The Digital Supply Chain podcast

Using AI And ML For Inventory Planning - A Chat With Flowlity CEO Jean-Baptiste Clouard

June 20, 2022 Tom Raftery / Jean Baptiste Clouard Season 1 Episode 235
The Digital Supply Chain podcast
Using AI And ML For Inventory Planning - A Chat With Flowlity CEO Jean-Baptiste Clouard
Show Notes Transcript

With all the disruptions to supply chains these days, inventory management is very much top-of-mind for supply chain professionals. Too much inventory is wasteful, too little risks stock-outs and missed sales.

Enter Flowlity - a French start-up helping companies better manage inventory planning using AI.

To learn more I invited their co-founder, and CEO Jean-Baptiste Clouard to come on the podcast to tell me all about it.

I learned loads, I hope you do too...

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And remember, stay healthy, stay safe, stay sane! 

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Jean-Baptiste Clouard:

Step one is we optimize their inventories internally. Step two is we deploy, particular supplier portal to their suppliers so that their suppliers are way more accurate, and have visibility, and reactive to whatever needs they have. And to do that, you need to be cloud and you need to be SaaS

Tom Raftery:

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 at 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 Jean Baptiste. Jean Baptiste, welcome to the podcast. Would you like to introduce yourself?

Jean-Baptiste Clouard:

Hello, Tom. Hello everybody. Uh, yes, for sure. Thank you very much. Thank you for inviting me. It's a pleasure to be here today. So I'm Jean Baptiste, uh, you can help me JB and, uh, I'm the CEO and co-founder of Flowlity. And, uh, now, we started for three years and a half ago and we are young startup, a young French startup, based in Paris. And we are building a supply chain planning software, that is aimed at, having better resiliency and better planning through the, the extended supply chain.

Tom Raftery:

Okay. And why, why did you, why did you get into this? What was, what was the Genesis of this? What, why did you wake up one morning and go, I think I'll make a supply chain planning company.

Jean-Baptiste Clouard:

Yeah. I guess it's not the kind of, of, of, you know, um, topics where you, you end up after college, you know, thinking about yes, let's revolutionize, uh, supply chain planning. So, no, no actually I spent my career working for, uh, in this field. Uh, so I started about 10 years ago, at a company, a Dutch company called Quintic. It was, uh, You know, doing supply chain software and supply chain planning. And that has been bought after, by Daso System. I was head of product at Desu System. And, and then I, I worked for, and American started called Anaplan. So I've been, you know, in this field for the past 10 years. What I noticed is that, you know, even, even though that. There are more, there are more issues, in supply chain than, than ever before. A lot of disruptions, a lot of stock outs, a lot, a lot of bad inventory, lot of waste. And so, you know, we, we felt that even though there was a lot of solutions existing, the problem was not really tackled and solved, and there was a big opportunity to to bring to the market, something different, something that, uh, would, do planning differently and would also bring satisfaction. And there are a lot of love from, from the users. So that's what we tried to do. And, uh, and yeah, three years ago we started, we thought big market opportunity. Let's, let's try to build something different, and bring it to the market to serve those inventory issues. Eventually.

Tom Raftery:

Okay. And what kind of inventory issues are you solving?

Jean-Baptiste Clouard:

Yeah. So it's, it's a very good question. So, so again, when I'm speaking about inventory issues, I'm, I'm speaking about having either, too much I inventories and so that results in, in waste and, and obsolete inventories or having, and not enough, and so in stockouts and missed sales and, and they are, um, actually two, idea, focus that we are focusing at Flowlity it's either raw materials or finished goods and in the end, yeah, it's, let's say the two, two extreme of, of the company. And, and we are trying to reduce those stockouts and those over stocks by, you know, providing better planning and especially what we think is that the planning software and the planning solutions have really been, you know, built and conceived in, in the eighties, nineties, all the concepts behind it, like the MRP, multi echelon inventory organizer, all those concepts have been built in the nineties where the world was much more centered, much more predictable. And you didn't have so much volatility, so much uncertainty. And, and so what we see now is that this is really not working in the modern world where there's so much disruptions. When you know, we are facing a world with Ukraine, uh, blockage of, of, of China harbours, where you have, a lot of information where there's always, we are living in the world of constant disruptions or constant, constant uncertainty. And we still have planning methodologies that are really deterministic that are really like, you know, you know what let's predict that we will, you know, sell 100 and then let's, you know, then buy 100 components, I would say. That's basically it. So that doesn't work in, in a world with so much volatility. And, and so we think we need to have a different approach of planning and that's what we call resilient planning, that capture this volatility that is uncertainty to be way more efficient and reduce stockouts in the end. That's what we are aiming at and, and bring to supply chain key decision maker, a much more robust and still efficient supply chain.

Tom Raftery:

Okay, how?

