Barcode Enabled Supply Chain Management for Organized

www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 70

Barcode Enabled Supply Chain Management for Organized Retail Stores- An

Empirical Case Study

Rajeev Gupta

Assistant Professor

Moradabad Institute of Technology

Moradabad, India

[email protected]

Ajay K. Garg

University Professor (Tenure-Track)

Fairleigh Dickinson University - Vancouver Campus

Vancouver V5X2R9 Canada

[email protected]

Abstract

The aim of this paper is to highlight the role of barcoding in Indian organized retail industry.

The major domain in which barcoding is helpful for smooth functioning of organized retail store is the

prime focus of this paper. The study includes the feedback given by the store managers of Shoppers

Stop, Pantaloon, Big Bazar, Globus and Vishal Mega-Mart on the issues related to protection from

theft, faster and improved service, reducing inventory errors, easy accessibility in showroom, time

saving, improved efficiency, reducing cost, fast inventory status in store, fast information

dissemination in store and tracking & tracing the material etc. This paper concludes that barcoding is

very much effective for the efficient store management. Major attention in this paper is paid towards

the role of barcoding in the organized retail stores.

Key Words: Barcode, supply chain, Inventory Management, Information Technology, Store

management.

Introduction

Global corporations like Lucent, Wal-Mart, Proctor and Gamble and Sun Microsystems have

confirmed that value can be produced through supply chain integration (Lee, 2000). The survival in the

competitive edge is possible only through utilizing the latest technologies for better customer service

in cost efficient manner. In Indian organized retail stores, the barcoding is most commonly used

technology for strengthening the supply chain. The results of the bar coding technology for smooth

conduction of organized retail stores operations are very much productive and encouraging. Bar coding

is a technology which identifies the objects and collects data without using key entries. Bar codes are

binary codes that are arranged in a parallel form using bars and gaps (Palmer, 1995). Maintaining and

managing inventory in the organized store for better customer service is the basic requirement and for

this barcodes are the better option. Wild (1997) examined that, through inventory control, products

made available to customers with the help of proper coordination among purchasing, manufacturing

and distribution functions.

This study focuses on the several benefits of barcoding in various dimensions such as

protection from theft, better customer service, inventory related issues, time saving, efficiency, cost

and tracking and tracing the products in the stores etc.


Objectives

1. To identify the contemporary supply chain techniques prevalent in the organized retail sector in

Indian market space.

2. To determine the impact of Barcoding on the overall business process effectiveness.

Literature Review

Barcodes has become the “ubiquitous standard for identifying and tracking products” (Wyld,

2006, p. 157). Barcodes are easy to use, inexpensive, and more reliable in terms of accuracy over

manual techniques (McCathie & Michael, 2005). Most of the research articles are focusing on the

elements of supply chain and some are on technological aspects. Inventory control practices are

important to all organizations, particularly for small and medium retail organizations that are more

vulnerable to inventory control issues (Gunasekaran, Forker, & Kobu, 2000; Zipkin, 2000). In Indian

organized retail stores, barcoding is commonly used but there are very few empirical researches are

available to justify the actual benefits of barcoding. Wyld (2006) analyses that barcoding is most

widely used technology on this planet with five billion barcodes scanned every day in the world. Retail

organizations are adopting supply chain practices not only for supply purposes but also for competitive

advantages. Zipkin (2000) analyses that technology advancements have major influence on inventory

decisions and these advancements have the potential to streamline entire industries. Jorge R. León-

Peña (2008) analyses the significance of e-business for improved control of demand and supply

aspects of the product assortment. E-business includes the concept of electronic data interchange

which means transferring the business data electronically for smooth functioning of business. Samuel

Fosso Wamba, Louis A. Lefebvre and Elisabeth Lefebvre (2007) focused on RFID technology and

Electronic Product Code (EPC) for improved retail supply chain. The Radio Frequency Identification

is the most popular technology based on the electronic product code which is used by the retail

organizations for tracking and tracing the goods. Brewer (2007) study points the benefits of tested

barcoding in comparision to casually adopted RFID and suggested about hybrid RFID-barcode system.

