WEBVTT 00:00:00.068 --> 00:00:02.110 PROFESSOR: Why don't we go ahead and get started. 00:00:02.110 --> 00:00:04.920 Thank you very much for making the trek and the logistics 00:00:04.920 --> 00:00:06.480 to get all the way across campus. 00:00:06.480 --> 00:00:09.210 And I'm pleased to see that most of us made it back here. 00:00:09.210 --> 00:00:12.870 And if people straggle in, we'll understand. 00:00:12.870 --> 00:00:17.700 So this afternoon, we're going to shift from a technology 00:00:17.700 --> 00:00:21.810 focus to now research groups that 00:00:21.810 --> 00:00:25.710 are concentrating on specific business problems to solve. 00:00:25.710 --> 00:00:28.140 And our first speaker is Dr. Elgar Fleisch, 00:00:28.140 --> 00:00:32.850 who is the head of the eyed labs at the University of St. Gallen 00:00:32.850 --> 00:00:35.430 and ETH in Zurich, Switzerland. 00:00:35.430 --> 00:00:39.870 And he's here to share about the flagship project 00:00:39.870 --> 00:00:44.260 for the auto-ID labs in the anti-counterfeit space. 00:00:44.260 --> 00:00:46.700 ELGAR FLEISCH: Thank you very much. 00:00:46.700 --> 00:00:50.990 So I hope you don't suffer the same jet lag as I do. 00:00:50.990 --> 00:00:53.180 And I have the best position in this day 00:00:53.180 --> 00:00:55.430 to talk about any counterfeiting. 00:00:55.430 --> 00:00:57.080 So this is really the flagship project. 00:00:57.080 --> 00:00:58.670 We have not only one flagship project. 00:00:58.670 --> 00:01:01.490 Soon others will pop up. 00:01:01.490 --> 00:01:04.640 But this is a project which goes across all the labs-- 00:01:04.640 --> 00:01:07.080 our seven labs across the world. 00:01:07.080 --> 00:01:09.470 What's the need, actually, for any counterfeiting? 00:01:09.470 --> 00:01:13.190 Why do we think this is important? 00:01:13.190 --> 00:01:16.870 Without going into details, you will find soon 00:01:16.870 --> 00:01:20.330 an extensive white paper on any counterfeiting using 00:01:20.330 --> 00:01:23.130 RFID and stuff like that on our web pages. 00:01:23.130 --> 00:01:24.470 You see the red line. 00:01:24.470 --> 00:01:28.280 This is really kind of the global trade, how it grows. 00:01:28.280 --> 00:01:32.060 And the blue one, that is really counterfeit, how those grow. 00:01:32.060 --> 00:01:35.720 It's different scales though, so it has to. 00:01:35.720 --> 00:01:37.970 We see that in recent years, counterfeiting 00:01:37.970 --> 00:01:39.560 is a good profession. 00:01:39.560 --> 00:01:45.110 And the topic is the punishments on counterfeit goods 00:01:45.110 --> 00:01:47.770 is really not very high. 00:01:47.770 --> 00:01:49.880 The punishments are low, but the returns-- 00:01:49.880 --> 00:01:52.790 the business cases for the fake producers, they're very good. 00:01:52.790 --> 00:01:54.440 It's like in drug business. 00:01:54.440 --> 00:01:57.470 So this is why counterfeiting seems 00:01:57.470 --> 00:02:00.440 to be an increasing topic. 00:02:00.440 --> 00:02:04.370 We all know who work in this area that in some literature, 00:02:04.370 --> 00:02:07.880 you find numbers that say about 7% to 10% 00:02:07.880 --> 00:02:11.330 of all goods traded internationally 00:02:11.330 --> 00:02:15.650 are trading with counterfeit goods, or gray market goods, 00:02:15.650 --> 00:02:16.940 or stuff like that. 00:02:16.940 --> 00:02:18.590 This number is way exaggerated. 00:02:18.590 --> 00:02:21.080 That's what we found out in our research. 00:02:21.080 --> 00:02:23.150 You have to divide it by 10 or so. 00:02:23.150 --> 00:02:26.000 However, still the problem is a big one. 00:02:26.000 --> 00:02:31.700 And we have not only impact on companies and on users, 00:02:31.700 --> 00:02:34.340 but also on the economy, so let's fight this one. 00:02:34.340 --> 00:02:37.160 And this, again, is an area where a lot of chaos 00:02:37.160 --> 00:02:39.980 in the system, and we've seen it in Sanjay's presentation. 00:02:39.980 --> 00:02:42.770 When you have somewhere chaos in a system, 00:02:42.770 --> 00:02:46.460 then RFID might be a good thing to use in order 00:02:46.460 --> 00:02:50.600 to get chaos straightened out a little bit. 00:02:50.600 --> 00:02:52.610 What we see, that we have actually 00:02:52.610 --> 00:02:56.180 very good methods of authentications already 00:02:56.180 --> 00:02:59.480 in place if you use a banking card or stuff like that. 00:02:59.480 --> 00:03:02.150 We have heard this morning from an Austrian gentleman-- 00:03:02.150 --> 00:03:04.310 so I'm also from Austria. 00:03:04.310 --> 00:03:06.460 I have to talk to you later on. 00:03:06.460 --> 00:03:08.210 Then we know that in the banking business, 00:03:08.210 --> 00:03:11.090 we have already good authentication 00:03:11.090 --> 00:03:12.390 technologies available. 00:03:12.390 --> 00:03:15.716 However, we don't have low-cost authentication. 00:03:15.716 --> 00:03:17.900 Beyond authentication, you have usually 00:03:17.900 --> 00:03:22.340 is made for things to humans. 00:03:22.340 --> 00:03:25.070 What we do usually, we authenticate humans. 00:03:25.070 --> 00:03:26.537 We don't authenticate things. 00:03:26.537 --> 00:03:28.120 But we are building the infrastructure 00:03:28.120 --> 00:03:29.670 for Internet of things. 00:03:29.670 --> 00:03:32.250 So let's talk about the machine-machine authentication 00:03:32.250 --> 00:03:32.840 now. 00:03:32.840 --> 00:03:36.680 And therefore, we need new technologies. 00:03:36.680 --> 00:03:39.560 For doing so, we created kind of a special interest group. 00:03:39.560 --> 00:03:42.380 We have some companies working with us. 00:03:42.380 --> 00:03:44.330 The number is strongly growing. 00:03:44.330 --> 00:03:45.668 And the question is really-- 00:03:45.668 --> 00:03:47.960 and this is a research question we didn't solve so far. 00:03:47.960 --> 00:03:50.420 We need support from all of you. 00:03:50.420 --> 00:03:54.620 And how can we use RFID and related technologies 00:03:54.620 --> 00:03:57.830 in combination with classical measurements 00:03:57.830 --> 00:04:01.070 to fight counterfeit, parallel trade, illicit trade, all 00:04:01.070 --> 00:04:02.030 that stuff. 00:04:02.030 --> 00:04:04.040 Well, what are the research questions? 00:04:04.040 --> 00:04:06.290 And I want you to think about what could 00:04:06.290 --> 00:04:07.850 be potential research question. 00:04:07.850 --> 00:04:12.200 Where could you propose some papers or help to us? 00:04:12.200 --> 00:04:15.013 Basically, there is four different categories 00:04:15.013 --> 00:04:15.930 of research questions. 00:04:15.930 --> 00:04:18.980 One is, what is really the economy of illicit trade? 00:04:18.980 --> 00:04:20.600 That seems to be a simple question. 00:04:20.600 --> 00:04:24.500 But you could ask anybody-- pharma industry, whatever, 00:04:24.500 --> 00:04:26.450 in the automotive industry. 00:04:26.450 --> 00:04:29.000 You receive different answers, unclear answers. 00:04:29.000 --> 00:04:31.290 So we would go into very detailed. 00:04:31.290 --> 00:04:33.380 This is the first work package, and I 00:04:33.380 --> 00:04:36.530 think we did some great work there already. 00:04:36.530 --> 00:04:38.780 To understand what's the economy of illicit trade. 00:04:38.780 --> 00:04:41.480 And Thorsten Staake over there, with the nice tie, 00:04:41.480 --> 00:04:44.280 is leading those research questions. 00:04:44.280 --> 00:04:48.050 The second one is how to quantify illicit trade. 00:04:48.050 --> 00:04:50.510 Because you need to have business cases. 00:04:50.510 --> 00:04:53.180 You need to understand, what is the effective on brands, 00:04:53.180 --> 00:04:54.800 what is the effect on revenue, what 00:04:54.800 --> 00:04:56.790 is the effective whatsoever. 00:04:56.790 --> 00:05:01.760 So come up with models which are not consulting models where 00:05:01.760 --> 00:05:03.710 we say you could save 1% or 2%. 00:05:03.710 --> 00:05:05.810 No, we have to go into the black box 00:05:05.810 --> 00:05:08.990 and come up with clear figures, clear calculations. 00:05:08.990 --> 00:05:14.780 And then, of course, if we know how the problem is structured, 00:05:14.780 --> 00:05:17.180 and if we know what the savings are-- 00:05:17.180 --> 00:05:20.150 so how expensive those technologies could be, 00:05:20.150 --> 00:05:23.510 solutions could be-- then we can derive the requirements, what 00:05:23.510 --> 00:05:26.390 are resolutions solutions in any counterfeiting auto-ID base. 00:05:26.390 --> 00:05:28.360 And basically, at the very end, then-- and this 00:05:28.360 --> 00:05:30.980 is the results for EPC globalizer. 00:05:30.980 --> 00:05:34.555 What's the impact on the infrastructure plus? 00:05:34.555 --> 00:05:36.680 So how would the infrastructure of things, internet 00:05:36.680 --> 00:05:39.230 of things would have to look like in order 00:05:39.230 --> 00:05:41.130 to serve all these requirements. 00:05:41.130 --> 00:05:43.360 Some of the primary results-- 00:05:43.360 --> 00:05:45.350 I go through those rather quickly-- 00:05:45.350 --> 00:05:47.810 is, of course, there is the good supply chain. 00:05:47.810 --> 00:05:50.270 We call it here, the illicit intended supply chain. 00:05:50.270 --> 00:05:53.090 There is the bad supply chain. 00:05:53.090 --> 00:05:56.930 And the bad supply chain, it's not only fakes. 00:05:56.930 --> 00:06:00.413 It's also if you run your machine beyond the hours 00:06:00.413 --> 00:06:01.580 you should run your machine. 00:06:01.580 --> 00:06:04.160 So you produce products for the grain market. 00:06:04.160 --> 00:06:05.690 It's parallel trade. 00:06:05.690 --> 00:06:07.160 It's theft. 00:06:07.160 --> 00:06:09.980 And if you have theft introduced to the illicit supply chain 00:06:09.980 --> 00:06:12.530 somewhere, you bring it back to the illicit supply chain. 00:06:12.530 --> 00:06:15.740 All those problems, actually, you 00:06:15.740 --> 00:06:18.530 would find behind the topic any counterfeiting. 00:06:18.530 --> 00:06:21.470 So we choose any counterfeiting because of marketing reason. 00:06:21.470 --> 00:06:23.750 Everybody understands-- any counterfeiting. 00:06:23.750 --> 00:06:25.880 But in fact, it's the most complicated 00:06:25.880 --> 00:06:28.370 track-and-trace thing you could solve, 00:06:28.370 --> 00:06:31.400 because you need to have a secure supply chain in order 00:06:31.400 --> 00:06:32.630 to solve those problems. 00:06:32.630 --> 00:06:36.710 One of the general findings is, you 00:06:36.710 --> 00:06:40.010 see many arrows crossing between the good and the bad supply 00:06:40.010 --> 00:06:41.460 chain. 00:06:41.460 --> 00:06:44.630 So what you have to think about first-- 00:06:44.630 --> 00:06:48.680 where are you able to leverage new technology 00:06:48.680 --> 00:06:54.470 in order to, let's say, do harm to the business 00:06:54.470 --> 00:06:56.810 case of fake producers? 