ECR Retail Loss
Enabling the Retail Sector to Sell More and Lose Less
ECR research has identified forty eight video analytic use cases in retail, and two years ago, we published a report on the adoption of these use cases. Click here to read and download the 2022 report. In that report, we evidenced a low overall utilisation rate of just 7% but with a clear trend towards significant future interest in and use of video analytics. In particular, the areas around the retail store checkout and in the aisles seem to be areas of considerable testing and future development. To explore these expected changes, we are launching a new survey and invite you to participate and share the extent to which your business has deployed, is trialling, has trialled these video analytics use cases. Your responses will be aggregated and anonymised to provide some very helpful benchmarks for the industry. We also think, based on what we heard from you the last time we ran the survey, that you will actually enjoy the exercise and the opportunity to reflect again on your adoption of video analytics. The survey is short and will not take long to complete, we need your responses by Friday January 12th. If you are not the right person for this survey, please can you forward to the right person. Thank you, Colin To take the survey, scan the QR Code.
IP Video technology is playing an ever-increasing role in the safety of store colleagues, security and retail operations across the whole retail sector, stores, distribution and supply chain Every year, our video working group gets to hear from the Founders and CEO's of leading edge video providers via a panel discussion, chaired by Professor Adrian Beck, with the questions this year to Pierre Racz (Genetec), Gerard Figols (i-PRO) and Martin Gren (Axis) prepared and shared in advance. In this years call, the questions covered different aspects of video technology, its applications, and the challenges and opportunities it presents. Here are a few highlights of that discussion. The Role of AI and Intelligent Automation Pierre Racz, CEO of Genetec, shed light on his distinction between AI and Intelligent Automation. He emphasized that while true AI may still be a distant goal, Intelligent Automation, which combines human oversight with machine learning, can yield better outcomes in retail video technology. Expanding Video Use Cases Gerard Figols, from i-PRO, highlighted how video technology extends beyond security to encompass various retail operations. This includes understanding customer traffic, optimising inventory, and personalized interactions. Cameras are no longer just cameras; they are IoT sensors that provide valuable insights to retailers. Ethical Considerations of Facial Recognition Facial recognition technology was a hot topic during the discussion. Panellists stressed the importance of ethical use cases, consent, and transparency and compliance. Pierre Racz cautioned against covert use and emphasised that satisfied customers can share their experiences, while dissatisfied customers have a far-reaching impact. Future-Proofing Video Investments To maximise flexibility and avoid vendor lock-in, the experts recommended adopting open architectures in such fast-moving space. Martin Gren highlighted the significance of backward and forward device compatibility when making infrastructure choices. Video Analytics in Self-Service Retail The discussion covered self-checkout and self-service trends and noted the increased use of video analytics to enhance efficiency while countering fraud. But again, the consensus was that although the AI tools are good, to get the best results, the systems need clear oversight from well-trained people. Innovations on the Horizon The experts agreed that the future of video analytics in retail will continue to evolve, focusing on factors like audio, image fidelity, storage efficiency, and analytic sophistication. They emphasised the need to integrate third-party analytics and sensors to gain deeper insights and maintain ethical practices. In conclusion.... The insights shared during the session provided a valuable look ahead at the role of video technology in the retail sector and a chance for the retailers in our group to reflect on the implications and how this new information can help them shape a video strategy and investment that can be more future proof. For a deeper understanding of the current and future landscape watch the full session or read a summary transcript below. . Summary transcript Please note this is not a verbatim transcript, rather an edited synopsis of many of the key points addressed. Please refer to the full video for verbatim quotes. Colin Peacock: We have an excellent session planned for our video working group. I should start with the usual reminders around antitrust laws – everyone please keep this top of mind in terms of what we should and shouldn’t say regarding competitive, proprietary information. So far we’ve successfully avoided jail for 25 years and I’d like to keep it that way. I have no plans to go to jail, how about you Adrian? Adrian Beck: No. Colin Peacock: For those less familiar, ECR is a global retail association that has been operating for over 25 years. We have a huge number of retailer members worldwide who participate