Reynolds (2007) also suggested the importance of barcoding as compared to RFID on the basis of

expert opinion. According to the survey conducted by Zebra Technologies (2006), 96% European

companies admitted that barcoding improves the overall efficiency. Operational improvements have

been monitored in the form of efficiency, consistency, data accuracy, and inventory and asset

management in the organizations with the barcode technology implementation (Zebra Technologies,

2007; Ellram, Londe, Weber, 1999). Almost all researches are directly related to the technological

aspects of retail supply chain like RFID (Radio Frequency Identification), EPC (Electronic Product

code), BARCODING, EDI (Electronic Data Interchange) and CIS (Corporate Information System) and

some are based on conceptual framework of JIT(Just-in-Time), Inventory management, warehousing

management etc. RFID technology is classified as a wireless automatic identification and data capture

(AIDC) technology (Swartz, 2000). Zhang et al. (2008) illustrate a smart Kanban system using RFID

technologies for shop-floor management. Hau L. Lee (2002) analyses that given the different nature of

demand and supply uncertainties of diverse products, different supply chain strategies are wanted for

different products. It is focused by the researchers that according to the product nature and market

scenario, different supply chain strategies like inventory decisions, warehousing management,

distribution channels etc. should be adopted by the organizations. Lambert and Stock (2001) define the

most important sources of data for the common database, which are the order processing system,

company records, industry data, and management data.


Sample Profile:

Age

Frequency Percent

Valid 21-35 Year 20 20.0

36-45 Year 46 46.0

More than 45 Year 34 34.0

Total 100 100.0

The sample that was taken for the study was inclusive of the respondents that were categorized in

various age groups. The sample representation of the age groups included 20% people from 21-35

years bracket, a majority of 46% sample was within 36-45 years and about 34% respondents were

above the age of 45 years.

Gender

Frequency Percent

Valid Male 80 80.0

Female 20 20.0

Total 100 100.0

Table shows that the covered sample was distributed among male and female category of the gender,

where 80% of the respondents were male and the remaining 20% were female.

Qualification

Frequency Percent

Valid Post Graduate 62 62.0

Graduate 26 26.0

Diploma 12 12.0

Total 100 100.0

Sample was distributed with different level of educational background, where 62% of the respondents

were post graduates, 26% of the respondents were graduates and the remaining 12% were diploma

holders.

Organization

Frequency Percent

Valid Shoppers Stop 20 20.0

Pantaloon 20 20.0

Big Bazar 20 20.0

Globus 20 20.0

Vishal Mega Mart 20 20.0

Total 100 100.0

The captured sample was evenly distributed among different organization with equal

respondent size. Hence the various contributory brands, namely- Shopper stop, Pantaloon, Big Bazar,

Globus and Vishal Mega Mart contributed each of 20% of the covered sample.

Analysis:

H1: The extent of technology adoption in present Supply Chain Management is better when compared

to the previously managed systems.

Descriptive Statistics

Mean Std. Deviation

RFID 2.00 .000

BARCODING 4.80 .492

EDI 4.46 .673

ERP 3.20 .569

Decision Support System 3.86 .964


Table shows that organizations were open to testing technologies like Bar coding, EDI, ERP and

Decision Support System for the supply chain management, only RFID is the technology that is not

being used by any organization(with st. dev.=0.000). Across organizations people were found to be

more acceptable to Bar Coding, which is also depicted by the minimum standard deviation as shown in

the table against the discussed category, thereby showing a consensus among the people using the

technology across organizations.

H2: The technology adoption techniques help in efficient Store Management.

Sub Hypothesis:

H2.1.0: Null Hypothesis: Barcoding do not help to protect from theft.

H2.1.1: Alternate hypothesis: Barcoding helps to protect from theft

Protection from theft * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Protection from theft 2 Count 2 0 0 2

% within

BARCODING

50.0% .0% .0% 2.0%

3 Count 0 2 8 10

% within

BARCODING

.0% 16.7% 9.5% 10.0%

4 Count 0 4 42 46

% within

BARCODING

.0% 33.3% 50.0% 46.0%

5 Count 2 6 34 42

% within

BARCODING

50.0% 50.0% 40.5% 42.0%

Total Count 4 12 84 100

% within

BARCODING

100.0% 100.0% 100.0% 100.0%

Table shows that organizations that are using the bar-coding in their supply chain management are of

the view that protection from the theft will be on a better note. Overall 82% people are of the

perception that bar-coding helps to protect from theft.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 51.793a 6 .000

Likelihood Ratio 18.842 6 .004

Linear-by-Linear Association 2.480 1 .115

N of Valid Cases 100

Here the table shows that significance value is less than 0.05, which implies that the null hypothesis

will be rejected and the alternate hypothesis will be accepted meaning the bar-coding helps to project

from theft.

