Tag Archives: what is forecasting

An imaginary conversation with a weather frog

European folklore believed that frogs kept in a glass would be able to forecast the weather. People filled some water in the glass to keep the amphibian happy, and then added a small ladder. A climbing frog would indicate good weather, whereas a frog hanging out in the water would show bad weather. This belief especially stuck with people in the German speaking countries where weather forecasters are typically called ‘Weather Frogs’ (Wetterfrosch). Well, weather forecasters do one thing well: forecasting. They are the true masters and I thought that we could get some insights from one of them. It is frog migration season in Bavaria and I happened to have found one who is willing to talk to me. Please meet Franz the Frog. Franz resides in Bavaria, Germany where people recognize him as a trusted master forecaster.

 

AN IMAGINARY CONVERSATION WITH THE FROG

Christoph: How is life as a master forecaster? Your pictures are every on the side of the roads these days. You must be pretty busy? Spring is known for its volatile weather.

Franz the Frog: All cool here in the pond. Thanks for asking. We were quite busy up until yesterday. That’s when we finished our quarterly forecast. I am looking forward to jumping around for the next few weeks.

Christoph: But wait a second! It’s a volatile climate out there. How can you just sit there, jump around and not forecast whenever things change?

Franz the Frog: Dude, I hear ya’. But the big boss here in the pond decided that 2-3 forecasts per year are totally fine. Plus we have so much other stuff to do. Also, do you realize how much work we have to do to complete a forecast?

A weather frog

Christoph: Sorry, this was news to me.  Then tell me, why exactly is this so much work?

Franz the Frog: Helllllooooooo!!!!! Each forecast starts with us looking at weather patterns for the past five years. Just simply gathering the data that is stored all over our pond takes us forever. Our big boss in the pond also wants us to create detailed variance reports for those years. That takes about a month. To create the actual forecast, we turn over every single leaf in our pond. And we record every little rain drop. That takes a lot of time. Get it?

Christoph: Oh…I see…a lot of detail. But a lot of detail should result in higher accuracy right? My wife Jen complained about your reliability the other day. She claimed that she would not even ‘pack a suitcase for vacation’ using your information. Errr…please don’t shoot the messenger.

Franz the Frog: Watch it, buddy! I will stick my tongue out here in a second. What do you expect? We get paid by our big boss in the pond. If the boss is happy, the flies are happy and we are happy. The boss decided that it’s best for us to provide a forecast that tells you guys exactly what you want to hear. Last year we saw rain coming. Your wife complained that she did not want any rain that particular week. She said:’Ahh, this stupid forecast. Rain is driving me crazy. It should be sunny!!!’. That got my big boss in the pond really upset. And guess what happened: no pay. That made our decision easy: we would eliminate a lot of pain and frustration if we simply forecast what everybody wants to hear.

Christoph: Oh…ok. That is so not cool. Let’s change topics. What type of tools do you guys use to do your forecasts? I mean, we’re in the year 2011 so  I suspect that you guys have some cool tools…like that PC game Frogger?

Franz the Frog: Frogger rules!!! If you like Frogger, you will be happy to hear that we are still using the same platform. No changes. Here, take a look: We have special leaves from a searose that was created in Redmond, WA’. Those leaves allow us to play with the data that we collect. The nice thing is that everybody in our pond has a ton of those searoses flying around. And the other guys love those leaves. The all create their own versions. But do me a favor and DO NOT talk to the green guy over there: he has to collect all the leaves at quarter-end. He hates his job. A bunch of my colleagues sometimes play a joke on him and swap out leaves or hide them. Others change the carefully thought-out leaf structures by ripping a holes in them or by chewing on them. You should see his face when the leaves don’t stack!!! Haha…RRRiibbitt.. Hilarious.

Christoph: Holy tadpole! That sounds like a tough job. Can you trust the data then?

Franz the Frog: Probably not. But hey…that’s the way the pond has been for a long time. It worked in the past it should work in the future, right? We have been around for thousands of years.  And the big boss is happy and we’re getting paid—what’s not to like?

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Future Ready? A discussion with Steve Morlidge

Steve Morlidge, Future Ready

The IBM Finance Forum 2011 events have officially started in Europe. These events are designed for Finance professionals seeking to deliver stronger business insight to their organizations. Apart from being a great networking opportunity, we focus on sharing a lot of best-practice knowledge. Customers share their stories. And IBM also bring in great guest speakers like Steve Morlidge who share their tremendous knowledge in the finance area.