00:06:56.810 --> 00:06:58.640 And this is two points. 00:06:58.640 --> 00:07:02.060 Because only at two points in these two supply chains, 00:07:02.060 --> 00:07:05.630 there is a good gatekeeper who watches 00:07:05.630 --> 00:07:08.960 when bad goods cross from the bad supply chain 00:07:08.960 --> 00:07:10.260 into the good supply chain. 00:07:10.260 --> 00:07:15.240 And this is with customs, and this is with the end consumers. 00:07:15.240 --> 00:07:20.870 So if we can build systems to enable customers and customs, 00:07:20.870 --> 00:07:25.580 then we can fight illicit trade considerably. 00:07:25.580 --> 00:07:28.000 We did some empirical studies here-- 00:07:28.000 --> 00:07:29.410 very funny results. 00:07:29.410 --> 00:07:33.220 We asked really who would buy fake goods? 00:07:33.220 --> 00:07:37.750 Because it's not clear who would really buy fake goods. 00:07:37.750 --> 00:07:40.030 And what are the reasons for it? 00:07:40.030 --> 00:07:43.000 Here, one of the interesting results is that most of them 00:07:43.000 --> 00:07:45.880 buy faked goods because they think the original one is 00:07:45.880 --> 00:07:47.390 too expensive. 00:07:47.390 --> 00:07:49.450 So it's kind of a strange argument. 00:07:49.450 --> 00:07:54.370 And the other argument is really that the cost ratio-- 00:07:54.370 --> 00:07:58.270 cost performance ratio is very good if you buy fake goods. 00:07:58.270 --> 00:08:01.060 Which is probably not true if you talk about drugs. 00:08:01.060 --> 00:08:03.820 I mean, we have to be anyway very carefully 00:08:03.820 --> 00:08:06.190 differentiating between different types of products 00:08:06.190 --> 00:08:06.850 you would fake. 00:08:06.850 --> 00:08:08.450 The ones the customer knows about, 00:08:08.450 --> 00:08:10.690 the others the customer won't know about it. 00:08:10.690 --> 00:08:13.330 What's really very interesting is that we learned 00:08:13.330 --> 00:08:15.070 that many of the people-- 00:08:15.070 --> 00:08:18.340 actually, most of the people, half of it-- we interviewed 00:08:18.340 --> 00:08:22.570 said they didn't buy faked goods because they 00:08:22.570 --> 00:08:24.370 had no opportunity so far. 00:08:24.370 --> 00:08:27.460 So the market problem is rather big. 00:08:27.460 --> 00:08:29.680 This is only true for products you 00:08:29.680 --> 00:08:32.799 would know they are fake, like the Rolex for $5 00:08:32.799 --> 00:08:34.530 or something like that. 00:08:34.530 --> 00:08:37.390 And most of your problems are with products 00:08:37.390 --> 00:08:39.880 you don't know as a user. 00:08:39.880 --> 00:08:44.440 So what's the impact of an auto-ID-based solution 00:08:44.440 --> 00:08:47.360 as one first framework for an answer to the last research 00:08:47.360 --> 00:08:47.860 question? 00:08:47.860 --> 00:08:51.340 OK, if we have automated product authentication, 00:08:51.340 --> 00:08:53.830 then just use the simple managed methodology 00:08:53.830 --> 00:08:55.660 we developed in the labs. 00:08:55.660 --> 00:09:01.390 The cost of checking whether something is identical or not 00:09:01.390 --> 00:09:02.380 decreases. 00:09:02.380 --> 00:09:04.690 It's always what we do with this infrastructure, 00:09:04.690 --> 00:09:08.440 we reduce the cost of measuring reality. 00:09:08.440 --> 00:09:12.640 So it's very simple and cheap to check whether something 00:09:12.640 --> 00:09:15.700 is identical if you have an RFID tag 00:09:15.700 --> 00:09:18.280 and some infrastructure available on the product 00:09:18.280 --> 00:09:19.660 and around the product. 00:09:19.660 --> 00:09:23.140 So you start checking more often in each warehouse, 00:09:23.140 --> 00:09:25.900 or with the mobile phone at home. 00:09:25.900 --> 00:09:29.920 If you check more often, you check not only 00:09:29.920 --> 00:09:34.780 on a statistical basis, but you may do a 100% check. 00:09:34.780 --> 00:09:37.960 So end of statistics is the key word there, 00:09:37.960 --> 00:09:39.970 which then will have impacts on the revenue 00:09:39.970 --> 00:09:42.610 model out of package too. 00:09:42.610 --> 00:09:44.110 Reputation would go up. 00:09:44.110 --> 00:09:47.890 And it's not so simple, because if you're Microsoft 00:09:47.890 --> 00:09:50.710 and your software got faked in China, 00:09:50.710 --> 00:09:52.810 it's probably good for you now. 00:09:52.810 --> 00:09:57.070 Because you're developing a kind of a standard there. 00:09:57.070 --> 00:10:01.240 In five or 10 years, you start using the laws which 00:10:01.240 --> 00:10:02.650 are building up currently. 00:10:02.650 --> 00:10:05.590 And then force people to buy your Microsoft products, 00:10:05.590 --> 00:10:07.400 but then it's standard already. 00:10:07.400 --> 00:10:09.520 So it's not always true that fakes are bad. 00:10:09.520 --> 00:10:12.020 We have to be careful about that. 00:10:12.020 --> 00:10:15.040 Anyway, so this would have an impact on the return. 00:10:15.040 --> 00:10:18.460 Now we could think about, OK, if we check a lot, 00:10:18.460 --> 00:10:22.300 we have a high-resolution data on those illicit actors. 00:10:22.300 --> 00:10:25.780 If we know how the actors work, we 00:10:25.780 --> 00:10:30.100 can do way more to fight those parties. 00:10:30.100 --> 00:10:32.140 And of course, we can also derive 00:10:32.140 --> 00:10:35.920 know-how and how to engineer the products so 00:10:35.920 --> 00:10:40.510 that the counterfeiting is getting more difficult. 00:10:40.510 --> 00:10:42.640 Which again, helps in our business case, 00:10:42.640 --> 00:10:45.220 but then also we can come up with 00:10:45.220 --> 00:10:47.830 nice competitive strategies. 00:10:47.830 --> 00:10:50.050 Because we learned that the real danger is 00:10:50.050 --> 00:10:55.720 if somebody produces fakes, uses your IP, at the beginning 00:10:55.720 --> 00:10:56.740 you lose revenue. 00:10:56.740 --> 00:10:59.170 But on the long run, these guys build up know-how. 00:10:59.170 --> 00:11:01.640 And they become a real competitor, 00:11:01.640 --> 00:11:04.120 so this is one thing we want to fight. 00:11:04.120 --> 00:11:07.630 And then of course, if we have a tool available for everybody 00:11:07.630 --> 00:11:08.890 where it's very simple. 00:11:08.890 --> 00:11:11.140 And I gave the signal-- 00:11:11.140 --> 00:11:14.380 I said the goal of this research is that we have a warehouse. 00:11:14.380 --> 00:11:16.690 And within one second, we can check the product, 00:11:16.690 --> 00:11:20.360 enter our product in a warehouse, whether they are OK 00:11:20.360 --> 00:11:20.860 or not. 00:11:20.860 --> 00:11:22.985 I mean, we cannot reach this within the next years. 00:11:22.985 --> 00:11:24.190 But this is a clear goal. 00:11:24.190 --> 00:11:26.980 But if we have a tool like that, then we 00:11:26.980 --> 00:11:31.630 can start giving to third-parity suppliers, this tool. 00:11:31.630 --> 00:11:34.810 And they should check for any counterfeited-- 00:11:34.810 --> 00:11:36.430 for faked goods. 00:11:36.430 --> 00:11:39.280 So enter here, new business models would come up. 00:11:39.280 --> 00:11:41.650 You probably could enable a little army 00:11:41.650 --> 00:11:43.730 in fighting these issues. 00:11:43.730 --> 00:11:46.552 So again, it's not really just the automation part, 00:11:46.552 --> 00:11:48.010 but it's the transformational part, 00:11:48.010 --> 00:11:50.140 which seems to be very interesting 00:11:50.140 --> 00:11:54.470 in this counterfeit research. 00:11:54.470 --> 00:11:56.380 Of course, there is 100 different ways 00:11:56.380 --> 00:11:58.280 in how to do any counterfeiting. 00:11:58.280 --> 00:12:00.940 That would be part of the solution-to-work package. 00:12:00.940 --> 00:12:05.020 Three, do it via pedigree and normal EPC tags, 00:12:05.020 --> 00:12:08.800 or do it via secure RFID tags. 00:12:08.800 --> 00:12:12.080 I think Friends of Austria are developing this direction. 00:12:12.080 --> 00:12:13.970 So it depends on the business case, 00:12:13.970 --> 00:12:15.430 it depends on the problem. 00:12:15.430 --> 00:12:19.810 And the nice part is, the EPC network, 00:12:19.810 --> 00:12:22.210 the auto-ID infrastructure should 00:12:22.210 --> 00:12:26.520 be able to cope with all those different varieties of how 00:12:26.520 --> 00:12:27.970 to secure the supply chain. 00:12:27.970 --> 00:12:32.430 Not just with one, and this is the tricky part in doing so. 00:12:32.430 --> 00:12:35.220 This is actually what we are planning to do. 00:12:35.220 --> 00:12:38.250 We have some considerable first results achieved. 00:12:38.250 --> 00:12:41.400 And we are planning that by the autumn, 00:12:41.400 --> 00:12:44.623 there will be a book out there showing the first results 00:12:44.623 --> 00:12:45.540 on any counterfeiting. 00:12:45.540 --> 00:12:52.180 And with this, I think I hand over to the cool chain 00:12:52.180 --> 00:12:56.578 where we have another different kind of how to secure a supply 00:12:56.578 --> 00:12:57.870 chain in a different direction. 00:12:57.870 --> 00:12:58.370 Thank you. 00:13:04.729 --> 00:13:06.250 PROFESSOR: So I'd like to present 00:13:06.250 --> 00:13:07.250 John Pierre [INAUDIBLE]. 00:13:07.250 --> 00:13:09.260 He is another member of the conference committee 00:13:09.260 --> 00:13:10.790 that helped to organize this event. 00:13:10.790 --> 00:13:14.390 He is a Associate Professor of Agricultural and Biological 00:13:14.390 --> 00:13:17.180 Engineering and a Co-Director for the Center for Food 00:13:17.180 --> 00:13:19.200 Distribution at-- 00:13:21.710 --> 00:13:24.030 FSU or the University of Florida? 00:13:24.030 --> 00:13:25.732 [INTERPOSING VOICES] 00:13:25.732 --> 00:13:27.940 GUEST SPEAKER: It's bad for you, I can tell you that. 00:13:31.130 --> 00:13:33.380 OK, I'm going to do that. 00:13:33.380 --> 00:13:34.850 Well, let me start that. 00:13:34.850 --> 00:13:38.000 Well, I'm going to talk about cold chain 00:13:38.000 --> 00:13:39.860 with RFID time [INAUDIBLE] writing. 00:13:39.860 --> 00:13:42.710 And I'm going to change my definition of cold chain, 00:13:42.710 --> 00:13:46.430 because after I saw us crossing the street this afternoon, 00:13:46.430 --> 00:13:48.830 I think this is really cold chain-- one by one, crossing 00:13:48.830 --> 00:13:49.830 the street in the slush. 00:13:49.830 --> 00:13:51.080 That was great. 00:13:51.080 --> 00:13:53.720 Well, you heard a lot of research group talking 00:13:53.720 --> 00:13:54.290 this morning. 00:13:54.290 --> 00:13:57.860 And I feel like a bit odd, because I'm more research 00:13:57.860 --> 00:13:59.930 group, but also end user. 00:13:59.930 --> 00:14:02.120 We developed some of the technology, 00:14:02.120 --> 00:14:03.950 but we are a big user of everything 00:14:03.950 --> 00:14:05.660 that you are providing-- this morning 00:14:05.660 --> 00:14:06.840 that you are talking about. 