Evolution of AR Forecasting Best Practices at Duracell: Case Study

[00:00] Steven Wurst:

Hey good morning everyone. Thanks for coming out. I hope you hear me. Okay, you hear me. Okay good. So my name’s Steve. I’m with Duracell. We’re going to be talking about the Cash Forecast before Accounts Receivable. And I get to kind of bring into it a little bit of the process where we started. And then to where we are now. With four years in HighRadius and collaborating to do it even better to and kind of bring it to the next level. So Duracell is a company that I’m thinking a lot of you might know about. We’re a major consumer product company. So we’re an American manufacturing company. We have batteries. And we make smart power systems. Some quick history, Duracell was for a while bought long term owned by Procter and Gamble. And four years ago there was a transition where we moved. We kind of started from scratch when it comes to many of our processes and finance as well. I mean we’ve got new people for my finance department. We got a new U.S. AC that’s in Pete’s system but we didn’t have a lot of those reporting capabilities or the other both times that are often valuable with companies particularly in the finance area. So we had to figure out how to do things pretty quickly. One thing that I should mention and maybe the goal of a lot of companies including your larger companies but the financial forecasting reforms within the various Duracell business and these and then it kind of rolls up to a corporate level. So when I’m talking about accounts receivable forecasting. The numbers get rolled up with all the ins and outs of getting your cash position and the Treasury people that get all that data.

Steven Wurst:

So I work with all the teams in terms of getting you to know all those ins and outs of cash. So what I’m talking about is you know the payable what’s being paid in taxes. How much we’re paying the vendors and some of the intercompany types of transactions that get in the mix. So, I have pieces but there are other people to better involve. So, first of all, how many people do cash forecasting for accounts receivable right here. There’s some of you and then I’m kind of thinking the people that are in here want to learn about the currency that you’re going to be doing it or maybe are in a treasury function and you interact with others that are doing it. So, you know I think at the end of the day we’re trying to get to a place where we have accurate forecasting. That’s the end goal. So we Treasury people know how much cash we have on here. And for a lot of you that know this, sometimes too much cash on hand is good. And too little is not good. So trying to get accurate cash forecast is important. So, this is my process. And then as we go through the next couple slides you get to see how it kind of runs parallel. And we hear a lot of good feedback. Here is a little bit about my process. I’m not kidding. You know I like a bit more of it. We are taking it to the next level. And how to do it better and how it could be automated. And that’s why we are going to be working closely with this. Anything that we are going to automate, none of it has the financial experiences as well as our technical experiences to work with HighRadius. Getting it to that next level. So, if you think we’re going to tell you about it, it’s more about a direct market up here. You can see there the indirect method for cash forecasting. And a lot of you probably know what it is. But in case you may not, the indirect is basically your FEMA people, your financial people who are doing these longer-term projections and they look at income statements balance sheets and they try to back into what they think the cash projections would be done if the direct cash forecasts. And this is kind of a bottom-up approach and actually going to the details of the invoices the customer level and looking out what is out there to be collected and then we have to build in another element which would be future sales. So if I’m looking at a very short period of time. It’s easy because we’ve already invoiced the customer and I can make some assumptions based on payment habitat. You know what we’re going to collect. If I go out further that’s when the difficulty happens and that’s why we’re going to be working with, you know, Bernie’s and why we are working with her. I’m trying to get that type of accurate information.

Okay. So you know for these manual pay scores better than we call a pay score. And it’s kind of an internal term that we have but it’s basically referring to customer payment habits. It’s a manual process. And again, I’m not going to kill you with all the details of how I do it. I would rather have the time where Bernie’s is showing you a better way of doing the future of it. Okay. So, we’re only doing monthly forecasts then we’re only going out for two months. I mean actually I do weekly but I’m not very good at it, to be honest with you and our forecast accuracy is very good in the current month. We’re ninety-five percent. But in the second month, it’s not as good. That’s when it starts to deteriorate. We’re about 75 percent and the business leaders and Treasury folks aren’t happy with that because they need to plan on the use of cash. What are we going to do? Are there going to be future obligations that begin to pay off in a couple of months from now and it’s only going to have the resources to do it? Are we going to maybe have to borrow or are we going to be able to make a payment on something that we thought was going to be? Okay. So right here, we’re talking about the high powered cash forecast. This is HighRadius. Where we are right now is in phase one.