across various activities and working groups. We also have excellent academic support, like Professor Adrian Beck from the University of Leicester. Additionally, we receive research funding and grants from a few selected partners, including Genetec, and their leaders often attend these sessions to stay abreast of the latest problems facing retailers that can inspire their innovation pipeline. Our group objectives focus on sharing video technology best practices, furthering video analytics, and exploring in-store applications beyond just security use cases. Today we’ll concentrate on the future. We have multiple retailers joining us and we’ll hear from several later. Adrian Beck will facilitate the discussion - we’ve provided him the questions to ask the panel via an earlier retail member brainstorm. Adrian Beck: Thanks Colin. Hello everyone, great to see you all again – hard to believe it’s been a year already! As Colin mentioned, we recently met with regular participating retailers to understand their most pressing video-related questions when thinking ahead. We have 12 questions here that we’ll work through as best we can over the next 55 minutes or so. Each speaker has seen these in advance, so with no further delay let’s dive right into the first question around AI... Adrian Beck: Let's start with you Pierre. What does AI mean to your Genetec business and how is it changing your product and service approach? Pierre Racz: OK, first of all the acronym AI stands for "Absolute Ignorance." People often misconstrue crafty guessing for actual intelligence when the technology remains completely ignorant. That's not to say these tools are useless though. In all seriousness, I was at a conference where Microsoft, Intel and Nvidia's research PhDs spoke. They mentioned we remain far from achieving the technological singularity or human-level AI. What we actually have now are statistical inference engines. These are great at surfacing past patterns for us, but cannot drive innovation or discover new things the way humans can. In the context of retail crime, real intelligent criminals can also find ways to trick these defenses through adversarial inputs. So in summary, true AI does not exist yet. What does exist is Intelligent Automation - this means smart systems doing the heavy lifting but with humans still playing an essential oversight role in the loop. This produces the best outcomes in my opinion. At Genetec, we leverage machine learning as an acceleration tool but ensure people stay involved so we don't propagate "Absolute Ignorance". Adrian Beck: Can you provide some real examples of how this intelligent automation philosophy manifests in your solutions? Pierre: Sure - take license plate recognition, which we have offered within Genetec since about 2002. We originally used an approach called support vector machines that required manually building feature extraction models for different plate fonts, jurisdictional contexts, etc - very labour intensive. About 10 years ago we switched to deep neural networks so the system could automatically self-learn features instead. However, because it's still fundamentally ignorant despite some clever illusion of intelligence, we caught unusual cases like it using license plate bolt patterns rather than printed numbers to guess at states. No matter how advanced these tools get, we must keep human oversight as part of the quality control loop. Adrian Beck: Gerard, what's your perspective on AI and its impacts for IPRO? Do you share some of Pierre's scepticism? Gerard Figols: Yes the hype around AI is definitely immense...I agree it's not truly intelligent or independent as Pierre said. However, video devices are still getting smarter. But smarter doesn't mean they can function autonomously without human guidance. As camera counts grow massively, operators face overload. These smarter systems can help them monitor more devices by providing more insightful data. We don't see cameras as just cameras anymore - they are IoT sensors. Deep learning allows them to classify objects within scenes so users can find the most useful events faster. But at the end of the day there must be a human operator analysing scenarios and deciding appropriate responses using these AI-generated insights. Fusing all the data from various sensors - like video cameras among other sources - remains imperative as well. Adrian Beck: Martin from Axis - where do you net out regarding the role and evolution of AI in the video surveillance domain? Martin Gren: We have incorporated AI and deep learning across our chipsets and entire camera line up for years now actually. But we don't use it just to grab headlines through fancy demos - the priority is improving core functions like image processing. We also believe strongly in a hybrid model where edge devices enhance lower-level capabilities but cloud platforms drive heavier analytical workloads leveraging metadata from the cameras. AI definitely provides lasting advances but claims around human-like reasoning remain overstated from what we've seen behind the scenes. Thoughtfully constructive applications paired with human interpretation lead to the best outcomes in the real world rather than radical autonomy. Adrian Beck: Let's continue walking through our industry