Webcast replay: Navigate turbulent times in your supply chain
Last week we hosted a webinar with our CEO Steve Pratt, our VP of Consumer Products Mike Hulbert, and Diginomica Co-Founder Den Howlett on the topic of navigating turbulence in your supply chain. This is definitely something that is top of mind for companies everywhere as we navigate the latest black swan event, a global pandemic.
This Otter.ai generated transcript of “Navigate turbulent times in your supply chain” has been lightly edited for clarity and length:
Leslie Poston 0:01
Welcome to our latest webcast, Navigate Turbulent Times for Your Supply Chain with AI. I’m Leslie Poston, Director of Content at Noodle.ai, and I’m so excited you could join us today. First, a little housekeeping: For this webcast we’ll be taking Q&A during the final 15 minutes. Feel free to submit your questions throughout the webcast, and we’ll get to as many as we can before our time is up. Second: We encourage live notes on Twitter and other social media. Our official hashtag today is #surviveturbulence – you’ll see a live tweet feature in your console. If you tweet using that console during the webcast, the system will automatically add the hashtag to your tweet for you. Last but not least, all those who registered will receive the recording of the webcast as soon as the final edit is complete. Additionally, you’ll find resources you can download at any time in the Resource Center of your webcast. Normally, I do a formal introduction of our panel, but we’ve got a guest host today, Den Howlett from Diginomica. Den, why don’t you get us started, and I’ll pop back in for the Q&A portion.
Den Howlett 0:58
Well, hi everybody. My name is Den Howlett, I’m co founder of Diginomica–the media company with a camel inspired name–and with me today is Steve Pratt, CEO of Noodle.ai and Mike Hulbert, VP Consumer Products of Noodle.ai. This is a live production so expect some ums and ahs, and maybe the occasional interruption by a dog, or maybe a child comes bouncing in… we don’t know yet. We’re all doing this from a variety of remote locations. To set the scene, I spoke with Steve a few weeks ago, during which time I asked if AI solutions could provide value for supply chain in the context of a pandemic such as the one that we all know about. At the time, he said that no technology by itself can predict a black swan event of the kind we’re seeing playing out today. But he believes these events provide a unique opportunity to better understand how to operate supply chains effectively. Okay, Steve, could you elaborate on that theme, and then we can maybe get into some examples of how this is working among customers.
Steve Pratt 2:01
Sure. I think that what’s happened recently is, obviously, there’s short term pain associated with this. I think if you look at the the opportunity that’s created from this from a data perspective, it’s really amazing. In fact, data scientists always try to convince people to wiggle demand to see what happens, and we have the mother of all wiggles going on right now. The amount of information that people can gather from their supply chains right now is amazing if they have the right tools to analyze it. What I’m concerned about is that we see a lot of companies taking a very traditional response to a crisis. They’re forming “war rooms” and bringing in consultants and things like that, and as a recovering consultant I see that this is probably not the right response. It’s fighting things that have amazing complexity with spreadsheets and other very simplistic tools. With the complexity of what we’re dealing with here the opportunity to listen to the data and to learn from the data and to emerge stronger is better than ever. I think bringing in this new generation of data science driven technologies, a very comprehensive set of data that you crunch on supercomputers to give your operators the best insights, that’s the right approach. As data scientists, we’re very excited about the opportunity. We’re mindful of being able to support customers in the short term, and in spinning up mission control rooms, and we’ve developed products that we think can help augment what you’re doing to give a proper response to this.
Den Howlett 4:12
So Steve, as a recovering consultant, I suppose step one is to admit we are powerless over viruses, and that our lives have become unmanageable, would you agree?
