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A STUDY ON INCREASING COMPETITIVENESS OF UNORGANIZED RETAIL IN INDIA Prof. Prem Vrat1

Akshay Jain2

Prateek Raj3

This paper focuses primarily on the Small and Medium Scale Retail Sector of India which is largely unorganized. Faced by stiff competition from major enterprises like Reliance and WalMart, that allocate considerable amount of resources on research and development of tools for increasing competitiveness and efficiency, a systematic study on the application of Industrial Engineering Principles in unorganized retail sector is an important area of study and of relevance to the Indian Economy. The paper attempts to present an in-depth study of retail sector of India, organized and unorganized, and identifies parameters for defining competitiveness in the context of Indian Retail. Integrating Quality Function Deployment (QFD) analysis with the Kano model the paper highlights major technical issues related to Kirana and Apparel Shops. A model for optimizing product assortment in a small unorganized store has been presented, with the introduction of the concept of linear cross elasticity. It highlights the managerial insights gained through the study.

Keywords- Unorganized retail sector, India, Kirana, Competitiveness, QFD analysis, Product Assortment, Heuristic

1

Professor of Eminence, Management Development Institute, Gurgaon Department of Mechanical Engineering, Indian Institute of Technology, Delhi 3 Department of Mechanical Engineering, Indian Institute of Technology, Delhi 2

1


A STUDY ON INCREASING COMPETITIVENESS OF UNORGANIZED RETAIL IN INDIA

1.

Introduction

1.1 The Retail Sector The retail industry is divided into organized and unorganized sectors. Organized retailing refers to trading activities undertaken by licensed retailers, who are registered for sales tax, income tax, etc. These include the corporate-backed hypermarkets and retail chains, and also the privately owned large retail businesses. Unorganized retailing, on the other hand, refers to the traditional formats of low-cost retailing, for example, the local kirana shops, owner manned general stores, paan/beedi shops, convenience stores, hand cart and pavement vendors, etc. Currently Organized sector occupies a small percentage of the retail sector with 96% of the total business being carried out by traditional unorganized trade outlets.

Retailing is a booming sector of the Indian Economy primarily owing to the opening of FDI in retail sector and coming up of hypermarkets and retail chains. Organized retailing is pitted to grow at a rate of 35% while the unorganized retailing only at the rate of 6%. It is expected that organized retailing will have 10-15% of the industry share by 2010. Thus faced by a stiff competition, the small traditional trade outlets need to enhance competitiveness of the unorganized retail sector of India, so that they can not only deal with the challenges faced by the coming up of multinationals and hypermarkets, but also capitalize from this retail boom.

1.2 The Paper Catering to the need of enhancing competitiveness of the unorganized retail sector of India, the paper focuses on a systematic study of the sector, both unorganized as well as organized, through primary and secondary data sources. Based on the analysis, parameters significant for grocery and apparel competitiveness are identified and an index is developed to measure and compare competitiveness of different retail formats. A competitor analysis is presented for retail and apparel stores, and using Kanoâ€&#x;s model, a rigorous QFD analysis is undertaken and major technical issues are highlighted for small kirana and apparel shops.

Working further on one of the major technical issues of product assortment, a case study has been presented. A model and its heuristic algorithm have been developed for effective product assortment of products over the existing demand, with the introduction of the parameter of linear cross elasticity.

2


2

General Study

Papers relating to the current state of retail sector in India were studied. The summary report by Boston Analytics, 2009 (1) provided factual information about the traditional retailing in India as compared to modern retailing and advised the companies to take into consideration the unique diversity of retail sector of India. It also discussed the issues multinational face due to the unorganized nature of traditional retailing in India. But nothing was discussed regarding the threats faced by modern retailing on traditional retailing.

The report by A.T. Kearney (2) on growth in retail sector in BRIC economies and CII - A.T. Kearney, 2006 (3) on the need of organized sector for sustained growth in India, showed the huge scope for growth in the sector though much of the potential was shown in organized retailing. The detailed report by Joseph, etal. 2008 (4) was very relevant to this study as it discussed in detail the impact of organized retailing over unorganized retailing. The unorganized retailers in the vicinity of organized retailers experienced a decline in sales and profit in the initial years of the entry of organized retailers. The adverse impact, however, weakened over time. The study indicated how consumers and farmers benefited from organized retailers. The study also examined the impact on intermediaries and manufacturers. Based on the results of the surveys, the study made a number of specific policy recommendations for regulating the interaction of large retailers with small suppliers and for strengthening the competitive response of the unorganized retailers.