questions. Let's move to our next question around the rising challenge of organized retail crime that so many retailers on our call have faced lately. Pierre Racz: There was an interesting article about a company in the United States that got into legal trouble for crowdsourcing some video labelling, and one person was wrongly tagged as a human trafficker. That individual was stopped aggressively by police but eventually released without any charges after 40 minutes of very rough detention. They are now suing that video camera manufacturer. So it highlights the great responsibility involved when interpreting video data. If you will label footage, be extremely accurate. Actual humans should be involved in analysis before unfounded assumptions spread downstream. You also need properly scoped usage policies governing who receives such sensitive information. For example NATO (North Atlantic Treaty Organization) has varying levels of intelligence data access across member states based on agreements and laws. Just like restaurants safeguarding food quality through supplier standards, video data requires thoughtful handling as well. Adrian Beck: Gerard from IPRO - what's your advice around seamlessly and securely sharing video data with law enforcement for criminal investigations? Gerard Figols: Two crucial points jump out right away. First is guaranteeing data integrity, so proper encryption to make certain the information remains intact in transit. The second consideration is bandwidth constraints, because sharing all raw video feeds from many retailers could overwhelm police departments and limit their capability to react. Instead of transferring entire video archives, it likely works better to share analysed incident metadata like alerts and insights so they know where to focus. But adequate infrastructure would need to support strong security protections as per regulations like GDPR (General Data Protection Regulation). Adrian Beck: Martin from Axis - what have you seen relative to external video data exchange needs? Martin Gren: Honestly the most common method I still encounter is people filming screens with their mobile phones, which is obviously not ideal. Proper encryption and auditing capabilities are absolute must-haves. Regarding formats, we have perceived greater comfort progressing the industry norm towards H.264-encoded MP4 video clips as needed. But overall this isn't an area we actively facilitate as a manufacturer beyond guidance. Adrian Beck: Let's combine questions 3 and 4 since they relate... Retailers are very interested in quantifying video's business value beyond a security foundation. So first, how might video use cases stretch deeper into retail operations? And second, which processes could be automated using video analytics? Pierre, over to you for initial thoughts. Pierre Racz: It's debatable how innovative this is since some customers have already achieved major results in these areas before using techniques that could now spread more broadly. For instance, leading retailers who have successfully reduced shrinkage well below industry averages routinely reposition mobile cameras over different store zones for marketing and operational analytics beyond just monitoring cash registers. We're also exploring camera-equipped robots autonomously patrolling locations after hours to detect restocking needs or inventory anomalies. So video and analytics are already transforming activities in measurable ways when deliberately leveraged, though mainly by big chains presently. Making such capabilities more pervasively accessible can catalyse the next evolution. Adrian Beck: What are you observing relative to expanding video use cases Gerard? Gerard Figols: Various insights come into focus through creative camera placements and AI-based apps - understanding customer traffic flows and hot spots; queue management to boost service levels; optimizing inventory; personalized interactions with VIP shoppers through face recognition and more. We have over 150 deep learning-capable cameras to enable this edge-based data extraction. Adrian Beck: Martin, what non-security use cases are you encountering? Martin Gren: Self-checkout stands out as a prominent arena actively using connected video to enhance loss prevention while increasing efficiency through less labour. That said, we have reservations around techniques like blacklisting. It's easy to overstep ethical boundaries, even inadvertently, in retail environments open to the general public. Claims we see around assessing falls also seem unrealistic despite advanced analytics. Humans still prove better at contextual judgment for now. Adrian Beck: Facial recognition persists as a lightning rod issue given the appetite retailers have for leveraging it to curb abuse towards staff, balanced by strong privacy concerns. Pierre, you've been vocal about related risks in the past - where do you stand given the promotional capabilities but simultaneously scary connotations? Pierre Racz: Companies should first be clear whether their intent is security or surveillance-driven advertising. Ethical use cases center on voluntarily enrolled participants who opted in fully informed of trade-offs. Any other covert identification breeds long-term distrust once uncovered. Genetec previously