Dominik Metzger: 0:04

The end goal of smart sensing, as I mentioned, is there to really use this information to automate a business process.

Tom Raftery: 0:16

Good morning, good afternoon or good evening wherever you are in the world. This is the digital supply chain podcast, the number one podcast focusing on the digitization of supply chain. And I'm your host, global vice president of SAP. Tom Raftery. Hi, everyone. Welcome to the digital supply chain podcast. My name is Tom Raftery with SAP. And with me on the show today I have my special guest, Dominic Dominic, would you like to reintroduce yourself? Welcome back to the show.

Dominik Metzger: 0:44

Yeah, perfect. Tom, thanks so much for having me back. Dominic Metzger, I'm part of sa P's product engineering organization, and I run product management for our manufacturing and industrial IoT solutions.

Tom Raftery: 0:56

Tremendous. And Dominic, we are inviting you back on the show because you wrote a blog post not too long ago, where you referred to something called Smart sensing. And it was the first time first time I'd come across that term in the context. And I'm sure many people listening to the podcast, may not have read the blog, and maybe you could explain to us what is smart sensing?

Dominik Metzger: 1:22

Yeah, absolutely. So smart sensing, really, to us is a way to automate and to really improve business processes, both in logistics in manufacturing, but also in what we call Supply Chain Management overall. And very specifically, it is all about using this wealth of information that we gain, for example, from sensors, from RFID tags from QR codes, or potentially even from images from cameras to identify supply chain objects, think pallets, trucks, boxes, goods that we are moving, and use this information use this, these sensor values to take an automated business decision, I will get much, much more specific. But just to give you already one example by for example, equipping pellets with RFID sensors or with sensors that can even measure the condition of whatever we are tracking, right temperature, humidity, whatever it is, we want to track. This allows us to not only find the position of whatever we are tracking, but also get a lot of contextual information. And the end goal of smart sensing, as I mentioned, is then to really use this information to automate a business process.

Tom Raftery: 2:55

Okay, because we have access to many, many sensors increasingly nowadays. But what you're the difference here you're saying is that rather than seeing an alert, come up and manually make a decision to act on something, that process is being automated?

Dominik Metzger: 3:12

Yeah, exactly. And I think this is the critical part, that we go all the way to steering business transactions. So exactly what you mentioned on rather than me as a user in a warehouse, or tracking a delivery for my customer getting an alert popped up by a why not automatically, if there's something wrong, for example, with my product on the road, or with the pallet that is driven through the warehouse, why not automatically post transaction so create a goods issue automatically a goods receipt, why not automatically create a new outbound delivery because I know the first one had an incident the temperature went too high, my product is actually seeing a problem cannot be delivered any longer. So I automatically trigger a new delivery, maybe even an automated notification to my end customer, that there is a challenge during the delivery.

Tom Raftery: 4:16

Okay, and I mean, what else is new about this? Is this something that can scale more or what are their differences or does this bring to the table?

Dominik Metzger: 4:26

Yeah, good question. Because the technology itself or the idea is not that you We also refer to it as Automatic Identification also ID so pretty much with the invention of sensor technology, and RFID reader gates, for example, this became a capability. Now what really has changed in my opinion are two aspects. First of all, what I would summarize really under the umbrella of total cost of operations, not only have sensors of the last day It's become significantly cheaper, but also the ITF. So the software capabilities have become much, much more scalable. Why is that because in a world of on premise computing, it basically means that every single warehouse, where you want to introduce this technology to, in order to automate a good issue with receipt process, for example, you need to install software, right, you need to run applications on premise, you need to configure them, you need to run projects, you need to provide all the contextual rules to interpret these events. But now, with the capabilities of cloud and edge computing, we are suddenly able across 1020 100, factories, warehouses, distribution centers, to introduce one smart sensing cloud, which contains all the intelligence, right because at the end, it's not so exciting any longer being able to capture sensor readings, right? That is the technology that has always been there. But the intelligence, the rules, the intelligence that interprets these events that interprets the data and says, alright, if it is a certain threshold that is reached for a certain product on a certain route, that is the point in time where I need to trigger a transaction aboc that is now completely supported in in our smart sensing cloud. And in order to ensure that still, we have the availability in in the warehouses in the factories of this world, we combined it with edge computing, so we are now able to have a very, very lean footprint that is necessary still in the factories or distribution centers, but significantly cheaper and more lean compared to a full blown on premise implementation. So I'm gonna say this is the first big aspect. And that immediately ties with the second scalability also, just from a performance perspective. So it is now with the help of cloud and edge computing, we have a significantly more robust architecture and scalability, which means we can just process 1000s and 1000s of events, and we see significantly lower latencies, or just higher performance.

Tom Raftery: 7:17

tremendous tremendous. Are there any examples you can talk to any kind of use cases that you can refer to?