H2.2.0: Null Hypothesis: Bar-coding do not help to faster and improved customer service

H2.2.1: Alternate hypothesis: Barcoding helps to faster and improved customer service


Faster and improved customer service * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Faster and

improved customer

service

2 Count 0 4 6 10

% within

BARCODING

.0% 33.3% 7.1% 10.0%

3 Count 2 0 4 6

% within

BARCODING

50.0% .0% 4.8% 6.0%

4 Count 2 0 24 26

% within

BARCODING

50.0% .0% 28.6% 26.0%

5 Count 0 8 50 58

% within

BARCODING

.0% 66.7% 59.5% 58.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%

Table shows 50% of the respondents saying that bar-coding is helping for better and improvement of

customer service at highly effective and 32% people are saying barcoding is most effective for faster

and improved customer service.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)






will be rejected and the alternate hypothesis will be accepted concluding that the bar-coding helps to

faster and improved customer service

H2.3.0: Null Hypothesis: Barcoding do not help for reducing inventory error

H2.3.1: Alternate hypothesis: Barcoding helps for reducing inventory error

Reducing inventory errors * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Reducing inventory

errors

3 Count 4 1 5 10

% within

BARCODING

100.0% 8.3% 6.0% 10.0%

4 Count 0 2 13 15

% within

BARCODING

.0% 16.7% 15.5% 15.0%

5 Count 0 9 66 75

% within

BARCODING

.0% 75.0% 78.6% 75.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%


Table shows that 66% of the respondents were of the view that bar-coding is helping for reducing

inventory error at highly effective level and 22% people were saying bar-coding is most effective for

reducing inventory error.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)






will be rejected and the alternate hypothesis will be accepted concluding the bar-coding helps for

reducing inventory error.

H2.4.0: Null Hypothesis: Barcoding do not help to easy accessibility in showroom

H2.4.1: Alternate hypothesis: Barcoding helps to easy accessibility in showroom

Easy accessibility in showroom * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Easy accessibility in

showroom

1 Count 4 6 50 60

% within

BARCODING

100.0% 50.0% 59.5% 60.0%

2 Count 0 6 30 36

% within

BARCODING

.0% 50.0% 35.7% 36.0%

3 Count 0 0 4 4

% within

BARCODING

.0% .0% 4.8% 4.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%

Table shows that when the organizations use the barcoding technology then they do not feel

that barcoding help in easy accessibility in showroom which is backed by the response rate where 96%

of the respondent say that the bar-coding do not helps in to easy accessibility in showroom at highly

ineffective level and 4% people are saying barcoding is neutral for easy accessibility in showroom

means neither helpful nor creating extra burden.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)



Linear-by-Linear Association .991 1 .320


Table shows that significance value is more than 0.05 hence the null hypothesis will be accepted and

the alternate hypothesis will be rejected concluding that the bar-coding do not helps for easy

accessibility in showroom.

H2.5.0: Null Hypothesis: Barcoding do not help in time saving

H2.5.1: Alternate hypothesis: Barcoding helps in time saving


Time saving * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Time saving 3 Count 4 0 4 8

% within

BARCODING

100.0% .0% 4.8% 8.0%

4 Count 0 2 38 40

% within

BARCODING

.0% 16.7% 45.2% 40.0%

5 Count 0 10 42 52

% within

BARCODING

.0% 83.3% 50.0% 52.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%

Table shows that when the organizations use the barcoding technology then they feel that

barcoding help in time saving in showroom which is very clearly delineated though the data where

92% of the respondent say that the bar-coding helps in time saving in showroom as highly effectively

and 8% people are saying barcoding is neutral for time saving in showroom where they meant to say

that it is neither helpful nor consuming extra time.

Chi-Square Tests

Value Df

Asymp. Sig. (2-

sided)





Table shows that significance value is less than 0.05 hence the null hypothesis will be rejected

and the alternate hypothesis will be accepted giving us a clear understanding that the bar-coding helps

in time saving to every retail outlets.

H2.6.0: Null Hypothesis: Barcoding do not help to improved efficiency

H2.6.1: Alternate hypothesis: Barcoding help to improved efficiency

Improved efficiency * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Improved efficiency 3 Count 2 0 7 9

% within

BARCODING

50.0% .0% 8.3% 9.0%

4 Count 2 8 53 63

% within

BARCODING

50.0% 66.7% 63.1% 63.0%

5 Count 0 4 24 28

% within

BARCODING

.0% 33.3% 28.6% 28.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%

Table shows that when the organizations use the barcoding technology then they feel that barcoding

help to improve efficiency because the 91% of the respondent said that bar-coding helps in improving


efficiency and only 9% people are saying barcoding is neutral to improving efficiency means it was

neither helpful nor unsupportive to these 9%.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)





Table shows that significance value is less than 0.05 hence the null hypothesis will be rejected and the

alternate hypothesis will be accepted giving us a view that bar-coding helps to improve efficiency of

retail outlets.