Based on the literature review and general study two types of separate segments of shopping for a general customer are identified. These two types are Routine Shopping and Occasional Shopping.

Routine Shopping comprises of shopping of products that are routinely required on a daily or monthly basis. It comprises mainly of the following segments of the retail market: 

Food and Beverages

Personal Care

Occasional Shopping comprises of shopping of products that are bought occasionally without a routine pattern. It comprises mainly of the following segments of the retail market. 

Clothing and Textile

Footwear

Consumer Durables

Books, Music and Gifts

Home Décor and Furnishing

The requirements and shopping patterns for the two segments tend to be different and therefore their analysis has been done separately.

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The routine shopping retail stores comprise of the following types: 

Unorganized Stores- Kiranas(mom and pop shops) found in neighborhood markets.

Organized Stores- Small stores like Reliance Fresh and Subhiksha in large neighborhood

markets and Hypermarkets like Big Bazaar in larger markets and shopping malls.

In occasional shopping only Apparels has been focused on in the paper as it is the most significant contributor to occasional shopping in retail and its stores comprise of the following types: 

Unorganized stores- Variety Shops selling a wide range of apparels and shops specializing in

specific garments like Saris etc. Such shops are generally found in large markets, like Sarojani Nagar in New Delhi. 

Organized stores- Large retail chains like Shoppers stop and Westside found generally in

Malls and markets like Cannaught Place and small franchise outlets, for example Reebok or Levis found in the same markets as well as markets like Sarojani or Lajpat Nagar in New Delhi.

Taking inputs from the designed questionnaires and parameters in related papers like Batt, 2008 (5) and, Mahua Dutta, 2008 (6) a pilot survey was conducted and the following eight factors are identified that cover most of the Retail Purchase Factors (RPF) affecting the choice of retail stores. An analysis on each of these factors is given: 

Proximity (The reach ability of the retail store)- In case of grocery it was observed that

importance of proximity was same across profiles while it is not an important factor in case of apparel. 

Product Assortment (The variety and quality of product) - Product Assortment is equally

important for grocery and apparel. But product assortment elements differed for apparel and grocery. 

Infrastructure (The layout, amenities and appearance) - This factor was perceived differently

by people shopping in different shopping formats. 

Price (The prices and offers provided by retailer) - Price is equally important for both grocery

and apparel however while price is the most important factor for grocery, it is not the most important factor in case of apparel. 

Communication (Marketing) - Difference in perception is observed across grocery and

apparel. 

Service (Response time, Home Delivery, Payment Options, Auxiliary Services etc) - Similar

difference in perception is observed across grocery and apparel.

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Relationship (Customer loyalty, establishment etc) - Relationship is an important factor for

purchase decisions in both grocery and apparel but not as important as product assortment and price. 

External Environment (Parking facilities, Location etc) - Factors like parking etc were

important in case of grocery stores while location and ambience was an important factor for apparel shopping.

3

Competitiveness Index

In order to measure the competiveness of retailers, a competitiveness index is developed based on the above eight parameters identified. Other parameters could be taken for competitiveness analysis like ‟supply chain performance” but such parameters are broader in concepts and difficult to compare. These eight factors are selected as these were easy to identify and rate. Using Paired Comparison method RPFs have been compared to each other to find out their overall weightage. The Tables 1 and 2 show the paired wise comparison for both Grocery and Apparel Shopping.

Price is found to be the most important competitive factor in case of retail (Table 1), comprising of a quarter of competitiveness, followed by assortment and proximity. Interestingly infrastructure, relationship and external environment are equally important with communication being the least important. Four factors- price, assortment, proximity and service account for more than 75% of the competitiveness of a Grocery store.

Price and Assortment are found to be the most important competitive factor in case of apparel stores, comprising of a nearly half of competitiveness. Interestingly service, relationship and external environment are equally important with communication and proximity (unlike Grocery) being the least important.

