Research Paper Sample

ASSESSING THE MARKETING EFFICIENCY OF SAMBALPURI BASTRALAYA HANDLOOM COOPERATIVESOCIETY: A DEA APPROACH ABSTRACT The handloom industry of India is one of the important manufacturing institutions of the world. There has been growth and innovation both with in and outside the co-operative sector that gives us clues regarding the potential of handloom industry in 21st century. It occupies a significant position in our rural economy in terms of potential for employment and income generation. Orissa is famous for his exquisite handloom products. The handloom sector plays a vital role in the state economy. Sambalpuri Bastralaya Handloom Co-operative Society has the distinction of being one of the largest handloom co-operative societies in the state as well as in the country. The paper deals with study of marketing channels of Sambalpuri handlooms and compares the performance of some selected sales branch of Sambalpuri Bastralaya Handloom Cooperative society Ltd, Bargarh. The motivation of the study originated from the loss suffered by the society. Some inputs and outputs of the sales branch are identified to apply the Data Envelopment Analysis (DEA) in order to measure the relative efficiency and reference units. The analysis has found that only sales cannot increase the efficiency of a sales branch.

Key Words: Efficiency, Data Envelopment Analysis, Marketing Channel Introduction: Sambalpuri Handloom Industry of India has a legacy of unrivalled craftsmanship. It occupies an important place in the rural economy of Orissa. The industry engages largest number of workers, next only to agriculture. The nature of the fabric is durable, strong, absorbent, washable and that held color permanently. Thousands of peoples are engaged in various

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activities like ginning, spinning, dyeing, bleaching, weaving and finally trading of the cloth.. Sambalpuri sari with motifs, tie and dye are reflected superb craftsmanship. Some of the typical varieties of Sambalpuri handloom saris from Orissa that have own acclaim in the world are Bomkai, Khandua, Bichitrapuri, Saktapar, Posapalli, Tarabali, Rupashree etc..in cotton silk, Bapta, patta and tassur.

It is fabricated using tie and dye method known as

IKAT. The craftsman conceptualizes the design, draws it and according to the design color is added. To protect and promote the interest of weavers, Sambalpuri Bastralaya Handloom Cooperative Society (SBHLCS) Ltd., Bargarh, Orissa was set up on 22.06.1954 by Padmashree Krutartha Acharya. Significance of the Study: The authors have developed their interest in handloom industry because its problems are talked regularly over daily newspapers, magazines and in other modes. A starting point of our analysis was to understand the problem. The gathered data are presented through Table-1 to Table-4 for the year 1999 to 2004.The data reveals that SBHLCS is suffering loss as well as saris worth millions of rupees laying in sales branch without being sold. This means not only loss of skill but also livelihood for thousands of weavers for whom weaving is a way of life. Stocks are pending due to the marketing inefficiency of sales branch. The demand of saris is still exist which is evident from the revisiting of the number of customer to sales branch. Marketing Channels of Handlooms: We observed that marketing of Sambalpuri handlooms are done through the following four channels. Marketing Model i.

Weaver

Consumer

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ii

Weaver

iii

Weaver

iv

Weaver

Consumer

Master Weaver

Consumer

Stockiest

SBHLCS S.Br.

Weaver Co.Op.

Consumer

In the first model the independent weaver may gain profit but the customer may not get proper satisfaction due to lack of variety, design and color etc. In the second model the weavers are neglected. Their labor is not taken into account. The master weaver becomes the center of profit. In the third model the weavers are also neglected because they sells their products to the stockiest, then it reaches to the customers. In the fourth model the product passes through the sales branch of Sambalupri Bastralaya to the customer. Here the customers as well as the weavers are benefited.

Objective of the Study: •

To measure the relative efficiency of sales branch of SBHLCS, Ltd., Bargarh.

•

To know the reference units of inefficient units to become efficient.

