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Finance Plan & Forecast

The case for continuous forecasting

Continuous Forecasting

Time for a confession. I really hated forecasting back in my old job. Here I was working with clients on improving their planning, budgeting & forecasting processes. Yet, I absolutely hated doing my own forecast. It just didn’t feel right. What was wrong? Well, I never really understood the template that our controller sent out. And it always took forever. Luckily, I had to do this only 2-4 times per year. But that was also part of the issue. Every time I received the forecasting template (a complex spreadsheet!) I had to collect and enter a ton of data. Also, I had to re-orient myself and figure out how the template worked this time. And then there was the reconciliation between my project plans and the prior forecast. To sum it up: The ramp-up time was simply too long. The result: I hated the forecast because the process took too long and it was too infrequent.

Fire-Drill

Indeed, the typical process for updating, distributing, collecting and aggregating forecasting templates can take up to a few weeks in most companies. It is critical to understand that the templates are typically unavailable to the user community during extended periods of time. Analysts are busy and need to take care of other tasks between forecasting cycles. As a result, forecasts are being conducted infrequently and the business owners feel like conducting a ‘fire-drill” when the templates are actually sent out.

The traditional spreadsheet-driven process

Forecasting Software

But there is a much better model that many of my clients have implemented. Modern planning & forecasting software allows us to keep our forecasting templates online nearly 24*7. We no longer have to collect our 100s of spreadsheets, fix formulas, manually load actuals, manually develop new calculations and the re-distribute the templates in long and manual cycles. Thanks to OLAP technology (sorry for the techie term), we can make model changes in one place only and they can automatically be pushed out to the different templates (e.g. cost centers, profit centers etc..). Automated interfaces between the ERP (for actuals) and the forecast models can be setup. We can automatically aggregate data in real-time and we can control the process flow. Overall maintenance is a lot easier and the templates are available pretty all the time and the users can work with their data around the clock and throughout the year.

Using this technology, Finance departments can allow the business users to work in their templates around the clock. A sales manager can update her data right after a critical customer meeting (e.g. change the sales quantity for a product). In other words, people can make quick incremental changes to their forecast data instead of performing time-consuming, infrequent larger data input exercises.

Continuous Forecasting

But the Finance department now has to carefully communicate with the business. They need to clearly communicate submission deadlines etc..

The continuous data collection process

But what is the advantage to the business users and the finance department? How would this technology have change my personal experience in the prior job?

Clients typically experience three main advantages:

  • The templates are available 99% of the time and users can work in them on a daily basis. As a result, users become a lot more familiar with the templates and their comfort levels rise.
  • The actual forecast process is a lot faster for the business users. They can make incremental changes which typically don’t take that much time. Contrast that to my case where I had to build a bottom-up forecast almost every quarter. The ramp up time can be considerable.
  • Forecasts tend to be more complete. In the case of an urgent ad-hoc forecast (imagine something critical happened), the business is able to compile a near complete forecast in very short time. This is where the incremental updates add serious value. Contrast that to the traditional spreadsheet process. People might be out on vacation or they are out traveling. The potential time-lag to get somewhat decent data can be quite long.

Let me clarify one last thing: A continuous process does NOT mean I can simply aggregate my data every night and obtain an updated forecast. No, I need to communicate to the business WHEN I need the data. But due to the 99% availability I can collect my data very quickly.

Let’s go continuous! Would love to hear your thoughts and experiences. Good or bad.