Copyright 2015 Society of Photo-Optical Instrumentation Engineers and IS&T-The Society for Imaging Science and Technology.

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1 Copyright 15 Society of Photo-Optical Instrumentation Engineers and IS&T-The Society for Imaging Science and Technology. This paper was published in the proceedings of the 15 IS&T/SPIE Electronic Imaging conference: Media Watermarking, Security, and Forensics, San Francisco, CA February 8, 15, volume 99 and is made available as an electronic reprint with permission of SPIE. Single print or electronic copies for personal use only are allowed. Systematic or multiple reproduction, or distribution to multiple locations through an electronic list server or other electronic means, or duplication of any material in this paper for a fee or for commercial purposes is prohibited. By choosing to view or print this document, you agree to all the provisions of the copyright law protecting it.

2 Scanning-Time Evaluation of Rebecca Gerlach, Dan Pinard, Matt Weaver, Adnan Alattar 1 Digimarc Corporation, 95 SW Gemini Drive, Beaverton, OR 98, USA ABSTRACT This paper presents a speed comparison between the use of s and the Universal Product Code (UPC) for customer checkout at point of sale (POS). The recently introduced promises to increase the speed of scanning packaged goods at POS. When this increase is exploited by workforce optimization systems, the retail industry could potentially save billions of dollars. The is based on Digimarc s watermarking technology, and it is imperceptible, very robust, and does not require any special ink, material, or printing processes. Using an image-based scanner, a checker can quickly scan consumer packaged goods (CPG) embedded with the without the need to reorient the packages with respect to the scanner. Faster scanning of packages saves money and enhances customer satisfaction. It reduces the length of the queues at checkout, reduces the cost of cashier labor, and makes self-checkout more convenient. This paper quantifies the increase in POS scanning rates resulting from the use of the versus the traditional UPC. It explains the testing methodology, describes the experimental setup, and analyzes the obtained results. It concludes that the increases number of items per minute (IPM) scanned at least % over traditional UPC. Keywords:, Watermarking, Universal Product Code, UPC, POS, Retail Industry, Consumer Packaged Goods (CPG), Speeding Checkout 1. INTRODUCTION The traditional Universal Product Code (UPC) was introduced about four decades ago to uniquely identify products in retail sales systems. Its use has become ubiquitous not only in the retail industry but in many other industries as well. In the retail industry it is being used to manage store inventory and to facilitate customer checkout at POS. It is also used along with store loyalty cards to analyze shoppers' habits, increase sales, promote products, reduce inventory, and enhance profit margins. The use of the UPC saves the industry billions of dollars yearly and its return-on-investment has far exceeded expectations. However, the UPC has not kept up with tremendous advances in technology over the past years, and there are significant opportunities to improve upon it. Digimarc has recently introduced the that maintains and extends the advantages of the traditional UPC. It is based on Digimarc's robust watermarking technology. Unlike the UPC, the is imperceptible and preserves the aesthetic value of packaging. It embeds the same GTIN-12 number that is usually encoded in the UPC. This makes the transition from the traditional UPC to the almost transparent to retailers and their information technology infrastructure. The is embedded over the whole surface of a package allowing packages to be scanned quickly at any orientation with respect to the POS scanner. Embedding the does not require special ink, material, or equipment, and it does not require any changes to standard printing processes. The offers a significant increase in scanning rates over the traditional UPC code. Initial testing indicates that is robust, accurate, and easy to scan by professional checkers as well as customers. During the National Retail Forum (NRF) held in January 14, two Digimarc executives with no professional experience as checkers challenged the Guinness World Record for the time required to scan and bag CPG items and easily set a new record of seconds, bettering the previous record by more than 35%. This success represents a scanning rate of 62 IPM, far more than what most professional checkers can achieve using UPC. Other participants at the NRF were able to achieve even higher rates than this. 1 { Rebecca.Gerlach, Dan.Pinard, Matt.Weaver, Adnan.Alattar }@digimarc.com

