In this webinar, Sahil Vijay, Senior Product Manager, ERP Cash Collections and A/R Reporting Lead from General Electric shares his first-hand experience of transforming the order to cash process. He also emphasizes the importance of analytics and why it should be the beginning point of your automation journey.

On Demand Webinar

GE’s Vision For Evolution Of Analytics In Order-To-Cash

Session Summary

In this webinar, Sahil Vijay, Senior Product Manager, ERP Cash Collections and A/R Reporting Lead from General Electric shares his first-hand experience of transforming the order to cash process. He also emphasizes the importance of analytics and why it should be the beginning point of your automation journey.


Key Takeaways

Evolution of analytics with implementation of dynamic reporting model


  • The one platform for everything approach
  • The importance of applying dynamic reporting model
  • Taking the drilling down approach for dashboard capabilities
Key characteristics of analytics in 2020


  • Building operational reporting and dashboards
  • Taking action-oriented approach with dynamic dashboards
  • Thinking of Robotics & AI as digital assistants to increase productivity
  • Improving report accessibility for different hierarchical levels
Understanding on-the-go analytics


  • Accessing the right content at the right time is crucial
  • Building a mobile app to access reports and dashboards on-the-go
  • Top reasons why you need mobile analytics
GE’s transformation story


  • Insights from GE’s cash, collections evolution journey

[0:00]  Moderator:

All right, let’s get this show on the road. Hi, everyone. Thank you so much for joining today’s session on how GE is shaping its future with the advancement of analytics in O2C. Your speaker for this session is going to be Sahil Vijay, Sahil is the senior product manager of ERP cash collections and AR reporting lead at General Electric. He has been in it for over 16 years with expertise in SAP products and various database platforms and has been working closely in the analytic space in recent years. Now that that housekeeping is out of the way. Sahil, the stage is yours.

[0:35] Sahil Vijay:

Thanks. So I would say from yesterday to today, there were many sessions about analytics. You see different areas of expertise, that hybrid is maintained with other expertise we heard about where I’m going to go today is more around how do I  put that all together. You know, what else can we do? On top of all the knowledge, we learned analytics is the key. And I think, in the keynote session, everybody mentioned like analytics. They do pick a case state and then go from there, they don’t pick the Big Bang, because of a small piece.

[1:25]Sahil Vijay:

So how analytics could transform the order to cash, that’s the key and then how to use looking at the vision to build a synergy between analytics and your platforms, your applications. And then we’re going to talk about the inside from GE, how we evolved our collection journey, especially acquiring Ahlstrom and then having multiple ERP. So we run a scenario where we have SAP, we had Oracle, not just one Oracle, multiple Oracles. I think we are down to 40 ERPs. We started at 200 plus. And then our journey is weighing it down to 8 ERPs or 4 by 2022.

So very simple questions.

[2:17] Sahil Vijay:When we look at the analytics platform, US audience, what are you looking into? Are you looking for the big dollar numbers or looking for the collections portfolio, cash app? And then there’s a team, you may have collections leader collecting all the data and pushing out to your teams to work on or putting them on a platform that we can work on. Why not? I can have one platform where I have everything in one space. It’s not just for my leadership, not for our CFOs or CEOs. It’s for the people who are doing the job-like collector or cash applications team, AI, automation. But now, how could analytics transform the space? So the whole theory is, it does not need to be static, the front end of all pictures that you present to your management, right? How it can drill down from top to bottom. So I’m going to talk about a dynamic reporting model where you’re sitting on top of all that knowledge acquired from analytics, and how do you apply towards your specific areas. So looking at a three-dimensional view, like we saw a lot of different models. I was the top of the line charts down there. But how does that go down to the lane and all of that has analytics behind it? The biggest challenge that GE had and I spent one year was on building their AR report which you just don’t have any idea how to collect because you’re working with the deductions team, you’re working with your collections team, everybody wants data. And then in SAP, what we use is upstream and downstream. So, upstream for us is projects and sales orders. So, you know, your project may or may not depend on your business, your platform or you have seen a server which is getting a purchase order, you build that upstream process, and then you do forecasting too before you conclude the business with the customer. Then it goes down the track too, you know, billing the customer, sending them invoices, all the way down to getting the cash paid. Then you run into scenarios like unapplied cash, applied cash. Look at all the scenarios.

