Forecasting Net Income - Case Analysis

8/11/2019 Forecasting Net Income - Case Analysis

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Forecasting Net Income for RDP

Enterprises

A C A S E A N A L Y S I S

Zarah Joy Paciente

2009-33529

BS Management III

University of the Philippines Visayas

October 6, 2011


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Background of the Case

RDP Enterprises is a fresh dressed chicken distributor that is based in Tacloban City which is

owned and managed by Mr. RDP. It started its distribution of fresh chicken on the year of 2005

and it is still ongoing up to this day. They provide better quality of fresh dressed chicken not

only to the wet market but to different supermarkets of Tacloban City and nearby towns like

Abuyog, Carigara, MacArthur, Lapaz, Sta. Fe, Palo, Tunga, Jaro, Tanauan, Tolosa , Dagami and

Dulag. Recently, theyve changed their supplier because of some certain issues due to some

problems with the previous suppliers management and supply.

The new supplier, Pura Farms, is a local business that grows chicken and eventually sells them

to distributors despite o the presence of big competitors like Magnolia, Bounty Fresh, etc. They

just have started the business because Mr. RDP convinced Mr. L to put up Pura Farms and help

him out in the dressed chicken distribution in Leyte. Because the supplier is a starting business,

the supply for RDP Enterprises isnt that stable compared before.

Mr. RDP wants to determine how much would be the sales that are going to be for 2012 & 2013

given the sales data since 2008 when RDP enterprises had Pura Farms as its supplier. Mr. RDP

also face a decision whether to continue having the new supplier as its partner in dressed

chicken distribution which would help him realize a good profitability.


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RDP Enterprises

Summary of Net Income

For the years 2008 to 2011

RDP Enterprises Net Income

Year

Month 2008 2009 2010 2011

January 60,806 -84,714 -10,649 185,697

February -36,299 -3,692 17,876 86,597

March 45,399 69,907 49,640 -38,767 40,221 47,447 32,380 304,674

April 4,299 126,302 212,580 17,291

May 67,554 -81,306 199,140 72,823

June 140,191 212,045 -113,070 -68,074 148,214 559,934 105,306 195,420

July 128,824 -36,076 -42,180 -90,755August 62,769 38,838 223,663 -116,082

September -40,836 150,758 84,196 86,958 39,144 220,627 0 -206,837

October -56,733 123,217 85,975 0

November -134,112 -63,174 6,363 0

December 210,673 19,828 282,037 342,080 126,873 219,211 0 0

Total 452,537 322,198 1,047,219 293,257


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CASE ANALYSIS

I. Introduction

RDP Enterprises is a local fresh dressed chicken distributor business at Tacloban City. It is

owned by Mr. RDP and had managed it since 2005. Last 2008 theyve changed their supplier

because of some certain management and supply issues with the previous supplier which made

a loss with the small business.

The new supplier, Pura Farms, had recently set up its business by growing and selling dressed

chicken to local distributors through the efforts of Mr. RDP in convincing Mr. L to start up the

business. Because it is a starting business, the supply of chickens for RDP Enterprises hasnt

been stable.

Mr. RDP now evaluates his net income statement and wants to know what would be his profit

by 2012 & 2013 given the data from 2008 August 2011. He also wanted to know whether he

would continue having the new supplier as its partner based on the forecasted net income to

ensure his profitability.

II. Statement of the Problem

What will be the forecasted net income of RDP Enterprises going to have for years 2012 and

2013? Will RDP Enterprises continue having Pura Farms as its supplier given the forecasted

data?


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III. Objective

To determine the forecasted net income for RDP Enterprises for years 2012 & 2013

To know whether Mr. RDP will continue having Pura Farms as its supplier given the

forecasted data

IV.Alternative Courses of Action

a) Continue with Pura Farms

Advantage: It would help Pura Farms to establish its name in the area and eventually

expand if continued.

Disadvantage:It is a starting business, supply wouldnt be stable until firmly establish

b) Discontinue with Pura Farms.

Advantage: It would allow RDP Enterprises to search for potential suppliers

Disadvantage:It would take time for RDP Enterprises to search for suppliers. Unstable

supply may also happen.

