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Analyze the current situation of the company: Only using the information and data provided, complete an Internal (Strengths and Weaknesses) analysis and an External analysis (Opportunities and Treats) – a SWOT analysis – and conclude with a few recommendations to increase the company market penetration.

Determine the profile of Haverland’s customers: Determine, from the information provided, what are the main characteristics of the customer profile. From your observation derive the main selling points.

Use data in Appendix II-2 (Sales) to calculate the seasonality of sales and draw the appropriate conclusions from this analysis. From the data given, compute the seasonal multiplicative factor for each month [seasonal factor for month M = (mean of same month for 2012 and 2013 / average monthly sales)]. Draw a conclusion regarding the seasonality.

Use Appendix II-2 (Sales) and I-1 (Market analysis) to determine Haverland’s estimated turnover for the year N+1, and comment on your results. Add the seasonal factors calculated in the previous question: Compute the sales trend (linear regression) for 2012 and 2013 and forecast sales for 2014 using both the linear regression results and the seasonal factors computed. Plot the estimated monthly sales for 2014 (N+1).

Design a loyalty strategy for dealers and fitters of electrical supplies (wholesalers, electricians). Define a strategy using social network and promotional tools to increase wholesalers’ and electricians’ loyalty.

The Electric Heating Market in France

Current Situation and SWOT Analysis:

In spite of the maturity, the electric heating market in France is still growing, driven by new technology that incorporates to innovative energy-efficient products. These days, Housing construction is another factor that is gearing up growth. It is believed that the market would continue to increase over next twenty years. The issues regarding heating represent an individual households as well as businesses. This concern is linked to the environmental challenges and urgency to establish energy-efficient solutions.

According to the SWOT analysis of market,

Strength: The strength of “Haverland” Electric-heater Company in France is that the company is attractive and particularly renovated. The quality insulation is very crucial for keeping energy consumption and costs down. The regulation of electric heating installation has permitted compliance with new regulation that needs the use of highly efficient heating equipment. More of it, a full range of electric inertia heaters is available to cover all the segments of market.

Weakness: Employees must know the affects of performance of company. Otherwise, new challenges for electric heating are its accountability. The lack of innovation might be a great issue.

Opportunity: Selling market of gas heating systems has a huge opportunity of huge enhancement. The opportunity is the growing trend value of heating projects. The purchase of equipment is significantly influenced by conditions of weather. “Haverland France” has constructed brand consciousness through their dealers. Haverland’s current communication strategy targets to be cheap and efficient. Therefore, the online communication is an outstanding choice of media for the company.

Threat: The demand for consistent renovation creates a modern business atmosphere. However, “Haverland” has no privacy or confidentiality clauses about facets of circumstances. The strategical move of Spanish heating market is a threat of this French company.

Haverland’s Customer Profile: 

The customers of Haverland customer profile relies upon different types of heating markets that are Heating system, Gas, electricity, Electricity and wood as supplement, renewable energy or hybrid, renewable energy combined with other energies and other heating systems. Company’s commitment to develop international markets led to strong growth of network of commercial agents and distributors.

Sustainable consumption for insulation work or equipment choice is synonymous with the prosperity of consumers to choose high end technology for their heating systems. Household consumption of energy for heating relies on housing types and socio-demographic factors involving level of income and structural methods such as replacement of equipments that ultimately relies upon available new products.

SWOT Analysis of Haverland

Consumers have a tendency to combine the performance and cost-effective attributes of electric heating with the superior comfort or convenience afforded by other systems. In reality, a large majority of people choose the system that is least costly to install and maintain. Financial considerations override the desires of consumers for making environment friendly choices. There are also other labels of safety and performance standards. Customers should ensure electric radiators as safe and reliable.

Multiplicative decomposition method (Month Wise)

Column 1

Column 2

Column 3

Column 4

Column 5

Column 6

Column 7

Column 8

Column 9

Year

Month

Time

Cases

MA

CMA

Sn*e

Sn

final Sn

2012

1

1

49451

1.53748166

2

2

60045

2.26354989

3

3

10584

35493.75

31772.875

0.33311433

0.46455965

0.7329619

4

4

21895

28052

25696.625

0.85205742

0.87505412

1.38062211

5

5

19684

23341.25

27426.5

0.71770003

0.76346297

1.20455848

6

6

41202

31511.75

36291.125

1.1353189

1.20018417

1.89359809

7

7

43266

41070.5

50158.875

0.86257915

0.83217031

1.31296191

8

8

60130

59247.25

65362.5

0.91994645

0.85158126

1.34358766

9

9

92391

71477.75

78616

1.17521878

1.23760609

1.95264077

10

10

90124

85754.25

90492.375

0.99592922

1.01095414

1.59503922

11

11

100372

95230.5

95136.5

1.05503145

1.05503145

1.66458249

12

12

98035

95042.5

98255

0.99776093

0.99776093

1.57422355

2013

1

13

91639

101467.5

94039.5

0.97447349

0.97447349

1.53748166

2

14

115824

86611.5

80732.5

1.43466386

1.43466386

2.26354989

3

15

40948

74853.5

68704.125

0.59600497

0.46455965

0.7329619

4

16

51003

62554.75

56793

0.89805082

0.87505412

1.38062211

5

17

42444

51031.25

52450.125

0.80922591

0.76346297

1.20455848

6

18

69730

53869

55120.375

1.26504945

1.20018417

1.89359809

7

19

52299

56371.75

65230.125

0.80176146

0.83217031

1.31296191

8

20

61014

74088.5

77901.875

0.78321607

0.85158126

1.34358766

9

21

113311

81715.25

87162.75

1.2999934

1.23760609

1.95264077

10

22

100237

92610.25

97698.875

1.02597906

1.01095414

1.59503922

11

23

95879

102787.5

1.66458249

12

24

101723

1.57422355

Average

18.9330752

Note:

MA= moving average

CMA= centred moving average

Sn = seasonl estimate

e = error

Computation of the normalization factor

       L = number of months in the year = 12

         Normalization factor = L/(sum of average monthly estimates)                                                                                              

L/12 =

1.577756

Monthly multiplicative indices are-

Jan

1.537482

Feb

2.26355

March

0.732962

April

1.380622

May

1.204558

June

1.893598

July

1.312962

August

1.343588

September

1.952641

October

1.595039

November

1.664582

December

1.574224

Sales data of Haverland (Month Wise)

Input:

Annual

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Total

2012

49451

60045

10584

21895

19684

41202

43266

60130

92391

90124

100372

98035

687179

2013

91639

115824

40948

51003

42444

69730

52299

61014

113311

100237

95879

101723

936051

Totals

141090

175869

51532

72898

62128

110932

95565

121144

205702

190361

196251

199758

1623230

Monthly Averages

70545

87934.5

25766

36449

31064

55466

47782.5

60572

102851

95180.5

98125.5

99879

67634.58

Monthly Indices

1.04303149

1.30014108

0.380958952

0.53891069

0.45929166

0.820083414

0.706480289

0.89557734

1.520686532

1.407275617

1.45081843

1.47674452

Analysis:

Year

Jan

Feb

Mar

Apr

Jun

Oct

Nov

Dec

2012

47410.841

46183.450

27782.521

40628.253

50241.231

64041.471

69183.020

66385.891

2013

87858.326

89085.717

107486.646

94640.913

85027.936

71227.696

66086.147

68883.276

Is there a trend in the data?

 

 

Quarters/

Cumulative

Year

Months

Month (x)

Sales (y)

sum x^2

sum x*y

2012

1

1

47410.841

1

47410.841

2

2

46183.45

4

92366.900

3

3

27782.521

9

83347.563

4

4

40628.253

16

162513.012

5

5

42857.299

25

214286.495

6

6

50241.231

36

301447.386

7

7

61241.231

49

428688.617

8

8

67141.047

64

537128.376

9

9

60756.111

81

546804.999

10

10

64041.471

100

640414.710

11

11

69183.02

121

761013.220

12

12

66385.891

144

796630.692

2013

1

13

87858.326

169

1142158.238

2

14

89085.717

196

1247200.038

3

15

107486.646

225

1612299.690

4

16

94640.913

256

1514254.608

5

17

92411.868

289

1571001.756

6

18

85027.936

324

1530502.848

7

19

74027.543

361

1406523.317

8

20

68128.120

400

1362562.400

9

21

74513.056

441

1564774.176

10

22

71227.696

484

1567009.312

11

23

66086.147

529

1519981.381

12

24

68883.276

576

       1653198.624

Total

210

1623229.610

2870

22303519.199

By linear regression equations:

b

a

7845.288244

-1011.705

Sales=b(Season)+a

 

The linear regression equation for model trend in this case is-

Sales = 7845.288244 *(t) - 1011.705

We may need to check r^2 to decide on the goodness of the fit.

Let us generate the deseasoned monthly forecasts for 2014

 

 

Year

Jan

Feb

Mar

Apr

May

Jul

Aug

Sep

Oct

Nov

Dec

2014

195120.501

202965.789

210811.078

218656.366

226501.654

242192.231

250037.519

257882.807

265728.095

273573.383

281418.672

Seasonality indices

1.026

1.279

0.375

0.530

0.452

0.695

0.881

1.496

1.385

1.427

1.453

Let us generate the monthly forecasts for 2014

 

Year

Jan

Feb

Mar

Apr

May

Jul

Aug

Sep

Oct

Nov

Dec

2014

200241.663

259637.515

79017.945

115939.933

102356.177

168350.488

220324.299

385847.962

367934.709

390517.974

408895.562

2012

49451

60045

10584

21895

19684

43266

60130

92391

90124

100372

98035

2013

91639

115824

40948

51003

42444

52299

61014

113311

100237

95879

101723


The calculations and results infer that the month wise sale of electrical products would grow in 2014 rather than 2012 and 2013. However, in March 2014, the sale is going to be less than March 2013. The sale of September to December in 2014 is going to be significantly greater than sales of September to December 2013.   

The loyalty strategy is used for dealers and fitters of electrical suppliers that are wholesalers and electricians. Nearly 35% of homes use electricity as its power source in France. Use of electricity in domestic purpose and primary power sources have increased in these days. It is useful to detect between individual private housing where individuals select their own heating system and collective housing or shared building. Consumers would be well suggested to consider their choice of heating system with care. This happened because new residential or commercial builds would cause for at least 20 to 25 years for it.  

 As Haverland is one of the leading companies in the manufacturing sector and sale of electric heaters, the French subsidiary gets benefit from the expertise and information of the Spanish company. The cost strategy of Haverland is serving customer requirements as they consider that the customer association is paramount. Although the company has an international perspective, the ability is to meet local requirements is a major success factor for their strategy.

Electric heating suppliers majorly communicate via internet and on television. Haverland like other companies have their own website for providing constant and borderless communication to their customers. In today’s world, social networks are crucial communication tools for carrying out business. Haverland consequently present on Facebook, Twitter and Youtube. The use of social networks permits brands to enhance their vision, promote loyalty and better understand their customers.

Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis: forecasting and control. John Wiley & Sons.

Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment, 114(1), 106-115.

Yamane, T., 1967. Statistics: An introductory analysis (Vol. 886). New York, NY: Harper & Row.

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