[07:39] Bernice van der Velden:

Yes. All right. So right now we’re in case one of our implementation with Jason Anderson is one of our early adopters. So. It’s really kind of a partnership. So he gives us feedback on what we’re doing currently. And already we’re seeing that we can forecast up to six months. So instead of just a few months. We can go up to six months and we can break that down into weekly segments as well. So up to 24 weeks. The accuracy is around the same right now around 95 percent. And we want to make that even better of course. Your forecasts, you want them to be able to rely on those who you want to have a level of confidence. And then right now we’re just focusing on a category since that is the most difficult to forecast. Can I give a raise of hands of who has the most difficult time forecasting there are right now? Yeah. So that’s what we’re focusing on with all of our early adopters and trying to focus on that first and then we’ll start working towards the next categories. And that’s where our faith comes in. We are going to look at different categories of AP payroll as well. Like Steve mentioned. And we’re definitely going to be targeting an improvement in our accuracy. So right now our target is. Increased 40 percent. And then to add an even more granular look of your forecast we want to look at it on a daily level as well. Back over to Steve.

[09:18] Steven Wurst:

Sure. So when I talked about we’re kind of doing it manually it’s not automated with the produce we’ll be talking about. It’s pretty rudimentary for some of you. Maybe this is still run as well. But basically what we do is we export out. We have some 50 working cash modules from a HighRadius and use their collection module where I export. Our data where I’ve set up filters. To look at certain receivables that I think are going to be collected within a certain period of time. So for example right now if I’m in February and I want to see what can be collected in the month of February every week right through to the end of March, I export out everything that I’m showing. It depends on your payment terms like customer or company. But we actually have a Q4 model which takes an assumption based on customer payment. I have data based on their customer discount date. If they are offered a discount some may be offered this. I know how our customers pay. I know by a dollar-weighted average whether certain customers pay. Early or some customers are paying me on average one two three four five days late for example. I know. Next. And when I dumped the data. I account for that mix into what is calling. Give. OK.

Then the next piece of the app the next element in this is pretty important if you are in the kitchen the business. Unfortunately, we have a lot of customers that take deductions. Deductions would be often referred to short names or you know they’re basically short pain. OK. So in our case, we have customers instead of maybe waiting for promotional money precisely because. We have customers that can deduct the damaged goods. They can deduct made because of pricing discrepancies. Of course. The perfect world is we don’t have deductions and we do everything we cause and make sure it doesn’t happen in the first place. But regardless. Certain customers are pretty aggressive. They will deduct anyway. I need to understand those habits or I need to be privy to some information current information that leads me to believe that the deduction happened. So what I’m doing is I think it shows up there a is basically the data dump based on certain criteria that I have such as the payment package. Then I backed out the deduction. And then the next piece is OK.

Steven Wurst:

Within this period of time that I’m forecasting. I am going to have some new sales because we have some customers that are short term that may fall due within that period of time that I’m forecasting. And that’s always a very difficult part for me and the reason why I’m saying it’s difficult because the timing and our sales are important. Terms very. Customer by customer sometimes. And is it going to show that it was the end of the month? Again based on a customer’s payment habit. Well, they probably passing that correct period of time but and certainly, they’re going to pay yes or no when do I adjust there. So. Then, of course, the last piece. Defended them to the cash for is actually A minus B which should be deducted plus maybe. So the key would be what I just talked about. And if you would be any manual adjustments and this is pretty key. And I think even when we talk about automation. You need. To have. Some keys. That will adjust your cash forecast. Because. There will be events that happen. I may be told Oh Wal-Mart is going to take this really large deduction because of there. Or maybe we have a big return coming back. Or in our case and I’m in the battery business. There’s. Seasonality. We sell a lot of our batteries during the Christmas holidays. So we have to get the signal when we apply for that.