00:14:06.840 --> 00:14:09.200 And let's go with that. 00:14:09.200 --> 00:14:12.710 Well, we are not the RFID lab. 00:14:12.710 --> 00:14:16.790 We are a research center that has a RFID lab inside. 00:14:16.790 --> 00:14:19.590 Or we call that the Center for Food Distribution 00:14:19.590 --> 00:14:21.410 Retailing, which is CFDR. 00:14:21.410 --> 00:14:23.890 And the mission of our center is to provide a food industry 00:14:23.890 --> 00:14:26.785 unique environment to assure food quality 00:14:26.785 --> 00:14:28.910 and safety throughout the whole distribution chain. 00:14:28.910 --> 00:14:32.060 So we don't take only one part, but we take the old thing, 00:14:32.060 --> 00:14:34.550 from the beginning in the field or the manufacturing plant 00:14:34.550 --> 00:14:37.813 to until the customer leaves the retail store. 00:14:37.813 --> 00:14:39.230 That way that we organize that, we 00:14:39.230 --> 00:14:41.570 have 28 faculty at University of Florida 00:14:41.570 --> 00:14:44.510 and six with our university worldwide. 00:14:44.510 --> 00:14:47.870 And all of us has a specific discipline, and we 00:14:47.870 --> 00:14:49.700 always joint effort together. 00:14:49.700 --> 00:14:53.540 The idea, in this case, is to look at different angles, what 00:14:53.540 --> 00:14:56.330 we can do with RFID. 00:14:56.330 --> 00:14:59.450 Our center is as an external advisory board, 00:14:59.450 --> 00:15:01.130 from the top down international decision 00:15:01.130 --> 00:15:03.770 maker in the food industry, retailer, food service. 00:15:03.770 --> 00:15:05.402 And to be advisory board member, you 00:15:05.402 --> 00:15:06.860 have to be president or senior vice 00:15:06.860 --> 00:15:09.420 president of a big company. 00:15:09.420 --> 00:15:12.320 So we have Walmart, Publix, Albertsons, Burger 00:15:12.320 --> 00:15:16.490 King, [INAUDIBLE] ShopRite, Preference Food Group, Outback. 00:15:16.490 --> 00:15:18.150 And are we going to add three more. 00:15:18.150 --> 00:15:20.420 So these people, twice a year, come in Gainesville 00:15:20.420 --> 00:15:22.980 and tell us what their industry is looking for. 00:15:22.980 --> 00:15:25.460 And of course, RFID is a big thing about it. 00:15:25.460 --> 00:15:29.858 But today, I'm going to talk to you about why temperature 00:15:29.858 --> 00:15:31.400 tracking is so important for the food 00:15:31.400 --> 00:15:34.572 industry and the pharmaceutical industry from our eyes. 00:15:34.572 --> 00:15:36.530 From the external advisory board, but also what 00:15:36.530 --> 00:15:39.020 we have done during the whole year. 00:15:39.020 --> 00:15:41.465 For the food industry, most personal products 00:15:41.465 --> 00:15:43.820 are really affected by temperature. 00:15:43.820 --> 00:15:45.982 It's a question of quality and safety. 00:15:45.982 --> 00:15:48.440 For some of you, last night I was flying with [INAUDIBLE],, 00:15:48.440 --> 00:15:49.850 and you can watch TV. 00:15:49.850 --> 00:15:51.260 And I was watching Dateline where 00:15:51.260 --> 00:15:55.430 they were promoting a big survey that they did about food 00:15:55.430 --> 00:15:57.200 safety and the retail store. 00:15:57.200 --> 00:16:00.140 And they were reporting that most of the infractions 00:16:00.140 --> 00:16:01.850 were about temperature management. 00:16:01.850 --> 00:16:04.833 We get food poisoning, not only at the retail store, 00:16:04.833 --> 00:16:06.500 but also at restaurant chains and things 00:16:06.500 --> 00:16:09.380 like that just because of poor management of temperature. 00:16:09.380 --> 00:16:11.960 And it makes a big difference, because each year, 00:16:11.960 --> 00:16:14.600 retail stores can lose about $400,000 00:16:14.600 --> 00:16:17.600 due to bad temperature management per store. 00:16:17.600 --> 00:16:20.990 For example, if I count all the stores that our advisory board 00:16:20.990 --> 00:16:23.640 has, it's about 8,000 stores. 00:16:23.640 --> 00:16:26.720 So if you add $400,000 for each of them, it's about $3 billion 00:16:26.720 --> 00:16:30.290 per year we lose, just because of poor temperature management, 00:16:30.290 --> 00:16:32.870 but also people that get sick too. 00:16:32.870 --> 00:16:35.090 So this is what we have been looking for. 00:16:35.090 --> 00:16:37.790 Trying to focus on what we can do with temperature tracking 00:16:37.790 --> 00:16:39.720 to help our industry. 00:16:39.720 --> 00:16:41.635 So where temperature may be a problem-- 00:16:41.635 --> 00:16:43.010 well, it can come from the field. 00:16:43.010 --> 00:16:44.390 You have to know what kind of temperature 00:16:44.390 --> 00:16:45.515 when you harvest something. 00:16:45.515 --> 00:16:46.820 Is it cold, is it warm? 00:16:46.820 --> 00:16:49.340 If it's very warm, I have to cool it down. 00:16:49.340 --> 00:16:52.230 And at the warehouse, also, during transit 00:16:52.230 --> 00:16:53.280 is a major thing. 00:16:53.280 --> 00:16:55.880 During transit, distribution, and in the store-- 00:16:55.880 --> 00:16:58.820 if I can have all this information live, 00:16:58.820 --> 00:17:01.190 I can manage much better things in terms 00:17:01.190 --> 00:17:02.600 of cold chain management. 00:17:02.600 --> 00:17:04.849 Also, I can prevent any problem that 00:17:04.849 --> 00:17:07.160 can happen during by transit without it happening 00:17:07.160 --> 00:17:09.690 and get too bad. 00:17:09.690 --> 00:17:12.089 Well, knowing real-time temperature 00:17:12.089 --> 00:17:14.579 can also predict residual shelf life 00:17:14.579 --> 00:17:16.540 and make decisions based on this knowledge. 00:17:16.540 --> 00:17:19.530 So what we have been spending many years in our center 00:17:19.530 --> 00:17:22.890 is that we are trying to have a good sense of what temperature 00:17:22.890 --> 00:17:26.079 has an effect on quality and safety of food. 00:17:26.079 --> 00:17:28.573 So we have been focusing a lot on produce, because we 00:17:28.573 --> 00:17:29.740 are in the state of Florida. 00:17:29.740 --> 00:17:30.960 So we have a lot of produce. 00:17:30.960 --> 00:17:33.300 And what we have developed in the last few years 00:17:33.300 --> 00:17:37.500 is a mathematical product to predict quality. 00:17:37.500 --> 00:17:39.510 So just to give you an example here, 00:17:39.510 --> 00:17:42.690 is that based on experimental data that we have done, 00:17:42.690 --> 00:17:45.030 we can predict, if you give me the temperature 00:17:45.030 --> 00:17:47.460 chart, the history of the temperature of your product, 00:17:47.460 --> 00:17:50.830 I can predict exactly how the product is going to look like 00:17:50.830 --> 00:17:54.090 and what kind of shelf life I still have in my store 00:17:54.090 --> 00:17:56.820 or at least at the moment that I read my temperature. 00:17:56.820 --> 00:17:59.570 So we have a huge database of experimental data. 00:17:59.570 --> 00:18:03.330 And all these models have been developed by our team. 00:18:03.330 --> 00:18:05.310 So if you provide me-- and also we 00:18:05.310 --> 00:18:07.140 can predict what kind of quality criteria 00:18:07.140 --> 00:18:09.150 you're looking for if you give me temperature. 00:18:09.150 --> 00:18:12.720 I can predict if it's the crispiness of lettuce you're 00:18:12.720 --> 00:18:17.100 looking forward, the color, if it's mold grown on raspberries, 00:18:17.100 --> 00:18:19.200 we can all predict these things if you 00:18:19.200 --> 00:18:21.760 provide me the temperature tracking of that. 00:18:21.760 --> 00:18:23.730 So we have been trying a lot to do that. 00:18:23.730 --> 00:18:25.782 One of the requests was from the restaurant chain 00:18:25.782 --> 00:18:26.490 and the retailer. 00:18:26.490 --> 00:18:29.702 They said, when we get something in our distribution center 00:18:29.702 --> 00:18:32.160 and we have this temperature monitoring, we can look at it, 00:18:32.160 --> 00:18:35.130 but it's very difficult to do something with it. 00:18:35.130 --> 00:18:36.640 We have to interpret that. 00:18:36.640 --> 00:18:38.970 We look for peaks of temperature and things like that, 00:18:38.970 --> 00:18:41.530 but not exactly what to make a decision. 00:18:41.530 --> 00:18:44.880 So if we can track that way before it gets to the DC, 00:18:44.880 --> 00:18:47.970 we can always manage our inventory and our distribution 00:18:47.970 --> 00:18:51.163 center and decide if my product that came yesterday, 00:18:51.163 --> 00:18:52.830 maybe it's in better shape that the ones 00:18:52.830 --> 00:18:54.300 coming in an hour from now. 00:18:54.300 --> 00:18:55.890 And maybe I ship that to my store 00:18:55.890 --> 00:18:58.260 right away, the one coming in, and keep the other one 00:18:58.260 --> 00:18:59.280 in my warehouse. 00:18:59.280 --> 00:19:04.300 Because it can still stay for a few days more without problem. 00:19:04.300 --> 00:19:08.160 So we can predict in terms of the quality, 00:19:08.160 --> 00:19:10.640 in terms of the pallet level, the case level, and the item 00:19:10.640 --> 00:19:11.140 level. 00:19:11.140 --> 00:19:12.630 And this is what we have been working. 00:19:12.630 --> 00:19:14.547 We have been working with temperature tracking 00:19:14.547 --> 00:19:18.120 tags available on the market from different suppliers 00:19:18.120 --> 00:19:19.740 at different frequencies. 00:19:19.740 --> 00:19:21.690 But still, the same problem is where 00:19:21.690 --> 00:19:23.640 I'm going to put my tag to give me 00:19:23.640 --> 00:19:25.680 a good idea of what is my temperature to predict 00:19:25.680 --> 00:19:27.450 the shelf life of that. 00:19:27.450 --> 00:19:28.920 One of the issues that we have done 00:19:28.920 --> 00:19:31.523 is that, well, the best way to measure 00:19:31.523 --> 00:19:32.940 my temperature of my product would 00:19:32.940 --> 00:19:35.520 be right in the core of my product at different locations, 00:19:35.520 --> 00:19:37.810 at least have a good reading of that. 00:19:37.810 --> 00:19:40.200 But as you know, a lot of these products, 00:19:40.200 --> 00:19:43.620 like if you take lettuce with 94% water, 00:19:43.620 --> 00:19:45.870 it's pretty difficult to get a signal through it. 00:19:45.870 --> 00:19:49.570 So I cannot read it, so I have to place my tag somewhere else. 00:19:49.570 --> 00:19:52.470 So at this point, we try with the pallet level one, 00:19:52.470 --> 00:19:55.380 where we have to place this thing on the outside, right 00:19:55.380 --> 00:19:58.750 at the bottom of the pallet at this point here. 00:19:58.750 --> 00:20:01.470 So what we did is that, hey, let's measure temperature 00:20:01.470 --> 00:20:03.120 inside the pallet and measure what 00:20:03.120 --> 00:20:06.090 the RFID tag is providing us. 00:20:06.090 --> 00:20:07.680 Well, that's the problem here. 00:20:07.680 --> 00:20:10.920 It's because, for example, I hope that you can see it. 00:20:10.920 --> 00:20:13.530 But the red line is the temperature of my tag, 00:20:13.530 --> 00:20:16.830 and all the other lines is the temperature inside my pallet 00:20:16.830 --> 00:20:17.710 load. 00:20:17.710 --> 00:20:20.820 So I'm pretty far for what I should measure. 