Steve Pratt 4:22
Yeah, absolutely. And I think that we were powerless to see this coming, other than in a very macro sense. You know, Bill Gates talked about “this is coming at some time in the future”, but it really wasn’t actionable. A lot of people will tell you that data science could have predicted to this, but that’s simply not true. That’s not the way data science works. But what it can do, and I think the good news is, is help us respond. We have the tools now to respond better than ever, so we’re certainly not helpless. We were maybe helpless to say this virus was going to come from this place and spread like this at this particular moment, but we’re far from powerless now to respond to it.
Den Howlett 5:18
Okay. When you talk about the “war room,” when we’ve spoken about this in the past, you’ve talked about the proliferation of spreadsheets and how those don’t really work in these environments. Could you just elaborate on that a little bit?
Steve Pratt 5:35
Yeah, I mean, the traditional way of doing things is that you have, usually, an isolated incident that maybe affects a few brands. Maybe you have a hurricane, or something like that. Then you bring in a bunch of people to analyze that and they download data from your ERP systems and your CRM systems, they put it in a spreadsheet. Then they analyze it for a couple weeks and they come back and say “here’s the answer.” This is far beyond that. The complexity of what we’re talking about is is immense. I mean, you’re talking, especially about your big complicated companies, especially CPG companies, where you could have millions of possible combinations and things that are changing on a daily basis. Doing spreadsheet analysis with very simplistic mathematics and very simple data sets is not the answer. The right answer is to take a massive amount of data with massive signals coming from all directions, letting a supercomputer find correlations to do your best at that sensing, demand sensing, allocate proper allocations, and very importantly, when we’re going to emerge from this and seeing the early warning signs of when we’re going to move from this. I think just doing it in a spreadsheet is very 1990s. And, hopefully, our also our health healthcare professionals are using AI to find a cure and innovate in a vaccine. But I think AI also plays even more of a direct impact on supply chain management.
Den Howlett 7:25
Okay, Mike, turning to you, if I may, please. I guess you’re getting calls fairly frequently from customers saying, hey, help me out. What kind of things are they saying to you at the moment?
Mike Hulbert 7:37
Yeah, maybe just to begin to ground, as Steve was starting to discuss, regarding what is the planning ecosystem if you’re running a global CPG? Obviously, it starts at a store and product level and propagates up into your customers’ DCs, then into your DCs, your internal and external manufacturing and ultimately your materials suppliers. In the best of times we have bullwhip, right? It’s one of the age old supply chain planning challenges that ripples at the front end to become giant dislocation. Once you get to factory planners, and then material planners, trying to get the material pipeline into your plants and third party plants. And so now, with a tsunami at the front, and, to Steve’s point, across all of your product lines, some of which may be completely stocked out selling like gangbusters, seeing both hoarding and elevated demand and elevated consumption, and others that have slowed down, and that creates an immensely challenging situation. That’s also compounded by the fact that most people have planning technology that’s set up in layers that begins to, for many folks, skew DC or even more granular forecasts. For the average company with 10s of thousands of products, potentially, you’re quickly in the hundreds of thousands, if not millions of plans, so to speak, that now are going in unpredictable directions so, I think that’s really at the core; initially, this kind of demand challenge and the signals and then the second piece that’s layering and now, with social distancing, it’s having an impact both in production and in warehousing in particular. So, irrespective of what some of the demand outlook looks like, now you also have some added supply uncertainty, both internal and external, that’s overlaid. It’s really a truly exceptional time. And if you think about trying to get in the middle of all of that and start to plan things manually, it’s really an immense challenge.
Den Howlett 10:15
And I presume customers are coming to you and saying, “now what do we do?”