We could also add that that actually wouldn’t be as much of an extraordinary event that’s more historically a known event. But I do also have with the seasonality I put in something like turkeys. The bad news is there’s. There’s you know. That one thing could happen. The good news for us. We sell a lot of batteries because people need to fill flashlights. Their devices. So we’re not we get a lot of sales and stuff like that so I’ve got to be able to. Defeat. Those situations into my numbers. So I think a lot of you are here because there are challenges.

Okay. So. You know I’m not going to elaborate on a lot of them but one is just giving accurate data in general and kind of assuming a lot of you here are having problems getting the type of back issues. And you know again if you have a lot of customers. And depending on. Having too few customers. Sometimes you got to be able to have their customer insight built into the logic. Of your methodology. OK. Some of you may be challenged by a lot of manual work. So what I’ve seen in my career. Is there’s a lot of manual dumping using spreadsheets sometimes revealing numbers with somebody else? Does it make sense? Yes. No. I mean the perfect world would be if we could just push a button. OK. And that gets into the lack of tools to try to. Conduct the exact analysis as well. So I should mention. We try. A couple of other tools. And it didn’t work well. And we got inaccurate numbers. What we found is. That it didn’t really adapt well based on our customer habits. And customer habits. Could change in our month to month or over time. So it’s not static.

One customer may not pay a certain way. Throughout the whole year. A customer may decide as it gets close to their urine they’re going to hold back cash. So when we talk about the future with technology we need to understand those types of happiness. OK. These systems we’ve looked at are too rigid. It didn’t have the manual adjustments that we talked about. And it just. You know the bottom line is. That. It didn’t give good numbers. It didn’t know about customer introductions happening. So. And the big key here is that you really know when these new customer sales are happening. In the timing of those sales. So we scrapped those. All right. So again I think a lot of you might know the implication. Of. It inaccurate forecasts. But at the end of the day if you’re with a company that is really trying to plan on using your cash. It’s got to be accurate. OK. So. So that’s really our main goal here. And you know the other thing is a waste of time wanting the variances. I hate that piece because it never fails. I give a forecast. The Treasury Department calls me back and says. Why is it this number can we do this number. And then I got to be able to explain a lot of things. Then if I’m off. So you know it’s kind of this.

Double-edged sword. I don’t want to give a number where they over-deliver and then by the Senate you know by the same token I get beat up to fight on to the other two. So a recent one month period that I mentioned there I am that ninety-five percent accuracy so that’s pretty good. But it says you are trying to get better and I mentioned before we’re kind of a new company these days. It’s about can we go out several more months and getting that accuracy. So. Pretty. You know we’re going to talk and I’m sorry. My slug. No. So again I don’t think. A lot of details because I think a lot of you understand the man the cloth. I mean there’s a lot of input. To be able to get. More and more. Adjustments that we’re talking about. You know we’re looking at customer invoices options. And then sometimes we have the FEMA people pointing into. All the things going on at the end of the day to call the numbers you know put in the midst of the war with all law. And when I’m delivering numbers to the Treasury Department and the CFO. So like I just said.

[18:32] Bernice van der Velden:

Sorry, Steve. Like, Steve said. There’s a lot of manual inputs and his process right now. But for. Our current process what we are doing and we have years of experience of course with our integrated receivables platforms we can. Integrate all of your information. Seamlessly gather all that data from the ERP. So all your accounts receivables from your key and your accounts payables eventually as well. And your payments from here straight from your banks. So we can just grab all of that information and set it up onto your cloud platform and that enables. You or another user and the CFO of your company to take a glance at our cloud system and be able to tell what the forecast is going to be for the next six months. This also allows you to work together on this. Platform. So like even mentioning. There are times where the data that we are getting. We might not have everything that you know with those manual inputs that he does right now as well. We want to have some kind of way of dealing with those and that would be very unusual circumstances. So in that aspect, we also want to have the ability to add a manual input. So we have that as well. And we can add different types of roles so the CFO might not want to. See the granular level of things but the. The audit calculator would want to see all the details so he could manually input that specifically. And of course, our whole platform is accessible through your mobile your iPod you can be checking it on the go. I know everyone who likes to travel. So you can just check what your forecast is right there and then everything. Last Friday someone asked or on Monday someone asked me on our meeting forecast going to be. And if he was using our system he could quickly check that out check it right now and use that. So let me actually show you life demo.

