing Department

 Consumer Analysis: Evaluating consumer characteristics, needs and purchase

process and selecting the group’s of consumers at which to aim marketing efforts

 Product Planning: Develop and maintain product (product images, brands,

packaging )

 Price Planning: Determining price levels, pricing techniques, price adjustment

 Promotion Planning: Communicating with customer through some types of

advertising, public relations, personal selling, sales promotion [ CITATION muhnd \l

1033 ]

b Decision Level

Management Level: Help marketing manager to control and analyze marketing

environment and performed marketing activities quickly and effectively

c Information Types

Decision Support System (DSS): It provide database about list product prices of

competitors Based on this, the manager can defines reasonable pricing strategies tocreate competitive advantages compared with competitors and meet customersatisfaction, improve speed up the process of decision making

d Instrument

Vistaar

Price: $590Vistaar software enables company to achieve pricing best practices through priceanalytics, price optimization, price list management [ CITATION capnd \l 1033 ]

2.3.3 Finance Department

a Functions of Finance Department

 Book keeping procedures: Keeping records of the purchases and sales made by a

business as well as capital spending

 Preparing final accounts: Profit, loss account and Balance Sheets

 Providing management information: Managers require ongoing financial information

to enable them to make better decisions (businessstudies, n.d.)

2.3.4 Accounting Department

a Functions of Accounting Department

 Accounts Payable and Receivable: The department records each accounts payable as

a liability and account receivable as assets

 Payroll: Accounting department ensures that the organization pays its employees

accurately, including bonus, commission and benefits The department monitorsemployees’ time off, vacation and sick day It pays the government taxes as well asunion dues and other withholding from an employee’s paycheck

 Inventory: Accounting department watches the cost of inventory over a specific

period against its revenues to ensure the costs of raw materials, labour do notnegatively impact cash flow [ CITATION Prind \l 1033 ]

b Decision Level

Operational level: Help manager track the organization’s day-to-day operational

activities

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