H2.7.0: Null Hypothesis: Barcoding do not help to reduce cost

H2.7.1: Alternate hypothesis: Barcoding help to reduce cost

Reduce Cost * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Reduce Cost 2 Count 0 2 10 12

% within

BARCODING

.0% 16.7% 11.9% 12.0%

3 Count 4 6 52 62

% within

BARCODING

100.0% 50.0% 61.9% 62.0%

4 Count 0 4 20 24

% within

BARCODING

.0% 33.3% 23.8% 24.0%

5 Count 0 0 2 2

% within

BARCODING

.0% .0% 2.4% 2.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%


does not helps to reduce cost because the 74% of the respondent saying the barcoding do not help for

reducing cost and only 26% people are saying barcoding is helpful for reducing the cost.

Chi-Square Tests

Value Df

Asymp. Sig. (2-

sided)



Linear-by-Linear Association .143 1 .705


Table shows that significance value is more than 0.05 hence the null hypothesis will be accepted and

the alternate hypothesis will be rejected means the barcoding do not helps to reduce the cost.

H2.8.0: Null Hypothesis: Barcoding do not help in fast inventory status in store

H2.8.1: Alternate hypothesis: Barcoding help in fast inventory status in store


Fast inventory status in store * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Fast inventory status in store 3 Count 0 4 9 13

% within BARCODING .0% 33.3% 10.7% 13.0%

4 Count 4 8 52 64

% within BARCODING 100.0% 66.7% 61.9% 64.0%

5 Count 0 0 23 23

% within BARCODING .0% .0% 27.4% 23.0%



Table shows that when the organizations use the barcoding technology then they feel that

barcoding help in getting fast inventory status in store because 87% of the respondent saying the

barcoding helps for improving efficiency as highly effective and effective and only 13% people are

saying barcoding is neutral for fast inventory status in store means neither helpful nor unsupportive.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)






alternate hypothesis will be accepted meaning that bar-coding helps for fast inventory status in store.

H2.9.0: Null Hypothesis: Barcoding do not help in Fast information dissemination in store

H2.9.1: Alternate hypothesis: Barcoding help in Fast information dissemination in store

Faster information dissemination * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Faster information dissemination 3 Count 2 2 8 12


4 Count 2 8 30 40


5 Count 0 2 46 48

% within BARCODING .0% 16.7% 54.8% 48.0%




help in fast information dissemination in store because the 88% of the respondent saying the barcoding

helps for Fast information dissemination as highly effective and effective and only 12% people are

saying barcoding is neutral for fast information dissemination in store meaning that neither helpful nor

unsupportive for fast information dissemination.


Chi-Square Tests

Value Df

Asymp. Sig. (2-

sided)






alternate hypothesis will be accepted which means that bar-coding helps for fast information

dissemination.

H2.10.0: Null Hypothesis: Barcoding do not help in Tracking and tracing the material

H2.10.1: Alternate hypothesis: Barcoding help in Tracking and tracing the material

Tracking and tracing the material * BARCODING Cross tabulation

BARCODING

Total 3 4 5

Tracking and tracing the

material

1 Count 0 2 0 2

% within

BARCODING

.0% 16.7% .0% 2.0%

2 Count 0 2 10 12

% within

BARCODING

.0% 16.7% 11.9% 12.0%

3 Count 4 8 58 70

% within

BARCODING

100.0% 66.7% 69.0% 70.0%

4 Count 0 0 16 16

% within

BARCODING

.0% .0% 19.0% 16.0%


% within

BARCODING

100.0% 100.0% 100.0% 100.0%


help to Tracking and tracing the material in store is easier in comparison to non user of barcoding

because 16% of the respondent say that bar-coding helps for Tracking and tracing the material as

highly effective and 70% were neutral for response to tracking and tracing the material in store which

implied that it was neither helpful nor unsupportive for Tracking and tracing the material.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)






alternate hypothesis will be accepted indicating that bar-coding helps for tracking and tracing the

material.


It was also found that all the issues that were analyzed against bar-coding were found to be positively

correlated with the technology. That is to say those respondents believed that the various queries were

positively affected by the technology (bar-coding) intervention. Hence based on the above sub

Hypotheses (H2.1- H2.9) it can be clearly said that technology adoption techniques helped in efficient

Store Management.

H6: The efficient store Management is positively correlated with bar-coding

Correlations

BARCODING Protection from theft

BARCODING Pearson Correlation 1 .158

Sig. (2-tailed) .116

N 100 100

Protection from theft Pearson Correlation .158 1


N 100 100

Table shows when we use bar-coding then the protection from theft increased because the table shows

positive correlation 0.158 between the bar-coding and protection from theft means both are directly

related with each other if we start using of one independent variable as bar-coding then the dependent

variable protection from theft automatically increased.