[20:50] Steven Wurst:

Right. This is actually the best part. That’s why I didn’t want to teach you too much about the manual process. I think the key takeaway is. The manual process which is a lot is what a lot of companies are doing may lack accuracy and it’s time-consuming and it’s not really the future. It will produce a show really is the future. And using technology is just like yours better than what I was describing.

[21:28] Bernice van der Velden:

OK. Category 1 seed is clearly. A. Little dark for them. This is what. People be logging into in the next couple of weeks. So we are starting our implementation process but this is what it looks like. So this is a demo system on-demand data. But he can right off the bat. Look at his forecast in the next three months and you can even toggle to the six months forecasts. And see what it looks like. And then we also spoke a lot about having that confidence in your forecasts. Even with an eye model, there is going to be a level of confidence. And. I say that but. Since it isn’t a model it does learn over time. So we do have that machine learning capability of course. So your forecast is. Going to be more accurate as time goes by. But to give you that that level of confidence we do also show. The forecast versus actual in the past. A couple of months. So that again.

[22:34] Steven Wurst:

Going to. The one thing I do want to mention this is a natural evolution didn’t change itself for confidentiality reasons I didn’t want you to think it’s our data. A little developer’s style.

[22:46] Bernice van der Velden:

Yeah. So you can look at the forecast versus actual in the last six months toggle back to the three months and you can do that too. And so what that actually shows you if you look at this. And so what that actually shows you if you look at this point right here for December 30th it shows the actual was one hundred and six million. And that forecast. So that was made a week before. As you see here. The forecast that was made a week before for December 30th was one hundred seven million. So there you can see. How is my forecast doing versus my actuals? Can I really rely on everything so far? And then this next card here gives you that same data. But maybe a fashion that you prefer. So it shows you the percentage shows and the direct difference as well. And then I know some of you also don’t like to just look at graphs. You want to look at the numbers so you can actually click on this graph and it’ll show you the forecasts in numeric form. And how we do it right now as we make forecasts each week. So every Monday we’ll create a new forecast for the next six months. So that’s how it tallies up here. Then the next thing that we have been talking about quite a lot this presentation is being able to have some kind of manual input. So that’s where we go here. Here. It’s almost so you won’t be just forgotten that Excel hell but you will. So this is kind of your excel on the web. We call it high sheets. So it allows you to enter a manual override. Say there’s a big return coming in that the customers told you over the phone that wouldn’t be applied in any of your PS or your banks. You could manually overwrite that right here. And then if you would go back you can actually check your forecasts.

Back to the graph. You can check your forecast what it looks like with that may override. And without. So you can toggle. Between. So this is kind of. What we’re. That’s this is our phase one with Duracell right now but we are looking for feedback and. Feedback from Steve especially. But. What is everyone thinking right now? Is everyone a little excited. Sir. I’m. Sorry. Our cash position that would be our future state. So if you go to a diner booth you can see the cash position specifically right now for a Duracell or focusing just on forecasting. They are specifically. But. Your actual is in flip. Yeah.

[25:48] Steven Wurst:

Yeah. So. So she’s kind of getting back to where we started in an inn question because the end of the day it’s really the cash position that counts. But when you look at the various elements that make up that cash position. The accounts receivable hopefully one of the bigger numbers affected is anything there’s going to be a champion one day.

But it’s an important element. It’s usually one of the largest and it’s important. And then also it tends to be I think what a lot of companies seem to struggle with. So if we were to look at the process. And live with me working with HighRadius it just seemed to make sense. Let’s focus.