Mike Hulbert 10:21
Yeah, obviously, people have different situations. And I think that’s an important thing to think about. On one hand, you have folks that are completely overwhelmed with demand. Step one is about narrowing the product line, trying to maximize throughput, and really just maximize the amount of product you can get out of your manufacturing capability, both internal and external, and then allocate that out to customers. But then there’s a much broader set there that everyone’s really trying to tease out. How much of this is elevated consumption? And how much of its hoarding? And, within the elevated consumption, how do we begin to react to that? So, the first challenge that people are asking about is ‘how do we get on top of that?’ Then secondarily, ‘how do we begin to plan that on an ongoing basis?’ And then the last aspect of this is around a lot of the tale of portfolios, which, frankly, are just all over the place. Some people have stopped manufacturing some of those SKUs and it’s extremely difficult to plan and for the most part, it can’t get a lot of attention right now. I think just trying to stabilize the signal is really job one, just so we get a clear sense of what do we need to manufacture and be able to firefight inbound materials to support that.
Den Howlett 12:06
I don’t know what you’re seeing over in America, I’m based in the UK. But what we’re seeing is supply issues, not necessarily across the board, but certainly geographical differences. In some places, you can find things, in other places, you can’t find things. There are daily deliveries that are turning up in other places, it’s hit and miss. Are you seeing similar patterns emerging there?
Mike Hulbert 12:37
Yeah, I think so. It’s really evolving week to week as well. Certainly the consumer dynamics, with some initial stock up and hoarding, really started six or seven weeks ago–more at the front end. It continued, there are a few CPGs that are starting to see a little bit of stability but with a lot of empty shelves. But with the belief that consumer buying is going to an elevated consumption level as opposed to filling up the pantry. It’s very hard to see within the planning signals with so many empty shelves what consumers may be ultimately even trying to buy. And I think the supply dynamics around social distancing and production facilities and warehousing is really more in the last couple of weeks.
Den Howlett 13:42
Okay, so are you seeing the bottlenecks change from, over time, the kinds of bottlenecks that you might see in the supply chain, under quote unquote ‘normal circumstances?’ Are they shifting in the current situation?
Mike Hulbert 14:03
Even the traditional notion of what a bottleneck might be is probably a different concept. Right now we’re seeing what we think are three phases to this. I think back to Winston Churchill, ‘we’re at the beginning of the beginning.’ We may be getting closer to the end of the beginning, but that period of really immense stocking up, elevated consumption, and whatnot may be waning. We do believe that over the next several weeks, as people are bedding in with work at home and learn at home, that some of those consumption patterns will change, especially around food and cleaning products, and the infamous toilet paper hoarding that’s going on. I don’t think consumption’s gone up, but purchasing sure has, you know, we’ll all begin to smooth out a little bit and start to have a shot at gaining a little bit of stability over that period of time. And then I think at the end, probably three months out, there’s more of the beginning of a transition back to normalcy, which is likely to be kind of a prolonged period where people stock down a little bit at some stage, and begin to, for instance, eat outside the home, and for some of those patterns to start to stabilize. But I think it’s important to think about the context of this reality from a supply chain planning perspective. We’re in for a year of real challenge, I think.
Steve Pratt 15:51
And I would say the opportunity here is that there are really interesting datasets emerging from this. Again, from a data science perspective – we have a one of our products, Athena Insights and then another, Demand Signal AI. And what it what we can do is to look at what is actually happening in Italy, and look at the pattern of the correlations and how they’re like what’s happening in New York now, and then you can overlay them on the future as those things spreads to different geographies. Then the models can get smarter and smarter as we get more experience. And then certain economies will emerge. And as the virus passes, and assuming people go go back to work at some point, you can apply this in a temporal manner across your supply chain and across brands. It’s really important to understand all the different signals, all the different data sets that can be very predictive of when things are going to come back. We absolutely believe that how companies emerge from this is equally important to how they handle it when they’re in the middle of it. I remember in 2007 and 2008, there were a lot of companies that were very slow to recover, because they weren’t managing the recovery. They weren’t ready for the recovery. They were so hunkered down. They weren’t thinking one step ahead. And I think that in supply chain it’s what are the signals that we will see that will say, “when and how do we need to adjust?” And also “do we adjust up or down?” Right? And to Mike’s point, we could have the mother of all bullwhips here. So it’s really important to manage coming out of this. As we say here it’s “winning in the turns,” that you had a super high g turn coming into this and you’re going to have a super high g turn coming out, hopefully, and how you navigate that is not a spreadsheet exercise. It’s a massive supercomputer crunching on massive data sets, finding correlations and every signal possible. And then you can make the best decisions as an executive.