00:20:20.820 --> 00:20:23.440 Of course, some people are going to tell you, well, 00:20:23.440 --> 00:20:25.950 if you know the package, you can always predict. 00:20:25.950 --> 00:20:29.550 With formula heat and mass transfer, 00:20:29.550 --> 00:20:30.630 I know all this stuff. 00:20:30.630 --> 00:20:31.240 That's fine. 00:20:31.240 --> 00:20:33.570 But the problem is that I deal with the food industry 00:20:33.570 --> 00:20:35.790 when we have about 650 different cases 00:20:35.790 --> 00:20:38.370 or different configurations of materials, 00:20:38.370 --> 00:20:40.170 more than 1,000 products. 00:20:40.170 --> 00:20:42.240 Do the math-- all the combinations 00:20:42.240 --> 00:20:43.410 that can come together. 00:20:43.410 --> 00:20:45.330 It's pretty difficult to get that. 00:20:45.330 --> 00:20:47.680 So at this point, we said, all right, 00:20:47.680 --> 00:20:48.810 let's go to the case level. 00:20:48.810 --> 00:20:50.500 Maybe we can get it better. 00:20:50.500 --> 00:20:53.100 So what we have done is that we have embedded the RFID temp 00:20:53.100 --> 00:20:56.010 tag on a reusable plastic container. 00:20:56.010 --> 00:20:58.860 So at least we are getting a little bit closer to that. 00:20:58.860 --> 00:21:02.820 That these are designed to be five on the pallet load, 00:21:02.820 --> 00:21:05.100 meaning that I always have one end of the container 00:21:05.100 --> 00:21:08.850 that they can see so I don't have a problem to read the tag. 00:21:08.850 --> 00:21:12.480 The problem is exactly the same if you look at it. 00:21:12.480 --> 00:21:16.680 The RFID tag is the blue line, and the purple one 00:21:16.680 --> 00:21:20.010 is the surface of my container, and the blue is what is inside. 00:21:20.010 --> 00:21:22.470 Even a small container like that, 00:21:22.470 --> 00:21:25.350 I still cannot get my temperature inside. 00:21:25.350 --> 00:21:28.140 Well, it's better because if I put that and embed 00:21:28.140 --> 00:21:30.570 that in a plastic container, I pretty much 00:21:30.570 --> 00:21:32.863 know what will be the heat transfer 00:21:32.863 --> 00:21:34.530 to predict what will be the temperature. 00:21:34.530 --> 00:21:36.820 So I am in a better prediction. 00:21:36.820 --> 00:21:39.720 Now I can predict what's going on inside my box, 00:21:39.720 --> 00:21:43.170 and we can get a better sense of prediction. 00:21:43.170 --> 00:21:46.320 At the item level, well, we didn't see very much use of it. 00:21:46.320 --> 00:21:49.380 Because we always move these items to a certain-- 00:21:49.380 --> 00:21:53.628 I don't decide, I don't get my box in my distribution center 00:21:53.628 --> 00:21:55.920 and just look at one lettuce, and this one is not good, 00:21:55.920 --> 00:21:56.490 this one is good. 00:21:56.490 --> 00:21:57.690 No, I don't have time to do that. 00:21:57.690 --> 00:21:58.898 They have to move my product. 00:21:58.898 --> 00:22:01.410 So case level would be the smallest item 00:22:01.410 --> 00:22:03.360 that I can use with temperature tracking, 00:22:03.360 --> 00:22:07.440 with temperature keeping in memory so I can download that 00:22:07.440 --> 00:22:08.980 at any time. 00:22:08.980 --> 00:22:11.670 So at the item level, it's more a punctual temperature. 00:22:11.670 --> 00:22:12.480 I just want some-- 00:22:12.480 --> 00:22:13.920 I don't want to store this data. 00:22:13.920 --> 00:22:16.590 Just tell me the temperature of my product right now. 00:22:16.590 --> 00:22:18.130 Is it at the right place? 00:22:18.130 --> 00:22:19.710 So what I can do is, because I can 00:22:19.710 --> 00:22:22.560 see if it was unbroken or not on the display. 00:22:22.560 --> 00:22:25.290 So we can see that if my refrigerator display doesn't 00:22:25.290 --> 00:22:27.990 work well, I should have a signal about that. 00:22:27.990 --> 00:22:30.570 Because right now, you see the difference 00:22:30.570 --> 00:22:34.020 between nine hours of not good refrigerator display 00:22:34.020 --> 00:22:36.420 compared to the unbroken cold chain. 00:22:36.420 --> 00:22:38.220 I prefer this one than this one. 00:22:38.220 --> 00:22:41.310 Well, in your case, it's this one here. 00:22:41.310 --> 00:22:41.900 It's snowy. 00:22:41.900 --> 00:22:44.270 You know, it looks like it will get some snow soon. 00:22:44.270 --> 00:22:47.760 Well, so punctual temperature at the item level, 00:22:47.760 --> 00:22:48.630 it's very important. 00:22:48.630 --> 00:22:50.088 Because it can prevent misplacement 00:22:50.088 --> 00:22:52.722 of the product in the store. 00:22:52.722 --> 00:22:54.180 It's bad to say that, but each time 00:22:54.180 --> 00:22:56.763 that you have a human decision in this old distribution chain. 00:22:56.763 --> 00:22:58.620 This is where you have losses. 00:22:58.620 --> 00:23:03.170 And this is what my wife told me each time that I shop. 00:23:03.170 --> 00:23:06.470 You made a decision to buy that, and that's the last. 00:23:06.470 --> 00:23:10.460 But the thing is that you put this item, 00:23:10.460 --> 00:23:12.110 and if it's not the right temperature, 00:23:12.110 --> 00:23:13.550 you kill your product. 00:23:13.550 --> 00:23:17.540 You will not have the top quality, meaning 00:23:17.540 --> 00:23:19.190 that people won't buy it. 00:23:19.190 --> 00:23:21.863 And now it's opened the door to smart display. 00:23:21.863 --> 00:23:23.780 We are already working on the copying on that. 00:23:23.780 --> 00:23:28.490 Where if I can read my tag, even if the precision is not 00:23:28.490 --> 00:23:30.950 there that much, at least I know if there's 00:23:30.950 --> 00:23:33.200 a problem with the airflow. 00:23:33.200 --> 00:23:37.200 Or if I should redirect my air flow in order to [INAUDIBLE].. 00:23:37.200 --> 00:23:40.610 And now, we should provide a good thing 00:23:40.610 --> 00:23:42.740 for-- like the food service company. 00:23:42.740 --> 00:23:45.470 Where I can have a best before date dynamic. 00:23:45.470 --> 00:23:48.140 Because the best before date, when I produce something, 00:23:48.140 --> 00:23:49.640 it's always the worst-case scenario, 00:23:49.640 --> 00:23:52.500 that somebody kept that in their car for that number of hours. 00:23:52.500 --> 00:23:56.060 So my best [INAUDIBLE] is going to be that date. 00:23:56.060 --> 00:24:00.223 But if I do a good job, I should have credit from that. 00:24:00.223 --> 00:24:02.390 If I have a good cold chain management, particularly 00:24:02.390 --> 00:24:04.340 for food service and restaurant chain, 00:24:04.340 --> 00:24:07.010 I should have my best before date maybe stretch a little bit 00:24:07.010 --> 00:24:10.670 longer, because my temperature management was great. 00:24:10.670 --> 00:24:14.360 So just to give you an idea, if I do that tomorrow morning, 00:24:14.360 --> 00:24:17.900 the food service company is going to save 30% of losses 00:24:17.900 --> 00:24:20.000 right away, because I allowed them 00:24:20.000 --> 00:24:21.560 to have a longer best before date, 00:24:21.560 --> 00:24:23.510 because they did a good job in coaching. 00:24:23.510 --> 00:24:25.370 That's a lot of money for them. 00:24:25.370 --> 00:24:27.980 At home application, we discussed about that 00:24:27.980 --> 00:24:28.530 this morning. 00:24:28.530 --> 00:24:30.980 You said something about refrigerator, thinking smart, 00:24:30.980 --> 00:24:32.660 and what you have in the refrigerator. 00:24:32.660 --> 00:24:35.720 Well, I can even tell you, later in the future, 00:24:35.720 --> 00:24:38.593 if my refrigerator is well, or if maybe I 00:24:38.593 --> 00:24:40.010 should eat this thing that I don't 00:24:40.010 --> 00:24:42.260 want to eat because tomorrow it's going to be too bad. 00:24:42.260 --> 00:24:44.450 Or maybe I should wait tomorrow. 00:24:44.450 --> 00:24:48.170 So prevention, also, of the DC is very important 00:24:48.170 --> 00:24:49.453 because we get all this load. 00:24:49.453 --> 00:24:51.620 We should make sure that they are at the right place 00:24:51.620 --> 00:24:52.670 at the right time. 00:24:52.670 --> 00:24:54.530 I can have smart transportation-- 00:24:54.530 --> 00:24:57.200 refrigerated trailer, C container. 00:24:57.200 --> 00:24:59.030 I can optimize my cooling at the form. 00:24:59.030 --> 00:25:00.950 And we're going see in the next presentation, 00:25:00.950 --> 00:25:04.310 that cooling is a critical thing on your shelf life. 00:25:04.310 --> 00:25:07.320 It prevents non-safe food to enter my distribution chain. 00:25:07.320 --> 00:25:09.410 So if my truck is coming and I know a day 00:25:09.410 --> 00:25:12.410 before that this thing is going rotten 00:25:12.410 --> 00:25:14.720 because of the temperature, I don't want 00:25:14.720 --> 00:25:16.280 to open this door in my DC. 00:25:16.280 --> 00:25:19.770 With all the airflow, spores flying everywhere, 00:25:19.770 --> 00:25:21.140 it's going to be a pain for us. 00:25:21.140 --> 00:25:23.720 But also, it's going to be a cold chain diagnostic tool. 00:25:23.720 --> 00:25:25.820 Because each time that I can fix something, 00:25:25.820 --> 00:25:29.540 I can backtrack very well, very precisely where my unit is not 00:25:29.540 --> 00:25:30.350 working properly. 00:25:30.350 --> 00:25:31.808 Because sometimes it can take weeks 00:25:31.808 --> 00:25:34.310 before you find out about that. 00:25:34.310 --> 00:25:36.930 Very quick, let's go with the pharma industry. 00:25:36.930 --> 00:25:38.930 The pharma industry has a very strict regulation 00:25:38.930 --> 00:25:39.930 about temperature range. 00:25:39.930 --> 00:25:42.650 And I'm talking about cold chain management here. 00:25:42.650 --> 00:25:45.380 Neither, very good accuracy-- if you provide me a temperature, 00:25:45.380 --> 00:25:48.530 it's better to be good, because my range is very, very small. 00:25:48.530 --> 00:25:50.570 Most of the vaccine is going to be between 2 00:25:50.570 --> 00:25:52.170 and 8 degrees Celsius. 00:25:52.170 --> 00:25:56.150 So if you tell me that your RFID tag is plus-minus 2 degrees 00:25:56.150 --> 00:25:59.210 Celsius, well, I'm not interested at all. 00:25:59.210 --> 00:26:02.210 And I have to be able to read it before I open the container. 