Jean-Baptiste Clouard:

How that's really a good question. So, I might get a bit technical. But, how do we do that? So the first thing is let's I dunno, talk about, a bike, uh, a bike is composed of many components, but let's talk about bikes and wheels. So, how do you, you know, in the end replenish, for example, wheels? The classical way of doing it is through deterministic method, uh, which is basically, you know, you forecast how many bikes you will sell, basically. And then you propagate it, through multiple operations and then you, you get in the end, how many wheels you will need. So it's a very deterministic process and it accumulates errors right? It accumulates errors. Errors on the fact that know there might be a lot of, production issues, uh, reschedule of, of the production batches. You might have also uncertainty on how much you will bike, you will sell in the end. And, and you will have also delays in, in when the components will be delivered. And so, all this error in the, you know, classical way of, of doing inventory replenishment is, accumulated when it's, you know, going up and propagated up in the supply chain. And so it creates a lot of stock outs and a lot of your trans planning when you do, inventory optimization and inventory management of those components and those wheels. So what is a different way of doing that? And so what, what do we do with gender electric. So, so what do we do is that we, we replenish directly, the wheels by putting an inventory buffer on, on this, on this wheels and then forecasting, how much, wheels we will, consume, in a probabalistic manner in, in a stochastic manner. So it's not a deterministic integration, but it's basically how many wheels will we need based on past wheels consumption, but also the NRP input. And we put those two things in a big algorithm, in a big AI algorithm that will in the end give us, um, A good sense of how much we will consume. We will also predict when the goods are going to arrive. So it's what we call lead time forecasting. And then we'll do those two things probabalisticly taking into account all the uncertainties and all the pot potential scenarios. And based on those uncertainties, we'll place the right inventory buffer that will mitigate all of this volatility and then giving a recommendation replenishment. So. It's quite techy, but in the end, what it means means we are really, you know, shifting from a very, static supply chain to dynamic one where we are capturing and measuring all the uncertainty at any point in time, both of demand and supply, and then adjusting with an inventory buffer to this variable uncertainty really going from static to dynamic supply chain and bringing a lot of efficiency and very reduced amount of, uh, of shortages. To give you a sense uh we are talking about. 30%, 20, 20, 30% of shortages, uh, reduction compared to traditional NRP. And we are doing that at multiple customers, multiple companies, big companies. So yeah, we have, we've quite some use case now working on this topic.

Tom Raftery:

Okay. So can you tell me about some of the, successful stories that you've had with customers?

Jean-Baptiste Clouard:

Yeah, maybe let's start with, uh, a very famous, watch, uh, manufacturer. so for, for example, for them, we are, replenishing, the components that are, used to produce a watch. And so for, for those guys, for example, there were a couple of topics. So first we knew that the NRP and, and so the dependent demand out of the NRP was, you know, quite, bad and, at low accuracy. And so we started was one of the first customer. We started by just, forecasting, the needs of the components based on the past conceptions. And, um, that was actually good. That was better than the NRP. Uh, but what, what we are seeing is that as, at first step, that there was, you know, it was really good for 80% of the product, but then for 20% of the product was not so good because, there was new products, for example, new watches with new components. And so, um, then what we did is that we did the second step and as the second step we included the MRP. So the dependent demand from the NRP into our, forecasting algorithm, basically we are using AI machine learning algorithms. And so we are taking both and, and the algorithm by itself. We find that for certain product and, certain patterns, well, the MRP is going to be, is going to have more influence. And for other patterns is going to be more the, the past historical consumptions. And by using both we have been able to crush actually the performances. And to be way better into predicting those, consumption patterns. And, on top of that, doing some kind of supply viability, taking into account the, the, supplier, say reliability by doing lead time forecasting, and then putting all that together we have been able to show very yeah, very good performance. And, a capability to reduce shortages that is, you know, really significant. And, you know, in the end it translates into, more availability of components. And so in the end, more, more sales because we are talking about luxury products and, uh, and also, you know, this was true for this customer, but we are doing, doing exactly the same in the food industry. So, uh, yeah, so it's, uh, it's.

Tom Raftery:

And what, what, what are you, what are you doing in the food industry?

Jean-Baptiste Clouard:

Yeah, it's exactly the same principle. So, um, for this industry, we're talking about the large dairy manufacturer, one of the worldwide leader in this industry and, and what they do is that they are, uh, same thing and they're forecasting the needs of the finished goods. And then, propagating propagating it through the, the NRP. What we saw is that the NRP and the dependen demand is at 30% accuracy at three months, which is really, really bad. And so we did exactly the same thing. And so we did the inventory optimization and replenishment on the, all the components, the net adherence in this, on, in those, those yogurt. And then what we showed is that we were capable of increasing this actuals here at 70% by, by doing this intelligent mix of, using the, past consumptions and, the, the, the a NRP the demand, and then forecasting the read times, and then certain. And so it's exactly the same thing.

Tom Raftery:

Okay, fantastic. when you talk about taking in information from the MRP,

Jean-Baptiste Clouard:

Yes.

Tom Raftery:

can you take in information from any MRP system or are there particular ones that you're more suited to?

