The performance ideas blog

Tag: forecasting software

  • Annual Budgeting – Our favorite season?

    BUDGETING

    Yes, it’s that time of the year. The time that is often filled with pain and fear. No, I am not talking about Halloween but about the corporate budgeting process. Much has been written about the annual budget. And most of the written stuff is not positive. Jack Welch alone has provided us with a few memorable quotes such as:

    “The budget is the bane of corporate America. It never should have existed.”

    “But the budgeting process at most companies has to be the most ineffective practice in management. It sucks the energy, time, fun and big dreams out of an organization. (…) And yet (….) companies sink countless hours into writing budgets. What a waste!”

    WHAT IS A BUDGET

    In theory, a budget should actually be a rewarding and important process. Why? Let’s look at the purpose of a budget: It should outline how we want the future to look. It details planned actions, outlines investment areas etc.. When you think about it, these are very important tasks. And it should not be that nerve racking. Budgeting and planning allow us to sit back, look at our past achievements and provide us with the opportunity to lay out a path towards future success. Doesn’t sound too bad, right?

    THE PROBLEM

    Indeed, the annual budgeting process is anything but popular. No wonder. It is usually very difficult and the resulting value is dubious. So, what’s wrong? Many things. Here are a few statistics that I pulled together from various articles, books and conferences. Depending on the specific sources, number tend to vary a bit here and there. But the general trend is the same.

    • Over 70% of all budgets loose their validity after the first quarter of the new fiscal year. The speed and volatility of our global connected world renders many assumptions useless.
    • And estimated 94% of all executives do not have confidence in the budget numbers. They believe the numbers are either outdated, they are padded or they are meaningless. Huh?
    • 75% of all companies need more than three months to complete the entire cycle. Even three months is a long time these days. Responding to changing market conditions becomes very difficult with these long cycle times. Also, think about the enormous amount of resources that are invested into the process. Do we really want to spend all that time only to find that the output doesn’t actually reflect reality?
    • Over 75% of all budgets are believed to be sandbagged. Gaming the numbers remains a popular competition: cost center managers exaggerate expenses to protect their turf. Sales managers express negative market views to maximize their earning potential. Not good.

    TIME FOR CHANGE

    It’s time for a change! In the next few weeks I will share a few ideas for making the budgeting process more valuable. On Thursday, business advisor Mike Duncan will discuss the overall purpose of the budget. He recently wrote a nice article called Six Ideas for Setting Successful Budgets.

    If you have stories and best practices to share, please get in touch with me.

  • How to reduce detail in your forecasts

    Rolling Forecasts are quite popular today. But to implement them properly it is usually imperative to reduce the detail in the forecasting models. Less detail speeds up the process and helps to increase the accuracy.  A recent post on this blog looked at some of the problems with too much detail. The big question though is to where and how to cut detail. While people tend to look at the chart of accounts first, many organizations actually have great success with making a few modifications to their timescale.

    THE BIG SCALE

    Take a look at the photo below. It symbolizes one of the key issues with forecasting: the further out we look the more diffuse our view gets. While we might have a good idea of what is going to happen next month, it is usually more difficult to do the same for the months after. That’s just the way it is.

    Rolling Forecasts - The time horizonUnfortunately, most forecasting templates do not reflect this fact of life. Take a look at the original time-scale from a customer that I used to work with. The organization wanted to look beyond fiscal year end. However, all months were treated equally:

    A traditional time-scale (208 data points)

    Notice how much detail is being generated. And detail requires effort. As a business person, I will have to sit down and try to provide an amazing amount of detail. This could take a while. The basic assumption of this template is that business people are able to precisely quantify when something is going to happen no matter if it’s tomorrow or next year. That is dangerous and it’s simply not possible. Here is an example: I might know that a certain customer will purchase my product next month. But I will most likely not be able to precisely identify the same thing for next year. The forecast will therefore most likely be wrong from a timing perspective. Why the detail then?

    A DIFFERENT TIMESCALE

    How about changing the timescale? Take a look at the final redesign in IBM Cognos TM1:

    Rolling Forecast Model
    Less detail. Probably more accurate (112 data points)

    The new version reduces the detail by almost 50%. And this approach pays tribute to the fact that the further out we look the more diffuse our view of the future becomes. Overall, we could argue that this template will produce more accurate forecasts while also making it easier for the business. This is a lot easier to work with! My client implemented a similar timescale with excellent results.

    YOUR MODELS

    Take a look at your current models. Is there an opportunity to alter the timescale? How much detail could you get rid of? If you want to embark on implementing a Rolling Forecast, you should most definitely look at this approach. Please let me know your thoughts and experiences.

  • 3 ways to analyze and communicate Forecast Accuracy

    Analyzing Forecast Accuracy

    What’s the best kept secret in your company? Well, hopefully not your forecast accuracy numbers? Forecast accuracy should not be a calculation that happens behind closed doors. But the numbers should be communicated and analyzed to be really useful. Here are three ways you can communicate and analyze your numbers:

    • The table of shame & glory: One good way to display forecast accuracy is to collect the numbers in a heat map. Collect the numbers for different organizational units in a table and color code the values based on tolerance ranges (green = acceptable, yellow = hmmm, red = absolutely not). The advantage of this approach is that we can easily spot trends and also compare different organizational units. This type of table can also be used to motivate people to take their forecasts seriously. But once again: be cautious with putting too much pressure on forecast accuracy.

     

    • The bar chart of absolute truth: You can also simply compare forecasts and actuals in a simple bar chart. This type of format works ok for a single organizational unit. Having more than one in there makes a messy chart that is not worth looking at. The advantage here is that we can easily spot the absolute differences between the values.

    • The run chart of truth: A very popular way to display the forecast error is to visualize the percentage error in a bar chart (a so-called run chart). This is a great way to very easily spot problem areas and trends. Also, we can easily compare different organizational units.

    Those are three great ways to analyze and communicate forecast accuracy. You will probably want to experiment with all three of them. Many organizations do use these in connection. 

    Good luck with your next few forecasts! If you want to learn more, please join one of our upcoming Rolling Forecast workshops. Simply get in touch with me for an updated schedule.

    P.S.: If you want to read more about measuring forecast accuracy, I highly recommend purchasing Future Ready by Steve Morlidge and Steve Player. It is one of the best books about business forecasting. You can read an interview with Steve Morlidge on this site.

  • 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.