V. Analysis

This case would deal with forecasting for us to determine what would the forecasted net

income for RDP Enterprises for 2012 & 2013. Mr. RDP is also deciding whether to continue his

partnership with Pura Farms, the supplier, or not.

In order to determine the net income for 2012 and 2013, different methods were used: Nave

Forecasting, Time Series Progression- per year, per quarter and the Seasonalized Trend

Progression. On the next pages are the computations.


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Nave Forecasting

Table 1.1Nave Forecasting

Nave Forecast

Year T Quarter Actual Net Income Forecast Net Income Absolute Value ofErrors(Deviation) |Actual -

Forecast|

2008 1 1 69,907

2 2 212,045 69,907 142,138

3 3 150,758 212,045 61,287

4 4 19,828 150,758 130,930

2009 5 1 -38,767 19,828 58,594

6 2 -68,074 -38,767 29,307

7 3 86,958 -68,074 155,031

8 4 342,080 86,958 255,123

2010 9 1 47,447 342,080 294,633

10 2 559,934 47,447 512,487

11 3 220,627 559,934 339,308

12 4 219,211 220,627 1,416

2011 13 1 304,674 219,211 85,463

14 2 195,420 304,674 109,254

15 3 -206,837 195,420 402,257

16 4 -206,837

Sum of Actual 2,115,210

Sum of Errors 2,577,229

MAD 184,088

MSE 738,011,957,497

MAPE 9%

Graph 1.1Graph of Nave Forecasted Data

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0

200,000

400,000

600,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


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Time Series RegressionPer Year

Table 2.1Time-Series Regression for year 2008

Year 2008Month t x tx t^2 Forecast Error

January 1 60,806 60,806 1 41,521 19,285

February 2 -36,299 -72,598 4 40,829 -77,128

March 3 45,399 136,198 9 40,136 5,264

April 4 4,299 17,198 16 39,443 -35,144

May 5 67,554 337,770 25 38,750 28,804

June 6 140,191 841,149 36 38,058 102,134

July 7 128,824 901,771 49 37,365 91,459

August 8 62,769 502,152 64 36,672 26,097

September 9 -40,836 -367,521 81 35,980 -76,815

October 10 -56,733 -567,333 100 35,287 -92,020November 11 -134,112 -1,475,228 121 34,594 -168,706

December 12 210,673 2,528,072 144 33,902 176,771

SUM 78 452,537 2,842,435 650

Average 7 37,711

b -693

a 42,214

Graph 2.1Graph of Forecasted Data against Raw Data (year 2008)

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100,000

150,000

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250,000

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

Series1

Series2


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Table 3.1 - Time-Series Regression for year 2009

Year 2009

Month t x tx t^2 Forecast Error

January 1 -84,714 -84,714 1 -54,705 -30,009

February 2 -3,692 -7,385 4 -39,877 36,185

March 3 49,640 148,920 9 -25,049 74,689

April 4 126,302 505,209 16 -10,221 136,523

May 5 -81,306 -406,531 25 4,608 -85,914

June 6 -113,070 -678,419 36 19,436 -132,505

July 7 -36,076 -252,529 49 34,264 -70,339

August 8 38,838 310,700 64 49,092 -10,255

September 9 84,196 757,761 81 63,920 20,275

October 10 123,217 1,232,168 100 78,748 44,468

November 11 -63,174 -694,913 121 93,577 -156,751

December 12 282,037 3,384,448 144 108,405 173,632

SUM 78 322,198 4,214,716 650Average 7 26,850

b 14,828

a -69,533

Graph 3.1 - Graph of Forecasted Data against Raw Data (year 2009)