00:26:02.210 --> 00:26:05.270 Because if I cannot, if I have to open it, well, 00:26:05.270 --> 00:26:09.320 this is where it gets to a conflict of what happened 00:26:09.320 --> 00:26:10.130 before I opened it. 00:26:10.130 --> 00:26:14.090 But also, I want to read that during my transit. 00:26:14.090 --> 00:26:15.530 For some of you who are aware, we 00:26:15.530 --> 00:26:17.480 have been contracted by the American Red Cross 00:26:17.480 --> 00:26:20.330 to redesign their distribution system in terms of packaging, 00:26:20.330 --> 00:26:22.170 but also on the network. 00:26:22.170 --> 00:26:24.890 And we are trying this thing, also the RFID unit. 00:26:24.890 --> 00:26:27.820 And what is the major thing is, for them, 00:26:27.820 --> 00:26:29.570 it's not a question of losing the product, 00:26:29.570 --> 00:26:31.740 but more not providing the supply. 00:26:31.740 --> 00:26:35.408 So if they can understand what is the temperature before it 00:26:35.408 --> 00:26:37.700 gets to this nation and decide if this product is going 00:26:37.700 --> 00:26:39.710 to be accepted or not, well, they 00:26:39.710 --> 00:26:42.052 can always ship another one to make sure 00:26:42.052 --> 00:26:43.010 that they get a supply. 00:26:43.010 --> 00:26:46.040 Because in 24 hours, you have to supply 4,000 different drop 00:26:46.040 --> 00:26:48.120 point hospitals in US with them. 00:26:48.120 --> 00:26:51.650 So you have to know this visibility about that. 00:26:51.650 --> 00:26:54.620 One of the problems with the pharma industry 00:26:54.620 --> 00:26:55.760 with the cold packaging-- 00:26:55.760 --> 00:26:57.380 I have two minutes? 00:26:57.380 --> 00:26:59.720 And very quick is that it's unfriendly. 00:26:59.720 --> 00:27:01.610 Many of the packaging components are 00:27:01.610 --> 00:27:04.220 very unfriendly to RF temperature tag. 00:27:04.220 --> 00:27:08.120 For example, if I take some of the components-- we have foil, 00:27:08.120 --> 00:27:11.540 we have gel packs, we have metal cans, and all these things. 00:27:11.540 --> 00:27:13.370 I can get a signal through it. 00:27:13.370 --> 00:27:16.400 Even some of the vaccines are so bulky that I can not 00:27:16.400 --> 00:27:17.150 do that either. 00:27:17.150 --> 00:27:21.240 For example, I just slice one packet in half. 00:27:21.240 --> 00:27:24.560 So it's the side of a styrofoam container with all the vaccine 00:27:24.560 --> 00:27:25.520 and all the gel packs. 00:27:25.520 --> 00:27:26.990 And I just cut it in half. 00:27:26.990 --> 00:27:29.660 And what happened is that many places, 00:27:29.660 --> 00:27:30.950 I cannot even read the tag. 00:27:30.950 --> 00:27:33.530 Maybe at the bottom, sometimes I have enough 00:27:33.530 --> 00:27:35.030 that I can read something. 00:27:35.030 --> 00:27:36.560 But most of the time, I cannot. 00:27:36.560 --> 00:27:39.050 So we have to find a way to get around that. 00:27:39.050 --> 00:27:41.480 Because the good value about it is 00:27:41.480 --> 00:27:44.180 because I don't want to open my container before. 00:27:44.180 --> 00:27:45.680 Another one is the vacuum panel. 00:27:45.680 --> 00:27:50.750 Vacuum panel is a vacuum that is wrapped with mylar or foil. 00:27:50.750 --> 00:27:53.480 And because of the vacuum, it has very high insulation. 00:27:53.480 --> 00:27:56.300 So the top-notch product in the pharma industry-- vaccine, 00:27:56.300 --> 00:27:57.470 very expensive one-- 00:27:57.470 --> 00:28:00.900 they all ship like that because it's very good for them. 00:28:00.900 --> 00:28:02.490 We have no signal through that. 00:28:02.490 --> 00:28:04.103 It's kind of completely shielded. 00:28:04.103 --> 00:28:06.270 So I cannot measure any temperature and read it from 00:28:06.270 --> 00:28:07.207 the outside. 00:28:07.207 --> 00:28:08.790 And we have been challenged with that, 00:28:08.790 --> 00:28:11.830 because a lot of companies are using that now. 00:28:11.830 --> 00:28:13.680 So just as a conclusion, I can say 00:28:13.680 --> 00:28:17.020 that RFID temp tags opened a new era for cold chain management. 00:28:17.020 --> 00:28:20.195 Now all our researchers are thrilled about that. 00:28:20.195 --> 00:28:21.570 Because it can predict things, it 00:28:21.570 --> 00:28:23.850 can have data that they didn't have before. 00:28:23.850 --> 00:28:26.760 The food industry should benefit from new smart technology. 00:28:26.760 --> 00:28:28.500 And the pharmaceutical industry can 00:28:28.500 --> 00:28:30.000 have the real-time visibility, which 00:28:30.000 --> 00:28:31.583 is going to change quite a lot the way 00:28:31.583 --> 00:28:33.510 that they decide which items they 00:28:33.510 --> 00:28:35.490 should send to the other one. 00:28:35.490 --> 00:28:36.610 That's it. 00:28:36.610 --> 00:28:37.110 Thank you. 00:28:47.211 --> 00:28:49.210 PROFESSOR: Thank you, very much. 00:28:49.210 --> 00:28:53.190 Our next speaker-- in place of Christian Helms who 00:28:53.190 --> 00:28:57.510 had a family emergency, he was kind enough 00:28:57.510 --> 00:29:03.930 to send Michael Nicometo who is Director at the Cold Chain 00:29:03.930 --> 00:29:06.840 Group AG in Bremen, Germany. 00:29:14.740 --> 00:29:16.685 MICHAEL NICOMETO: OK, basically, I'm 00:29:16.685 --> 00:29:19.060 here to talk about what some of the problems that we have 00:29:19.060 --> 00:29:21.850 are when we think about embracing or adopting 00:29:21.850 --> 00:29:25.180 RFID technology within the cool chain. 00:29:25.180 --> 00:29:26.620 We have a board of directors. 00:29:26.620 --> 00:29:29.020 We have investors who expect an ROI. 00:29:29.020 --> 00:29:32.680 We have customers who don't want to pay for something 00:29:32.680 --> 00:29:34.840 unless they really see added value. 00:29:34.840 --> 00:29:38.530 Added cost is one thing, added value is a different thing. 00:29:38.530 --> 00:29:42.940 So where we fit-- just so you know a little bit about us-- 00:29:42.940 --> 00:29:48.430 is we're a global cool chain logistics provider. 00:29:48.430 --> 00:29:50.860 Christian Helms is the CEO and Managing Director. 00:29:50.860 --> 00:29:54.070 I'm the Director and I'm also responsible for global IT 00:29:54.070 --> 00:29:55.840 and IS. 00:29:55.840 --> 00:30:00.010 We were formed as a new company in February of 2005. 00:30:00.010 --> 00:30:02.860 We announced it through logistic in Berlin last year. 00:30:02.860 --> 00:30:05.080 But our experience goes far back beyond that. 00:30:05.080 --> 00:30:07.390 Christian has been-- he's built the perishable network 00:30:07.390 --> 00:30:10.870 for Kuehne and Nagel, which is a global logistics provider. 00:30:10.870 --> 00:30:12.370 And we work together with Hellmann. 00:30:12.370 --> 00:30:15.220 And at Hellmann, I was sitting second to the CIO 00:30:15.220 --> 00:30:17.860 where we had offices in 80 countries-- 00:30:17.860 --> 00:30:20.720 130 offices and 3,500 users. 00:30:20.720 --> 00:30:23.680 So a lot of our experience comes from before this. 00:30:23.680 --> 00:30:25.900 This is a new company that's dedicated 00:30:25.900 --> 00:30:28.270 to perishable temperature-sensitive types 00:30:28.270 --> 00:30:29.470 of handling. 00:30:29.470 --> 00:30:33.010 In July of 2005, we had a slight addition to our business plan, 00:30:33.010 --> 00:30:36.130 because we acquired a company that's like a mini-Cisco, 00:30:36.130 --> 00:30:36.660 if you will. 00:30:36.660 --> 00:30:38.077 It's food distribution in Germany. 00:30:38.077 --> 00:30:40.720 It's very high-end fresh, gourmet food 00:30:40.720 --> 00:30:43.060 that lost 7.5 million euros last year. 00:30:43.060 --> 00:30:45.850 So we had the task of turning that around very quickly, 00:30:45.850 --> 00:30:48.590 which we have in the six months that we've run it this year. 00:30:48.590 --> 00:30:49.855 That's called RUNGIS Express. 00:30:49.855 --> 00:30:51.940 It's in Meckenheim Germany. 00:30:51.940 --> 00:30:58.690 Our focus really, again, is providing a global network 00:30:58.690 --> 00:31:01.270 of offices that are dedicated to the specialized 00:31:01.270 --> 00:31:03.400 handling of temperature-sensitive products 00:31:03.400 --> 00:31:06.130 with skilled and experienced people. 00:31:06.130 --> 00:31:09.670 Locations are very important, gateways, what we're doing. 00:31:09.670 --> 00:31:12.460 We worked with John Pierre for the last seven or eight years, 00:31:12.460 --> 00:31:13.720 I think it is now. 00:31:13.720 --> 00:31:16.780 Back to the days that he wrote the perishable handling manual 00:31:16.780 --> 00:31:18.940 for [INAUDIBLE] when he was up in Quebec. 00:31:18.940 --> 00:31:21.100 And we continue to work with him very closely 00:31:21.100 --> 00:31:23.740 through the University of Florida. 00:31:23.740 --> 00:31:26.950 There's an awful lot of services that Cool Chain Group does. 00:31:26.950 --> 00:31:31.450 And RFID is one of the solutions sets that we're looking at 00:31:31.450 --> 00:31:33.880 to apply to all of the solutions that we have 00:31:33.880 --> 00:31:36.790 with handling these products. 00:31:36.790 --> 00:31:38.860 And John Pierre has really talked a lot 00:31:38.860 --> 00:31:41.110 about exactly the same things I'm going to talk about. 00:31:41.110 --> 00:31:45.340 I'm going to look at them from a more summarized business format 00:31:45.340 --> 00:31:48.730 rather than looking at it from a research, and a design, 00:31:48.730 --> 00:31:52.150 and optimization of all of the different protocols 00:31:52.150 --> 00:31:54.190 and everything that's involved. 00:31:54.190 --> 00:31:59.380 With the cool chain, we really need to optimize transit time. 00:31:59.380 --> 00:32:03.880 If we can capture a broken transit point in the supply 00:32:03.880 --> 00:32:07.210 chain immediately-- let's say that a container doesn't 00:32:07.210 --> 00:32:10.750 get on a plane, and we don't know if it's confirmed on board 00:32:10.750 --> 00:32:11.470 or not. 00:32:11.470 --> 00:32:13.240 If it's sitting in Miami and it's 00:32:13.240 --> 00:32:16.420 on the tarmac at 110 degrees and it's blackberries 00:32:16.420 --> 00:32:18.370 and raspberries, we don't have very 00:32:18.370 --> 00:32:21.483 long to recover it to get it back inside of a cool chain. 00:32:21.483 --> 00:32:22.900 A lot of times, we don't even know 00:32:22.900 --> 00:32:25.450 that until the products become a product 00:32:25.450 --> 00:32:26.590 that you take to the dump. 00:32:26.590 --> 00:32:27.965 Or make wine out of it, if you're 00:32:27.965 --> 00:32:29.720 a winemaker, that might work. 00:32:29.720 --> 00:32:32.295 So if we actually had the infrastructure in place 00:32:32.295 --> 00:32:34.420 to where we would know whether or not that actually 00:32:34.420 --> 00:32:37.300 got on the aircraft or not, we could actually 00:32:37.300 --> 00:32:40.475 have some dynamic reporting, exception reporting, 00:32:40.475 --> 00:32:42.100 and get to it, and bring it back to us. 00:32:42.100 --> 00:32:45.340 So optimizing transit time is extremely important. 