Barcode Enabled Supply Chain Management for Organized Retail Stores- An Empirical Case Study

Barcode Enabled Supply Chain Management for Organized Retail Stores- An Empirical Case Study Abstract Rajeev Gupta Assistant Professor Moradabad Institute of Technology Moradabad, India prof.rajeevgupta@gmail.com Ajay K. Garg University Professor (Tenure-Track) Fairleigh Dickinson University - Vancouver Campus Vancouver V5X2R9 Canada ajeyakgarg@gmail.com 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. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 70

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),, 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. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 71

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 4.80.492 EDI 4.46.673 ERP 3.20.569 Decision Support System 3.86.964 www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 72

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 * Cross tabulation Protection from theft 2 Count 2 0 0 2 50.0%.0%.0% 2.0% 3 Count 0 2 8 10.0% 16.7% 9.5% 10.0% 4 Count 0 4 42 46.0% 33.3% 50.0% 46.0% 5 Count 2 6 34 42 50.0% 50.0% 40.5% 42.0% 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. df Pearson Chi-Square 51.793 a 6.000 Likelihood Ratio 18.842 6.004 Linear-by-Linear Association 2.480 1.115 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 www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 73

Faster and improved customer service * Cross tabulation Faster and 2 Count 0 4 6 10 improved customer.0% 33.3% 7.1% 10.0% service 3 Count 2 0 4 6 50.0%.0% 4.8% 6.0% 4 Count 2 0 24 26 50.0%.0% 28.6% 26.0% 5 Count 0 8 50 58.0% 66.7% 59.5% 58.0% 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. df Pearson Chi-Square 28.189 a 6.000 Likelihood Ratio 24.190 6.000 Linear-by-Linear Association 4.759 1.029 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 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 * Cross tabulation Reducing inventory 3 Count 4 1 5 10 errors 100.0% 8.3% 6.0% 10.0% 4 Count 0 2 13 15.0% 16.7% 15.5% 15.0% 5 Count 0 9 66 75.0% 75.0% 78.6% 75.0% www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 74

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. df Pearson Chi-Square 37.587 a 4.000 Likelihood Ratio 20.243 4.000 Linear-by-Linear Association 16.307 1.000 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 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 * Cross tabulation Easy accessibility in 1 Count 4 6 50 60 showroom 100.0% 50.0% 59.5% 60.0% 2 Count 0 6 30 36.0% 50.0% 35.7% 36.0% 3 Count 0 0 4 4.0%.0% 4.8% 4.0% 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. df Pearson Chi-Square 4.127 a 4.389 Likelihood Ratio 5.961 4.202 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 www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 75

Time saving * Cross tabulation Time saving 3 Count 4 0 4 8 100.0%.0% 4.8% 8.0% 4 Count 0 2 38 40.0% 16.7% 45.2% 40.0% 5 Count 0 10 42 52.0% 83.3% 50.0% 52.0% 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. Df Pearson Chi-Square 52.601 a 4.000 Likelihood Ratio 28.044 4.000 Linear-by-Linear Association 4.693 1.030 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 * Cross tabulation Improved efficiency 3 Count 2 0 7 9 50.0%.0% 8.3% 9.0% 4 Count 2 8 53 63 50.0% 66.7% 63.1% 63.0% 5 Count 0 4 24 28.0% 33.3% 28.6% 28.0% 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 www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 76

efficiency and only 9% people are saying barcoding is neutral to improving efficiency means it was neither helpful nor unsupportive to these 9%. df Pearson Chi-Square 9.977 a 4.041 Likelihood Ratio 8.287 4.082 Linear-by-Linear Association 1.784 1.182 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 * Cross tabulation Reduce Cost 2 Count 0 2 10 12.0% 16.7% 11.9% 12.0% 3 Count 4 6 52 62 100.0% 50.0% 61.9% 62.0% 4 Count 0 4 20 24.0% 33.3% 23.8% 24.0% 5 Count 0 0 2 2.0%.0% 2.4% 2.0% Table shows that when the organizations use the barcoding technology then they feel that barcoding 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. Df Pearson Chi-Square 3.687 a 6.719 Likelihood Ratio 5.244 6.513 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 www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 77

Fast inventory status in store * Cross tabulation Fast inventory status in store 3 Count 0 4 9 13.0% 33.3% 10.7% 13.0% 4 Count 4 8 52 64 100.0% 66.7% 61.9% 64.0% 5 Count 0 0 23 23.0%.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. df Pearson Chi-Square 9.936 a 4.042 Likelihood Ratio 12.834 4.012 Linear-by-Linear Association 4.243 1.039 Table shows that significance value is less than 0.05 hence the null hypothesis will be rejected and the 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 * Cross tabulation Faster information dissemination 3 Count 2 2 8 12 50.0% 16.7% 9.5% 12.0% 4 Count 2 8 30 40 50.0% 66.7% 35.7% 40.0% 5 Count 0 2 46 48.0% 16.7% 54.8% 48.0% Table shows that when the organizations use the barcoding technology then they feel that barcoding 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. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 78