Den Howlett 18:19
How well prepared do you think some supply chain planners are for being able to consume the kind of information that you’re talking about here? Steve, what would you say?
Steve Pratt 18:31
Well, I mean, it’s our job to make it simple. We worked very hard on you know, our Athena Insights. Our supply chain AI suite is designed for operators. It’s designed for planners, and executives. We don’t build things for data scientists, we build applications for business people. So we have Athena insights, which is really like what’s going on in your supply chain and gives you deep insights that you didn’t have before. There’s also Demand Signal AI, which is really de-noising demand signals. I mean, right now most stat forecasts are meaningless, right? Just throw them out. The stuff that’s coming out of your ERP system that uses, you know, simple exponential smoothing is, you know, it’s… just throw it out. It’s not giving you any information. And then we have supply execution. Once you know the demand, how do you expedite? How do you not expedite? And where do you allocate things, what SKU DC combinations are using much more sophisticated technology? You know, these are the these that that people need to be relying on. I’m very proud of our applications. I’m sure there are other other companies that also offer these applications. I strongly encourage people to add finished data science applications to your war rooms. It is insufficient to add a data scientist who’s staring at a blinking cursor and starting to type code. But by the time they finish it, we’re going to be in 2021 and this thing is going to be past. So the time to act is right now.
Den Howlett 20:19
That brings me to a point that I wanted to bring up with you anyway, Steve, and this is about AI, as it’s currently understood, is a relatively new branch of technology enabled solutions. And history teaches us, doesn’t it, that when we when we find new technology, we like to tinker with it. We like to be able to play with it. And AI has that kind of halo around it, which encourages developers to say, “hey, let me get my hands on this stuff”, right. But history again teaches us that that isn’t necessarily the right way to go about things is it? Build or buy: the classic build or buy. In this current environment, I don’t think you’ve even got a choice. I think you’ve got to buy rather than even attempt to build, which is fine up to a point. So how do you? How do you persuade companies in particular?
Steve Pratt 21:18
Well, it is trust, but verify. So I can show you the results that we’re getting for our customers right now, which is dramatic. It is a step change in technology. To get back to your point of build versus buy, the AI, for better or for worse, has been around a long time. I remember back in the early days of customer relationship management, people were saying this technology was so important to us. And that “we’re so different that we’re going to custom code our own CRM system.” And they have hired, 10s, sometimes hundreds of developers. Then, a year or two later after they’ve invested, you know, 10s, sometimes even more millions of dollars, that they basically threw it out because they said, “Why are we doing this? Why can’t we just buy?” And I think that AI is going to follow that same trajectory, that, unfortunately, there are a lot of management consulting firms who are telling companies they should custom develop 10 to 50 applications. You can imagine a company trying to custom code 50 AI applications. I can tell you, it’s really hard, very complicated, and very slow. And AI by its nature is kind of a science experiment. So I think that it’ll follow the same trajectory. That people are going to try to custom code their AI applications and realize that that’s a fool’s errand and their entire company is set up to solve one specific problem, right, or two specific problems, and you should partner with them. And so, you know, that’s that’s why we exist. We realize the complexity of solving, of applying Advanced Data science to very complicated supply chain problems. And so, I think we’re on the right path.
Den Howlett 23:39
I sincerely hope you’re wrong. I sincerely hope that, especially in this environment, that people will actually wake up and smell the coffee and realize, “you know what, I ain’t got the time to build this.”