Grocery Store Table 1 Grocery Store- Pair wise analysis Proxi

Assort

Infrastruc

Pri

Communi

Servic

Relatio

Ext.

mity

ment

ture

ce

cation

e

nship

Envi ron

Proximity

Proximity

Pri

Proximity

ce Assortment

Assort

Pri

Proxi mity

Assort

Assort

ce

5

Infrastructu

Pri

re

ce

Prox.

Prox .

Assort

Ass ort

Infra

Servic e


Price

Price

Price

Price

Pric e

Communica

Servic

Relatio

tion

e

n

Service

Service

Ext

Serv ice

Relationshi p Ext. Environ

Unit

Count

Normalized

Our Score

Proximity

5

6

Assortment

5

6

18%

Price

7

8

25%

Infrastructure

1

2

7%

Relationship

1

2

7%

External Environment

1

2

7%

Service

4

5

15%

Communication

0

1

3%

18%

Apparel Store Table 2 Apparel- Pair wise Analysis Proximity

Assortment

Infrastructure

Price

Communication

Service

Relationship

Ext. Environ

Proximity

Assort

Assortment

Infra

Price

Assort

Infrastructure Price Communication Service Relationship Ext. Environ

6

Price

Service

Relation

Ext

Assort

Assort

Assort

Assort

Infra

Service

Relation

Ext

Price

Price

Price

Price

Service

Relation

Ext


Unit

Normalized

Our Score

Proximity

0

1

4%

Assortment

6

7

23%

Price

6

7

23%

Infrastructure

1

2

7%

Relationship

3

4

13%

External Environment

3

4

13%

Service

3

4

13%

Communication

0

1

4%

4.

Count

QFD Analysis

4.1 Competitor Analysis The primary objective of competitor analysis is to identify areas of strengths and weaknesses and to measure the relative importance of retail purchase factors, for the organization. For Example, when comparing different grocery retail formats, a weakness in one of the retail factors would suggest that the given retail purchase factor is of greater importance to the retail format, than what has been attributed in the general competitiveness index. The different factors have been compared based on customer satisfaction scores compiled in a retail survey that also provided for the 21 subcategories of retail purchase factors relevant to customers and their relative importance. In traditional importance adjustment technique, there is a linear relationship between customer satisfaction improvement ratio and importance increment ratio. M. Xie, 2003 (7) provides a thorough discussion and implementation of the Kano Model in competitor analysis of House of Quality of QFD that has been applied in this paper. It states that “for some customer attributes, customer satisfaction can be greatly increased with only a small improvement in performance; while for some other features customer satisfaction will only marginally increase even when the performance of the product or services has been greatly improved. Using the traditional way of adjusting the raw importance, possibly the customer will not be satisfied with a certain customer attribute or perhaps the customer satisfaction target will be over fulfilled.

To overcome this problem Kano Model was implemented in the QFD analysis. The Kano model helped in gaining profound understanding of customer satisfaction. It divides product features into the following three distinct categories, each of which affects customer satisfaction in a different way: 

Must-be Attributes – Customers take them for granted when fulfilled. However, if the product

does not meet this basic need sufficiently, the customer may become very dissatisfied.

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One Dimensional Attributes – These attributes result in customer satisfaction when fulfilled

and dissatisfaction when not fulfilled. The better the attributes are, the better the customer likes them. 

Attractive Attributes - The absence of attractive attributes does not cause dissatisfaction,

because they are not expected by customers. However, strong achievement in these attributes delights customers.”

4.2 Application of Kano Model in QFD Kano Model suggests that attractive attributes are more likely to result in customer delight than must-be attributes. Thus if s and p respectively represent the customer satisfaction degree and service performance level and parameter k represents the proportionate increment over the parameter p, then the relationship can be expressed by the following equation: Δs/s = k. Δp/p where for attractive attributes, k>1; for one dimensional, k=1; for must be attributes, 0<k<1

For finding the adjusted improvement ratio, the following formula was used: IRadj = (IRo)1/k where IRadj is the adjusted improvement ratio; IRo is the original improvement ratio and k is the Kano parameter varying for different categories. For the present analysis the following values of k were used:

Service Category

Value of k

Must-be Attributes

0.5

One dimensional Attributes

1

Attractive Attributes

2

The competitor analysis with the Kano Model implementation for Kirana and Apparel stores is provided in Figure 1 of the exhibits given in the paper.