00:32:45.340 --> 00:32:47.830 Finding where there's an exception to what we anticipate 00:32:47.830 --> 00:32:49.830 is going to happen is very important. 00:32:49.830 --> 00:32:52.330 And the other thing is, if we have automated data collection 00:32:52.330 --> 00:32:55.600 at receiving and shipping, that saves us a lot of time. 00:32:55.600 --> 00:32:58.840 If you receive 2,000 or 3,000 containers 00:32:58.840 --> 00:33:02.950 of goods off of an LD7 or a couple of different containers 00:33:02.950 --> 00:33:05.010 off of an airline, and you have trucks backed up 00:33:05.010 --> 00:33:06.760 to your warehouse door honking their horns 00:33:06.760 --> 00:33:10.180 because they want to get it, and you have to receive and enter 00:33:10.180 --> 00:33:12.910 every single box, whether it's by barcode or manually, 00:33:12.910 --> 00:33:14.800 you're spending an awful lot of time. 00:33:14.800 --> 00:33:17.620 If you could take that same product through a portal, 00:33:17.620 --> 00:33:19.210 or with a handheld or anything else 00:33:19.210 --> 00:33:22.600 you want to think of, to read it for a reader configuration, 00:33:22.600 --> 00:33:25.510 you really cut down the receiving time, as well as 00:33:25.510 --> 00:33:27.580 finding out if there's any errors. 00:33:27.580 --> 00:33:31.493 The temperature and monitor problems-- as JP talked about, 00:33:31.493 --> 00:33:32.410 that's very important. 00:33:32.410 --> 00:33:33.760 You saw what happens to product when 00:33:33.760 --> 00:33:34.885 it's out of the cool chain. 00:33:34.885 --> 00:33:36.640 For sensitive products like berries, 00:33:36.640 --> 00:33:38.440 one hour above temperature equals 00:33:38.440 --> 00:33:41.260 one day of lost shelf life. 00:33:41.260 --> 00:33:45.050 It's an interesting statement, because as JP pointed out, 00:33:45.050 --> 00:33:47.920 when you looked at the charts measuring surface temperature 00:33:47.920 --> 00:33:49.950 on the outside of a pallet or a case, 00:33:49.950 --> 00:33:52.450 it doesn't necessarily tell you what the pulp temperature is 00:33:52.450 --> 00:33:53.650 of the product. 00:33:53.650 --> 00:33:56.997 And when somebody receives product, 00:33:56.997 --> 00:33:59.080 a lot of times if they get a truckload of product, 00:33:59.080 --> 00:34:01.270 they've got one or two traditional temperature records 00:34:01.270 --> 00:34:01.770 in there. 00:34:01.770 --> 00:34:04.000 If it's out of control, they'll reject the load. 00:34:04.000 --> 00:34:07.270 Or they'll set it off until they can do further QC. 00:34:07.270 --> 00:34:09.530 And then they either reject the whole load, 00:34:09.530 --> 00:34:11.380 or they don't reject the whole load 00:34:11.380 --> 00:34:13.750 based on what they see on one or two measuring points. 00:34:13.750 --> 00:34:20.190 With RFID, we think we can put a tag on every single pallet 00:34:20.190 --> 00:34:22.170 in different locations on a pallet, map 00:34:22.170 --> 00:34:24.308 the whole container, and basically 00:34:24.308 --> 00:34:26.100 then be able to tell if some of the pallets 00:34:26.100 --> 00:34:28.560 were exposed to a bad temperature- either too 00:34:28.560 --> 00:34:31.380 warm, or too cool, or both in the same container. 00:34:31.380 --> 00:34:33.750 And through research that JP is doing 00:34:33.750 --> 00:34:35.699 at the university with his team, we 00:34:35.699 --> 00:34:38.580 can actually map what the effect is 00:34:38.580 --> 00:34:41.010 for different types of packaging versus different types 00:34:41.010 --> 00:34:44.153 of commodities, as to what the ambient temperature is, 00:34:44.153 --> 00:34:46.320 and how far you go out of temperature upper or lower 00:34:46.320 --> 00:34:48.050 control limit, and the length of time 00:34:48.050 --> 00:34:49.800 that you're out as to what the effect will 00:34:49.800 --> 00:34:51.120 be on the pulp temperature. 00:34:51.120 --> 00:34:53.610 So we can start to assign an awful lot of business 00:34:53.610 --> 00:34:56.940 intelligence to being able to really tell what that's 00:34:56.940 --> 00:34:59.640 doing to the product, not just look at the surface temperature 00:34:59.640 --> 00:35:02.160 and make a decision. 00:35:02.160 --> 00:35:03.840 Real-time alerts during transit-- 00:35:03.840 --> 00:35:05.850 if there is a problem, then we can have an alert 00:35:05.850 --> 00:35:08.910 and go back through cellular or other types of communication. 00:35:08.910 --> 00:35:10.350 Or to the driver, we can actually 00:35:10.350 --> 00:35:13.298 take remedial action during the transit process 00:35:13.298 --> 00:35:15.090 rather than wait until we open up the doors 00:35:15.090 --> 00:35:17.070 and find out that we have something wrong. 00:35:17.070 --> 00:35:19.680 And the shelf-life predictability-- 00:35:19.680 --> 00:35:21.360 not just accepting or rejecting, but 00:35:21.360 --> 00:35:23.460 the shelf-life predictability becomes 00:35:23.460 --> 00:35:28.200 much more doable than it is with current technologies. 00:35:28.200 --> 00:35:30.390 Trace and track is a very important thing 00:35:30.390 --> 00:35:31.890 for safety and recall. 00:35:31.890 --> 00:35:34.440 Also for tracking, like with food and other items-- 00:35:34.440 --> 00:35:36.030 what did we actually sell the thing 00:35:36.030 --> 00:35:37.830 for at the retail shelf versus what 00:35:37.830 --> 00:35:40.530 lot did it come from-- so we know how to do accounting 00:35:40.530 --> 00:35:43.210 and to send the revenues to the right people. 00:35:43.210 --> 00:35:46.770 But that's very pervasive. 00:35:46.770 --> 00:35:49.740 That's very time intensive when you have human people trying 00:35:49.740 --> 00:35:52.050 to do all this, and you have ERP systems that 00:35:52.050 --> 00:35:54.420 are trying to take different receiving and shipping, 00:35:54.420 --> 00:35:56.040 and different logistics packages, 00:35:56.040 --> 00:35:57.420 and different airline tracking. 00:35:57.420 --> 00:35:59.190 And you try to put that all together, it 00:35:59.190 --> 00:36:02.400 becomes very difficult. If you're able to do it with RFID, 00:36:02.400 --> 00:36:05.610 then it becomes transparent, and it becomes non-pervasive 00:36:05.610 --> 00:36:09.160 or less pervasive to the actual process of the business flow 00:36:09.160 --> 00:36:11.430 so you can actually start to do that. 00:36:11.430 --> 00:36:13.770 Humidity and atmospheric conditions 00:36:13.770 --> 00:36:15.490 are similar to temperature. 00:36:15.490 --> 00:36:17.490 Their second level, they're also very important. 00:36:17.490 --> 00:36:19.740 I didn't list any bullets here because it's really 00:36:19.740 --> 00:36:21.210 similar types of things just using 00:36:21.210 --> 00:36:23.340 different types of sensors. 00:36:23.340 --> 00:36:25.845 Warehouse and handling efficiency, we touched on. 00:36:25.845 --> 00:36:28.950 Real-time receiving and shipping and pick detail-- 00:36:28.950 --> 00:36:33.210 not only do you save time, but you also gain on accuracy. 00:36:33.210 --> 00:36:35.760 With barcodes, sometimes people will read a box-- 00:36:35.760 --> 00:36:38.570 they can't read this box, and they go scan one they can. 00:36:38.570 --> 00:36:40.080 Until you get to the end of the lot, 00:36:40.080 --> 00:36:41.700 you don't really know what's going out the last door. 00:36:41.700 --> 00:36:43.700 The last door is whatever is still in the system 00:36:43.700 --> 00:36:44.850 and they ship it. 00:36:44.850 --> 00:36:47.010 Warehouse workers that are responsible for picking 00:36:47.010 --> 00:36:50.070 and packing, in many cases, are very ingenious into how 00:36:50.070 --> 00:36:51.400 to get around a system. 00:36:51.400 --> 00:36:54.000 And so, good RFID will handle all of that. 00:36:54.000 --> 00:36:56.167 Also, we can do time studies on which people 00:36:56.167 --> 00:36:57.750 are doing their job better than others 00:36:57.750 --> 00:36:59.760 by observing different activities 00:36:59.760 --> 00:37:01.080 throughout different points. 00:37:01.080 --> 00:37:05.610 And we can have automated inventory with bin polling. 00:37:05.610 --> 00:37:07.320 So what are our challenges? 00:37:07.320 --> 00:37:10.363 We need to be able to establish measurable baseline costs. 00:37:10.363 --> 00:37:12.780 Everybody wants an ROI, but they don't know what it really 00:37:12.780 --> 00:37:14.910 costs them right now. 00:37:14.910 --> 00:37:16.410 We don't know in the business world, 00:37:16.410 --> 00:37:18.300 a lot of times, how much we're really losing. 00:37:18.300 --> 00:37:21.210 If Christian was here, he'd give you all kinds of percentages 00:37:21.210 --> 00:37:23.640 and talk about everything from Marks and Spencers-- 00:37:23.640 --> 00:37:26.010 which is one of the best with shelf-life, 00:37:26.010 --> 00:37:28.047 not having a lot of shelf life loss-- 00:37:28.047 --> 00:37:29.130 to some of the worst ones. 00:37:29.130 --> 00:37:32.160 But really, people don't have a good baseline. 00:37:32.160 --> 00:37:33.870 You have to establish a baseline so 00:37:33.870 --> 00:37:36.270 that when you look at an ROI, you can now actually start 00:37:36.270 --> 00:37:38.010 to measure the differences and know what you're really 00:37:38.010 --> 00:37:39.000 coming up with. 00:37:39.000 --> 00:37:40.590 And then you've got a sustainable ROI, 00:37:40.590 --> 00:37:42.497 so it's just not a flash in the pan. 00:37:42.497 --> 00:37:44.580 Where you put ROI in, you run it for three months, 00:37:44.580 --> 00:37:47.250 and then you quit doing it. 00:37:47.250 --> 00:37:50.880 We need to understand that not all RFID technology is 00:37:50.880 --> 00:37:51.870 EPC RFID. 00:37:51.870 --> 00:37:54.120 And there are some closed applications, 00:37:54.120 --> 00:37:58.710 low-frequency temperature tags, for example, 125 kilohertz, 00:37:58.710 --> 00:38:01.290 probably, in some of these packages with a close read 00:38:01.290 --> 00:38:04.870 range could work, whereas, 900 megahertz wouldn't work. 00:38:04.870 --> 00:38:07.920 And so, we have to really figure out what fits where. 00:38:07.920 --> 00:38:10.410 But still, as we gain experience, 00:38:10.410 --> 00:38:12.510 then we need to move towards how do we get this 00:38:12.510 --> 00:38:14.250 all into a standardized infrastructure 00:38:14.250 --> 00:38:15.958 so that we don't have high infrastructure 00:38:15.958 --> 00:38:18.858 costs for different types of frequencies. 00:38:18.858 --> 00:38:20.400 We have to have tags that are applied 00:38:20.400 --> 00:38:21.817 at the first stage of the process. 