So what management is looking at is that we are struggling. First, we acquire companies, you have company codes and profit centers that are split. And then now I have to build in a collection model, we didn’t have collection for one of our ERPs. But I need all the data together because my collector needs to know who the biller is and the biller needs to know who the project manager is. So in all the chains, we have to put together that that’s where we were looking at an analytical scenario. First, let’s capture the data. This was very important because no outside businesses can come in and collect your data. You have to bring your folks your business to come in and collect data. So dashboard. You all see that right? It’s a vital thing, which is very true. It’s a single source of truth. But if I tell you here, I have a dashboard for invoice to cash, I have a dashboard from my cash collection, I have a dashboard for say deductions, then you’re sending your team three different ways to collect the data. But if I have all of that in one zone, and I give you all kinds of front end tools, they’re like, I want to look into deductions. And somebody says, “Well, I looked into deductions, but I want to step back. And you know, maybe I want to see what happened to my order. What was the purchase order? Can I see the purchase order? Can I see the invoice, putting all that together?” And the goal is to bring efficiency by doing that, right. So the concept is this. I have a dashboard. See, I’m a collector, right? Click on the dashboard, only see what I need to see. I don’t care about the odds for the moment. But I just want to see what I need to work on. I’m a collector leader, I want to see how my collectors are performing. Or if they need insights from the billing team, who are my contacts, and of course, the dollar value.

[7:18]Sahil Vijay:

From a management perspective, they want to see how the processes are working. So all the data is fed in and out, right. Now, what is the journey in 2020 in order to cash analytics as a synergy? So a very simple example, which I’m going to bring in. So, we collaborated with HighRadius to implement FSCM in our SAP space with a company executive. So what we’re going to have is our 40% portfolio since we are in a thinking mode, and cut down on a cost from the ERPs and all that so we decided to retain one of the biggest upheavals for 40% portfolio like- there are no collections, there is no reporting tool. And then we started looking down the numbers. And we were in a situation where we’re strapped with unapplied cash, unallocated cash. There’s no collections module, what to do? We were like- let’s first understand analytics again. You have to pick a use case to start with. So we started looking at the downstream process rather than the upstream process. While one side of the house is working on putting FSCM together another side of the house is saying that you’re putting everything together from an analytics perspective.

[8:42] Sahil Vijay:

Now the vision is that I want a three-dimensional view of analytics like I said, you have a universal picture of everything in there. I have seen reports going around 150-160 columns that are feeding AR, AP order, purchase order, FSCM data, ILP hybrid is the last one. So number one is making it more universal. With that, we are saying reporting as a front end dashboard should also be operational. So I can look at the charts, but I can also work through it. And the whole concept of bringing all the data together to the platform is simplified in that full motion. Because think about operation, there’s one aspect of reporting and one aspect of operations. So you build a platform to bring all the data in, which is more static. Where we are going is we bring all the data to a platform where we can say, “Yes, this is static. But I’m giving you capability when you click on it, you do your cast and it’s going to feedback to your ERPs”. So a collector, for instance, would go in and get the customer details. So we have MDN hooked up for customer information right up front so you make the calls, then you have all the prioritization factors. So everything is kind of put behind a platform or a dashboard, which drills down to the respective areas. Now, how do we do that?

So, we’re looking at an action-oriented approach, based on what action somebody has to perform. As an individual, I’m interested to look at the big picture, or I want to go to a specific area. Say I’m a biller and more concerned about billing, I want to post an invoice. I’m a collector, I want to go down to the collection for you. So we are controlling all of that through different views that we are putting in. Now, the buzzword is when we say RPA or robotics. It scares a lot of people off. You know, I talked to a lot of people, the moment I say RPA, they never accept. They will just cancel those meetings and make excuses. So we are kind of transforming that. You know, think about the future, you can’t avoid robotics. But think of it more as your digital assistant. You know, that’s the first word we are using digital assistance. It’s not about performing all the work you do for what you do is beyond capability, right? We don’t have any bot, AI, ML capability. What you do is that we’re giving you an assistant to give you back four hours of your time, while you focus on the other areas. As a good example, we had our OTR finance team making sure that the forecasting in the projects and everything on the purchase order side is done right. And then they work with project managers who are like, I work with my sales team. But I have a task at hand where I have to create sales orders in the system. And I have to work with my OTR finance team to approve a project to go further, look at one concept and they came back; their cycle time was 45 days. And they said it takes us about a week or two given everybody’s response on email to create a sales order and the project and the system with forecasts and settlement everything like that’s a lot of time lost, whatever you’d like to do. Like I want somebody or something to go in. Just set up the project for me. It is a painful process in itself. It is not easy if you are in a transactional team and you create a sales order for me. I’m going to give you milestones. I’m going to give you all the data.