Jean-Baptiste Clouard:

Yeah, of course we have a partnership with SAP and, and we are, uh, you know, tightly integrated with, with SAP. So it's easier for us to, to work with SAP but all MRPs are working a bit the same and they're the same kind of, of calculations and in the end Flowlity is kind of an add-on, on top of an MRP and an ERP to make it more efficient and, and better. And so that's how we work. And so there's kind of continuous improvement loop in between the ERP and us. And so what we do is that the MRP is computing. They're getting the first dependent demand, purchase requisitions if we're I talking about SAP terms. Those purchase requisitions, we upload them into um Flowlity. Our algorithms are learning on them. And so we then offer to the users a platform where they can see our recommendations compared to the NRP output. And then, they take the decisions then, and then we put back into SAP and we replace the purchase requisition with our own. So that's, that's how it works. So we really add, our add-on on top of the ERP to make it more efficient. And so we do that both for raw materials, for finished, uh, product, because we also do DRP kind of use cases. And yeah, working is actually the same, uh, with the same idea that for the DRP, we are doing a forecast that each echelon, of the, description network and we are not running, uh, or so deterministic enough, uh, calculations. And, and here, for example, working with, uh, big companies, like, like Sango bank, for example. So, yeah, very, um, um, good results on that, on that topic as well. So it's both, say the upstream part and the downstream part of the supply chain.

Tom Raftery:

Ok superb. And I'm guessing it's kind of cloud delivered subscription model.

Jean-Baptiste Clouard:

Yeah, cloud delivered subscription model, very classical in that sense. And we do only SaaS only web based SaaS. The other topic that makes us quite, you know, unique is that we are building. In the end, what we try to build is also the first supply chain planning software that is also embedding the suppliers into the planning of the, the daily planning of, of the, replenishment, for example, because we think that, you know, to really truly solve inventory issues because that's, our goal, that's our moonshot. There is a component that is achieved internally. So, you know, by better, you know, predicting the, the volatility that I just spoke about. And there is a component that is, you know, done externally by making sure your suppliers are more reliable. That not just delivering on time to do that. What we do is that when we do the planning, we do, and we optimize, I inventories and replenishment for one company. Then what we do is that we offer to all of the, all of the suppliers directly, the capability to have, dynamic and, real time, uh, forecast and alerts so that they can, you know, be connected to the, to the demand of their customers at any point in time. And, uh, and so that's kind of the step two that we deploy with our customer. So, you know, step one is we optimize their inventories internally. Step two is we deploy, particular supplier portal to their suppliers so that their suppliers are way more, accurate and, and have visibility and reactive to whatever needs they have. And to do that, you need to be cloud and you need to be SaaS

Tom Raftery:

Sure. sure, sure. sure. And where to from here, what, what are next steps for you guys in Flowlity?

Jean-Baptiste Clouard:

It's a very good question. So of course, you know, we are startups, so our aim is to grow, uh, on our target market and, and to go fast and to, to do a lot of those use cases and to replicate. The end goal for Flowlity is to, is to, to really, to build, and to build a digital, replicate of the physical supply chain and to automate all the flow of goods, starting from the planning. And, and the way we do that it's exactly like step one. As I said, we optimize inventories and we optimize replenishment for one company. Step two is we involve the suppliers into the planning and so we deploy this supplier portal. Step three, uh, is to help those suppliers actually better manage their, their demand and forecast in, in the most possible and most accurate way. And forecast no, by using the inputs they get from their customers, because, you know, we do, you know, so working with the customers so they can get directly the inputs and have better forecast. And then, by doing that in the end, what we try to achieve is to be connected both to the customers, and the suppliers, to do cross company inventory optimization, to really be, you know, like this, this guy, like , balancing the inventories in between those two partners, to be way more efficient because we're breaking information silos and you know, what we want to do is build on top of that all the additional capabilities that allow us to really automate this flow in between a customer and its supplier and make them way more efficient in their collaborations.

Tom Raftery:

Very cool. Very cool. Jean Baptiste we're coming towards the end of the podcast now, is there any question I have not asked that you wish I had, or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Jean-Baptiste Clouard:

Maybe one thing. So, you know, we are a very fast growing company. So now we are, thirty five people, very soon 50 people where, you know, we're getting a lot of customers. Uh, customers are, you know, really happy, but if you want to work on those topics about, reducing your strategies, collaborating in a better way with your suppliers or with your customers. That's vice versa. Uh, then I think we are the right company to talk to, and to help you with your challenges.

Tom Raftery:

Ok, super. And if people want to know more about yourself or about Flowlity or any of the things we discussed in the podcast today, where would you have me direct them?

Jean-Baptiste Clouard:

Uh, sure. I mean, you can just write me a mail, uh, or you can contact me on LinkedIn. So, Jean Baptiste Clouard on LinkedIn, There is only one with this name. So yeah, don't hesitate. And, uh, you know, I will give you Tom, our contacts, our emails, and, and please do not hesitate also to contact us directly by our emails.

Tom Raftery:

Okay, perfect. John Baptiste been really interesting. Thanks a million for coming on the podcast today

Jean-Baptiste Clouard:

Thank you very much, Tom. And I wish everybody a very good day.

Tom Raftery:

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 sap.com/digital supply chain, or, or simply drop me an email to Tom dot Raftery @sap.com. If you like the show, please, don't forget to subscribe to it in your podcast application at choice to get new episodes, as soon as they are 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 time.