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100,000

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200,000

250,000

300,000

350,000

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

Series1

Series2


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Year 2010


January 1 -10,649 -10,649 1 72,934 -83,583

February 2 17,876 35,751 4 75,540 -57,664

March 3 40,221 120,662 9 78,146 -37,925

April 4 212,580 850,321 16 80,753 131,828

May 5 199,140 995,700 25 83,359 115,781

June 6 148,214 889,285 36 85,965 62,249

July 7 -42,180 -295,262 49 88,571 -130,752

August 8 223,663 1,789,302 64 91,178 132,485

September 9 39,144 352,299 81 93,784 -54,640

October 10 85,975 859,746 100 96,390 -10,416

November 11 6,363 69,997 121 98,997 -92,633

December 12 126,873 1,522,471 144 101,603 25,270

SUM 78 1,047,219 7,179,622 650

Average 7 87,268

b 2,606

a 70,327


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0

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100,000

150,000

200,000

250,000

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

Series1

Series2


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Year 2011


January 1 185,697 185,697 1 99,398 86,298

February 2 86,597 173,195 4 85,769 828

March 3 32,380 97,139 9 72,140 -39,760

April 4 17,291 69,163 16 58,511 -41,220

May 5 72,823 364,116 25 44,882 27,942

June 6 105,306 631,835 36 31,253 74,053

July 7 -90,755 -635,284 49 17,624 -108,378

August 8 -116,082 -928,656 64 3,994 -120,076

September 9 0 0 81 -9,635 9,635

October 10 0 0 100 -23,264 23,264

November 11 0 0 121 -36,893 36,893

December 12 0 0 144 -50,522 50,522

SUM 78 293,257 -42,795 650Average 7 24,438

b -13,629

a 113,027


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100,000

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

Series1

Series2


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Time Series RegressionPer quarter

Table 6.1 - Time-Series Regression for 1st

Quarter

1st QuarterYear Time Period Net Income tX t^2

2008 1 69907 69907 1

2009 2 -38767 -77533 4

2010 3 47447 142342 9

2011 4 304674 1218696 16

10 383261 1353411 30

Table 6.2Forecast for 1st

quarter

Forecast

Year Time Forecast Raw Data

2008 1 -22762 69907

2009 2 56290 -38767

2010 3 135341 47447

2011 4 214393 304674

2012 5 293444

2013 6 372496

Graph 6.1

Graph of Forecasted Data against Raw Data (1stQuarter)

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Series1

Series2


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Table 7.1 - - Time-Series Regression for 2nd

Quarter

2nd Quarter

Year Time Period Net Income tX t^2

2008 1 212045 212045 1

2009 2 -68074 -136147 4

2010 3 559934 1679803 9

2011 4 195420 781679 16

10 899325 2537380 30

Table 7.2Forecast for 2nd

Quarter

Forecast

Year Time Forecast Raw Data2008 1 138111 212045

2009 2 195925 -68074

2010 3 253738 559934

2011 4 311551 195420

2012 5 369365

2013 6 427178

Graph 7.1 - Graph of Forecasted Data against Raw Data (2nd

Quarter)

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Series1

Series2


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Table 9.1- Time-Series Regression for 4th Quarter

4th Quarter

Year Time Period Net Income tX t^2

2008 1 19828 19828 1

2009 2 342080 684161 4

2010 3 219211 657632 9

2011 4 0 0 16

10 581118 1361620 30

Table 9.2 - Forecast for 4th

Quarter

Forecast

Year Time Forecast Raw Data

2008 1 172633 19828

2009 2 154397 342080

2010 3 136162 219211

2011 4 117927 0

2012 5 99691

2013 6 81456

Graph 9.1- Graph of Forecasted Data against Raw Data (4th

Quarter)

0

50000

100000

150000

200000

250000

300000

350000

400000

1 2 3 4 5 6

Series1

Series2


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Table 10.1Summary of Time Series Regression per quarter condensed and projected from

years 2008-2013

Summary

Year Time Period Qtr Net Income Forecast

2008 1 1 69907 -22762

2 212045 138111

3 150758 203744

4 19828 172633

2009 2 1 -38767 56290

2 -68074 195925

3 86958 109832

4 342080 154397

2010 3 1 47447 135341

2 559934 253738

3 220627 15921

4 219211 136162

2011 4 1 304674 214393

2 195420 311551

3 -206837 -77991

4 0 117927

2012 5 1 293444

2 369365

3 -171902

4 99691

2013 6 1 372496

2 427178

3 -265814

4 81456

MSE 22,319,865,948


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Graph 10.1- Table of Forecasted Data against Raw Data using Time-Series Regression per

Quarter

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Series1

Series2


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Seasonalized Trend Projection