had retail customers switch off facial recognition after overwhelmingly negative customer responses - it came across as creepy with minimal crime deterrence value. The core lesson here, reinforced by Dave Carroll's viral United Breaks Guitars masterclass, is that satisfied customers may tell a few others about their experience whereas dissatisfied customers have reach to tell millions instantaneously. Avoid anything that can be perceived as intrusive. Adrian Beck: But doesn't that contradict with protecting staff from abusive repeat offenders for example? What's your take Gerard? Gerard Figols: The technology capability definitely exists - it's more about how its applied. As with Internet cookies, obtaining consumer consent remains imperative. We're seeing more airport security and check-in protocols offer facial recognition as an opt-in acceleration boon for travelers willing to enroll their biometrics. When ethically deployed with proper data safeguards, it can enhance safety and operations. Geographies have unique regulations guiding appropriate usage as well that must stay centrally in focus. Adrian Beck: Martin, thoughts from Axis? Martin Gren: Some Middle Eastern regions mandate facial tracking in ways unacceptable elsewhere, like schools exploring it only to face legal injunctions. Beyond confirming local laws, retailers must keenly evaluate risks to their brand reputation. No organisation wants to star in the next PR crisis video gone viral! That said, for consenting users in restricted areas like warehouses, it provides value. Just exercise extreme diligence given societal sensitivities. Pierre Racz: A European bank discouraged in-branch verbal abuse by informing volatile customers they remained visible through slightly blurred public view monitors. Utter transparency combined with technology fusion for ethics can bolster environments substantially. For example, Genetec solutions certified by the respected European Privacy Seal guarantee encrypted video auditing without explicit profiling. Avoid anything covert yet pursue creative strategies promoting honourable behaviour. Adrian Beck: Let's continue exploring other areas of interest that surfaced during our retailer round tables. The next logical discussion area based on retailer polling centres on future proofing costly video investments spanning years typically before refresh cycles...So what advice do you have for maximizing flexibility? Pierre, please kick us off. Pierre Racz: It's "telling" how all of the technology partners on this call here embrace open architectures rather than vertical integration or custom stacks. Retailers must avoid vendor lock-in scenarios yielding few options. Open platforms demonstrate committed innovation for customers versus stagnant reliance on fragile monopolies. The promise lies in continuously harnessing advances across technology shifts rather than just exploiting temporary competitive advantages. Adrian Beck: How do you guide customers on strategic system design principles Gerard? Gerard Figols: We wholly agree openness injects crucial upgrade flexibility paired with interoperability for component swapping. On top of that consider full lifecycle total cost of ownership, not merely upfront capital assets. One lens should be cyber risk - failing to implement adequate authentication and data encryption makes expenditures wasteful. Regional security regulations have complex nuances as well - conformity helps future proof platforms. Adrian Beck: Martin, how do you advocate retailers approach these sizable video infrastructure choices? Martin Gren: We acknowledge technology continually progresses so client refresh actually drives our roadmap prioritization. Ensuring both forward and backward device compatibility minimizes disruption when modernising. Beyond gear, systemic considerations like warranties, maintenance policies and sustainability standards matter for total cost. Particularly retail chains face increasing pressure to demonstrate environmental awareness and social governance. We must exhibit those ourselves as suppliers too. But adjusting incrementally on integrated foundations enables scaling securely. Adrian Beck: Self-checkout and self-service boom across categories, bringing both perks and perils like efficiency gains countered by fraud upticks. What's the video analytics outlook here from each of your vantage points? Pierre Racz: Visible cameras historically tempered misbehaviour through passive peer pressure. Anonymizing self checkouts now erode that effect. While technology can help significantly, the right staffing models retain importance - rewarding personnel supporting customers across physical and digital commerce may provide one path. Store managers would gain from analysing shrinkage metrics across various checkout hybrid strategies rather than just eliminating cashiers outright. Gerard Figols: Video devices monitoring consumers certainly assist. But corralling machine sensor data like weight discrepancies provides powerful fusion. Surfacing self imagery triggers subconscious social cues we leverage for gentle self-policing too. Integrated analytics sift operational data flows to enable internal loss prevention teams appropriately. No doubt self checkout usage will continue rising, so sensible