Evolution of AR Forecasting Best Practices at Duracell: Case Study


Steven Wurst

OTC Leader,

Ganadeep Rey Patlolla

Business Systems Consultant and Program Manager,


[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…

What you'll learn

In this session, Order to Cash leader Steven Wurst speaks about the 4 stages of evolution which the receivables cash forecasting process at Duracell underwent, observing the challenges faced in each stage and how it overcame them using continuous improvement and technology.

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HighRadius Cash Forecasting Cloud – an advanced forecasting system – leverages the proven RivanaTM Artificial Intelligence (AI) platform to provide the most accurate cash flow forecasts – right from a ledger account level and rolling up to the organizational level. Delivered as a Software as a Service (SaaS), the solution seamlessly integrates with your company’s ERPs, accounting systems, banks and order management systems. Multiple AI and Machine Learning algorithms process datasets including bank statement inflows/outflows, sales orders/customers invoices, purchase orders/vendor invoices and expense reimbursements for comprehensive as well as accurate cash flow forecasts. The closed-loop, machine learning feedback system ensures that the forecast models become more accurate with time.