MBA6063: Managerial EconomicsAsia Metropolitan University

ByA. Haris Awang

MBA2016-04-1001

Submitted to:Assoc. Prof. Dr. Abdul Rahim Abdul Samad

Department of Economics,Faculty of Economics and Management,

Universiti Putra Malaysia

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ASSIGNMENT SUBMISSION AND ASSESSMENT

MANAGERIAL ECONOMICS SEPTEMBER 2016

INSTRUCTIONS TO STUDENTS

1. This assignment contains question that is set in English.

2. Answer in English.

3. Learners are to submit assignment only in MsWord (.docx) format unless specified otherwise. Please refrain from converting text / phrases into picture format such as .gif / .jpeg / print screen / etc.

4. You can submit your assignment ONCE only in a SINGLE file.

5. Your assignment should be prepared individually. You should not copy another person’s assignment. You should also not plagiarize another person’s work as your own.

EVALUATION

This assignment accounts for 60% of the total marks for the course and shall be assessed based on the Rubrics attached.

You would be given feedback on the assignment before the Final Semester Examination commences.

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ASSIGNMENT QUESTION

PART 1PURPOSE: The purpose of PART 1 of this assignment is to enable students to understand fundamental economic concepts such as expected returns, profits and prices; and to apply his / her knowledge on factors affecting elasticity of demand and elasticity of supply of goods and services.

REQUIREMENT: 1. Two investments have the following expected returns (net present values) and standard deviation of returns.

(4 Marks) PROJECT EXPECTED RETURNS STANDARD DEVIATION

A RM50,000 RM40,000 B RM250,000 RM125,000

Which one has a higher risk? Justify your answer.

ANSWER:To solve this problem, the coefficient of variation for each project is analyzed using a formula of V = σ/r where,

V = coefficient of variation,σ = ratio of the standard deviation,r = expected valueVA = RM40,000/RM50,000 = 0.8VB = RM125,000/RM250,000 = 0.5

Since VA > VB , Project A has a higher risk because it has a higher coefficient of variation.

2. The AIROD Aircraft Company manufactures small, pleasure–use aircraft. Based on past experience, sales volume appears to be affected by changes in the price of the planes and by the state of the economy as measured by consumers’ disposal personal income. The following data pertaining to AIROD Aircraft Company sales, selling prices and consumers’ personal income were collected:

YEAR AIRCRAFT SALES (Q) (UNITS)

AVERAGE PRICE (P) (RM MILLIONS)

DISPOSIBLE PERSONAL INCOME (IN CONSTANT 2013 in RM BILLIONS)

2013 8,000 100 650 2014 10,000 89.50 610 2015 8,000 109.5 590

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a) Estimate the arc price elasticity of demand using the 2013 and 2014 data. (4 Marks)

ANSWER:To compute arc price elasticity of demand, the Mid-Point Formula is used.

Price Elasticity of Demand, Ed =

Percentage change in Qd

Percentage change in P

Where, percentage change =

End value - Start valueMidpoint x 100%

P:End value = 89.5Start value = 100Midpoint = (100 + 89.5)/2 = 94.75

Percentage change in P =

89. 5 - 10094 .75 x 100% = - 11.08%

Q:End value = 10,000Start value = 8,000Midpoint = (10,000 + 8,000)/2 = 9,000

Percentage change in Qd =

10,000 - 8,0009,000 x 100% = 22.22%

Price Elasticity of Demand, Ed= 22.22%/-11.08% = 2.01 (ignoring the negative sign)

Interpretation: When the elasticity value > 1, it is elastic, there’s a strong impact and response, consumers are really sensitive upon any price changes.

b) Estimate the arc income elasticity of demand using the 2013 and 2014 data. (4 Marks)

ANSWER:To compute arc income elasticity of income, the Mid-Point Formula is used.