Table 12.1- Table of Centered Moving Averages

Step 1 Step 2

t Quarter Net Income 4-Qtr Moving Ave Ratio

1 1 69,907

2 2 212,045

3 3 150,758

4 4 19,828

5 1 -38,767

6 2 -68,074

7 3 86,958 80,549 1.079556272

8 4 342,080 102,103 3.350349344

9 1 47,447 259,105 0.183119724

10 2 559,934 292,522 1.914160584

11 3 220,627 261,805 0.842715007

12 4 219,211 326,111 0.672195248

13 1 304,674 234,983 1.296579756

14 2 195,420 128,117 1.525325331

15 3 -206,83716 4 0

Table 12.2Table of Mean Ratios

Step 3

Quarter Mean Ratio

1 0.49323316

2 1.146495305

3 0.640757093

4 1.340848197

Sum 3.621333756


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Table 12.3Table of Normalization Factor

Step 4

Normalization Factor

Factor 1.104565409

Table 12.4Table of Final Seasonal Indices

Step 5

Quarter Index

1 0.544808287

2 1.266379055

3 0.70775812

4 1.481054537Total 4

Table 12.5Table of Deseasonalized Data

Step 6: Deaseasonalized Data

t Quarter Deseasonalized X

1 1 38,086

2 2 268,529

3 3 106,7004 4 29,366

5 1 -21,120

6 2 -86,207

7 3 61,545

8 4 506,640

9 1 25,850

10 2 709,089

11 3 156,15012 4 324,663

13 1 165,989

14 2 247,476

15 3 -146,391

16 4 0


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Table 12.6Table of Trend Regression using Deseasonalized Data

Step 7: Trend Progression

t Deseasonalized X Xt t2

1 38,086 38,086 1

2 268,529 537,058 4

3 106,700 320,100 9

4 29,366 117,464 16

5 -21,120 -105,602 25

6 -86,207 -517,242 36

7 61,545 430,815 49

8 506,640 4,053,116 64

9 25,850 232,647 81

10 709,089 7,090,891 100

11 156,150 1,717,654 121

12 324,663 3,895,953 144

13 165,989 2,157,855 169

14 247,476 3,464,658 196

15 -146,391 -2,195,858 225

16 0 0 256

SUM 136 2,386,364 324,545,472 1,496

Average 9 149,148

b 894,886

a -7,457,387


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Table 12.7Table of Final Forecasted Data from years 2008 - 2013

Forecasted Data

t Quarter Deseasonalized Forecast Seasonalized Forecast

1 1 894,886 441,388

2 2 1,789,773 2,051,966

3 3 2,684,659 1,720,214

4 4 3,579,546 4,799,627

5 1 4,474,432 2,206,938

6 2 5,369,318 6,155,898

7 3 6,264,205 4,013,834

8 4 7,159,091 9,599,255

9 1 8,053,978 3,972,489

10 2 8,948,864 10,259,831

11 3 9,843,751 6,307,453

12 4 10,738,637 14,398,882

13 1 11,633,523 5,738,039

14 2 12,528,410 14,363,763

15 3 13,423,296 8,601,072

16 4 14,318,183 19,198,509

17 1 15,213,069 7,503,59018 2 16,107,955 18,467,695

19 3 17,002,842 10,894,691

20 4 17,897,728 23,998,137

21 1 18,792,615 9,269,141

22 2 19,687,501 22,571,628

23 3 20,582,387 13,188,311

24 4 21,477,274 28,797,764


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VI. Conclusion

Based on the computation of different forecasting method, RDP Enterprises will be able to gain

at least a positive income by 2012 and 2013. Even though there were a lot of negative numbers

from the raw data, I am able to forecast a positive net income for the next 2 years. Because of

this, I conclude that RDP Enterprises through the management of Mr. RDP will still be profitable

for 2012 and 2013. Mr. RDP should also retain his partnership with the management of Pura

Farms even if it is a starting business of growing chickens and selling them to distributors.

Through the partnership, a series of improvement might happen given that assumption that

RDP Enterprises is gaining profit for 2012 and 2013. This would eventually lead to improvement

of quality and services provided by RDP Enterprises to its customers and to Pura Farms as well.

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Forecasting Net Income - Case Analysis