4.3 House of Quality Based on the new assigned weightages, the House of Quality was developed for Kirana and Apparel Stores. The QFD Analysis is provided in Figure 2 and 3 of the exhibits in the paper.

The following results were obtained in the QFD analysis:

For small grocery stores the following technical issues came out to be the most relevant: 

8

Profit Margin and Cost of Purchase


Strategic Location

Quality Guarantees

Stock Keeping Unit

For small grocery stores the following technical issues came out to be the most relevant:

9

Profit Margin and Cost of Purchase

Service Time

Strategic Location

Retail Schemes


Figure 1 Kano Analyis for Grocery and Apparel

10


Figure 2 QFD Analysis of Kirana Store

11


Figure 3 QFD Analysis for Apparel Store

12


5. Product Assortment Optimization Based on the Analysis, Product Assortment was chosen as an area relevant for optimization for both Grocery and Apparel. The problem of allocating which product in which quantity at what placeâ&#x20AC;&#x; is one of the central problems in retailing directly related to Product Assortment and technically primarily to Stock Keeping Units. We attempt propose a simple optimization model and a related heuristic that can be easily applied by small Kirana as well as Apparel stores.

5.1 Available Literature The literature review of the related papers provided with the existing models in the area. LindaSilva, etal. (8) provided an insight of optimizing shelf space specifically for small retail stores. This paper building on the works of other authors who have worked on supermarkets develops a model for small retail shops. Though a rigorous mathematical solution to the problem cannot be given as the application of such a solution will not be viable in a small retail store owing to its complexity, thus a heuristic model based on the existing models is developed by the authors for selecting products and allocating them shelf space.

Several alternate methods have been provided to allocate and arrange products. Corstjens & Doyle, 1981 (9) introduced the idea of cross elasticity that measures the effect of demand of one product over others, for example, the effect of demand of soaps over face wash or effect of demand of tshirt over shirts. Though this factor did help in optimizing the profit further but on the level of a small store the increment in overall profit (that is the objective statement in all optimizing models) is not significant when compared to the amount of complexity involved. Moreover the study also suggested that the factor used for sorting products (profitability, demand, size etc) were not as important as compared to a sorted arrangement itself as these different factors optimized the profit almost to the same value over the existing values.

5.2 Proposed model for Unorganized Retail Formats Problem Statement- A small Kirana shop wants to optimally select the quantity of each product in its catalogue over its already existing average forecasted demands for each product. The retailer expects that by effectively placing products that have a potential for greater demand owing to their greater cross elasticity, it can earn more profit. The mathematical model captures the retailerâ&#x20AC;&#x;s problem and tries to find an optimum mathematical solution, followed by a more generalized methodology (thumb rule) for selecting and placing products in a retail store.

Abbreveations di = Demand for product i over average demand Di pi = Profit per unit of product i

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si = Volume occupied per unit of product i ci = Cost of purchase per unit of product i δi = Elasticity in demand of product i S = Total space that can be allocated to products C = Total budget that can be allocated to products n = Number of products εi = Elasticity in demand of other products by demand in product i εij = Elasticity in demand of products j by demand in product i αi = Demand for product i when only product i is bought at a purchase decision βij = Demand for product i when product j is bought as it complements with product i γij = Demand for product i when product j is not bought as it alternates with product i Demand Variable: di Objective Fuction

Z = max ∑ di (pi + εi)

(Profit function)

where: εi = ∑ pj εij εij = (βij - γij)/di note that: di = (αi + βij + γij + ∑ βji) Such That: 1. ∑ si di ≤ s where s = S - ∑ si Di 2. ∑ ci di ≤ c where c = C - ∑ ci Di Constraint) 3. 0 ≤ di ≤ Di δi Constraint)

(Space Constraint) (Cost (Demand

Discussion on profit function Let there be two products i and j, then between these two products the following purchase scenarios can occur:1.

Customer purchases product i. Let the number of such purchase be αi

2.

Customer purchases product i and complementarily purchases j. Let the number of such

purchase be βij 3.

Customer purchases product i and alternatively drops purchasing j. Let the number of such

purchase be γij 4.

Customer purchases product j and complementarily purchases i. The number of such purchase

is βji 5.

Customer purchases product j and alternatively drops purchasing i. The number of such

purchase is γji 6.