3 Such increases in scanning rates have many advantages to retailers. Checkout queues are shorter and self-checkout is more convenient, effects that both save money and enhance customer satisfaction. When exploited with the workforce management systems usually used by big-box stores, the increase in scan rates could save the retail industry billions of dollars. The amount of savings for the retail industry depends mainly on the increase in scanning rates that the Digimarc Barcode offers over traditional UPC. The efficiency of the workforce management system and other store-related factors may also affect the amount of savings. For the aforementioned reasons, the warrants a formal study that quantifies the increase in scanning rates (or, equivalently, the decrease in scanning times). Hence an experiment was conducted at Digimarc to assess and compare the performance of the with that of the traditional UPC. Although robustness (as measured by false positives and false negatives) is very important, it is outside the scope of this experiment. In this experiment, people without professional checkout experience were asked to first scan twenty CPG items marked with the UPC, and the time required to scan these items was recorded. Then they were asked to scan equivalent items marked with the Digimarc Barcode. UPCs decoding was disabled when scanning for s. Finally, the obtained scanning times were compared and analyzed to draw some conclusions. This paper reports on this experiment and its findings. The experimental setup and procedures are explained in the next section. Section 3 describes the obtained results and analysis. Section 4 includes our conclusions. 2. EXPERIMENT SETUP 2.1. Test setup Forty items of CPG, similar to those found in a shopper s basket, were used in this experiment. These items varied in size, shape, and form factor. They included carton boxes, metal cans, plastic bottles, and plastic bags. They also included large, medium, and small items. These forty items were divided into two equivalent, but not identical, groups of twenty items each. One group will be referred to as the UPC group; the other will be referred to as the group. Each item in the UPC group was marked only with the traditional UPC code, while the art work on each item in the group was embedded with the. The strength of the was carefully adjusted to maximize robustness while minimizing visibility. Some of the embedding methods used for watermarking these packages are detailed in a companion paper in this conference s proceedings 1. The resolution of embedding allowed the packages to be scanned at 4-8 on a Datalogic Magellan 98i scanner. Some packages carried 47-bit payloads and the rest carried 64-bit payloads. Both sizes of payloads are supported by the reader. Carton boxes were printed on card stock and labels for cans and other package shapes were printed on proofing paper stock using a professional web press. Flexible packages were printed using an HP Indigo on clear laminate. These were reverseprinted and laminated onto various flexible substrates, white polys, silver Mylar, etc. Each embedded label was glued on its corresponding item. Appropriate materials were placed inside each package to simulate a typical weight and feel. Two stations, each similar to the POS found in a typical grocery store, were used for testing. The two stations were almost identical except for the catch (discharge area) for the scanned items. To compensate for this difference a bagger on each station removed the scanned items after scanning. As pictured in Figure 1 below, each station has a conveyor belt and a Datalogic Magellan 98i scanner. The Magellan scanner is a new image-based scanner currently being deployed in many retail stores. It was equipped with a detector to read s, and an in-house software application was used to manage the experiment and record the scanning time. 2

4 Figure 1. The POS Station Used in the Experiment. Twenty-six Digimarc employees, all of whom lacked professional cashier training and experience, were used as test subjects. The subjects were allowed to watch other subjects scanning items before their sessions. They were also allowed to help other subjects by bagging the scanned items. Each test subject was asked to scan the UPC group of CPG at one station ten times. Each subject was also asked to scan the group at the other station ten times. A bagger assisted each test subject to bag the items during each run. The time it took the test subject to scan each group once was recorded and analyzed. This experiment produced a total of 5 data points, or 2 for each group Performance measure Instead of using the scan time as a measure for evaluating how fast the scanning is, the number of items per minute (IPM) was used in this experiment. This measure works well for comparisons as it does not depend on the number of items used in the experiment. It is also similar to, but not the same as, the Ring-Time 2 that is widely used in the retail industry for measuring the performance of people working as cashiers. The IPM accounts only for the time spent on scanning CPG. Unlike Ring-Time, it does not include the time spent scanning produce, bagging items, or transacting payments. Simply put, it is N divided by the time spent scanning N CPG items. CTOs from some of the largest retailers have stated that increasing the IPM represents millions of dollars in savings to their retailers. In this experiment, care was taken to describe under what ideal conditions such an IPM increase could be realized. 3. RESULTS AND ANALYSIS The data obtained from scanning the UPC and groups for all twenty-six subjects has been aggregated and listed in Table 1. The first run for each group was considered a training run and has been removed from the data analysis. The table lists the average scan time and the average IPM for runs 2-9 of both UPCs and s for each of the 26 test subjects. Each run is a scan of items. The table also lists the average percent improvement in time and IPM for each test subject. The overall percentage improvement in average time and IPM for all test subjects are 31.41% and 53.18% respectively. These results clearly show that the has a significant advantage over traditional UPCs. The average of 54 IPM obtained in this experiment was slightly lower than, but consistent with, the 62 IPM achieved at the Guinness World Record challenge. The difference may be due to lack of practice of the test subjects, as for many of them this was their first experience at checking. 3