To meet the desired objectives we have followed the data envelopment analysis which is described below. Data source and Methodology:

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The data collection was done mainly through questionnaires, interviewing weavers, stockiest, employees of SBHLCS,Ltd., attending the public meetings, study of press reports, study of websites and various other sources. Data Envelopment Analysis technique is used to assess the efficiency of SBHLCS,Ltd.. Data Envelopment Analysis: Farrell (1957) attempt to measure the efficiency of production in the single input and single output case. Farrell’s study involved the measurement of price and technical efficiency of U.S agricultural output relative to other countries. But he failed in providing a way to summarize all the various inputs and outputs into a single virtual output. Charnes, Cooper, and Rhodes (1978) extended Farrell’s idea and proposed a model that generalizes the single input, single output measure of efficiency of a decision making unit (DMU) to a multiples input multiples output setting. A DMU is an entity that uses inputs to produce outputs. This approach to performance measurement is called Data Envelopment Analysis (DEA). The DEA technique involves the use of linear programming (LP) to solve a set of interrelated problems to determine the relative efficiency of DMUs. The cooperative society’s sales branch constitutes a DMU as we consider the performance of various sales branches. The efficiency of a DMU is computed as follows Efficiency = weighted sum of outputs / weighted sum of inputs DEA is an approach comparing the efficiency of organizational units such as schools, hospitals, shops, sales branch and similar instances where there is a relatively homogeneous set of units. It focuses on individual observations as represented by optimization of each observation in contrast to the focus on the averages and estimation (regression) of parameters that are associated single optimization statistical approaches. The analysis will ensure outputs

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achieved from the inputs provided and will compare the group of DMUs by their strength in turning input into output. At the end of the analysis the DEA will be able to say which units are (relatively) efficient and which are (relatively) inefficient. The term ‘relative’ is important here which a DMU identified by DEA, as an efficient unit in a given data set may be deemed inefficient when compared using another set of data. The relatively “most efficient” units become the efficient frontier and the relative efficiency of other units is compared with the efficient frontier. An advantage of DEA is that it uses actual sample data to derive the efficient frontier that each unit in the sample is evaluated without prior information on the most important inputs and outputs in the evaluation.

Sales branches of SBHLCS Ltd. are considered here as DMUs. One of the goal of the management is to ensure that each of the branch achieve the best possible performance, the problem, there fore is deciding what this means and how best to go about measuring it. The inputs of sales branches of SBHLCS can be inventory of saris, number of employees, floor space, wages of staff and overhead costs. Managers can develop the performance of a sales unit such as business per employee or profit per unit of office space utilized. All these attempts to measure performance may not produce a clear picture as branches may exhibit considerable variation depending on the performance indication chosen. In most cases attempts are made to find a comprehensive performance measures by giving a priori weight to a variety of performance ratios. This is where DEA helps as it provides more comprehensive measure of efficiency by taking into account all-important factors.

In the present study following three inputs and two outputs are considered.

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Inputs • • •

Inventory of Sambalpuri Saris Number of employees Wages of employees

Outputs • •

Yearly sales Customer’s satisfaction.

The customer satisfaction is calculated as number of customer visiting to the particular sales branch twice or more than twice. Mathematical Model The basic DEA model for “n” DMUs with “m” inputs and “s” outputs proposed by Charnes et.al. (1978), the relative efficiency score of pth DMUs is given by s

Max

v ∑

zp =

k= 1 m

u ∑ j= 1

s

s.t

v ∑ k= 1 m

k

u ∑ j= 1

k

j

yki

j

ykp x jp (1)

≤1∀ i

x ji

vk , u j ≥0∀ k, j Where, k=1 to s (no. of outputs} j= 1 to m (no. of inputs) i= 1 to n ( no.of DMUs) yki= amount of output k produced by DMU i xji = amount of input j utilized by DMU i v k = weight given to output k u j = weight given to input j

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The fractional program (1) can be solved as the linear programming problem s

z p = ∑vk ykp

Max

k =1

s.t

m

∑u j =1

j

x jp = 1

s

m

k =1

j =1

(2)