Income Elasticity of Demand, Ey =

Percentage change in Qd

Percentage change in I

Where, percentage change =

End value - Start valueMidpoint x 100%

I:End value = 610Start value = 650

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Midpoint = (650 + 610)/2 = 630

Percentage change in I =

610 - 650630 x 100% = - 6.35%

Q:End value = 10,000Start value = 8,000Midpoint = (10,000 + 8,000)/2 = 9,000

Percentage change in Qd =

10,000 - 8,0009,000 x 100% = 22.22%

Income Elasticity of Demand, Ey= 22.22%/-6.35% = 3.50 (ignoring the negative sign)

Interpretation: Since the elasticity value > 1, it is elastic, there’s a strong impact and response, and consumers are really sensitive upon any income changes. An increase in income causes an increase in demand for a normal goods.

c) Assuming that these estimates are expected to remain stable during 2015. Forecast 2015 levels for AIROD Aircraft Company assuming that its aircraft prices remain constant at 2014 level and that disposable personal income will increase by RM40 billion. Also assume that arc income elasticity computed in (b) above is the best available estimate of income elasticity.

(7 Marks) ANSWER:Find Q2 if Q1 = 10,000, Y2 = 650, Y1 = 610, Ey = -3.5 (from d)

Ey =

(Q2 - Q1 )(Q2+ Q1 ) /2(Y 2 - Y1 )(Y 2+ Y1 ) /2

=(Q2 - Q1)(Q2+ Q1)

×(Y 2+ Y1 )(Y 2− Y1 )

-3 . 5=(Q2 - 10,000 )(Q2+ 10,000)

×(650 + 610)(650-610 )

-3 .5=(Q2 - 10,000 )(Q2+ 10,000)

×1,26040

-3 . 5=(Q2 - 10,000 )(Q2+ 10,000)

×31 .5

-3 . 5=31. 5Q2 - 315,000

Q2+ 10,000

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-3. 5(Q2+ 10,000 )= 31 .5Q2−315 ,000

-3 . 5Q2− 35,000= 31.5Q2−315 , 000

315,000 − 35,000= 31. 5Q2+3.5Q2

280,000= 35Q2

Q2= 280,000/35

= 8,000

d) Forecast 2015 sales for AIROD Aircraft Company given that its aircraft prices will increase by RM20 from 2014 levels and that disposable personal income will increase by RM40 billion. Assume that the price and income affects are independent and additive and that the arc income and price elasticities computed in parts (a) and (b) are the best available estimates of these elasticities to be used in making the forecast.

(6 Marks)

The Forecast 2015 sales has to be calculated separately for each variable’s impact. Then each impact will be added to calculate the end result.

i) The impact on RM40 billion increase in personal disposable income as calculated in c): 8,000 – 10,000 = -2,000

ii) The impact on the RM20 (in millions) increase in price:

Ed =

(Q2 - Q1 )(Q2+ Q1 ) /2( P2 - P1 )( P2+ P1 ) /2

=(Q2 - Q1)(Q2+ Q1)

×(P2+ P1)( P2− P1 )

-2 .01=(Q 2 - 10,000 )(Q 2+ 10,000)

×(109 .5 + 89 .5 )(109 .5 - 89 .5 )

-2 . 01=(Q2 - 10,000 )(Q2+ 10,000)

×9 . 95

-2 .01 Q2 -20,100=9 .95Q2−99 ,500

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99 ,500 -20,100=9 . 95Q2+2 .01 Q2

79,400=11. 96Q2

Q2=79 ,400/11. 96= 6,639The impact is 6,639 - 10,000 = -3,361Therefore, the forecast 2015 sales is 10,000 -2,000 - 3,361 = 4,639

[TOTAL MARKS: 25]

-END of PART 1-

PART 2PURPOSE: The purpose of PART 2 of this assignment is to enhance the analytical skill of the students on production functions, cost minimization, profits maximization and to appreciate their usefulness in the real world.

REQUIREMENT: 1. Data on Gross Domestic Product (GDP), Labor and Real Capital for Mexico from 1955-1974 is shown in Table 1 below:

Table 1: Real GDP, Labor and Real Capital for Mexico from 1955-1974 YEAR GDP (millions of 1960

Peso) Labor (thousands of

people) CAPITAL (millions of 1960

Peso)1955 114,043 8,310 182,113 1956 120,410 8,529 193,749 1957 129,187 8,738 205,192 1958 134,705 8,952 215,130 1959 139,960 9,171 225,021 1960 150,511 9,569 237,026 1961 157,897 9,527 248,897 1962 165,286 9,662 260,661 1963 178,491 10,334 275,466 1964 199,457 10,981 295,378 1965 212,323 11,746 315,715 1966 226,977 11,521 337,642 1967 241,194 11,540 363,599 1968 260,881 12,066 391,847 1969 277,498 12,297 422,382 1970 296,530 12,955 455,049