This is where we brought in RPA.

[13:07] Sahil Vijay:

So far we have launched our product. I’m involved in three different sites. Customer response was- You know what? I like it, what else can you do? You just gave me back two hours a day. Now I can focus that time working for my sales team because I don’t want to do this manual task. So the buzzword again is the manual task. It’s not about what you do. We are taking what you don’t want to do, what you hate to do, what is the pain point and then we also bring in error handling. So I was in a session talking about error handling. What is the error handling today? Somebody made a mistake picking the wrong customer, creating a sales order, canceling it, or even billed it. You have reversals. Here comes a deduction. If I am giving you a platform, you give me the data and I’m going to do the AI, machine learning to say that this data is correct or not correct. In ERPs, you have a customer ID concept or maybe a customer name. So I asked how do you get a customer ID like why go into SAP system or Oracle or some other system? And then I research it in different transactions, they search through it. So the time spent is around 10 minutes, 5minutes, or maybe less, depending on if I find multiple customers. Let me just take that picture and then do it. So this was our case study. How do I take those 10 minutes which you hate and you will make a mistake, most likely, and our ratio was about, 40% times and it was wrong for the creation, right? So we took that and we asked them what they thought. So we took that. And then we said, “What’s next? Can you create a project for me? Can you create a sales order for me?” So the window opens.

So even today they’re like- let’s have more engagement. So again, it’s all about productivity. It’s not about taking somebody with Java, of course, it saves your activity as a corporate. GE decided not to hire more people in some areas. Now, people are complaining that they don’t have time. You know that our cycle time is 45 days because I’m spending this much time. So we put in this process, which I think every company is doing these days when they look into the cloud solution, your AS-IS process, so you break down each timeline and decide how much time you spend in each sector. Then you focus on that particular section as a use case, we pick projects and CSR in the transactional world. So like I said, digital assistants also communicate action. So we think about workflow. How do I call it a digital assistant, not our PA, we don’t let them do whatever they want because the user has given me the input? Now, we’re going to make you accountable. You own it. But we’re going to do the right communication tell you exactly what we did. That was our goal. And then once you do that, once you mature that process, that’s when you bring an AI and machine learning. I always go with examples because I like to relate it to real scenarios. For example, we said to a customer- Well, you gave me two or three parameters. And we still find like 10 different customers, and what better can we do? So we said, let’s bring AI, which we have not implemented yet. But we’re looking into that space to say, let’s bring AI to educate that if the purchase order has this customer name, that’s going to be the customer. That’s going to be the customer who has these milestones. So I’m pre-filling the front. So think about it, AI is doing the work. AI is just like Google when you search, you have four or five different options. So we are saying, “You’re entering 20 inputs to my front line, I’m going to reduce it to 10”. That’s pretty much the first in that space. I hope that makes sense. So this is my theory. When I say drill down from outward to inward, outward being the dashboard and inward being going to the real actions to be performed, all control to access, which is the criticality. We talked about all the integration happening there. We know other auditable stuff. So one of the tools is that we build around the workflow, I can see the trail when the human individual inputs the data to what Bob did, at what time, wherever the feelings work with the fearless, it’s audited from the day somebody submits a request. That’s one accessibility point is more around who needs to see what I think about that area. But if I’m an analyst, I care about the front feature. So think about all the signboards in there, you have analytics, then you have collections, where you’re going from downstream to upstream. So analytics collects credit reductions. In the end, you have your Treasury. So if I’m a Treasury person, I’ll see only the Treasury Board. So all those dashboards, I’m bringing together as one view. It depends on what your area of work is. That’s all you’re going to see. So it cuts cost on your building multiple dashboards, for instance, it’s going from the top. So it’s one single source of truth.

[19:35]Sahil Vijay:

So, now, as a collection leader, I have a lot of time as a collector.