Steve MorlidgeSteve Morlidge will be joining many events across Europe this year. He is a true thought-leader in the area of financial performance management. In 2010, he released a ground-breaking book called ‘Future Ready – How to Master Business Forecasting’. Together with co-author Steve Player, Steve shares a lot of valuable knowledge that he gained in over 25 years as a senior finance executive working for international companies like Unilever. He is also an active member of the Beyond Budgeting Roundtable (BBRT).

Steve Morlidge and I were able to talk over the phone right before the first Finance Forum event in Zurich.

Christoph Papenfuss: Many companies are still developing annual budgets. Is this approach outdated or is there a place for the annual budget?

Steve Morlidge: I believe that conventional budgeting is dead, or at least very much on the way out. It takes too long, hinders responsiveness and fosters all kinds of damaging political behavior in enterprises. Companies still need to do things like setting targets, and this may still be called ‘budgeting’, but it is a long way from the traditional process many of us grew up with.

Christoph Papenfuss: What are some of the key issues associated with the traditional forecasting process?

Steve Morlidge: In my view most companies do not understand the difference between budgeting and forecasting. As a result, forecasting is done in too much detail, but not frequently enough. More importantly, the mindset is very often all wrong. Budgeting teaches us that gaps (between target and prognosis) are bad, whereas the primary purpose of forecasting is to detect deviations from plan so that corrective action can be taken; so unearthing such discrepancies should be positively encouraged, not punished.

Christoph Papenfuss: Many people talk about rolling forecasts. Are rolling forecasts a viable approach?

Steve Morlidge: They are, but too often people underestimate the task. In my book, rolling forecasts are forecasts with a consistent horizon: 12 months, 15 months or whatever. As a result, at any one time a significant chunk of the horizon may extend beyond the fiscal year end. Many of the processes upon which forecasting relies – like activity planning and so on – are anchored on the annual budgeting process so sourcing the information you need beyond the financial year end can sometimes be a challenge, unless these supporting processes are remodeled at the same time. Also, conventional annual target setting, particularly if it is tied to incentives, can distort a rolling forecast process to the point that it falls into disrepute. As a result, my advice to people is to fix the ‘in year’ forecast process first, before you tackle rolling horizons and the ‘out year’.

Christoph Papenfuss: We all know the saying ‘You get what you measure.’ Does this apply to the forecasting process?

Steve Morlidge: Absolutely. In fact, if you don’t measure the quality of your forecast process and, most importantly, act upon it, you have no kind of guarantee that the forecast can be relied upon. Proper measurement – closing the feedback loop – is the only thing that separates forecasting from guesswork, and in my book, 95% of corporate forecasts fall into the latter category.

Christoph Papenfuss: Many organizations utilize spreadsheets to manager their forecasts. What role does technology play to improve the forecasting and planning processes?

Steve Morlidge: At one level technology isn’t important at all – the main deficiency with business forecasting is the processes used and the thinking that lies behind it – not the toolset. Having said that, few companies can sustain a successful forecasting process without technology that enables them to streamline processes, provide appropriate modeling capabilities, support rapid reiteration, provide insightful measures, communicate results effectively and so on. Tools don’t make a master craftsman, but without them nothing would ever get built.

Christoph Papenfuss: You will be delivering a keynote presentation at many IBM Finance Forum events. Can you share a few things you will be talking about?

Steve Morlidge: My main message is that the practice of forecasting is broken, not because we don’t have the tools, but because we don’t know how to use the tools we have. I will be sharing what I have learned about mastering forecasting articulated in the form of six simple principles.

You can find out more about Steve on his website: http://www.satoripartners.co.uk. To see a full list of the Finance Forums 2011 events and to sign up, click here.

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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.

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4 additional things to know about Forecast Accuracy

How is your forecast accuracy measurement project going? I hope the last post convinced you to start measuring this. But there are still some open questions. Let’s take a look at some critical items that you should consider.

TIME SPAN

One of the things people often get confused about is the type of forecast accuracy that they should measure. We often create forecasts for many months out. Technically speaking, I could therefore calculate 1,2,3,4,5,etc month forecast accuracy (e.g. I take a forecast value from 6 months ago and compare it to the actuals from today or I take my forecast from last month and compare it with the actuals that just came in). That’s a lot of data! Based on my own experience and discussions with many controllers, I have come to believe that most businesses should focus on a short-term measure (say 1-3 months). The reason for that is simple: the further out we look, the higher the probability for random errors (who can forecast the eruption of an Icelandic volcanoe?). Short-term accuracy is usually more important (think: adjusting production volumes, etc.) and we should have way more control over it than over longer-term accuracy. So, pick a shorter-term accuracy and start measuring it.

