Tag Archives: planning software

Better Forecasting And Budgeting Starts With Analysis – IBM Cognos 10 in Action

FORECAST ANALYSIS

Much has been written about developing better forecasting and budgeting templates or improving the overall process. But to my surprise there is hardly any focus on the role of analysis. I have seen many organizations where managers ‘survive’ the forecasting and budgeting cycle without ever spending time performing meaningful analysis of their data. They simply focus on getting the numbers in to satisfy finance and senior management.

This is a wasted opportunity. People should use that occasion to gain insights about their business. Lack thereof is likely to result in forecasts and budgets that are not meaningful. Some of you might say: ‘Wait a second! Managers do obtain some reports.’ True. They get the classic variance report with a ton of detail. But working with this is time-consuming and it is extremely difficult to identify critical trends and to see the big picture.

Forecasting Report

A traditional variance report. What does it tell us?

BETTER FORECASTING WITH ANALYSIS

Using a Business Analytics platform like IBM Cognos 10, you can make is easier for managers to gain critical insights. Here are a few ideas that you might find useful. Let’s look at the example of a sales manager for a European division of a global company. This manager has to forecast revenue and associated expense.

1. GO VISUAL

First of all, toss those detailed variance reports. Line of Business managers will most likely not obtain any information from them. Human beings do much better processing visual information. You can find a lot of information about this topic on this blog. So, try to swap out those hundreds of data points with a few meaningful charts. Your teams will be thankful.

2. CONSIDER EXTERNAL DATA

The variance report does not really tell us anything about our business potential. We could therefore consider looking at external data such as market trends. More and more of my clients do that. It helps them with assessing their overall position and it also helps them set realistic but ambitious targets. The example below shows that market growth in Europe is a bit limited compared to North America and Asia.

Market Size chart

The situation in Europe is not looking good

3. STUDY HISTORY

History is not necessarily a predictor of the future. But we should not ignore it. We might be able to identify seasonality and to detect general trends. Pick the critical measures. Line charts are usually a great choice to display this type of data. The example below shows that revenue is cyclical and that the general trend is positive:

Revenue Reporting

On the rise: Revenue trend for Europe

 

4. CHANGE YOUR PERSPECTIVE

One of the nice things about modern Business Analytics tools like Cognos 10 is that we can view data from multiple different angles. Use that capability to your advantage! Try to explore different perspectives. Look at the example above. Now, compare this to the view below. Same data. Just a simple change in Cognos 10:

IBM Cognos 10 dashboard

A different perspective

Our biggest months used to be in summer time. But that has shifted towards year-end. Same data – different perspective. Explore!

5. BENCHMARK YOURSELF

It makes sense to learn from others as well. We could do some internal benchmarking as well. In our example, we could look at deal sizes (looks like Europe’s deals are growing nicely and they are above company average):

Deal size chart

The average deal has grown bigger

Ok. That sounds good. But does the deal size come at a cost? Once again, let’s do some internal benchmarking and look at the ratio of expenses and the associated revenue. It looks like Europe is slightly higher which might explain the higher deal size.

Expense Ration chart

Every penny that is earned in Europe requires higher expenses

That information is valuable. It also leads us to think further and to ask some critical questions (Does it make sense to review our spending? Does the higher spending lead to bigger deals?). We should obviously not stop right here.

6. LOOK AT LEADING INDICATORS

What about other non-financial data as well? For revenue budgeting, I might also want to look at a leading indicator like customer satisfaction. And I might also want to look at our track record of winning deals (win-loss-ratio). Take a look:

Customer satisfaction chart

Customer satisfaction is rising again. A leading indicator for sales?

BETTER INSIGHTS

This is a simple example. The manager is now equipped with a few key insights:

  • Market growth is low
  • Our revenue trend is still positive
  • Buying patterns have shifted
  • Our strategy of investing in selling activities has increased the deal size
  • Customer satisfaction is increasing which could lead to higher sales

These are valuable insights. And it did not take much time to obtain them. The old variance report would not have provided that insight and it would have consumed a lot of time.

Try to incorporate a few of those ideas in your forecasting and budgeting processes. Doing this with spreadsheets is obviously difficult and probably explains why so many organizations are stuck with the traditional approach. Business Analytics software like IBM Cognos 10 makes it a lot easier to do that.