safeguards and oversight stay vital. Martin Gren: We have seen steep adoption for self checkout driven by tangible retailer savings from needing fewer front end staff. Yet effectively maintaining these at scale takes systems integration mastery - what works in small trials may degrade across hundreds of locations. Tools like product recognition have reached new levels of precision to speed flows in assisted modes as well. Various data harnessing assistance through video and adjacent innovations can collectively improve manned and unmanned store experiences. Adrian Beck: Our last question...what do each of you believe retail video analytics innovations on the horizon beyond the current state? Pierre Racz: Top retailers approach this holistically, embedding technology within processes, infrastructure and talent strategies concurrently. Perhaps checkout personnel could enjoy career growth graduating into specialised loss prevention roles versus feeling threatened. Humans and smart machines collaboratively outperform either in isolation. Consistently analysing the interplay across dimensions unlocks lasting progress. Gerard Figols: Being open to third party analytics and sensors multiplies insights fed through shared data repositories. Acting upon amplified intelligence distilled from fuse data requires culture and capability depth. But meticulously aggregating observations from video, point of sale, inventory and more enables realization of bigger opportunities. Martin Gren: Core fundamentals like audio, image fidelity, storage economy and analytic sophistication will all keep maturing thanks to ongoing advances. As sentry perimeter notions dissolve under perceptive uniformity, hitherto unmonitored areas like parking and loading docks warrant attention too. Customers expect retailers to exhibit digital sophistication with sustainable purpose. We have shifted from merely selling standalone products to promoting ethical client outcomes via managed services spanning hardware through software. Priorities like transparency and accessibility go hand-in-hand with technology growth to serve and protect people both online and in physical spaces. Colin Peacock: Adrian please provide any final comments before we conclude this thought provoking session. Adrian Beck: I would like to thank our panel - Pierre, Gerard and Martin - for these open insightful dialogues exploring so many intriguing themes on the future of video in retail. Colin back to you for final remarks… Colin Peacock: Thanks Adrian. And thanks indeed to all our speakers along with the audience for joining what has been a stimulating discussion as we wrap up this year and gear up for 2024. We have captured excellent notes from today's conversation to shape ongoing working group activities. Please reach out with any additional questions. Happy holidays everyone!
The latest discussion in our self-checkout working group, attended by over 120 participants, revealed ground-breaking advancements in retail technology and improvements in the Product Lookup Menu (PLU) process. As retailers move towards sustainability and an increased offering of loose products, the importance of scales and a PLU menu becomes more important than ever. This shift not only addresses environmental concerns but also brings unique challenges in inventory management and customer experience. One of the key insights discussed at the meeting was the significant role AI is starting to play in enhancing the accuracy of product identification. Retailers are adopting AI-driven systems at self-checkout stations and weigh stations in the aisle which are proving increasingly effective. Some of these systems have demonstrated remarkable accuracy rates, as high as 99.96%, in recognising products like fruits and vegetables. Such technological prowess not only reduces the likelihood of mislabelling, said to be 4% by one retailer, but also streamlines the customer's shopping experience. However, discussion at the meeting also highlighted the ongoing issue of product misrepresentation. One retailer noted selling more carrots than they actually bought into the business! Some customers are clearly mis-identifying products at weigh-stations and at SCOs, which underscores the necessity for more sophisticated recognition systems. The discussion also touched on the challenge of distinguishing between organic and non-organic produce, suggesting the use of stickers for easier identification. As the industry moves towards more sustainable practices including using brown paper bags, the technology must adapt to maintain its effectiveness. The video below recaps the key findings, with the transcript below. Colin: Well, this week we had our self-checkout group. I think we had over 120 participants registered for a discussion around the product lookup menu. Increasingly, retailers, in order to move away from plastic, are looking to use loose products, perhaps, fruits and vegetables, bakery in other areas. And that requires increased use of the product lookup menu, which is integrated into SCO, but also, we know it's also in the aisle as well. And as we've documented, this is an area of vulnerability for retailers in terms of mistakes and errors. But we also know it's a huge consumption of time and effort. And we have also