∑vk yki − ∑u j x ji ≤ 0 vk , u j ≥ 0

∀i

∀ k, j

The above LPP is run n times for each year over the data (Table-1 to Table-3) to get the relative efficiency of all the DMUs. The reference units and relative efficiency has been shown in Table-4 and Table-5. Table 1 1999

2000

Inventor y on any month in lakhs

No. of employees

Wages per year in lakhs

Sales in lakhs

No. of satisfied customers in year in hundreds

Inventor y on any month in lakhs

No. of employees

Wages per year in

1

8.00

6

2.30

67.2

8.80

6

2.41

2

1.00

5

1.89

67.7 1 10.6

13.25

1.80

5

2.1

3 4

0.45 3.00

4 4

1.9 1.9

2.10 55.63

0.45 3.50

3 4

1.3 2

5

1.55

4

2

21.96

1.60

3

1.4

6

1.60

5

1.88

24.86

1.70

5

2.41

7

9.45

6

2.29

57.92

9.50

4

2.1

8

0.60

4

1.4

15.01

0.65

4

1.45

9 10

0.42 0.80

4 4

1.4 1.4

4.93 5.01

0.52 0.82

4 4

1.5 1.5

DMU

1.47 44.5 1 27.4 6 27.9 7 57.9 2 15.0 1 4.32 3.76

7

lakhs

Sales in lakhs

No. of satisfied customers in year in hundreds

85.4 3 10.3 4 3 79

85.3

52.3 9 35.8 2 68.8 1 20.9 4 3.45 5.14

12.92 5.00 59.25 65.48 35.82 55.04 26.17 3.83 6.42

Table 2 2001

2002

Inventor y on any month in lakhs

No. of employees

Wages per year in lakhs

Sales in lakhs

No. of satisfied customers in year in hundreds

Inventor y on any month in lakhs

No. of employees

Wages per year in

1 2 3 4 5 6 7

9.50 2.00 0.46 4.00 1.80 1.95 9.80

5 4 4 4 6 5 3

2.1 1.45 1.5 1.5 2.52 2.21 1.4

68.81 20.94 3.45 5.14 93.81 9.83 1.33

75.40 12.28 2.00 70.82 66.80 50.74 69.65

9.80 2.40 0.45 4.28 1.80 1.95 9.80

6 5 4 4 4 5 4

2.65 2.31 1.68 1.65 2.25 2.31 1.64

8 9 10

0.65 0.62 0.83

4 3 6

2.12 1.51 2.52

88.53 53.44 38.06

17.58 3.40 6.67

0.70 0.50 0.83

4 4 3

1.60 1.72 1.60

Inventor y on any month in lakhs

No. of employees

No. of satisfied customers in year in hundreds

Inventor y on any month in lakhs

No. of employees

1 2 3 4

10.50 2.50 0.44 5.00

5

DMU

Sales in lakhs

No. of satisfied customers in year in hundreds

89.82 16.21 1.74 87.74 58.36 35.4 108.0 3 18.10 2.62 5.50

112.27 16.24 2.90 109.67 58.36 44.25 129.63

lakhs

27.15 1.96 4.12

Table 3 2003

2004

Wages per year in lakhs

Sales in lakhs

6 5 3 4

2.74 2.40 1.44 1.72

61.30 13.60 0.53 71.86

36.78 15.54 1.06 47.90

12.00 3.00 0.45 5.28

6 5 3 4

2.84 2.48 1.50 1.80

1.85

4

2.34

41.87

41.87

1.00

4

1.83

6 7

2.00 9.25

5 5

2.39 1.71

28.89 81.65

1.30 11.20

5 5

2.4 2.46

8

0.73

4

1.70

28.89 102.0 7 14.14

17.67

0.73

4

1.8

DMU

8

Wages per year in

Sales in lakhs

No. of satisfied customers in year in hundreds

19.11 5.61 0.55 29.7 7 16.2 4 11.03 32.0 3 5.96

25.48 11.22 1.23 34.00

lakhs

20.30 16.54 45.75 7.15

9 10

0.42 0.73

4 3

1.82 1.70

1.46 2.50

2.19 3.33

0.52 0.83

4 3

1.86 1.6

Table-4: Reference units and relative efficiency. 1999

DMU Ref units 1 -

2000

Z

Ref units

1

4,5 5

2

4,8

3

8

4 5

1

6

4,8

7

-

0.566077 6 0.181666 8 1 0.976747 8 0.931067 9 1

8

-

1

5

9

8

5

10

4,8

0.462360 4 0.269694 3

5 5

2001

Z 0.922351 8 0.175436 4 0.271499 4 1 1 0.643499 6 0.834134 1 0.983863 7 0.202622 3 0.191434 8

4,5

5

4,5,7

Z 0.7896264