5 UPC Test Subject Average of UPC Total Scan Time (sec) Average of UPC Items per Minute Average of Digimarc Barcode Total Scan Time (sec) Average of Digimarc Barcode Item Per Minute Percent Improvement in Average Time for over UPC Percent Improvement in Average Item per Minute for over UPC Checker % 89% Checker % % Checker % 13% Checker % 22% Checker % 64% Checker % 65% Checker % 57% Checker % % Checker % 11% Checker % % Checker % 48% Checker % 49% Checker % 78% Checker % 53% Checker % 32% Checker % 26% Checker % 62% Checker % % Checker % 14% Checker % 59% Checker % 58% Checker % 46% Checker % 81% Checker % 35% Checker % 54% Checker % 86% Grand Total % 53.18% Table 1. UPC and Timing Data 3.1. Scan rate by technology type The distribution of the scanning rate using UPCs and s is shown in Figure 2. The mean and the standard deviation are and 7.63 for the UPC, while they are and 9.77 for the. 4

6 Checker1 Checker2 Checker3 Checker4 Checker5 Checker6 Checker7 Checker8 Checker9 Checker1 Checker11 Checker12 Checker13 Checker14 Checker15 Checker16 Checker17 Checker18 Checker19 Checker Checker21 Checker22 Checker23 Checker24 Checker25 Checker26 Items Per Minute Frequency Histogram of Scanning Rate Items Per Minute UPC Figure 2. Distribution of the Scanning Rate Using UPC and scan times are significantly lower than UPCs. Using the raw data, the mean total scan time for Digimarc Barcodes is 23.8 seconds 33% faster than the UPC time of seconds. The standard deviation of scan times is lower than that of the UPC: 4.5 versus 6.9 respectively Scan rate by checker The average IPM for each test subject is plotted in Figure 3. Although scanning rate varies from one test subject to the other, all test subjects scanned significantly faster with s than with UPCs. 8 1 Average IPM per Test Subject Test Subject UPC Figure 3. Average Scanning Rate for All Test Subjects 5

7 Checker1 Checker2 Checker3 Checker4 Checker5 Checker6 Checker7 Checker8 Checker9 Checker1 Checker11 Checker12 Checker13 Checker14 Checker15 Checker16 Checker17 Checker18 Checker19 Checker Checker21 Checker22 Checker23 Checker24 Checker25 Checker26 Number of Items Per Minute Checker1 Checker2 Checker3 Checker4 Checker5 Checker6 Checker7 Checker8 Checker9 Checker1 Checker11 Checker12 Checker13 Checker14 Checker15 Checker16 Checker17 Checker18 Checker19 Checker Checker21 Checker22 Checker23 Checker24 Checker25 Checker26 Number of Items Per Minute The variations in scanning speed from one run to another are plotted in box-and-whisker plots as shown in Figures 4 and 5 for UPC and, respectively. In these diagrams, the horizontal sides of the box represent the 1st and 3rd quartiles. The difference between the 1st and 3rd quartiles is called the interquartile range (IRQ). The lines extending above and below each box are called whiskers. They extend to the outermost data point Scanning Rate for UPC Test Subject Figure 4. Variations in Scanning Speed Using UPC for All Test Subjects Scanning Rate for Test Subject Figure 5. Variations in Scanning Speed Using for All Test Subjects 3.3. Scan rate by run The variation in each of the runs of the experiment is shown in Figures 6 and 7 as box-and-whisker plots. 6