[26:43] Bernice van der Velden:

Yes. Any art piece under control and then there are all those other elements that will go into it as well. Is this your question. And we of course also have a lot of history working with accounts receivable so we and then. The production and our model is actually also based off of customer nuances. So let’s see you mentioned that other solutions might not have added. As we look at the history of customer pain. And apply that to our model.

[27:07] Steven Wurst:

And I know I’m sorry there are other questions we’ll get to you but adding on to that I think it’s important. So the model that you just saw. It’s learning. So we have. HighRadius modules we have one. The collections deduction in the cash shop and so forth. We have it where it’s actually. Now this new module that we’re showing here is learning based upon.

The data that we have. So it knows the timing and the types of customer deductions that are happening. It knows the payment terms and when those customers. How those customers are saying it’s learning all that it knows the sales whether it’s going up and down based on our customer habits when they’re buying as well. So. So this isn’t just you know happening kind of out of nowhere what we think. I think it’s important for you to understand it’s learning based on the data that we have in our system. And it’s getting pretty good at it. Because. When you use the technology you’re going to do. Maybe you could always elaborate or four years but it’s really about the water use it. The more it learns and then it can better anticipate what’s happening. So. We don’t currently have that directly.

[28:54] Bernice van der Velden:

In here. But that is a good suggestion. I think we’ve heard that sedition actually added another meaning as well. So we are actually continuously writing down everyone’s feedback so we do appreciate all your feedback.

[29:33] Bernice van der Velden:

I’m not quite sure about the direct integration with Salesforce but I do know with. Different ERP systems. We have. A flawless integration there so you can just grab the necessary information from the. Site. He was asking. Go ahead. So basically what I was asking is obviously sales and sales linearity throughout the quarter have a pretty significant impact on collections in terms and that sort of thing. So really the question was based on this interface with Salesforce or any specific sales forecasting model that would contribute a review component of variance with.

[30:16] Sayeed:

Hello, I’m Sayeed, the chief product officer for the company and I’ve been very focused on the Treasury lots of new products. So maybe. Directly to your question we are increasing the number of beta sources. Right. So ERP banks are definitely there and we were thinking off have been the answer will have been in systems where there are typically plants. The longer the plants of saints and how to bring that information in. So these definitely would factor. As we hold this product forward.

[31:49] Bernice van der Velden:

And even the previous question I think there was about what contributes to the radiance that is definitely a focus area. So as an example it could be I mean in this particular case we are looking at a particular set of company codes particular regions. What. What of that. Can you drill down and say is contributing a lot to the medians. When you come to multiple cash flows right. Cash flow categories then which of those are contributing highest to the variance, of course, we had done some historical analysis A.R. typically was the highest contributor to the variance but once you have all those flows you want to be able to break that up and say you variances let’s say 5 percent. And guess what sleepless end of that is actually contemplated by A/R right. So that kind of. The analysis is also definitely something that is showing. In fact, when you have multiple cash flow categories we already showed the breakdown by region by a company called by cash flow.
Categories etc.. And actually we do have another presentation that goes through the roadmap of what is to come. This is just going to get it was quite exciting. Yeah.

[32:07] Audience:

Mine is very quick Steven. Just a quick clarification about what you’ve mentioned in your forecast in Spain some data in the system right. So are you leveraging the historical purchase information from the customer to predict what the customer is going to buy in the next few weeks or quarter miles? Is that how you do your forecast?

[32:30] Steven Wurst:

Yes. Sure. But it’s not a perfect world.

[32:46] Steven Wurst:

A nation based on our planning to what’s kind of out there. What’s going to go coupled with the folks from the FEMA team with the projected sales are. So. Again this is. Why we’re talking about the future state the complexity behind. Getting those sales are pretty challenging versus if you have. A tool that could do more predictive. Pieces for that sale for you.

[33:30] Audience:

My question for you is a requirement to have a reading on your cash applications tool to be able to use this as our forecast now.