5

0.1655501

5

0.1171570

-

1 1

5

0.7011515

-

1

5

0.9109857

5

0.1477690

5

0.2706785

Ref units

Table-5: Reference units and relative efficiency 2002

DMU Ref units 1 4 2

4,5

Z 0.682471 6 0.337344 1

2003 Ref units

4,7 5,7

2004

Z 0.520734 3 0.334039 6

9

5,7

Z 0.4730662

5,7

0.3430893

Ref units

1.48 2.12

1.70 3.18

3

8

4 5 6

4,5

7 8 9

5

10

5

0.166155 1 1 1 0.781289 2 1 1 0.161617 5 0.204381 6

8

0.024671 6 1 1 0.838719 0 1 1 0.215417 6 0.191907 3

5,7 8 5,8

5

0.1312106

5,7

1 1 0.6360301

5 5

1 0.4973333 0.1720228

5

0.2030010

Figure-1. Efiiciency Vs Sales star

sleepers

80 Sales

60

dogs

40

?

20 0 0

0.2

0.4

0.6

0.8

1

1.2

Efficiency

Figure 1 shows an illustrative scatter diagram with two axes, one for sales and the other for efficiency of the sales branch. Each one of the two axes is split into two ranges of values Reviewing the long term prospects of each sales branch:

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The long-term prospects of each sales branch cannot be ascertained from its historical sales alone. Historical sales may not reflect the potential of the market of a sales branch which better management could exploit. Once the market efficiency and sales of a sales branch are known we can judge its longer-term prospects by means of an “efficiency sales matrix” depicted in figure1. Mean efficiency and mean sales of all the DMUs are taken into account. Conclusions The paper presents applications of DEA to determine relative efficiency of sales branch of SBHLCS, Ltd. The sales of DMUs 4 and 5 can be increased. The management has to think of about DMUs 2, 3, 9 and 10 because of their low efficiency and poor sales. They may be made mobile to increase their both sales and efficiency. It is interesting if one can take the non-discretionary inputs like location and others to measure the efficiency. Weavers must be made aware to prepare the fabric according to changing life style of people.

References: • • • • • • • • • •

Annual Report of “Sambalpuri Bastralaya Handloom Cooperative Society Ltd.” Bargarh. Banker R.D, Charnes A and Cooper W.W “Some models for estimating technical and scale efficiencies in data envelopment analysis” Management Science, 30 (1984) 10781092. Charnes, A; W Cooper; A.Y Lewin and L.M Seiford, Data Envelopment Analysis: Theory, Methodology and Application, Kluwer Academic Publishers, Boston, 1994. Emmanuel Thanassoulis “Assessing the market efficiency of Pubs.”Journal of OR Insight” Vol.10 issue 4 91997) 3-8. Farrel M. J “The measurement of Productivity efficiency” Journal of Royal Statistical Society (A general) 120 (1957) 1040-1043. Kotler Philip (2001) “Marketing Management”, the millennium edition, Prentice Hall Of India Pvt. Ltd. New Delhi,. Mohaptra ,P.C, (1986) “Economic Of Cotton Handloom Industry in India”, Ashish Publication, New Delhi. R.Ramanathan “A Data Envelopment Analysis Of Comparative Performance Of school in Netherland”, OPSEARCH Vol.38 No.2, (2001) 3-8. www.orissahandloomtosambalpurisaris.com www.sambalpuri.com

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