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1971 306,712 13,338 484,677 1972 329,030 13,738 520,553 1973 354,057 15,924 561,531 1974 374,977 14,154 609,825

a) Fit a regression equation using GDP as the dependent variable and Labor & Capital as the independent variables. (Use logarithm for all variables)

(10 Marks) Step 1: Convert into log for all variables for each observation

YEAR GDP

(millions of 1960 Peso)

Labor (thousands of

people)

CAPITAL (millions of 1960 Peso)

Log (GDP) Log (Labor) Log (Capital)

1955 114,043 8,310 182,113 11.6443 9.0252 12.11241956 120,410 8,529 193,749 11.6987 9.0512 12.17431957 129,187 8,738 205,192 11.7690 9.0754 12.23171958 134,705 8,952 215,130 11.8108 9.0996 12.27901959 139,960 9,171 225,021 11.8491 9.1238 12.32391960 150,511 9,569 237,026 11.9218 9.1663 12.37591961 157,897 9,527 248,897 11.9697 9.1619 12.42481962 165,286 9,662 260,661 12.0154 9.1760 12.47101963 178,491 10,334 275,466 12.0923 9.2432 12.52621964 199,457 10,981 295,378 12.2034 9.3039 12.59601965 212,323 11,746 315,715 12.2659 9.3713 12.66261966 226,977 11,521 337,642 12.3326 9.3519 12.72971967 241,194 11,540 363,599 12.3934 9.3536 12.80381968 260,881 12,066 391,847 12.4718 9.3981 12.87861969 277,498 12,297 422,382 12.5336 9.4171 12.95371970 296,530 12,955 455,049 12.5999 9.4692 13.02821971 306,712 13,338 484,677 12.6337 9.4984 13.09121972 329,030 13,738 520,553 12.7039 9.5279 13.16261973 354,057 15,924 561,531 12.7772 9.6756 13.23841974 374,977 14,154 609,825 12.8346 9.5578 13.3209

Step 2: Do a regression analysis by Excel on the converted variables

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.9975R Square 0.9951Adjusted R Square 0.9945Standard Error 0.0283Observations 20

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ANOVA df SS MS F Significance F

Regression 2 2.7517 1.3758 1719.2311 0.0000Residual 17 0.0136 0.0008Total 19 2.7653

Coefficient

sStandard

Error t Stat P-value Lower 95% Upper 95%Intercept -1.6524 0.6062 -2.7259 0.0144 -2.9314 -0.3735X Variable 1 0.3397 0.1857 1.8295 0.0849 -0.0520 0.7315X Variable 2 0.8460 0.0934 9.0625 0.0000 0.6490 1.0430

Step 3: Identify the suitable equationDouble log: log Q = a + b•log P + c•log YWhere, Q is GDP, P is Labor, Y is Capital

From the ANOVA table,a = -1.6524, b = 0.3397, c = 0.8460

So, the regression equation is log Q = -1.6524 + 0.3397(log P) + 0.8460(log Y)

Step 4: InterpretationAt 95% confidence interval, the p-value is < 0.05 for Labor which means it is statistically significant. However, the p-value is > 0.05 for Capital which means it is not statistically significant. A 1 % increase in Labor, will increase 0.3397% in GDP. On the other hand, a 1% increase in Capital, will increase 0.8460% in GDP cannot be concluded.

Following the logarithm regression, R-squared is equal to 0.9951 meaning that, 99.51% per cent of the GDP is explained by labor and capital. Another 0.49% is explained by other factors. As for the goodness of fit, the R squared value at 0.9951 indicates that the data is 99.51% close to the fitted regression line. In other words, 99.51% of the observed values are equal to the fitted values. By knowing Labor & Capital, we can predict GDP 99.51% of the time.