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Customer purchases product i. The number of such purchase is αj


Monetary earning due to purchase of product i = pi (αi + βij + γij + ∑ βji) Hidden earning due to purchase of product i = ∑ pj βij Hidden loss due to purchase of product i = ∑ pj γij Total earning due to purchase of i = Zi = pi (αi + βij + γij + ∑ βji) + ∑ pj (βij -γij) di = αi + βij + γij + ∑ βji Hence Zi = pi di + ∑ pj (βij -γij) = di ( pi + ∑ pj (βij -γij)/ di) = di ( pi + ∑ pj εij) Z = ∑ di (pi + ∑ pj εij) = ∑ di (pi + εi) 5.3 Heuristic Model The Following Heuristic Model was developed as a decision rule, to assist the shop owners in developing an optimum product assortment.

Case 1- When space is not a major constraint (The most common scenario for unorganized Kirana shops in India) Step 1: Filling the Data Table: Step 2: Calculating (pi + εi)/ ci for each product and arranging in descending order. Step 3: Calculating Di δi for maximum limit of products Step 4: Fill the given space with the product of highest order calculated in Step 2, until either the cost constraint or the maximum limit of the product is reached. Continue the process until, space, cost or maximum limits of all products are reached. Step 5: Place the products of high positive cross elasticity together, while high negative cross elasicities distant from each other, with the products of the highest order in Step 2 getting placed at the spaces most visible to the customer.

Case 2- When cost is not a major constraint Step 1: Filling the Data Table: Step 2: Calculating (pi + εi)/ si for each product and arranging in descending order. Step 3: Calculating Di δi for maximum limit of products Step 4: Invest the given money with the product of highest order calculated in Step 2, until either the space constraint or the maximum limit of the product is reached. Continue the process until, space, cost or maximum limits of all products are reached.

15


Step 5: Place the products of high posetive cross elasticity together, while high negative cross elasicities distant from each other, with the products of the highest order in Step 2 getting placed at the spaces most visible to the customer. Figure 4 shows in a flow chart form the steps required in applying the proposed heuristic algonthm.

Figure 4 Flow Chart for Proposed Heuristic

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5.4 Implementation on a Kirana Store Product Selection For the implementation of the model, data were to be collected from the retail store. As no records are generally kept by Kirana stores, it was not feasible to collect information about every product stocked by the store. Moreover according to Pareto‟s law, a vital few “A” class items should contribute to the majority of the profits. Thus application of the Optimization model is most apt over the vital few items of the store.

If x is the average profit per product category, then thumb rule of ABC analysis suggests, items with more than 6x profit are “A” class items.

The total profit of the store is reported as = Rs. 15000 Number of product categories as estimated = 100 Thus x = 150 Product categories with more than 6x = 900 Rs profits are: 

Bathing Soaps

Flour

Washing soaps and detergents

Biscuit

Toothpaste

Beverages

Rice

Snacks

Pulses

Parameter Estimation The next step involved is estimation of parameters enlisted in the model for each of these product categories. As mentioned earlier, no records were kept by the store owner, thus estimation of parameters required other tools for attaining realistic values. For data collection we recorded optimistic, pessimistic and most likely values of the demand Di and then calculated the expected value using the following formula where a= optimistic value; b= pessimistic value and m= most likely value

E= 1/3{2m+(a+b)/2} Demand Elasticity δi was estimated as δi= Optimistic Di/Expected Value Di. Table 3 shows the estimated value of parameters.

Estimation of Cross Elasticity As no values were available for cross elasticity, a different system for estimation was proposed. Mutual cross elasticity for each product was rated as ++, +, 0, - and --; ++ being the most positively correlated and – being the most negative.

17


An analysis of buying pattern was carried, monitoring the buying behavior for some highly and slightly correlated products and these values were assigned to each of the ratings. 

++ is assigned a value 0.5

- is assigned a value -0.25

+ is assigned a value 0.25

-- is assigned a value -0.5

0 is assigned a value 0

εi was calculated using the formula

εi = ∑ pj εij Table 4 shows the estimation of cross elasticity of products chosen. Very rigorous values are not needed for this parameter as they only account for the hidden costs of the company, and thus it is the relative value of the parameter that is more significant than the absolute values. Table 5 provides the final data table.