FREQUENCY

How often should we measure forecast accuracy? Every time we forecast! Why wouldn’t we? Measuring once in a while won’t help us much. The most interesting aspect of this measure is the ability to detect issues such as cultural and model problems. Just make sure to setup the models correctly and the calculations will be automatic and easy to handle. You will soon have plenty of data that will provide you with excellent insights.

LEVEL

Where should we measure forecast accuracy? We simply calculate this for each and every line item, correct? Hmm…better now! We already have so much data. I would suggest to look at two key dimensions to consider (in addition to time): the organizational hierarchy and the measure. The first one is simple: Somebody is responsible for the forecast. Let’s measure there. We could probably look at higher level managers (say: measure accuracy at a sales district level as opposed to each sales rep). In terms of the specific measures, experience shows that we should not go too granular. Focus on the top 2-3 key metrics of your forecast. They could be Revenue, Units, Travel Expenses for a sales forecast. The higher up we go in the hierarchy we would obviously focus on things such as Margin, Profit etc.. The general advice is to balance thirst for knowledge with practical management aspects. Generating too much data is easy. But it is the balance that turns the data into a useful management instrument. So, you should measure this at a level where people can take accountability and where the finance department doesn’t have to do too much manual follow-up.

CAUTION

But before I finish here, just a quick word of caution. Inaccurate forecasts can have different causes. Don’t just look at the plain numbers and start blaming people. There are always things that are out of our control (think about that unexpected event). Also, there are timing differences that occur for various reasons (think about a deal that is pushed to next month).  We need to go after those differences that are due to sloppy forecasts.

What about analyzing and communicating forecast accuracy? More about that in the next post. Do you have any other experiences that are worth sharing?

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Three things every controller should know about forecast accuracy

Forecast Accuracy

Forecast accuracy is one of those strange things: most people agree that it should be measured, yet hardly anybody does it. And the crazy thing is that it is not all that hard. If you utilize a planning tool like IBM Cognos TM1, Cognos Planning or any other package, the calculations are merely a by-product – a highly useful by-product.

Accuracy defined

Forecast accuracy is defined as the percentage difference between a forecast and the according actuals (in hindsight). Let’s say I forecast 100 sales units for next month but end up selling 105, we are looking at a 95% accuracy or a 5% forecast error. Pretty simple. Right?

And why?

Why should we measure forecast accuracy? Very simple. We invest a lot of time into the forecast process, we utilize the final forecast to make sound business decisions and the forecast should therefore be fairly accurate. But keep in mind that forecasts will never be 100% accurate for the obvious reason that we cannot predict the future. Forecast accuracy provides us with a simple measure to help us assess the quality of our forecasts. I personally believe that things need to get measured. Here are three key benefits of measuring forecast accuracy:

  1. Detect Problems with Models: Forecast accuracy can act like a sniffing dog: we can detect issues with our models. One of my clients found that their driver calculations were off resulting in a 10% higher value. A time-series analysis of their forecast error clearly revealed this after just a few months of collecting data.
  2. Surface Cultural Problems: Accuracy can also help us detect cultural problems like sandbagging. People are often afraid to submit an objective forecast to avoid potential monetary disadvantages (think about a sales manager holding back information to avoid higher sales targets). I recently met a company where a few sales guys used to bump up their sales forecast to ‘reserve’ inventory of their hot products in case they were able to sign some new deals. Well, that worked ok until the crisis hit. The company ended up with a ton of inventory sitting on the shelves. Forecast accuracy can easily help us detect these type of problems. And once we know the problem is there and we can quantify it, we can do something about it!
  3. Focus, focus, focus: Measuring and communicating forecast accuracy drives attention and focus. By publishing accuracy numbers we are effectively telling the business that they really need to pay attention to their forecast process. I have seen many cases where people submit a forecast ‘just because’. But once you notice that somebody is tracking the accuracy, you suddenly start paying more attention to the numbers that you put into the template. Nobody wants to see their name on a list of people that are submitting poor forecasts, right?

BUT……

Overall, forecast accuracy is a highly useful measure. But it has to be used in the right way. We cannot expect that every forecast will be 100% accurate. It just can’t be. There is too much volatility in the markets and none of us are qualified crystal-ball handlers. There is a lot more to consider, though. Over the next few days, I will share some additional tips & tricks that you might want to consider. So, start measuring forecast accuracy today!

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