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b) Fit a regression equation using GDP/Labor as the dependent variable and GDP/Capital as the independent variable. (Use logarithm for all variables)

(10 Marks) Step 1: Convert into log for all variables for each observationLOG first then divide

YEAR

GDP (millions of 1960 Peso)

Labor (thousand

s of people)

CAPITAL (millions of 1960 Peso)

Log (GDP)

Log (Labor)

Log (Capital)

Log(GDP)/Log(Lab)

Log(GDP)/Log(Cap)

1955 114,043 8,310 182,113 11.6443 9.0252 12.1124 1.2902 0.96141956 120,410 8,529 193,749 11.6987 9.0512 12.1743 1.2925 0.96091957 129,187 8,738 205,192 11.7690 9.0754 12.2317 1.2968 0.96221958 134,705 8,952 215,130 11.8108 9.0996 12.2790 1.2979 0.96191959 139,960 9,171 225,021 11.8491 9.1238 12.3239 1.2987 0.96151960 150,511 9,569 237,026 11.9218 9.1663 12.3759 1.3006 0.96331961 157,897 9,527 248,897 11.9697 9.1619 12.4248 1.3065 0.96341962 165,286 9,662 260,661 12.0154 9.1760 12.4710 1.3094 0.96351963 178,491 10,334 275,466 12.0923 9.2432 12.5262 1.3082 0.96541964 199,457 10,981 295,378 12.2034 9.3039 12.5960 1.3116 0.96881965 212,323 11,746 315,715 12.2659 9.3713 12.6626 1.3089 0.96871966 226,977 11,521 337,642 12.3326 9.3519 12.7297 1.3187 0.96881967 241,194 11,540 363,599 12.3934 9.3536 12.8038 1.3250 0.96791968 260,881 12,066 391,847 12.4718 9.3981 12.8786 1.3271 0.96841969 277,498 12,297 422,382 12.5336 9.4171 12.9537 1.3309 0.96761970 296,530 12,955 455,049 12.5999 9.4692 13.0282 1.3306 0.96711971 306,712 13,338 484,677 12.6337 9.4984 13.0912 1.3301 0.96501972 329,030 13,738 520,553 12.7039 9.5279 13.1626 1.3333 0.96511973 354,057 15,924 561,531 12.7772 9.6756 13.2384 1.3206 0.96521974 374,977 14,154 609,825 12.8346 9.5578 13.3209 1.3428 0.9635

Step 2: Do a regression analysis by Excel on the converted variablesSUMMARY OUTPUT

Regression StatisticsMultiple R 0.58661R Square 0.34411Adjusted R Square 0.30767Standard Error 0.01284Observations 20

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ANOVA

df SS MS FSignificance

FRegression 1 0.0016 0.0016 9.4436 0.0066Residual 18 0.0030 0.0002Total 19 0.0045

CoefficientsStandard

Error t Stat P-value Lower 95% Upper 95%Intercept -1.8542 1.0310 -1.7985 0.0889 -4.0203 0.3118X Variable 1 3.2833 1.0684 3.0730 0.0066 1.0386 5.5279

Step 3: Identify the suitable equationDouble log: log Q = a + b•log PWhere, Q is GDP/Labor, P is GDP/Capital

From the ANOVA table,a = -1.8542b = 3.2833

So, the regression equation is log Q = -1.8542 + 3.2833(log P)

Step 4: InterpretationAt 95% confidence interval, the p-value (0.0066) is < 0.05 for GDP/Capital and therefore, the variable is statistically significant to influence the GDP. A 1 % increase in GDP/Capital, will increase 3.2833% in GDP/Labor.

Following the logarithm regression, R-squared is equal to 0.3441 meaning that, 34.41% per cent of the GDP is explained by the ratio of GDP and Capital. Another 65.59% is explained by other factors. As for the goodness of fit, the R squared value at 0.3441 indicates that the data is only 34.41% close to the fitted regression line. In other words, 34.41% of the observed values are equal to the fitted values. By knowing the ratio of GDP and Capital we can only predict GDP 34.41% of the time.

c) Determine whether this production function exhibits increasing, decreasing or constant returns to scale and show with suitable diagram.

(5 Marks)

The first approach is by doing a simulation to test and see how an increase in inputs affect the predicted GDP by using the regression equation log Q = -1.6524 + 0.3397(log P) + 0.8460(log Y)

In the table below, for any given % increase in inputs, the % increase in GDP is always higher than the % increase in inputs, e.g. a 10% increase in all inputs gives rise to 12% in GDP and so on.