Steve Pratt 23:51
Yeah, well, the good news is that when we go in and show customers what we’ve already built, like, “here’s the finished product” it becomes very clear. Like, why would you try to make this? Why would you ever? “I’m not gonna buy a CRM system now I’m gonna custom code it” is a ridiculous notion. I think a lot of companies just don’t know these applications exist. When we show it to them (and we love to demo our system, because people’s eyes bug out about what they can do with the data they have when adding external data). It’s pretty awesome. It’s a super exciting time because it’s this step change and in technology, you just to boil it down AI is just math, and most ERP systems use addition, subtraction, multiplication, division, most planning systems are deterministic, and ignore probability theory, and what these systems are is very high dimensional multivariate calculus, right, combined with probability theory and a learning mechanism. It’s all those things together, which makes the old technology look like a spit wad. AI is more of a bazooka.
Den Howlett 25:23
Yeah. Okay. Mike, what else can we say? What else can we say to customers?
Mike Hulbert 25:29
I think there are a couple of factors that are worth highlighting. People are responding to all this. The first is that this outset, this beginning phase, even with the AI tools, this phase is a little more brute force. As we progress over the weeks we can get more and more refined, but even now just the ability to data wrangle millions of forecasts, for instance, and get some stability in the signals is a big benefit. And, if you’re doing that in a spreadsheet, and then you’re trying to recast your plan next week, because things have shifted, we’ve got new information, we want to start to adjust, then you’re in a very difficult environment to try and continue to sense and adjust to this environment. And I think it’s back to “No, we don’t know,” we don’t have crystal balls about the epidemiology and how all this will flow out. But we are going to get better information every week. And we can improve the quality of the signals literally every week. It is a very incremental thing. And I think that’s also where AI is really a new ballgame. As we improve and retrain the models on a weekly basis, we can continue to incorporate new data sets and continue to latch on as we start to get signal. And, I think I mentioned before, that probably in the next couple of weeks we’ll start to have enough of that, that you see, we’re able to even leverage more and more, we’re pinning these models down. I think there’ll be a lot of volatility for quite a while, even after we get through the work-at-home and learn-at-home phase, it’ll be really choppy. Looking at the horizon and getting ready to move right on into that period with some planning capabilities that are going to help get us better signals is really important. The other thing I’d highlight as a part of that, you have to support the planning process and roles as they sit today, thier planners out there today, planning their business today. And I think, overlaying the power and insights, that you can’t be reengineering, you can’t be changing people’s jobs in the midst of something like this. So, the other thing we’re seeing as very powerful is how do you get really really good information, the best information you can get, into the hands of planners in terms of “what are the exceptions”, “what do I need to go troubleshoot today” – that’s going to be really important as well. Especially with people working at home, it’s hard to coordinate and manage really big teams and keep them all on the same page.
Den Howlett 28:52
Steve, you’ve sold me. I don’t want you to spitball, what I want is a bazooka, right. Which is great. I really want to hear about return on investment, and don’t talk to me about TCO because I don’t care about that. But what I want to know is how am I going to save my money on this thing?
Steve Pratt 29:13
I can tell you, we’re way underpricing our products. From a from a return perspective. The areas in which we’re focusing are so sensitive, right? So a 1% change in in fill rate or a an increase of inventory churn – matters even during normal times – everyone’s situation is different right now. It’s all about getting to the right place in the right time. These are 10s of millions, hundreds of millions of dollars of benefit, of value, that people are seeing from our applications per year on a recurring basis. I can tell you our pricing is nowhere near that. So, ironically, what we’re seeing are customers coming back and saying, can you tone down the business case? Because it’s too high. Right? This is not “you’re getting a point two five return, we’re talking multiples of investments that you get very quickly. And that’s in the short and the short term. There’s probably a broader ROI, which is keeping the world’s supply chains running, which is a very important societal benefit right now, that, if we’re making decisions as a society, based on bad information, or we’re using old tools that work in normal times or isolated disruptions, if we apply those old tools to this globally comprehensive shift we’re going to be worse off as a society. I think part of what excites us about coming to work every day is to be a part of the solution of keeping the world’s supply chains running. And I think that our customers really appreciate that. I think, you know, economically it’s that our customers look back and say working with us was a no brainer. It’s a new technology. Some people have a little fear of the unknown. Now is the time to try it. And that’s what I I’ll say about ROI.