Correlations

BARCODING

Faster and improved

customer service

BARCODING Pearson Correlation 1 .219*


N 100 100

Faster and improved

customer service

Pearson Correlation .219* 1


N 100 100

Table shows when we use bar-coding then faster and improved customer increased because the table

shows positive correlation 0.219 between the bar-coding and faster and improved customer means both

are directly related with each other if we start using of one independent variable as bar-coding then the

dependent variable faster and improved customer automatically increased.

Correlations

BARCODING

Reducing inventory

errors

BARCODING Pearson Correlation 1 .406**


N 100 100

Reducing inventory

errors

Pearson Correlation .406**

1


N 100 100

Table shows when we use bar-coding then reducing inventory errors increased because the table shows

positive correlation 0.406 between the bar-coding and reducing inventory errors means both are

directly related with each other if we start using of one independent variable as bar-coding then the

dependent variable reducing inventory errors automatically increased.


Correlations

BARCODING


showroom



N 100 100


showroom

Pearson Correlation .100 1


N 100 100

Table shows when we use bar-coding then easy accessibility in storeroom increased because the table

shows positive correlation 0.100 between the bar-coding and easy accessibility in storeroom means

both are directly related with each other if we start using of one independent variable as bar-coding

then the dependent variable easy accessibility in storeroom automatically increased.

Correlations

BARCODING Time saving



N 100 100

Time saving Pearson Correlation .218* 1


N 100 100

Table shows when we use bar-coding then Time Saving in storeroom increased because the table

shows positive correlation 0.218 between the bar-coding and Time Saving in storeroom means both

are directly related with each other if we start using of one independent variable as bar-coding then the

dependent variable Time Saving in storeroom automatically increased.

Correlations

BARCODING Improved efficiency



N 100 100

Improved efficiency Pearson Correlation .134 1


N 100 100

Table shows when we use bar-coding then improved efficiency in storeroom increased because the

table shows positive correlation 0.134 between the bar-coding and improved efficiency in storeroom

means both are directly related with each other if we start using of one independent variable as bar-

coding then the dependent variable improved efficiency in storeroom automatically increased.

Correlations

BARCODING Reduce Cost



N 100 100

Reduce Cost Pearson Correlation .038 1


N 100 100

Table shows when we use bar-coding then cost comes in running the storeroom is goes down because

the table shows positive correlation 0.038 between the bar-coding and cost reduced in storeroom



coding then the dependent variable reducing cost in storeroom automatically increased means the cost

becomes low.

Correlations

BARCODING

Fast inventory status in

store



N 100 100

Fast inventory status

in store



N 100 100

Table shows when we use bar-coding then fast inventory status in storeroom increased because the

table shows positive correlation 0.207 between the bar-coding and fast inventory status in storeroom


coding then the dependent variable fast inventory status in storeroom automatically increased.

Correlations

BARCODING

Faster information

dissemination

BARCODING Pearson Correlation 1 .333**


N 100 100

Faster information

dissemination

Pearson Correlation .333**

1


N 100 100

Table shows when we use bar-coding then fast information dissemination in storeroom increased

because the table shows positive correlation 0.333 between the bar-coding and fast information

dissemination in storeroom means both are directly related with each other if we start using of one

independent variable as bar-coding then the dependent variable fast information dissemination in

storeroom automatically increased.

Correlations

BARCODING

Tracking and tracing

the material



N 100 100

Tracking and tracing

the material



N 100 100

Table shows when we use bar-coding then tracking and tracking the material in storeroom increased

because the table shows positive correlation 0.204 between the bar-coding and tracking and tracking

the material in storeroom means both are directly related with each other if we start using of one

independent variable as bar-coding then the dependent variable tracking and tracking the material in

storeroom automatically increased.

The overall picture of the data set helped in analyzing the fact that efficient store management is

positively correlated with Bar-coding.


Conclusion

The research outcome clearly indicates that barcode is most commonly used technology in Indian

organized retail whereas RFID is still untouched by all the retail organizations. Apart from barcode,

EDI, ERP and DSS are some techniques which are contributing to the success of these retailers. The

present supply chain management is better when compared to previously managed systems because

now the management of retail stores is much better by using latest technologies. Various issues related

to efficient store management are checked with barcode and it is found that barcode helps the

managers of retail organizations in all dimensions which are responsible for efficient store

management.

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Documents

Barcode Enabled Supply Chain Management for Organized