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% increase in all

inputs

Labor (thousands of

people)

CAPITAL (millions of 1960 Peso)

Log Q Q (GDP) %

increase in GDP

0% 10,000 270,000 12.0566 172,229 0%5% 10,500 283,500 12.1144 182,486 6%

10% 11,000 297,000 12.1696 192,835 12%15% 11,500 310,500 12.2223 203,271 18%20% 12,000 324,000 12.2728 213,792 24%25% 12,500 337,500 12.3212 224,394 30%30% 13,000 351,000 12.3677 235,076 36%35% 13,500 364,500 12.4124 245,834 43%40% 14,000 378,000 12.4555 256,667 49%45% 14,500 391,500 12.4971 267,571 55%50% 15,000 405,000 12.5373 278,546 62%55% 15,500 418,500 12.5762 289,589 68%60% 16,000 432,000 12.6139 300,698 75%65% 16,500 445,500 12.6503 311,872 81%70% 17,000 459,000 12.6857 323,109 88%75% 17,500 472,500 12.7201 334,407 94%80% 18,000 486,000 12.7535 345,766 101%85% 18,500 499,500 12.7860 357,183 107%90% 19,000 513,000 12.8176 368,658 114%95% 19,500 526,500 12.8484 380,189 121%

100% 20,000 540,000 12.8784 391,775 127%

In conclusion, because of the % increase in all inputs gives rise to more % in output, this simulation proves the production function to exhibit increasing returns to scale.

The second approach is to use the Cobb-Douglas production function, given by

ln(Output) = β1 + β2 ln(Labor) + β3 ln(Capital) + u

- if β2 + β3 = 1, there are constant returns to scale.

- if β2 + β3 > 1, there are increasing returns to scale.

- if β2 + β3 < 1, there are decreasing returns to scale.

In this case, β2 = 0.3397, β3 = 0.8460

β2 + β3 = 0.3397 + 0.8460 = 1.1857

Therefore, the production function exhibits increasing returns to scale.

The third approach is to plot a graph and see the gap. If the gap widens as the inputs are increased at a constant, then it is increasing returns to scale. As the gap narrows for a constant increase in GDP (10,257) i.e. A > B, it can be concluded that the production function exhibits increasing returns to scale.

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Note: Curve was plotted based on a simulation as per table below.

Labor (thousands of people)

CAPITAL (millions of 1960 Peso)

Log Q Q (GDP) %

increase in GDP

% increase

in all inputs2

+/- Q (up)

10,000 270,000 12.0566 172,229 0% 0.00% 0.00%10,500 283,500 12.1144 182,486 6% 5.00% 5.00% 10,257 10,996 296,881 12.1691 192,743 12% 9.96% 4.96% 10,257

[TOTAL MARKS: 25]

-END of PART 2-

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PART 3 By A. HARIS AWANG (MBA6063 Managerial Economics, 2016)

PURPOSE:

The purpose of this assignment question is to ensure that the students are able to apply their

knowledge on economic theories in assessing current events in the competitive real world.

REQUIREMENT:

Flat Screen Televisions Lose $126 per Unit Sold: Sony Corporation

Most of the cost of a flat screen TV involves the LCD panel. Globally, 220 million flat screen

TVs were sold in 2011 for $115 billion. Although scale economies in massive factories and

volume discounts on electronic input components have driven the cost of LCDs down from

$2,400 to $500 the last decade, the price has fallen even faster. In 2001, the average selling

price of a large LCD panel was over $4,000. By 2011, this price had fallen below $600. Sony

Corporation finds its flat screen TVs now fail to cover the full cost of the LCD panels and

instead impose a $126 ($500 - $374) loss per TV sold. Nevertheless, the indirect fixed costs of

the LCD factories including Korean Samsung, Japanese Sharp, Panasonic, and Sony

constructed are partially covered by operating (continue production). Losses would be greater

in the short run if they shut down.

Please read the article above and answer the following questions:

a) Use relevant graphs to explain the above phenomena. You are required to search for

appropriate data to support your answers.