Table 3 Parameter Estimation

1 2 3 4 5 6 7 8 9

S.No

Optimistic Di (units)

Soaps- Lux

350

450

400

400

0.125

Soaps- Rin

270

330

300

300

0.1

Toothpaste- Colgate

225

275

250

250

0.1

Rice- 1 kg

180

220

200

200

0.1

Pulses- 1 kg

135

165

150

150

0.1

Flour- 1 kg

360

440

400

400

0.1

Biscuit

400

600

500

500

0.2

Beverages- Pepsi

500

800

600

616.6667

0.297297

Snacks- Lays (70 gm)

400

750

600

591.6667

0.267606

Product 1

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Expected Di (units)

Pessimistic Di (units)

1 2 3 4 5 6

Soaps- Lux Soaps- Rin Toothpaste- Colgate Rice- 1 kg Pulses- 1 kg Flour- 1 kg

7

Biscuit

8 9

Beverages- Pepsi Snacks-Lays (70gm)

+ + 0 0 0

Most Likely Di (units)

Table 4 Cross Elasticity estimation CROSS ELASTICITY 2 3 4 5 6 7 + + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + + 0 0 0 + + 0 0 0 + + 0

0

0

0

0

0

0

0 0

0 0

0 0

0 0

0 0

0 0

+ -

δi

8 0 0 0 0 0 0

9 0 0 0 0 0 0

+

++

++

Εi 1.81 0.56 0.56 2.44 2.44 3.00 0.13 1.50 0.25


Table 5 Final Data Table

5.5 Implementation in a Kirana Store The Tables 6, 7, 8, 9 show the results obtained by the proposed mathematical and heuristic models, and the results that would be generated, if the assortment is done using the traditional methods employed by of the retailer. Two forms of heuristic methods are generally used by the retailer. 

The first involves selection of the products solely based on profits.

The second method is distributing the whole capital left based on the proportionate demand

elasticity.

It is evident that there is an improvement in profits by 10-20 % by the use of the model based on the existing heuristic methods available. The heuristic model proposed here achieves a similar profit as the simplex model. The heuristic thus provides a „thumb rule‟ for solving the retailer‟s problem. Table 6 Results obtained by Mathematical Model No. of units Cost Profit (Simplex) (Rs.) (Rs.)

19

Space (cm3)

1

Soaps- Lux

50

1137.5

112.5

15000

2

Soaps- Rin

0

0

0

0

3

Toothpaste- Colgate

0

0

0

0

4

Rice- 1 kg

20

680

120

26000

5

Pulses- 1 kg

15

660

90

18000

6

Flour- 1 kg

40

1250

150

80000

7

Biscuit

100

1300

200

25000

8

Beverages- Pepsi (500 ml)

2

43

3

2000

9

Snacks- Lays (70 gm)

0

0

0

0

5070.5

675.5

166000


Table 7 Results obtained using profit margin based assortment No. of units Cost Profit (Margin) (Rs.) (Rs.)

Space (cm3)

1

Soaps- Lux

50

1137.5

112.5

15000

2

Soaps- Rin

7

190.75

19.25

2100

3

Toothpaste- Colgate

25

1137.5

112.5

6250

4

Rice- 1 kg

20

680

120

26000

5

Pulses- 1 kg

15

660

90

18000

6

Flour- 1 kg

40

1250

150

80000

7

Biscuit

0

0

0

0

8

Beverages- Pepsi (500 ml)

0

0

0

0

9

Snacks- Lays (70 gm)

0

0

0

0

5055.75

604.25

147350

Table 8 Results obtained using proportionate demand elasticity based assortment No. of units Profit Space (Demand) Cost (Rs.) (Rs.) (cm3) 1

Soaps- Lux

20

455

45

6000

2

Soaps- Rin

14

381.5

38.5

4200

3

Toothpaste- Colgate

8

364

36

2000

4

Rice- 1 kg

11

374

66

14300

5

Pulses- 1 kg

8

352

48

9600

6

Flour- 1 kg

12

375

45

24000

7

Biscuit

57

741

114

14250

8

Beverages- Pepsi (500 ml)

51

1096.5

76.5

51000

9

Snacks- Lays (70 gm)

51

918

102

102000

5057

571

227350

Table 9 Result obtained by Heuristic Model No. of units Cost Profit (Heuristic) (Rs.) (Rs.)