[19:48] Sahil Vijay:

I have to switch my accounts, which are critical skills. There’s an SLA clock running. We have SLAs rounded so the biggest complaint was first it’s very manual and tedious. Why don’t you give me a tool on my phone that I can quickly switch? So our model has a dashboard, where we build a mobile app for individuals, based on what they need to do, they can tell us what feature in the app they want to activate, as simple as I need to assign a customer to my specialist, or I want to see so I’m in front of my CFO, and she just asked me about the number of collectors and accounts, those kinds of data. So I have it on the screen. That’s beauty. And again, the concept of dashboard comes in. Another flavor of that when I talk about accessibility and mobile connection is that-

[21:00] Sahil Vijay:

So we have two models, we have a model of direct collections where our collectors come in. And then we have a model where customers don’t want collectors coming in. They want to talk to sales or project managers, or the government doesn’t allow us to do those things. So, now, the thing I want to show you is the FSM and how it works for collections. Can you guys use it? Like, it’s the biggest challenge ahead.

[21:28]Sahil Vijay:

They will say why it’s complex. I don’t have time to learn this new application. And I don’t have time for you know, going in and feeding all that 10 different elements. I’m not a collector. So we improvise FSM. Using Fury, we said, well, what if I give you a list of customers for the ranking of past two, for instance, whatever parameters you said, and I’m going to stay in the end- All I asked you on this one screen, give me the inverse status, right? And then the whole concept of output-input is feeding that data that is registered in the ERP. Anybody can see. Right? That’s the whole theory. So we had those two different tracks that we worked with and built a very easy-to-do mobile application. So we didn’t have to hire folks to do it. In the Fury app, all we need to understand is what data we need to put into the application that matters. And then the front end, I would say it took us like two weeks to build the rollout.

[22:42] Sahil Vijay:

So a couple of other reasons that we ran into with project manager specifically or I’m always on the go on site. I can do whatever you ask me, but it has to be when I’m traveling because my routine is Monday to Thursday travel or I’m accessible most of the time. When I’m talking to my customer, provide me something where I can say, we are doing the new sales. But by the way, you haven’t paid us these three invoices and anything wrong with it, like we didn’t get the invoice or can you send me the invoice copy? We’ll look into it right away, you know when you are building that relationship, so we get that capital, we ask them to click and send the invoice or tell them that this is where the correspondence comes in, auto correspondence or manual correspondence. But coming from a project manager or sales manager, not coming from collections, you have to think about where collections fit in. This is where the mobile app is working. Well, we are expanding it to billers, expanding it to people who are responsible to release sales orders for milestone billing, for instance.

[23:56]Sahil Vijay:

So with GE, We started with a new SFTP acquired from all storms. And our CEO at that time, Jeff Emerald said, this is much superior to what we have at GE. So let’s put it into GE and decide to repeat as part of the service that retained it.

[24:23]Sahil Vijay:

This was SAP, all they didn’t have was collections and auto cash. So those features were not there. It was very nice around collections.

[24:37]Sahil Vijay:

When we acquired it, my team picked it up, like what should we do? Should we bring into our internal collections to build something on top, but I don’t have money. I’m not going to pay you much. So they were like- let me implement the FSE module and see how it goes. And that’s when the whole analytic stuff and everything opened up. So we are in a bad situation where our cash flow was negative. And what we realized was that we were stuck in a lot of customers that have not paid.

[25:18]Sahil Vijay:

I don’t know, for some reason, what I and my colleagues felt too is that while when the acquisition happened, somebody decided to stop building. Probably they were not comfortable with the merger. But that’s one of the challenges we had. We realized a lot of customers said I never received an invoice or while usually, I get a call and then I pay.

[25:42]Sahil Vijay:

So the model was not systematic, not standardized. That’s what we adopted for our goal to get that cash quickly. So we went ahead and brought in HighRadius, to add on and to improve our performance. And this was our journey with picking up analytics and the FSM collection. So analytics, I’m reading it out here is not just for downstream processes and this is where I was going. So downstream means in collections, cash applications, when your orders are not processed correctly. We are also looking into automating purchase orders. So the challenges, I cannot automate purchase orders. Why? Because every company has its standards of the purchase order, language barriers. What if I have a system that can read the purchase order, I can just log in and do everything? But it didn’t happen for us and we realized to come up with at least a thousand different variations of how the forms would look like from the purchase order which is from companies.