learned with our online grocery team, that when online pickers look to pick loose items from customers online for their online orders, again, they have to use the product lookup menu, so it's also a significant point of friction for online grocery pickers in the stores too. So, we've got all that going on. It really was a topic of very high interest, hence the number of people registered to attend and who participated. And we had some excellent speakers sharing what they're doing to try to at least detect some of the problems that might be arising and helping to make the whole process more easy and faster. What did you take as your key notes? Adrian: Yes, it was a very popular session. And I think the reason for that, Colin, is it is a real and present issue, I think, for all the grocers who are using self-checkout. The vast majority of whom expect their shoppers to select loose fruit and veg. It's one of their attractive propositions of groceries - you can choose your own. Isn't it? So, it's been a long part of the use of self-checkout. How do you get the customer to weigh the products that they want to purchase, but critically also to identify what they are, so they can get the right price. It's now almost become legion, hasn't it, in terms of arguably one of the most common ways in which people abuse SCO is to misrepresent one piece of fruit for another, or one piece of vegetable for another. Colin: [joking] Yes, everything looks like a brown onion. Adrian: Exactly, and I think a number of retailers have shared over time now just the extent to which this is potentially being abused. There was one retailer who came out with a startling statistic, that they sell more carrots than they actually bring into their business. Which was highly indicative that perhaps people are choosing carrots when they may not have been carrots. We know it's an issue, but it's not only an issue in terms of abuse. As you rightly say, it can be complicated for customers to use these. When you have potentially, some readers could have upwards of 50, 60 different types of fruit and vegetables in their stores, try to organize a product lookup screen that enables you to get to that papaya or whatever it is, that sweet potato that you purchased. It can take time. There can be multiple menus, can't there, for people to work through, which isn't easy because nobody's ever trained us to use these technologies. There's that issue around just how difficult this can be for customers to do that, and there's the potential abuse as well of, as I say, the older carrots for grapes trick. Then the third element that you mentioned was this productivity piece, which actually came up in the session, and it wasn't something we'd really thought about very much in terms of that audience benefiting from this. Of course, what a lot of the interventions and solutions focused upon this are to do with product recognition, aren’t they? They've got to be able to say, that's a banana, and I can choose that for you on your behalf from the product lookup. That is the critical element. How well can these video analytics systems identify fruit and vegetables accurately to be able to deal with this issue. And so we heard from three retailers who shared their experiences. The first one was slightly different in that it was mainly focused upon the weighing of fruit and vegetables away from self-checkout. So it was related, but it was different. It was in their in-aisle weigh stations, wasn't it? And they were using a very simple system, which was a camera above the weigh station. And it had AI linked to it. And it had a learning algorithm in there. And they were using it simply to look at what was in the weigh tray and say to the consumer, they're bananas. Is that what you want to buy? And they claimed they were getting 99.96% accuracy of the system, recognising the things that were put into that weigh scale. Colin: So that was a remarkable statistic. What was also fantastic about what they did, I thought, was they were able to put it into silent mode and actually work out how many people were mislabelling, which was incredible. You know, so I think the statistic was around 4% were being mislabeled, which is a lot of carrots or a lot of, not carrots. Or whatever it is. Adrian: Yeah, that's right. I like the surveillance mode ideas you can have with these things, because what surveillance mode is really good at this silent mode is it really helps you to get a measure of the size of the prize, doesn't it? Because you can see - and retailers often don't like it, or at least don't like having it on for too long - because they can see the loss. Visibly across the screen. But it's a great insight into just how much is happening. They were recognising something like 4% of the products that were put into that weigh scale were incorrectly chosen from the product lookup. So that's a really big win just in terms of reducing that. But they also talked about the reduction in customer time. They reckoned it would reduce customer time by as much as 80%. Just because if you put the bananas in it, it just says bananas. And all you have to do is say yes. And that's so much quicker than going… bananas, need to find bananas, click on bananas, and then print. So that was really good. And then