Mike Hulbert 32:10
Yeah, I think that you have business cases constructed around normal operations, and then you have all the uncertainty of what we’re faced with now. And so I think some of the ROI in this moment won’t be known until we’re through it. I think the two aspects are trying to get people focused on the right thing to maximize throughput in all sorts of different ways. But as we start to move into the transition out of this, to a point that Steve made about the mother of all bullwhips, we could see some real challenges in terms of materials and excesses across the board, whether it’s finished goods, raw materials, a lot of dislocation… it’s not easily dealt with coming out of this. So, I think that both aspects to that are a big deal right now.
Den Howlett 33:21
So, supply chain planners, manufacturing planners, people who are in your universe, they shouldn’t be frightened of what this delivers. They should be welcoming it in the sense that it makes it makes their lives doable. I think I’d like to be able to do a job where I felt was I was actually getting somewhere.
Mike Hulbert 33:45
Yeah, I think that’s the challenge. If you go and sit with folks it’s the data that they’ve got on the screen in front of them. They just can’t even make sense of it. So the stress comes in knowing they’ve got to make decisions like what they’re going to run on the line over the next four weeks. It’s a really hard thing to figure that out. Likewise, for a material planner that just has an ocean of material expedites and not knowing with great clarity which ones are going to be the most important ones to work on, those types of things are tough. As you look at the whole signal issue, our philosophy is that you really have to start at the front – with old value stream mapping, starting with the consumer and some of those indices and coming up through the supply chain, because trying to optimize the factory when the signals are all over the place it’s a losing proposition. It’s really about getting those signals calmed down, so that each planner in their chair can make the best decisions and feel good about it. And and I’ll tell you, there are a lot of people feeling immense stress, because the further you get back in that that supply chain, the harder your job is today. There are a lot of folks under a lot of stress trying to do the best they can to keep stuff moving. That’s what we’re ultimately trying to target.
Steve Pratt 35:24
Yeah, I would say in relatively stable times that there are still noisy signals coming out of advanced planning systems. And we have customers where their planners would spend the first two, three days of the week each week just trying to trying to understand what’s going on and then make decisions. So it’s very frustrating, right? You can imagine now that the noise level level is amplified, and the pressure to make decisions is amplified. So you’re getting stressed from both sides and it becomes intolerable. I think you need to give your planners better information, and then they can make tough decisions. And there are still trade offs, which is still a very human thing. But I think that this is about empowering planners and helping them make better decisions, ultimately.
Mike Hulbert 36:27
I might also add that if you look at the senior executive seats, having your hands around what in the world is going on in the global supply chain, again, a lot of the metrics that we look at in normal times go haywire, and some of the traditional BI that’s developed… all of a sudden it’s really not helping me as much to understand the totality of that. And I think that’s the other issue. We’re obviously very focused on individual planners and arming them with better information to make better decisions. But you also have this issue that it’s very hard to look at my global portfolio, for instance, how much acceleration do I have in demand? Where’s it decelerating? Where’s my demand-supply balance getting out of whack and at what greater degree? Where are those things moving in opposite directions, with the greatest pace? It’s that change of direction stuff, acceleration and deceleration, any kind of traditional reporting, it’s very hard to see those things. And even for us, we’ve taken views that we already have from a data science perspective, and we’re looking at new ways of “how do we get that on a simple to use dashboard and get it in front of a senior executive?” It even makes us work hard to continue to up our game and present that core data science insight in a manner that a senior executive can look at it and make better macro decisions.