[33:37] Bernice van der Velden:

So actually I mean. The implementation for it with Duracell was maybe a lot quicker because we already were integrated with all of their systems. So we didn’t have to go through that but. I think half of our early adopters are actually nine existing customers and they’re going through the same there at the same stage right now pretty much as well. And we have 400 plus customers already that we haven’t agreed with both with their banks and with their earpiece. So an hour and that’s. There’s no difficulty there.

[34:16] Ganadeep Ray Patlolla:

So the way I would like you all to kind of imagine this is like think about this as the ecosystem. This transformation journey that Duracell is on is to kind of. The big picture. The entire A.R. position that we want to. Make it. More digital nimble and at the same time have more accurate. And on the flight. Numbers. The idea came. Last year you only have about 80 information. How can you make this better? From that point. We are building on this. So I think even if you the way I imagine that even if you don’t have all the modules that you also have collections of actions and. Application. Even contact. Even if there’s only one. Module. You’ll all be feeding the information. They already like the high rate to set sitting on a big data plate if you want to imagine it’s how you want them. Processed and data further. So and. The more we use the system we are seeing that they can be something more additional tools. That help across the organization from FEMA the primary because I think when we did the other forecasting. Implementations I’d do it put personnel their focus from the type of insight. They were incoming from the bottom-up approach. So there was a big mismatch. So here we think too. Attack the problem from a different perspective. From the sales. And from AR giving the big. Plus in your balance sheet instead of all the minuses, you can get that number. Better put. Higher cost. Confidence level and then that’ll be better.

[35:59] Audience:

I just have a question, does this tool forecast DSO as a part of this.

[36:10] Bernice van der Velden:

Not for the forecast right now. Yes. You can get it. If you would like to add to that.

[36:27] Sayeed:

You know ADP average days to pay as he was talking about. That’s often a metric that folks used to predict. Right. And that’s usually across all customers. And some of them might get sophisticated enough to have. A face good on average needs to be by the customer but then it gets pretty hairy pretty soon if you have thousands of customers. Right. So that’s why it’s really easy right. Easy within codes to consider all kinds of variables in the model. Right. Instead of just taking. How does a customer average these to be? You can also consider that among for example. All right. So a simple way a one to one is you have a bunch of. Inputs that you have. Right. Customer, You want to predict the payment date for example. Right on an invoice. That’s. The crux of the underlying problem. Often you use one particular variable like iVillage dates to be but now the air it can consider all kinds of variables to predict that. Average days to be right. You might be using the dollar amount you might be using the timing when they actually write that customer always runs that AP On a Tuesday or a Friday. So the day of the week. So the machine can now suddenly start looking a whole bunch of. Factors. Right. That manually in an excel sheet was too hard. Right. And that’s why we’re able to get that kind of accuracy. Hopefully, that’s done. The answer to the simple metric of GDP average needs to be we are beyond that. Right. Because we have all this data from an ERP and payment behavior that we want to consider in the forecast. Rather than single point metrics.

[38:18] Audience:

Good presentation see. So. You talk about accuracy in your current model and the one that is in transition. I mean are you going down all the way to like day or day or are we just talking about like you know? We get a time one at a time.

[38:28] Steven Wurst:

Yes. So my model. The manual labor and I sure do what we’re doing. I do give data to Treasury on a daily basis. But it’s not very good. I can hardly fathom. Forecasting on a daily basis. My current model. I’m pretty good on a monthly basis. In the first month and then it deteriorates thereafter. The future state the digital feeds the effort he’s talking about. Will bring me to that level of granularity on the daily forecast.

[39:07] Audience:

Thank you. Good question. So I think it’s a great tool and a great idea. Can you export it to the Treasury team?

[39:15] Bernice van der Velden:

You Mean what do you mean exporting.

[39:18] Audience:

Well if you do the forecasting and trade which is what we do. Then 80 percent of the sales just have information for us too.

[39:32] Audience:

Communicate. So you find that the future is doing with the digital. Are you talking about how their face value that we’re going to?

[39:39] Steven Wurst:

See this. I mean I didn’t produce it. That’s a question one for you. So I think are you referring to when you saw that right up there with all the numbers and let’s work that out.