00:38:21.817 --> 00:38:24.150 Because it's much harder to cost justify 00:38:24.150 --> 00:38:28.020 applying tags midstream in any logistic process, or bar code, 00:38:28.020 --> 00:38:30.120 or any other type of labeling. 00:38:30.120 --> 00:38:32.370 So it has to really start at the beginning of whatever 00:38:32.370 --> 00:38:35.400 the process is so that we can utilize the benefit of that 00:38:35.400 --> 00:38:38.520 through the whole supply chain. 00:38:38.520 --> 00:38:40.530 Right now, read accuracy is a big problem. 00:38:40.530 --> 00:38:43.320 If we've got 95% of the data, it's really not good enough. 00:38:43.320 --> 00:38:46.560 If we have to verify what's on there through traditional means 00:38:46.560 --> 00:38:49.380 of measurement or identification, 00:38:49.380 --> 00:38:51.780 then RFID is just an extra layer that's not really 00:38:51.780 --> 00:38:53.268 displacing that other cost. 00:38:53.268 --> 00:38:54.810 And it's getting better all the time. 00:38:54.810 --> 00:38:57.422 Some things are going to be close enough to 100%, 00:38:57.422 --> 00:38:59.130 I think, to where we can start using them 00:38:59.130 --> 00:39:01.306 pretty rapidly in real time. 00:39:01.306 --> 00:39:03.270 The infrastructure has to be affordable, 00:39:03.270 --> 00:39:05.582 maintainable through the whole supply chain. 00:39:05.582 --> 00:39:08.040 Listening to some of the things you thought about with cost 00:39:08.040 --> 00:39:10.260 of readers early on, where they're not there yet, 00:39:10.260 --> 00:39:11.040 I agree with it. 00:39:11.040 --> 00:39:13.867 You know, electronics normally come down in cost. 00:39:13.867 --> 00:39:15.450 I'm sure the readers haven't come down 00:39:15.450 --> 00:39:17.520 as much as we expected them to yet. 00:39:17.520 --> 00:39:20.250 But as compared to moving parts or scanners with lasers, 00:39:20.250 --> 00:39:22.830 definitely, the electronic reader for RFID 00:39:22.830 --> 00:39:25.170 is going to be far less to install and maintain 00:39:25.170 --> 00:39:27.990 than anything with any mechanical moving parts in it. 00:39:27.990 --> 00:39:29.525 But it has to be there, and it has 00:39:29.525 --> 00:39:30.900 to be affordable and maintainable 00:39:30.900 --> 00:39:33.570 through the whole supply chain. 00:39:33.570 --> 00:39:35.800 We should approach the container level, pallet level, 00:39:35.800 --> 00:39:39.270 and item level, and sensor level independently. 00:39:39.270 --> 00:39:43.780 Sometimes people try to put together the total solution. 00:39:43.780 --> 00:39:45.630 And you never finish what you start. 00:39:45.630 --> 00:39:47.610 If we break it down into pieces, and that's 00:39:47.610 --> 00:39:48.985 what we're trying to do with some 00:39:48.985 --> 00:39:51.150 of our pilots and our tests, we can actually 00:39:51.150 --> 00:39:52.950 do something, gain some experience, 00:39:52.950 --> 00:39:55.660 and actually get an ROI on different pieces of that 00:39:55.660 --> 00:40:00.180 and then work towards building a complete infrastructure 00:40:00.180 --> 00:40:02.100 where everything works together. 00:40:02.100 --> 00:40:04.450 And we have to find ways to lower costs, 00:40:04.450 --> 00:40:06.660 even with the current technology. 00:40:06.660 --> 00:40:08.850 And it's a lot easier to justify on a pallet 00:40:08.850 --> 00:40:12.210 basis or a container basis than it is on a case 00:40:12.210 --> 00:40:13.710 basis or a level basis. 00:40:13.710 --> 00:40:17.340 So we're focusing on that on the front end. 00:40:17.340 --> 00:40:19.860 What are some of the concepts, some of the pilots 00:40:19.860 --> 00:40:22.030 that we're thinking about? 00:40:22.030 --> 00:40:25.350 Well, one of the things is to identify the right clients 00:40:25.350 --> 00:40:26.910 and commodities where we can actually 00:40:26.910 --> 00:40:30.443 put RFID tags onto cases when they produce them, 00:40:30.443 --> 00:40:32.610 when they label them, so we can run them all the way 00:40:32.610 --> 00:40:34.680 through the supply chain. 00:40:34.680 --> 00:40:37.320 And we can read them and not fight 00:40:37.320 --> 00:40:39.600 the commodities that have poor read rates, 00:40:39.600 --> 00:40:43.020 or the commodities that have very little profit in them 00:40:43.020 --> 00:40:43.980 or very low value. 00:40:43.980 --> 00:40:45.653 Because nobody is going to accept that. 00:40:45.653 --> 00:40:47.070 So we want to get to where there's 00:40:47.070 --> 00:40:50.640 a low threshold for adoption for the concept in general, 00:40:50.640 --> 00:40:54.150 and for a reasonable expectation of an ROI, 00:40:54.150 --> 00:40:55.920 so that we can go ahead and do the pilots 00:40:55.920 --> 00:40:58.625 with those types of commodities and those types of customers. 00:40:58.625 --> 00:41:00.000 Then we'll determine the accuracy 00:41:00.000 --> 00:41:02.208 of the technology and the impact on the process flow. 00:41:02.208 --> 00:41:04.590 Do we really speed the actual process flow up, 00:41:04.590 --> 00:41:07.410 or do we slow it down? 00:41:07.410 --> 00:41:11.310 We want to find carriers to test RFID tags on container level. 00:41:11.310 --> 00:41:13.890 And it's not traditionally RFID. 00:41:13.890 --> 00:41:17.745 It may just be RF with other types of communication devices. 00:41:17.745 --> 00:41:19.620 A lot of ocean containers have modems in them 00:41:19.620 --> 00:41:20.730 now, with the gensets. 00:41:20.730 --> 00:41:22.272 And there's a lot of things that they 00:41:22.272 --> 00:41:26.280 do where we can start to slowly purvey that with RFID 00:41:26.280 --> 00:41:28.620 as we think of it here, so that we 00:41:28.620 --> 00:41:29.870 can start tracking containers. 00:41:29.870 --> 00:41:31.453 But we have to have the infrastructure 00:41:31.453 --> 00:41:32.640 throughout the supply chain. 00:41:32.640 --> 00:41:37.350 And it may be that we can find an ROI just from point G 00:41:37.350 --> 00:41:42.030 to point K, and not from A to Z. We don't necessarily 00:41:42.030 --> 00:41:44.190 have to try to do the complete chain. 00:41:44.190 --> 00:41:46.920 And again, look at what the accuracy of the technology 00:41:46.920 --> 00:41:49.650 is, the impact on the process flow, and then 00:41:49.650 --> 00:41:51.900 we establish our ROI. 00:41:51.900 --> 00:41:57.060 And the final thing is talking about temperature mapping, 00:41:57.060 --> 00:41:58.920 where we're talking about whether it's 00:41:58.920 --> 00:42:01.560 on a case level or a pallet level, like I said earlier. 00:42:01.560 --> 00:42:03.320 If we can map that whole container 00:42:03.320 --> 00:42:05.310 or that whole truckload, we can actually 00:42:05.310 --> 00:42:07.890 start getting a lot more information. 00:42:07.890 --> 00:42:09.810 And then we can take business intelligence 00:42:09.810 --> 00:42:12.030 that we build into the final application, 00:42:12.030 --> 00:42:15.180 and we can say, all right, now we're going to accept or reject 00:42:15.180 --> 00:42:16.470 this load. 00:42:16.470 --> 00:42:19.560 These pallets have a longer shelf life on the same load 00:42:19.560 --> 00:42:20.910 than these pallets do. 00:42:20.910 --> 00:42:23.760 So instead of doing FIFO, or first-in first-out inventory 00:42:23.760 --> 00:42:25.470 management, we're going to actually start 00:42:25.470 --> 00:42:27.630 doing shelf-life inventory management. 00:42:27.630 --> 00:42:29.160 Instead of sending the stuff that's 00:42:29.160 --> 00:42:32.370 been stressed by temperature across the state 00:42:32.370 --> 00:42:34.980 or whatever the longest distance from this DC is, 00:42:34.980 --> 00:42:37.230 we're going to send it to the store across the street. 00:42:37.230 --> 00:42:39.272 We're going to send the stuff with the most legs, 00:42:39.272 --> 00:42:42.840 or the most shelf life left on it, to the father points. 00:42:42.840 --> 00:42:45.990 And so, by mapping the complete trailer, 00:42:45.990 --> 00:42:48.040 we can start to get much better experience. 00:42:48.040 --> 00:42:49.665 And even though our temperatures aren't 00:42:49.665 --> 00:42:52.350 going to be exact comparing surface to pulp, 00:42:52.350 --> 00:42:54.760 we're going to get a lot better than we are today. 00:42:54.760 --> 00:42:56.340 When I sit down and talk to people 00:42:56.340 --> 00:42:59.760 who are very technical and very accurate in their analysis 00:42:59.760 --> 00:43:02.430 of this, they're saying, oh, we can't do all these things. 00:43:02.430 --> 00:43:05.070 And I laugh and I say, well, what are we doing today? 00:43:05.070 --> 00:43:07.650 We have two temperature, or one temperature recorder 00:43:07.650 --> 00:43:09.252 in the truck that's not accurate. 00:43:09.252 --> 00:43:10.710 And half the time, the truck driver 00:43:10.710 --> 00:43:13.382 throws it away if he has a problem. 00:43:13.382 --> 00:43:15.840 Anything we do is going to be a gross improvement over what 00:43:15.840 --> 00:43:16.660 we've got today. 00:43:16.660 --> 00:43:20.110 So let's go ahead and get the experience and take it forward. 00:43:20.110 --> 00:43:20.860 So that's it. 00:43:20.860 --> 00:43:23.790 I think I just really wanted to give you a brief overview 00:43:23.790 --> 00:43:25.985 from our perspective. 00:43:25.985 --> 00:43:27.360 You know, we're not a big company 00:43:27.360 --> 00:43:28.500 that's going to be an industry leader, 00:43:28.500 --> 00:43:29.460 investing a lot of money. 00:43:29.460 --> 00:43:30.630 But we're also not a company that's 00:43:30.630 --> 00:43:32.310 going to wait and see what everybody else does. 00:43:32.310 --> 00:43:33.560 We're somewhere in the middle. 00:43:39.268 --> 00:43:40.560 PROFESSOR: Thank you very much. 00:43:40.560 --> 00:43:42.930 Questions, if you could come down to the microphones, 00:43:42.930 --> 00:43:44.820 please. 00:43:44.820 --> 00:43:46.860 One question, we actually had a student 00:43:46.860 --> 00:43:48.510 working on this last year. 00:43:48.510 --> 00:43:50.610 And the question came up around sampling rates 00:43:50.610 --> 00:43:53.970 and how significant that was in the temperature of business. 00:43:53.970 --> 00:43:57.750 I mean, if we take a read event and then we have some kind 00:43:57.750 --> 00:44:00.510 of telemetry stream-- so this would be characteristic of any 00:44:00.510 --> 00:44:01.230 sensor-- 00:44:01.230 --> 00:44:04.290 at what point do you marry that up, per an earlier question 00:44:04.290 --> 00:44:06.690 today, with your XML representation of the read 00:44:06.690 --> 00:44:07.290 event? 00:44:07.290 --> 00:44:09.850 And how often do you need to do that? 00:44:09.850 --> 00:44:12.030 Clearly a different time cycle than what 00:44:12.030 --> 00:44:16.100 we're doing in the ALE filtering of the actual EPC read event. 