Den Howlett 38:12
Well, gentlemen, we’ve covered a decent amount of ground, I think and I’m really pleased to hear you guys talking in these terms. Because in the last couple of days the most common question that I’ve been faced with from people calling in to me is “how do I get my stress levels down so I can actually do my job right?” Maybe this is one way to start that.
Den Howlett 38:40
It’s time to get to the questions. Leslie, should we get into this?
Leslie Poston 38:58
Absolutely. So we have nine questions so far, and we are taking more questions in the last few minutes. You were so complete and so thorough, you actually answered four of them already. We’ll get to as many as we can. Don’t rush your answers – if we can’t get to all of them I’ll write a blog post with you about it later.
Den Howlett 39:29
Leslie, we do mind reading as well. That’s why we’ve been able to answer four questions. Right.
Leslie Poston 39:34
I love it.
So the first one is from Sunil, who says, “I know that companies are hesitant to talk about their products in detail. But this high level approach might make sense for some someone who does this all the time. For someone like me, who does not, it’s hard to wrap my head around this use case, how can I help?” So, he’s kind of struggling to see some of your examples. I think if you could help them out with some examples that someone who doesn’t do this everyday might understand, that would be great.
Mike Hulbert 40:03
I’m gonna take a shot at that. What we do in the first phase is build data pipelines to ingest a lot of data out of the ERP. And so that’s orders, inventory levels, stock transfers, a lot of historic data about all the orders and levels and what you’ve done in the past, but then also what many people call planning books, at least in the SAP world, in terms of “how did my plans evolve over time in terms of my demand plans, my distribution plans, my production plans?” When you suck all that data in, the first piece of this is around being able to find the interrelationships in that data, that’s very hard to get out of just a typical kind of Tableau BI or graphical BI tool. And that’s what we call insights. And I mentioned one of the things that we’re looking at right now is the ability to kind of see my product portfolio and see where I have both dynamic kind of skew, DC locations, stable ones. Unfortunately, some of the stable ones are stable because they’re pegged at zero or crazy things like that these days. But that’s kind of the first step. The next phase, looking at things like our demand signal product, where we would actually generate a full demand signal and whether that’s something you use for execution, or it’s something you use on demand planning on more of a 12 to 18 month horizon is bringing to bear both internal and external data sets to kind of run a full demand signal, which then we can basically either overwrite or do exception base changing to existing plans? So those are a couple of quick examples. When we implement it in a fuller extent to get to full supply execution we get to the full inventory projections at a very granular kind of SKU DC level, and then compute what we call value at risk, which is where am I most likely to stock out. And that’s based on the probabilities of very uncertain demand, and in some cases, uncertain supply as well. Instead of a planner looking at hundreds of thousands of alerts, kind of giving them a processed list of the highest impact items that I need to look at today.
Steve Pratt 42:52
So yeah, I’ll say that there are a lot of companies that are building tools for data scientists, that’s not what we do. So if this isn’t something that you do every day, it’s probably not applicable to you. So we’re solving a very specific problem, for very specific people. So it’s tailored to that, right? There are other companies are taking the approach of, we’re going to make it easy for you to do data science yourself. More, we’ve built the full end to end application for business users.
Den Howlett 43:37
Next question, Leslie
Leslie Poston 43:38
Since we’re running up on time, I actually want to make sure that we get to this specific one. Do you think there is a silver lining? Do you think there’s a silver lining to all of this for supply chain leaders?
Steve Pratt 44:05
Yeah, I absolutely do. I think on on multiple dimensions, this initial pain is going to expose some things that today has been uncomfortable or somewhat painful issues with existing systems and the in and the shortcomings of existing systems. And in this environment, it just becomes they don’t work right there. It’s intolerable. So, I think getting people to adopt the next generation of technology and sophistication of running supply chains is one. I would also say the data sets generated from this could give people really interesting insights into their business and their consumers and I think the third is the intention of Noodle.ai is to create a world without waste, that if people do put in these technologies and more tightly manage supply chains and and get inventory levels down while getting fill rates up, that we’re going to have a lower footprint and lower carbon footprint for supply chains and how you define waste. I think we are much more adaptive and much more data driven and supply chains on the other side.
