Current Situation And SWOT Analysis Of Electric Heating In France

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.