Dominik Metzger: 7:25

Absolutely, three come to mind, the first one really happens in most of our customers warehouses, which is classic, inbound and outbound movements. This could be very simply when you have a distribution center. But this could also be in a, in a production context. You have a truck arriving, you're unloading a container, a truck, whatever it is, and you equip your premises, as we discussed, it could be an RFID. reader gate, you could work with ultra wideband technologies, it could even be as we mentioned, there are camera technologies available that also have basically video recognition features, which can give us indications on the goods, their position and their condition. But at the end of the day, it's it's all for the purpose of automating warehouse logistics processes that we can very much more efficiently post goods receipt goods issue without anyone needing to get out a handheld and scanning codes or typing in numbers on a on a handheld device in a warehouse, for example. The second use case is more in a production context, in Kanban scenarios, so Kanban is a very, very well established scenario, which means nothing else. It's a pool principle, where if I'm, let's say production operator, and I'm at a, an assembly station, but I need certain boxes, certain parts that automatically based on, let's say, equipping a Kanban box, right, which contains the parts that I'm using to assemble my product. But this box automatically sends a signal but it's empty, almost empty, near empty, we can do this very simply with smart shells, or with basically weight tracking, right, we know how heavy the box still is. And this automatically sends a signal to my warehouse system, which triggers for example, a warehouse task warehouse order to send replenishment to me. And this could happen in various facets. But it's just another way to further automate this Kanban process. And the third, what I find is actually one of the most exciting cases is when it comes to measuring, also the condition of whatever I'm transporting or whatever I'm tracking. So this could be a classic cold chain scenario, right, so I have my container on the road with perishables with pharmaceutical products. cannot go above or beyond a certain temperature. So also here I can equip smart sensing technology to measure this. But even other conditions could be tracked right we have customers for example, in their production facilities, they are assembling or producing parts, which are highly sensitive to certain conditions for example, shocks, right? Because there's a chance that they will later on in the finished product will be a damage if one of the parts right has been exposed to too much of a shock during production, it fell down or a forklift had a little, little incident right? Or even again, temperature or other conditions. So preventing these, let me say downstream quality issues. Also this can handle it with smart sensing that these sensitive parts let's call it are grouped with sensors. And we can during the production process, track them and their conditions thoroughly and to achieve end to end parts traceability.

Tom Raftery: 11:10

Super, super. The last time Dominic, you were on this podcast you were talking about industry for Dotto, when you know we had Neil's on as well talking about the whole ecosystem around industry for Dotto, how does how does something like smart sensing fit into that? What's the kind of big picture for it there?

Dominik Metzger: 11:27

Yeah, very good point. So the industry 4.0 strategy that we follow, really, let me say builds on multiple pillars. And a lot of the scenarios that we discussed happens in a factory right happens in a warehouse. So it really addresses our intelligent factory, or intelligent logistics strategy in the context of industry fell zero. And I personally see it as a technology enabler, right? The technology that we are talking about here is really, as we mentioned, the highly scalable possibility to leverage this wealth of data, sensor data, camera data, but again, most exciting to enriches with rules with intelligence to drive business processes in a factory, right? So if I go back to my Kanban scenario, or even the handling unit condition scenario that I described, where we want to know, did anything go wrong with a subpart that I'm assembling? This really feeds into improving productivity right into improving the quality of the products that I'm creating, and is therefore an integral part of a intelligent factory and intelligent plant?

Tom Raftery: 12:41

Okay, okay, great. And where to from here? Where does the kind of smart sensing go after this.

Dominik Metzger: 12:49

So we have actually launched this capability in the mid this year, we are working at the moment with first core innovation customers in rolling this out across multiple factories that distribution centers, for example, in the automotive supplier segment. So we will definitely be sharing some news in the future about some very first exciting customer success stories. In the meantime, what I recommend us to take a peek onto our YouTube channel, we will be sharing the link where you can find a more detailed demo how such a scenario for example, a combat scenario, or a goods receipt scenario looks like how we leverage these rules, these actions, this intelligence that I've talked about, but also, why not visit our industry forum Now perhaps, because that's really a perfect place to interact with us and our teams and talk about what the major challenges are of our customers of our listeners, so that we can go into a dialogue of can we support the requirements that they see with smart sentencing?

Tom Raftery: 13:58

Super, super, Dominic, we're coming towards the end of the podcast. Now, is there any question I've not asked you that you wish I had? Or is there any topic we've not covered off that you think it's important for people to be aware of?

Dominik Metzger: 14:12

The only thing that comes to mind, since you also mentioned our colleague meals, is ecosystems. So obviously, the smart sensing is a really great ecosystem plane, because it's an interplay between the sensor technologies, so your actual tags and devices, but also with, let's say, the applications that we provide the smart sensing infrastructure, the cloud computing capabilities, the edge devices, edge gateways, that at the end, connect with the sensors. And what we have launched here is an initiative by the name of industrial internet of things in x space, which is really a partner initiative and we call it in stays. Because we are. The goal of this initiative is to really have a turnkey solution together with selected hardware partners that can really provide seamlessly this end to end landscape, the sensors and devices, the gateways, the edge devices, and then of course, from SAP, the applications and cloud and edge computing capabilities. So therefore, industrial IoT next days, smart sensing is a big part of it. And we will be keen to share more who those partners are, but I can already give a little hints of that they are leaders in smart sensing technology. So yes, very exciting news to come.

Tom Raftery: 15:43

Super Bennett's x days, not x months or years, so

Dominik Metzger: 15:48

not even x weeks. Exactly.

Tom Raftery: 15:51

tremendous, tremendous. Dominic, if people wanted to know more about yourself or smart sensing or any of the things we talked about today, where would you have me direct them?

Dominik Metzger: 16:01

definitely pay a visit to my LinkedIn profile. Feel free to shoot me a message. Other than this, I would love to work with our customers. Maybe in a visit in our industry forum, no hubs.

Tom Raftery: 16:12

Tremendous. Dominic has been great. Thanks a million for coming on the podcast today. Thank you, Tom. Okay, we've come to the end of the show. Thanks everyone for listening. If you'd like to know more about digital supply chains, head on over to sa p.com slash digital supply chain or, or simply drop me an email to Tom Raftery of sap.com. If you'd like to show, please don't forget to subscribe to it and your podcast application of choice to get new episodes as soon as they're published. Also, please don't forget to rate and review the podcast. It really does help new people to find the show. Thanks. Catch you all next

Unknown: 16:51

time.