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Success with Forecasting – A discussion with Pieter Coens

Please meet Pieter Coens. Pieter is the Director of Finance & Control at Landal GreenParks in the Netherlands. He started his career in public accounting and joined Landal over 16 years ago. Pieter has held various positions in finance at Landal.

Landal GreenParks is a leader in bungalow-park management and rental. Landal has over 65 parks with a total of approximately 11,000 chalets. With 47 parks in the Netherlands, Landal leads the Dutch bungalow -park market. Outside the Netherlands, Landal has parks in Germany, Belgium, Austria, Switzerland and the Czech Republic.

Pieter gave a great presentation about Landal’s planning and forecasting processes at the IBM Finance Forum in Amsterdam on May 24th, 2011. We were able to have a quick chat at the event.

Christoph Papenfuss: You have implemented IBM Cognos to automate your budgeting and forecasting processes. What have you accomplished so far?

Pieter Coens: IBM Cognos currently helps us create an annual budget along with a monthly forecast. For that purpose, we have implemented several elements including models for Rental Revenue and our P&L.

Christoph Papenfuss: How did you manage your processes before that?

Pieter Coens: We used to manage our processes with a myriad of Excel files. It was very difficult. We ran into various issues such as managing excessive file sizes that slowed down the network, dealing with sluggish recalculations, difficulties tracing interdependencies etc.. Aggregating the different files was extremely cumbersome and time-consuming. And of course, there are the associated audit issues with spreadsheets.

Christoph Papenfuss: How are you benefiting from the implementation?

Pieter Coens: IBM Cognos has allowed us to automate a lot of the steps in the process such as preparing, distributing and aggregating planning templates. We are also able to develop more intricate models that provide us with better insights. Overall, we feel that our finance team and the business users are now able to focus more on the actual planning activities rather than the administrative tasks that I described earlier. My team is much more productive.

Christoph Papenfuss: You have an annual budget and also a monthly forecast. Who is involved in the process?

Pieter Coens: Finance is in charge of executing the process. But the business owners have to work and develop their own budgets and forecasts. They are in charge of entering their data in the models. Finance plays the role of the coach: we help the business make sense of the numbers and we guide them through the forecasts and budget iterations. This approach provides us with several advantages: By actively involving the business we can obtain more accurate and timely data. We also feel that the business is able to gain better business insights by actively working with their budgets and forecasts and the associated monthly actuals. Last but not least, Finance has more time to focus on value-added tasks such as performing analysis.

Christoph Papenfuss: You have a solid forecasting process. How often do you update the forecast and how far do you look into the future?

Pieter Coens: We currently use a monthly forecast. This allows us to anticipate and react to market changes. We ask the business to perform a detailed forecast for the next two months only. The remaining months until year-end are automatically calculated as a trend of the 2-month forecast. We found that creating a detailed forecast further out than 2 months does not necessarily result in very accurate data and it also takes a lot effort. We want the business to focus their energy on the short time-horizon and only forecast the know effects throughout the Full Year.

Christoph Papenfuss: You are proponent of driver-based models. Can you give us an example of how you have implemented this? Also, what are the benefits for the organization.

Pieter Coens: Driver-based models allow us to increase the speed of the budgeting and forecasting exercise. Also, we are able to perform better analysis at month-end and during the planning activities: Instead of just looking at an absolute variance, drivers allow us to review this from different angles such as price or volume effects. Food & Beverage Revenue, for example, can be calculated as Number of Guestnights * Average Spend on Food & Beverage.  The associated Cost of Sales are a percentage of the Food & Beverage Revenue that has been calculated.

Christoph Papenfuss: How did you go about implementing the IBM Cognos solution?

Pieter Coens: We decided to follow a modular approach and started with a few smaller projects. This allowed us to build critical skills and develop success much earlier. This in turn led to a situation where the business heard about our accomplishments and they started asking for additional projects e.g. forecasting on Operational Management Information.. Change management is a lot easier if the business users ask for projects instead of us pushing them to accept

Christoph Papenfuss: What else are you planning to do?

Pieter Coens: We are definitely looking to reduce the level of detail in our models. More detail does not mean higher accuracy. On the contrary, more detail requires more work and it does not necessarily drive accuracy. We are also looking to implement additional models such as cash flow and predictive modeling/forecasting for our Yield department.

Christoph Papenfuss: Thank you very much, Pieter! Good luck with your implementation.

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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.

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