8 Number of Items Per Minute Number of Items Per Minute Scanning Rate for UPC Run Figure 6. Variations in Each Run Using UPC Scanning Rate for Run Figure 7. Variations in Each Run Using Digimarc UPC scan rates increase with successive runs as checkers learn the positions of the UPCs. mean scan rates also indicate a slight learning curve. A conservative comparison of just the final runs average gives Digimarc Barcodes a 37% faster scan rate (56.4 IPM versus 41.1 IPM). The learning effect on scan rates for the test subjects is shown in Figure 8. Note that all ten test runs are included to demonstrate the first time training effect which led to their removal from all other analyses in this paper. 7

9 Items Per Minute AVerage IPM per Test Run Test Run Number UPC Figure 8. Scan Rate Variations in Each Run Using 3.4. Further remarks The following remarks about the experiment need to be addressed in future experiments to produce a more accurate assessment. 1. Demographics: The test subjects were checkers with no professional experience. Even those who use selfcheckout would not have been used to a belt driven check out system. This probably means that learning/training had a larger impact on the results than desired. 2. Packages: The number of packages was the same for both stations, but the packages were not an exact match. For instance one station may have used the tomato soup while the other used the parsnip soup. 3. Number of trials: With only 1 runs per user per system, there is a risk of other factors influencing the results: a. Learning: Users could still be learning both technologies, which may have a biasing effect on the calculated averages. b. Fatigue: Professional checkers have shifts that last multiple hours. The speed that can be maintained across 1 runs may not be sustainable over an entire shift. c. Data spikes: A particularly bad run can have a large effect on the average with this number of samples. 4. Randomization: Although most test subjects started with UPCs first, some did not. Since these were checkers unused to the belt driven system, training from the first system could have helped on the second system. By the same token, fatigue could set in between systems. Tracking and balancing the distribution of how many users started with which system could help to determine if there are any biases here. This experiment focused on speed benefits and not robustness as traditionally defined by false positive and false negative rates. A formal analysis of false negative rates seems unnecessary as it is built into the IPM metric. For example, a package that fails to scan successfully on the first swipe must be swiped again until it does scan. This re-swipe action increases the amount of time needed to scan a group of CPG and therefore reduces the IPM reported. The detector embedded in the barcode scanner device utilizes error correction methods traditionally used to limit false positives. Similar algorithms have been used in similar print and scan applications such as Digimarc Discover and have achieved very low false positive rates. 8

10 4. CONCLUSIONS This study indicated a conservative gain of % in scanning time or % in IPM for CPG at POS can be easily achieved by using s instead of the traditional UPC. Some minor differences between testing the and testing the UPC code may have affected the result, but that effect is not expected to be significant. Experienced cashiers are expected to achieve similar or higher gains than the non-experienced cashiers used in this experiment, though it may take a few trials for experienced cashiers to realize that they do not need to reorient packages relative to the scanner as they do with UPCs. Stores may exploit the gain due to using s to enhance customer satisfaction or save in labor cost. Assuming that retailers use an efficient workforce optimization system, Digimarc conducted another study to estimate the resulting savings from using the and showed that retailers could potentially save billions of dollars. This study also showed that retailers would recoup their cost of using a professional service to embed their CPG in less than a year. The reader is referred to Digimarc s 3 white paper for more details of this study 4. REFERENCES [1] Alastair Reed, Tomas Filler, Kristyn Falkenstern, and Yang Bai, Watermarking Spot Colors in Packaging, Proceedings of the SPIE, Media Watermarking, Security and Forensics 15, San Francisco, California, Feb. 15, accepted for publication. [2] [3] [4] 9