(20 Marks)

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The above phenomena is explained by answering the following questions:

Why did the LCD price fall? As described by The Economist (2012) (see figure 3.1), the price of LCD panel has fallen

very drastically from 2001 to 2011. During the period, none of the LCD producing companies

makes money from it. Many suppliers expanded capacity thus resulting in an excessively

abundant supply of LCD panels. Between 2004 and 2008, LCD panel price fell by 80% while

manufacturing cost fell by 50% and both eventually caught up until margin was gradually

diminished to approximately nothing. Some suppliers even had to sell at a loss.

As explained by Chang, SC (2005), faced by

intense price war started by Korea, Japan

decided to transfer LCD manufacturing

technologies to Taiwan which increased its

market share from 1% in 1998 to 30% in 2002.

More facilities were developed to improve

economies of scale which in 2002, resulted in

overproduction. This subsequently led to price

reduction which most manufacturers suffered financially. Another reason for the price

reduction has got to do with one of the biggest mergers in the LCD panel industry. In 2001,

Taiwan’s ADT merged with Unipac to become AUO which was second only to Samsung. The

merger enabled AUO to achieve economies of scale, product line diversification, expand

production capacity, and improve efficient

resource allocation.

The aggressive expansion by suppliers in Korea

and Taiwan has only contributed to the decline

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Figure 3.1 The fall of LCD cost and price. Source: http://www.economist.com/node/21543215

in the price. This is in conformance to the supply and demand change of equilibrium when

there’s a shift in supply as described in figure 3.2. The shift in the supply curve to the right

has lowered the LCD panel price from P1 to P2.

Why did the LCD TV price fall?

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Figure 3.3. The fall in LCD TV Price. Source: https://www.wired.com/2011/04/st_infoporn_lcds/

Figure 3.3 shows a downward trend for

LCD TV price. Since LCD panel is the major component in LCD TV in term of cost, the fall

in the price significantly contributes to the fall in finished product price. This has enabled

manufacturers to produce more LCD TVs for consumers. Another factor that contributed to

the price decrease is the phasing out of cathode ray tube (CRT) TVs being replaced by LCD

TVs resulting in a spike in LCD industry (Chang, 2005). Manufacturers saw this as an

opportunity to gain market share by ramping up LCD TVs production which eventually led to

a surplus. To reduce surplus, manufacturers had to reduce price as illustrated in figure 3.4.

Price continues to fall until it reaches equilibrium.

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Why Sony chose not to shut down despite making at a loss?The loss in LCD TV is not only affecting Sony but also other leading corporations including

Samsung, Sharp and Panasonic. From the general economic perspective, a firm must have

revenue R ≥ TC, total costs, in order to avoid losses. In the short run a firm should continue to

operate if price exceeds average variable costs. If the revenue the firm is receiving is greater

than its variable cost (R >VC) then the firm is covering all variable cost plus there is

additional revenue which partially or entirely offsets fixed costs. In this case, the revenue is

only enough to partially cover the fixed cost of factories constructed. Nonetheless, Sony along

with others chose to stay in the LCD TV business in order to maintain their market share and

had other divisions to cover their losses for at least temporarily (see fig. 3.5). Moreover, TV

business has a very promising future looking at its potential convergence with the internet and

new technologies. A company lacking capital, diversification and not as diversified as Sony

would have shut down or even exit the business when faced with this situation.

b) As an economic consultant, what would you advise Sony Corporation to do in light of

the competition from the other manufacturers? Be as specific as possible in your advice; and

justify your answers.

(30 Marks)

As this assignment is written in 2016, an assumption has to be established that it is from the

2012 context of what happened in 2011 and before. In light of the competition from other

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Figure 3.4. Price continues to fall until it reaches equilibrium.

manufacturers, as a consultant, I would advise Sony Corporation based on SWOT (strengths,

weaknesses, opportunities, and threats) analysis.

StrengthsOther than TV, Sony is also known for other consumer goods such as Blu-Ray, DVD, Home

Theatre, Car Audio, Home Audio, PlayStation as well as entertainment business which are

Sony Music and Sony Pictures. It is understandable that a big corporation as diversified as

Sony has other portfolios to cover for losses suffered by another portfolio. In this case, losses

made in LCD TV could be covered by other more profitable operations for as long as the

consolidated business is profitable. Sony also maintains large reserves of cash, with ¥895

billion (US$10.95 billion) on hand as of 2012. In May 2012, Sony shares were valued at about

US$15 billion (Wikipedia, 2016).