the other two were looking more specifically at how they were using it at self-checkout. So it was integrated more into their fixed SCUs. And that was a little bit more checkered, wasn't it? I think one had gone through a number of iterations of this technology, trying to get it to be sufficiently accurate to offer value. And then the other one was a pretty archaic system, really, which it wasn't very good at recognizing things other than if it was perfectly square or had square edges. So it's trying to identify where people were putting things on the weigh scale that were patently not fruit and veg. There were very few fruit and vegetables that were perfectly straight and have got square edges. And so that system was trying to say, you've put a bottle of wine on there. And you're claiming it's bananas. We don't think that's the case. We saw different ways of doing this. But there were lots of other retailers on the call who were clearly very interested in this and trialing it out as well. Because I think everybody recognises there's much work to be done here. And a lot of benefit can be derived from these much improved product recognition systems that are coming to market. Colin: Yeah, as you say, I think it really was an area where there will be much more. And that's why I think we said we would anniversary this meeting. And the cloud did seem to make a big difference. I think we heard a couple of times if you can actually have all this intelligence. Every machine learns at the same rate. Yeah. And perhaps that prize of, you know, recognition, and speed, might trump some of the other reasons we might want to do this around the sort of malicious types, you know, putting Lego on the scales. I don't know how often that happens, you know. Adrian: No, but a couple of other issues came up. One is: what do you do if you want customers to put things into bags? Obviously, there's a drive to move away from C2 plastic for sustainability issues. So if you're saying to customers, we want you to put them into brown paper bags, the best product recognition system in the world is going to struggle to look through a brown paper bag and see what's in there. So that's an issue that needs to be addressed. It's not the end of it, but it needs to be thought about. And then of course, there's a thorny old issue that we always talk about, which is organic versus non-organic vegetables and fruit. And how can these systems try and possibly identify the difference between an apple that is organic and an apple that's non-organic? And I think there was basic recognition that you need to help the system in that place by having some sort of sticker on there. So it can actually hone in on the sticker and say, I can see that that's organic because I recognise that particular sticker. Otherwise, I doubt whether there's going to be any systems because the idea that there are subtly different shades of green between them is asking a lot. I think it's much easier if you just put a sticker on these things. Colin: Yeah, hopefully you can just grow the sticker on there as well. Yeah, it grows with the sticker on it. But this seems to be the solution for one retailer, although I guess putting a sticker on it is an extra cost. That’s one way round it, but the brown paper bags, again, are a different thing. Well, thanks for that, Adrian. We're next together on December the 13th. There should be a QR code somewhere. And on December the 13th, we're looking at self-checkouts in non-groceries. So we've got a couple of retailers who are saying, yes, it's working. Here's some differences. And a couple saying, well, it didn't really work for us you know and for these reasons. So it'll be very interesting to hear the different travails of retailers as they have experimented with self-checkouts outside of the grocery. So looking forward to that. Thank you again and have a great weekend. Thanks Colin, bye bye. Adrian: Bye bye.
ECR is on the search for new innovative ways and technologies that can help retailers and CPG's improve OSA. The search starts with a clear set of problem statements. These problem statements emerged from interviews and discussions with the OSA working group members, made up of the OSA experts from the retailers and CPG's represented in the group and the academics who support the group, who have have in depth knowledge of the OSA problem, including Professor Daniel Corsten and Thomas Gruen. Broken down into four areas, upstream design, supply chain, back room and shop floor, there are in total twenty two problem statements, where new innovation, technologies and approaches would be welcomed. By mid-December, the retailer and CPG judges will have received a curated long list of innovations that meet the briefs. By early February, the Top 30 OSA innovations, as rated by the judges will be published. On May 22nd, the top ten will pitch at the online showcase finale. If you would like to help with the short-listing process, please email email@example.com
The research priorities are determined by its members – they drive the agenda to ensure ECR delivers research that meets the need of the industry bringing new insights, tools and techniques that enables retailers to sell more and lose less.
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