00:44:16.100 --> 00:44:20.850 I would be interested in your experiences in that regard. 00:44:20.850 --> 00:44:26.410 MICHAEL NICOMETO: I think, from a temperature perspective, 00:44:26.410 --> 00:44:29.110 what we're really interested in are two things. 00:44:29.110 --> 00:44:31.080 The most obvious one is whether we're out 00:44:31.080 --> 00:44:33.190 of an upper or lower control limit. 00:44:33.190 --> 00:44:34.590 And if we are, we want to measure 00:44:34.590 --> 00:44:36.630 all of the events that happened once we go out 00:44:36.630 --> 00:44:38.200 of the control limit. 00:44:38.200 --> 00:44:42.420 So the relationship is going to be 00:44:42.420 --> 00:44:45.630 that we have a certain amount of temperature 00:44:45.630 --> 00:44:48.060 variation over a certain amount of time 00:44:48.060 --> 00:44:51.790 that's going to affect the pulp temperature of that product. 00:44:51.790 --> 00:44:54.300 A quick rise in surface temperature and a drop 00:44:54.300 --> 00:44:57.510 probably isn't going to affect the pulp temperature very much. 00:44:57.510 --> 00:45:01.560 We can actually-- at this level of the analysis-- say, 00:45:01.560 --> 00:45:04.260 we're going to ignore all temperature readings that 00:45:04.260 --> 00:45:06.220 are in the temperature range. 00:45:06.220 --> 00:45:09.015 So we can actually say we want a temperature recorder that's 00:45:09.015 --> 00:45:11.140 going to make sure we're within our control limits. 00:45:11.140 --> 00:45:14.910 As long as we are, don't use the memory on the tag or the module 00:45:14.910 --> 00:45:15.745 to store any data. 00:45:15.745 --> 00:45:17.370 The only time we want to store the data 00:45:17.370 --> 00:45:18.990 is once you go out of limit. 00:45:18.990 --> 00:45:20.610 We may even say we only want to store 00:45:20.610 --> 00:45:23.980 certain incremental adjustments over a certain amount of time 00:45:23.980 --> 00:45:27.040 so we can then predict what the effect has been on it. 00:45:27.040 --> 00:45:31.050 However, at another level, if we can get the proper memory 00:45:31.050 --> 00:45:34.920 storage and also the readability within a bandwidth-- 00:45:34.920 --> 00:45:37.230 like when we come through a portal-- 00:45:37.230 --> 00:45:41.400 it would be nice to know how much variations there 00:45:41.400 --> 00:45:44.050 has been within the limits, within the upper-lower control 00:45:44.050 --> 00:45:44.550 limit. 00:45:44.550 --> 00:45:46.717 Because you have to make some judgments when you set 00:45:46.717 --> 00:45:47.760 those control limits. 00:45:47.760 --> 00:45:50.790 And a variation of temperature even within the limits 00:45:50.790 --> 00:45:52.980 affects the quality of some products. 00:45:52.980 --> 00:45:54.555 And so, it depends on where we're at. 00:45:54.555 --> 00:45:57.180 But again, when we compare it to what traditional equipment is, 00:45:57.180 --> 00:45:59.100 anything we do is better. 00:45:59.100 --> 00:46:02.970 And so, it's all a matter of, what's the right balance? 00:46:02.970 --> 00:46:05.250 And if we have 10,000 data points, 00:46:05.250 --> 00:46:06.780 and we're coming through a portal, 00:46:06.780 --> 00:46:10.710 and we're reading a pallet, then the first thing we have to do 00:46:10.710 --> 00:46:14.400 is be able to read whether or not there's an alert situation. 00:46:14.400 --> 00:46:16.100 So we're looking at, how do we set-- 00:46:16.100 --> 00:46:17.850 if there is some type of an alert-- how do 00:46:17.850 --> 00:46:20.473 we set the profile for this commodity on his pallet 00:46:20.473 --> 00:46:22.140 so that when we come through the portal, 00:46:22.140 --> 00:46:24.930 we only have to read enough data to know if there's a problem. 00:46:24.930 --> 00:46:27.660 If we try to read all of the data with the bandwidth 00:46:27.660 --> 00:46:30.155 that we have available through RFID today, 00:46:30.155 --> 00:46:31.530 with that many storage points, we 00:46:31.530 --> 00:46:34.958 don't have enough time as the [INAUDIBLE] comes through. 00:46:34.958 --> 00:46:36.500 GUEST SPEAKER: Well, maybe I can just 00:46:36.500 --> 00:46:38.760 say a work note, as it were. 00:46:38.760 --> 00:46:40.040 Yes. 00:46:40.040 --> 00:46:42.590 One thing that I just want to say is that, of course, 00:46:42.590 --> 00:46:44.630 if you get the temperature of the tag, what 00:46:44.630 --> 00:46:47.970 is outside the package, it's kind of a problem, an issue 00:46:47.970 --> 00:46:48.470 right now. 00:46:48.470 --> 00:46:50.750 Because if I take the temperature of my product 00:46:50.750 --> 00:46:54.260 inside and I do regular data acquisition, 00:46:54.260 --> 00:46:55.490 I don't take that much data-- 00:46:55.490 --> 00:46:58.070 10 minutes, 15 minutes, 20 minutes interval. 00:46:58.070 --> 00:47:00.950 That's way enough, because the lag 00:47:00.950 --> 00:47:03.560 that the time that it takes to change temperature of a product 00:47:03.560 --> 00:47:04.730 is pretty slow. 00:47:04.730 --> 00:47:06.890 But if I take on the outside of my package, 00:47:06.890 --> 00:47:08.420 we have a big issue like that. 00:47:08.420 --> 00:47:10.520 Because if I take every 20 minutes, 00:47:10.520 --> 00:47:13.880 my picture can look much worse than it is in fact. 00:47:13.880 --> 00:47:16.580 So this is what, as Mike said. 00:47:16.580 --> 00:47:21.150 Sometimes we try to work on the range and fine tune that. 00:47:21.150 --> 00:47:25.320 But don't think that it's a ton of data, to be honest. 00:47:25.320 --> 00:47:28.160 Only a few per hour would be way enough in our case. 00:47:30.918 --> 00:47:32.460 PROFESSOR: Well, thank you very much. 00:47:32.460 --> 00:47:34.070 Oh, we have a question. 00:47:34.070 --> 00:47:35.610 AUDIENCE: Just out of curiosity, I 00:47:35.610 --> 00:47:38.160 always wonder, if you have a rule saying 00:47:38.160 --> 00:47:42.600 a product has to be within the range of 60 to 90 degrees 00:47:42.600 --> 00:47:46.840 in the distribution network, what happens if it's 59 or 81? 00:47:46.840 --> 00:47:49.710 do you really care? 00:47:49.710 --> 00:47:51.270 GUEST SPEAKER: What kind of product? 00:47:51.270 --> 00:47:53.437 AUDIENCE: I don't know, just any perishable product. 00:47:53.437 --> 00:47:54.390 You have this rule. 00:47:54.390 --> 00:47:56.190 If I encode it in my system-- 00:47:56.190 --> 00:47:58.410 GUEST SPEAKER: If it's in the pharma, 00:47:58.410 --> 00:48:01.900 by some regulation, if it goes out of the range, it's out. 00:48:01.900 --> 00:48:04.370 You can not use it at all. 00:48:04.370 --> 00:48:07.290 The reason why it's going like that is because-- 00:48:07.290 --> 00:48:11.100 we cannot prove it-- but if you have a time and temperature 00:48:11.100 --> 00:48:14.640 relationship, we may be more able to release some 00:48:14.640 --> 00:48:18.355 of the product that has been out of range because we can prove 00:48:18.355 --> 00:48:20.730 that it didn't affect the temperature of the product very 00:48:20.730 --> 00:48:22.260 much at that time. 00:48:22.260 --> 00:48:25.710 In terms of food, you have some thresholds sometimes. 00:48:25.710 --> 00:48:27.660 Some products like fruit and vegetables, 00:48:27.660 --> 00:48:30.750 if you reach a certain threshold, it's gone. 00:48:30.750 --> 00:48:33.300 So I'm talking about freezing products. 00:48:33.300 --> 00:48:35.730 Some products are like minus 1, minus 1.5. 00:48:35.730 --> 00:48:37.840 If you go down over that, it's over. 00:48:37.840 --> 00:48:40.440 So sometimes, some of the ranges are pretty precise that you 00:48:40.440 --> 00:48:41.375 have to be there. 00:48:41.375 --> 00:48:44.380 AUDIENCE: Yeah, just I'm the systems person. 00:48:44.380 --> 00:48:46.020 I'm wondering if it would be beneficial 00:48:46.020 --> 00:48:48.720 if I put in my system, say, it's perishable, 00:48:48.720 --> 00:48:51.540 but if confidence is 90%, does that help at all? 00:48:51.540 --> 00:48:52.540 GUEST SPEAKER: Oh, yeah. 00:48:52.540 --> 00:48:53.250 AUDIENCE: OK. 00:48:53.250 --> 00:48:56.160 Yeah, sometimes those magic numbers are hard to explain. 00:48:56.160 --> 00:48:57.713 It's 90, the absolute magic number? 00:48:57.713 --> 00:48:59.130 GUEST SPEAKER: And the worst thing 00:48:59.130 --> 00:49:01.380 is that we are working with biological products. 00:49:01.380 --> 00:49:03.150 And all of them have an attitude. 00:49:03.150 --> 00:49:04.570 So they all behave differently. 00:49:04.570 --> 00:49:07.740 So of course, we learn enough after a while doing research 00:49:07.740 --> 00:49:12.300 that the precision is something that you have to take slightly 00:49:12.300 --> 00:49:14.910 sometimes, because the variation is pretty wide. 00:49:14.910 --> 00:49:17.065 But we know some guidelines, at least, 00:49:17.065 --> 00:49:18.907 that you have to be in this time. 00:49:18.907 --> 00:49:20.490 MICHAEL NICOMETO: In addition to that, 00:49:20.490 --> 00:49:22.382 we've got some conditions that we 00:49:22.382 --> 00:49:24.090 don't know about the product at the point 00:49:24.090 --> 00:49:25.500 that we start to measure temperature. 00:49:25.500 --> 00:49:27.420 For example, what are the harvest conditions? 00:49:27.420 --> 00:49:29.820 Was it rainy for one week beforehand? 00:49:29.820 --> 00:49:30.660 Was it dry? 00:49:30.660 --> 00:49:34.390 All of that affects the beginning life of the product. 00:49:34.390 --> 00:49:37.800 And so, what we're measuring when we measure the product is, 00:49:37.800 --> 00:49:40.260 we're measuring what's the optimal conditions. 00:49:40.260 --> 00:49:43.140 And this is where I was talking about some of the software 00:49:43.140 --> 00:49:45.842 that John Pierre's team is working on developing. 00:49:45.842 --> 00:49:47.550 It's giving you exactly the type of thing 00:49:47.550 --> 00:49:49.660 of that you're talking about, which says, 00:49:49.660 --> 00:49:51.120 well, if something's out of limit, 00:49:51.120 --> 00:49:53.550 we don't just reject it other than in case of controlled 00:49:53.550 --> 00:49:55.470 substances or pharmaceuticals. 00:49:55.470 --> 00:49:59.790 But if something is out of limit for a short period of time, 00:49:59.790 --> 00:50:01.140 what was the out of limit? 00:50:01.140 --> 00:50:03.720 You know, so it's a time and temperature factor. 00:50:03.720 --> 00:50:06.367 How far out of temperature, and how long 00:50:06.367 --> 00:50:07.950 was it out of temperature, that really 00:50:07.950 --> 00:50:10.680 makes that a predictable situation. 00:50:10.680 --> 00:50:12.450 And again, if it's food products, 00:50:12.450 --> 00:50:14.490 we still have some other uncontrollable things 00:50:14.490 --> 00:50:15.570 that are there. 00:50:15.570 --> 00:50:17.410 AUDIENCE: Thank you. 00:50:17.410 --> 00:50:20.060 PROFESSOR: Well, thank you very much to the panel.