[39:50] Bernice van der Velden:

So actually they would be able to access it right on there. On the platform as well they wouldn’t have to export it. So you know. You wouldn’t have to know your colleagues.

[40:01] Audience:

The frequency and. The type of information. Churn rates if you wanted to see. If. This changes everything. You. Think. It’s. Just. Like hey, our 5 percent pay rate is now. We can say.

[40:26] Ganadeep Ray Patlolla:

I think you are seeing that this is just the information by the team that needs to plug this information along with a payroll all the other parts. So I think it’s a get some export feature. I don’t know. Maybe I can explain.

[40:44] Sayeed:

So the integration aspect of this of getting the forecasts out. Right. So I think of forecasting as if you already have a theme a system. That we are actually on top of it. Because a lot of pharmacies talk about forecasting but don’t really get into the meat of actually you know what is the logic for forecasting. You often have to end up doing it in excel and then you are uploading these exiles and. The DNS is often just aggregate the soft information for you. Right. So. Big that the heart of the problem of forecasting is what we are tracking here because that’s a hard problem. And nobody was really solving it well. Right. And with all the data sources now you don’t have to replace if you have a team as you don’t have to replace it with you. This is information that can then be fed to the M.S. instead of doing a can of course export and excel and then upload as you do normally for aggregation. But the reality is we can integrate. So we’re not trying to replace your core themes but we are Frank to help you solve the cash forecasting problem. You can integrate with things.

[41:57] Audience:

Thank you, its quite a good presentation and appreciate that. So it. So that’s an amazing configuration that Cho put together over the top two or three biggest challenges you had completeness. Putting this together.

[42:14] Steven Wurst:

The question early on I mean we were out in a phase with the human element and the change. I mean I don’t I put the two biggest pieces are predicting the sales. OK. Yeah. And when those sales are going. Actually it’s not predicting the sales as much as. Predicting. What day those sales are going to happen based on returns when it’s going to be due. That’s my world right now. Future state. That’s what they’re trying to solve. OK because I’ll just make it up I want to give away the terms. This is sure for our customers. But let you say for example customer pays in 30-day terms. I mean if it’s the end of February. Will the customer pay that amount may be in the month. Well, not all customers are created equal. Sure that some customers may be late which means it won’t be paid in fact next month. If you. Look at it. So that’s one of the biggest challenges I find. I mean in the sales in this is what it is for the month it’s more than the timing of those sales. What customer it’s going to be and what those payment terms that have been negotiated with those customers are. And then the last piece is. What customers have? A Right. That’s the granularity of it. The U.S. for two the other is predicting deductions. What I really like a lot also from this the future state. Which I digitizing is. My world will underscore. A lot of those customer habits. How do you do that? Again. It’s not perfect. Sure every customer you know the numbers mentioned Wal-Mart just because Wal-Mart is great. Wal-Mart conducted in a certain way or a certain amount or a certain pattern. Last year does not repeat itself. But I would say it’s probably about at least 90 percent. Yes.

[44:19] Ganadeep Ray Patlolla:

Can I add to that? So I think the other challenge on this journey that we saw was. Hiring already had AR information to the populace. The easier one. I think that we want to ramp it up to the next level then we want to kind of take this further on when we are getting are trying to get. The other inputs are focused. And we have to get them buying from. Accounts. Payables payroll intercompany FEMA.

So I think we’ll have to do it. Buy. In from others. Prostate areas. So I think. Right now it has kind of been. In the very infancy status. If you are playing with numbers. Right. So I think that’s kind of the longer vision would be that we have to get broader. Buying. Or a combination. Instead of just doing a little project.

[45:13] Steven Wurst:

Still, a patient mentioned and keep getting profit. We’re pretty much done. So. I think. There are a lot of good questions there were more slides. I don’t think it’s as important as the questions. So we’re going to keep an eye will. See. Properties. That may behave a little time after. If you guys have questions. I don’t want you to touch us after this meeting. We were under the. Question. And not always available for chat. And I will also be at this outside.