[27:01]Sahil Vijay:

This is where we brought in our template. Reading through the purchase order gave us the key points. Now, that’s the upstream process that I’m working on. Now, taking that environment, we were over 1 billion past to about, I would say close to if I remember, right 80 millions were over 180 days. From that, within one year, we came down to close to 40%.

[27:39]Sahil Vijay:

And then how did we start a journey? We started with one pilot run with Americans. Then we expand it to seven regions. And this is a challenging journey where you’re bringing a new tool to an organization and the whole collections team, like how’s that going to work for us? Like you have no choice. But that’s what we represent and what we did. We made it so simple that our adoption rate increased, our leaders accepted. And we went with seven sites. So now we have sites in Europe, America, Latin Manasa, SSA, and we have Singapore as well, from the Asia side. So this was a team of 70.

[28:38]Sahil Vijay:

Collecting leaders and collectors. So the whole expectation from analytics was to understand the cash flow, and all that so we just didn’t focus on collections. On the tool front from analysts, we realized that you can put pressure on collections if you haven’t billed the customers.

[29:07]Sahil Vijay:

And that’s what we learned through analytical deviations or derivatives.

[29:14]Sahil Vijay:

And then we went looking into our unapplied cash. There is a lot of money paid, but it was never counted towards a customer. So we ran into those scenarios. And then we built tools around it that provided the reporting capability which didn’t exist at that time. As simple as a hundred million in allocated cash was just cleaned out in a month, a hundred million in a month was unallocated. And our total reductions were 40 million. So if I’m right, today, we are down to about 250 million, and there were other steps taken, but our 180 days are gone. We still have 90 plus days, but we are in the 30 days period past 30 days.

[30:14]Sahil Vijay:

So our investment to dollar value was so high. So we looked into how much you save and enhance it to make it better. And we use HighRadius to do that first.

[30:35] Sahil Vijay:

So, we always talk about benefits, ROI. So a lot of benefits, which we observe is that it was easy to lay down daily goals. So when we talk about collections, you have your workplace, you have your prioritization scores. Is that beneficial? It is. It makes a lot of difference.

[31:00] Sahil Vijay:

You can tell your frontline collections team about what you need to work on daily. And then we provided visibility to our stakeholders. Why is that important to me as a subset of a big organization? I need funding. If I don’t show my numbers, who’s going to sponsor me, right? So it was very important for us. And then, with reporting, we were able to publish KPIs, like we saw below, which we didn’t have before.

[31:37]Sahil Vijay:

So those were the few capabilities, benefits that we had. And this was our GE journey. I joined in 2017, spent one year in analytic space, then they pushed me into collections in 2018. Now I’m upstream looking into the process in order.

[31:55]Sahil Vijay:

So it’s always a learning curve.

[32:00] Sahil Vijay:

It kept building in between for a different reason: the building and everything you looked at. So, they put me in cash and I do not know how that works. But the challenge is not SAP challenges. How do I integrate everything? This is where we have to look into Cloud because if you’re in one ERP, that’s so simple, right? I can bring it in, Hana. I can use BW Hana using SLT tools. If I have all the recipes here, I can put data in and do whatever and it can be my operational footprint. That’s the capability. But when it’s legacy systems, Oracle’s, you have different formats, building interfaces are costly. Whenever you have to spend money on a huge development, that’s going to be costly. Back in my days, I’ve run into projects where SAP came in and said that we cannot do any announcement for you. I’m like-Why do you customize it so much?

[33:11]Sahil Vijay:

25% of your code is customized. So if I enhance it, I have to spend all that money to bring that up. So SAP was proactive, it lets you standardize your code. There’s a capability that a product does not have, so you bring your development and enhance it. But some people do not realize that you just need to adopt it. So adoption is the problem.

[33:54]Sahil Vijay:

So that’s all I have as the food for thought. So what do you think about that? Analytics evolution, like we said, from left to right, we look at analytics to Treasury. And then how you envision all the data coming together on an easy platform. So, never overcomplicate things. That’s the journey we are on. You want to ease our pain to do it easily, but have to be more efficient. Thank you very much.

Sahil vijay

Senior Manager-ERP & A/R
General Electric

I can have one platform where I have everything in one place. It’s not just for my leadership, not for our CFOs or CEOs. It’s for the people who are doing the job - like the collector or someone from the cash applications team.


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