20

Space (cm3)

1

Soaps- Lux

50

1137.5

112.5

15000

2

Soaps- Rin

0

0

0

0

3

Toothpaste- Colgate

0

0

0

0

4

Rice- 1 kg

20

680

120

26000

5

Pulses- 1 kg

15

660

90

18000

6

Flour- 1 kg

40

1250

150

80000

7

Biscuit

100

1300

200

25000

8

Beverages- Pepsi (500 ml)

2

43

3

2000

9

Snacks- Lays (70 gm)

0

0

0

0

5070.5

675.5

166000


6.

Managerial Insights

The unorganized sector is a very diverse and complex area of study. Unlike the traditional areas of application of Management Sciences, which are more structured and organized, the study of unorganized sector requires a very different approach owing to the complexities involved. Some of the issues confronted during such a study include lack of necessary data, and much greater diversity in organizations and processes.

A study of the unorganized sector can be undertaken; provided that a suitable selection of analytical tools is done that can deal with the subjective as well as the quantitative information involved. A major portion of such a study in unorganized sector involves creating a systematic framework that can capture the diversity of the sector, given the constraints like non availability of quantitative data, and can also provide for a platform for further analysis of its sub-parts. Quality Function Deployment is a very powerful analytical tool that has the potential of great applications in the area of unorganized sector. It at once analyzes the subject based on different parameters, with respect to its different stakeholders. In this paper the House of Quality for unorganized grocery and apparel stores has been developed. Further work in the area, employing the different stages of QFD, can provide an interesting framework for further investigation in the area. Another important aspect is simplicity of results, and development of effective â&#x20AC;&#x17E;thumb rulesâ&#x20AC;&#x; that can be conveniently applied by managers of these unorganized sectors who generally would not be comfortable with complex management science techniques, or unequipped for rigorous mathematical optimizations. Development of such simplified applications can greatly improve the competitiveness of unorganized sectors.

In the given paper, after contributing to the overall framework that can reflect the diverse aspects of retail, we have proposed a heuristic to optimize the product assortment. The model introduces the concept of linear cross elasticity, which is an area of further research in retail. If a database of cross elasticity between different products can be created, based on a major statistical study, the values can be of considerable significance to retailers while deciding their optimum product assortment strategy. The optimization of product assortment is one of the many areas where further research can be conducted in the context of unorganized retail, and these can provide for a creating significant body of knowledge base that can greatly improve the competitiveness of unorganized retail in India. The future work involves the validation of the model with further analysis of Kirana and Apparel stores and comparing actual practices with model based approaches to estimate the quantum of improvement using industrial engineering and O.R. approaches

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To conclude, the study of unorganized sector in general, and unorganized retail in specific provides innovative challenges for researchers in the area of management, and work in this field can greatly enhance the competitiveness of the sector and impact the growth in developing economies as a whole.

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Authors Profile Prof. Prem Vrat is former Director IIT Roorke and Former Vice Chancellor, UP Tech. University and was Director â&#x20AC;&#x201C; in â&#x20AC;&#x201C; charge, Dy. Director, Dean and Head at IIT Delhi. Currently he is Professor of Eminence, MDI, Gurgaon. He has published more than 390 research papers and articles in journals and conference proceedings and has guided 36 Ph.D. theses. He is Honorary Member of IIIE and Fellow of IIIE; INAE; NASI, ISTE, WAPS and has won many prizes and awards.

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Prateek Raj is an Engineering Graduate from Indian Institute of Technology Delhi holding a B.Tech Degree in Production and Industrial Engineering. His research interests lie in the study of unorganized institutions of Developing Economies, which he considers is an area of potential economic growth. The paper on Unorganized Retail is his attempt to understand the complex dynamics of unorganized retailing using Industrial Engineering principles, is based on his major project at IIT Delhi.

Akshay Jain is an Engineering Graduate from Indian Institute of Technology Delhi holding a B.Tech degree in Production and Industrial Engineering. He is interested in the area of Operations Research and Industrial Engineering and their applications in non--traditional areas of study. The paper on Unorganized Retailing is based on his major project at IIT Delhi.

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A Study on Unorganized Retail in India