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Figure 3.5 illustrates a breakdown of the operating income for each of Sony’s divisions in the

quarter ending Sep 20, 2011 (Yarow J & Terbush J, 2011). It shows that while LCD TV,

which is under the Consumer & Products & Services division, is losing money, other

divisions i.e. Music, Pictures and Financial Services (which is made up of insurance and

banking services) are making profit. So, on a consolidated basis Sony was not in the red

despite making losses in LCD TV and other divisions.

Strengths: Diversified, cash reserves.

Advice: To allocate more capital for research and be the innovative leader like Sony once was.

Sony used to be a leader in innovative products such as Walkman, CD Player, DVD Player,

3.5” Micro Floppy Disk Drives, Handycam, Trinitron, Digital Camera and PlayStation. Now

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Figure 3.5. How Sony makes money. Source: http://www.businessinsider.com/chart-of-the-day-sony-operating-income-september-2011-2011-11

it seems to be doing what others are doing e.g. LCD TV. It has to rise above the rest to claim

its position as market leader.

WeaknessesWhile being diversified is good, being too diversified has its downside. This is the case for

Sony which has resulted in a distortion of its Sony brand. When Steve Jobs took over Apple

as CEO in 1997, he cut the number of Apple products by 70% down to only four. This has

enabled the company to focus on just a few products, and made them incredibly successful.

Weaknesses: Too diversified, not focused.

Advice: Sony has to look at what matters the most to its philosophy of existence. It is the

profit or is it the brand reputation or is it its legacy in creating a better world for consumers?

Sony is to downsize divisions that are making losses. This may involve lay-offs and assets

divestment.

OpportunitiesThe opportunity to leverage on internet companies like Google or Netflix can be the future.

Everyone is talking apps and internet TV these days. Sony can add values to its TV by

including Google apps or Netflix in their TVs.

Another opportunity that was realized by Sony is the acquisition of camera maker, Minolta

which proved to be successful. Through this, Sony also acquired the lens technology from

Minolta which has enabled Sony to venture into DSLRs market and managed to capture 12%

global market share after Canon and Nikon in 2011.

Opportunities: Acquisition of technology companies.

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Advice: Sony has to look at new technology development not only through research but also

through acquisition of high technology companies. Technologies have a lot of advantages

when it comes to setting the world’s market.

ThreatsOne of the threats faced by Sony is the stiff price competitions from competitors such as

Samsung and LG who are doing very well in products such as mobile devices and televisions.

Another threat is from tier 3 companies from China and Taiwan who have the price advantage

selling brands very few have heard of. Sony’s online network also faces threats from hackers.

The PlayStation network was hacked which compromised customer data such as credit card.

Threats: Price competition, tier 3 companies, hackers.

Advice: Sony should not be competing on low-end products that can be massively produced

but instead should shift focus on high-end technology products. Sony should also invest more

into internet security as this will provide a good platform for its future high-tech products to

launch.

(TOTAL: 50 MARKS)

Reference: Chang, S.C. (2005). The TFT–LCD industry in Taiwan: competitive advantages and future developments. Technology in Society, 27, 199–215.

McGuigan, JR et al.. (2014). Managerial Economics: Applications, Strategies and Tactics. (13thed.). US. Cengage Learning.

The Economist. (2012). Television-making: Cracking Up. Retrieved 30 September, 2016, from http://www.economist.com/node/21543215

Tweney, D. (2011). Infoporn: How Flatscreen TVs Get Cheaper. Retrieved 30 September, 2016, from https://www.wired.com/2011/04/st_infoporn_lcds/

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Wikipedia. (2016). Sony. Retrieved 05 October, 2016, from https://en.wikipedia.org/wiki/Sony

Yarow J & Terbush J. (2011). CHART OF THE DAY: How Sony Actually Makes Money Is Very Surprising. Retrieved 04 October, 2016, from http://www.businessinsider.com/chart-of-the-day-sony-operating-income-september-2011-2011-11

-END of PART 3-

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