Den Howlett 45:55
Incremental value being added all the way right.
Steve Pratt 45:57
Mike Hulbert 45:59
Yeah. I think so. And hey, we might add, given the broader context that, I think it’s likely to also have the silver lining that beyond the supply chain world, that just the world itself, can now get serious about hardening and getting ready for one of these that may be worse. Hopefully this ultimately is something that leads us to do a lot of other things beyond what we’ve discussed today. And then ultimately, they’re going to prepare us better, as a global society for another black swan whenever it occurs.
Leslie Poston 46:49
Okay, fantastic. I have two two more. One I’m actually not going to read the question. I just want to acknowledge Clive, who’s been a very active and engaged with us and who’s asking some great questions about data set sharing and external data sets. And Clive, I actually think that your questions will be answered on our webcast to follow this one next week. And I’ll send you a personal email with a link to that so that you can get your questions answered, because they’re great questions, and I don’t want you to feel ignored or unheard.
Den Howlett 47:21
Leslie, I know who Clive is.
Leslie Poston 47:25
I’m going to do one more question so that we don’t run over time from listener Jon, who says, “How do supply chain planners have to change their mindset or approach in order to be more agile in times like these if you have new tools? Don’t you also need a new mindset?”
Mike Hulbert 47:47
That’s a great question. I think that the opportunity for individual planners is that we make them more effective in the sense that the the difficulty of sifting through thousands and thousands of alerts and trying to figure out what you need to focus on, we’re able to kind of take that out of the day and give you a really crisp list of the key exceptions that need a plan or to go route and ultimately initiate an action. I think it’s that that flexibility and agility to kind of move into that part of the job in a much bigger way. At least in some of the work that we’ve done, and in my my old roles, I think it was really great to see how excited individual planners see it. We’re about working with these tool sets and really how it made them have a root cause or a problem solving action taken. A lot of this stuff that ends up being really clerical, about trying to hunt for the needle in the haystack of “what what do I actually need to focus on?” So I think that’s actually one of the things that’s really pleasing in this is how excited some of those really great planners get about it because it really unlocks them to be problem solvers.
Den Howlett 49:33
Steve, last words from you.
Steve Pratt 49:36
I just want to echo that. I think there are a lot of first responders in this case. I think inventory, the people who are running supply chain right now are akin to the these first responders because if the supply chain shut down we’ve got a big massive problem and these people are faced with doing heroics on a day to day basis, these planners. I think they’re very smart, dedicated people and are justifiably frustrated with the information that they’re getting and making very high stakes decisions. And so, I think this is less about changing the planner, it’s more about supporting the planner to be successful, right, and say, “help us help them.” And I think that one other silver lining out of this is I see an increase of that among people, right, that we’re all in this together. And I think that, we’re trying to do our small part in this to to help supply chains in the world keep running during this during this really difficult time and to emerge out of it quickly. We want to make this a V rather than a protracted U that we’re coming out of this. So that’s our focus right now every day.
Den Howlett 51:18
That’s a great place to to pause, I think. Are there any more questions or are we pretty much done?
Leslie Poston 51:24
I think that we’re done with Q&A. I’m going to take the questions we didn’t answer and address them in a blog post and in our next webcast. Everyone has been heard today, and I thank you very much for coming and participating.
Transcribed by https://otter.ai
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- MMSteelClub and Noodle.ai: Webcast Video Highlights from the Steel Making Technologies Session
- Advanced Supply Chain Planning with Noodle.ai: Plain-Speak for Supply Chain Leaders
- Data Science in the Time of COVID-19
- Introduction to MLflow for MLOps Part 3: Database Tracking, Minio Artifact Storage, and Registry