Df Pearson Chi-Square 13.254 a 4.010 Likelihood Ratio 13.485 4.009 Linear-by-Linear Association 11.000 1.001 Table shows that significance value is less than 0.05 hence the null hypothesis will be rejected and the 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 * Cross tabulation Tracking and tracing the 1 Count 0 2 0 2 material.0% 16.7%.0% 2.0% 2 Count 0 2 10 12.0% 16.7% 11.9% 12.0% 3 Count 4 8 58 70 100.0% 66.7% 69.0% 70.0% 4 Count 0 0 16 16.0%.0% 19.0% 16.0% Table shows that when the organizations use the barcoding technology then they feel that barcoding 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. df Pearson Chi-Square 18.957 a 6.004 Likelihood Ratio 15.699 6.015 Linear-by-Linear Association 4.125 1.042 Table shows that significance value is less than 0.05 hence the null hypothesis will be rejected and the alternate hypothesis will be accepted indicating that bar-coding helps for tracking and tracing the material. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 79

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 Protection from theft Pearson Correlation 1.158 Sig. (2-tailed).116 Protection from theft Pearson Correlation.158 1 Sig. (2-tailed).116 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. Faster and improved customer service Pearson Correlation 1.219 * Sig. (2-tailed).028 Faster and improved customer service Pearson Correlation.219 * 1 Sig. (2-tailed).028 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. Reducing inventory errors Pearson Correlation 1.406 ** Sig. (2-tailed).000 Reducing inventory errors Pearson Correlation.406 ** 1 Sig. (2-tailed).000 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. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 80

Easy accessibility in showroom Pearson Correlation 1.100 Sig. (2-tailed).322 Easy accessibility in showroom Pearson Correlation.100 1 Sig. (2-tailed).322 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. Time saving Pearson Correlation 1.218 * Sig. (2-tailed).030 Time saving Pearson Correlation.218 * 1 Sig. (2-tailed).030 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. Improved efficiency Pearson Correlation 1.134 Sig. (2-tailed).183 Improved efficiency Pearson Correlation.134 1 Sig. (2-tailed).183 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 barcoding then the dependent variable improved efficiency in storeroom automatically increased. Reduce Cost Pearson Correlation 1.038 Sig. (2-tailed).707 Reduce Cost Pearson Correlation.038 1 Sig. (2-tailed).707 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 means both are directly related with each other if we start using of one independent variable as bar- www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 81

coding then the dependent variable reducing cost in storeroom automatically increased means the cost becomes low. Fast inventory status in store Pearson Correlation 1.207 * Sig. (2-tailed).039 Fast inventory status in store Pearson Correlation.207 * 1 Sig. (2-tailed).039 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 means both are directly related with each other if we start using of one independent variable as barcoding then the dependent variable fast inventory status in storeroom automatically increased. Faster information dissemination Pearson Correlation 1.333 ** Sig. (2-tailed).001 Faster information dissemination Pearson Correlation.333 ** 1 Sig. (2-tailed).001 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. Tracking and tracing the material Pearson Correlation 1.204 * Sig. (2-tailed).042 Tracking and tracing the material Pearson Correlation.204 * 1 Sig. (2-tailed).042 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. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 82

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. References 1. Brewer, M. (2007). RFID adoption hurdles start to crumble. Wirelessdesignmag.com, October, 3-4. 2. Ellram, L., Londe, B. L., & Weber, M. (1999). Retail logistics. International Journal of Physical Distribution and Logistics Management, 29(7), 477-494. 3. Gunasekaran, A., Forker, L., & Kobu, B. (2000). Improving operations performance in a small company: a case study. International Journal of Operations & Production Management, 20(3), 316-335. 4. Jorge R. León-Peña (2008), e-business and the Supply Chain Management, Business Intelligence Journal, 2008,p 1. 5. McCathie, L., & Michael, K. (2005). Is it the end of barcodes in supply chain management? Paper presented at the Proceedings of the Collaborative Electronic Commerce Technology and Research Conference LatAm, Talca, Chile. 6. Michael, K., & McCathie, L. (2005). The pros and cons of RFID in supply chain management. Paper presented at the International Conference on Mobile Business, Sydney, Australia. 7. Samuel Fosso Wamba, Louis A. Lefebvre and Elisabeth Lefebvre, 2007, Integrating RFID Technology and EPC Network into a B2B Retail Supply Chain: A Step toward Intelligent Business Processes, Journal of Technology Management and Innovation, 2007. 8. Swartz J. (2000), Changing retail trends, new technologies and supply chain. Technology in Society, Vol.22, pp.123-132. 9. Zhang, Y.F., Jiang, P. Y., & Huang, G. Q.(2008). RFID-based smart kanbans for just-in-time manufacturing. International Journal of Materials and Product Technology, 33(1-2), 170-184. 10. Hau L. Lee(2002), Aligning Supply Chain Strategies with Product Uncertainties, California Management Review, Vol. 44, No. 3, P 10. 11. Lambert, D.M. and J. R. Stock, 2001. Strategic Logistics Management. 4 th Edn., Irwin McGraw-Hill, New York. 12. Lee H (2000). Creating value through supply chain integration, Supply Chain Management Review, September/October:30-36. 13. Palmer, R. C. (1995). The Bar Code Book: Revised and Expanded. New Hampshire: Helmers Publishing, Inc. 14. Reynolds, M. (2007). RFID take-off will not kill the barcode, says expert, Electronics Weekly, Issue 2297, 12. 15. Wyld, D. (2006). RFID101: The next big thing in management. Management Research News, 29(4), 154-173. 16. Wild, T. (1997). Best Practices in Inventory Management. New York: John Wiley & Sons. 17. Zipkin, P. (2000). Foundations of Inventory Management. Boston: McGraw-Hill. www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 02, June-2013 Page 83