Tag Archives: sales forecasting

Improve your forecasts – 6 things we can learn from weather forecasters

Back in April, I posted an interview with a master forecaster: Franz the Frog. Interestingly enough, this post is one of the most popular entries on this blog. But all jokes and irony aside: Weather forecasters are indeed world champions in forecasting and there are some lessons that we as finance professionals can learn from them. Let’s take a look:

LESSONS FROM MASTER FORECASTERS

1. Forecasts should be objective: Have you ever seen a subjective weather forecast? Well, it may feel like that sometimes. But weather forecasters do not publish what they think the public or the managers of the TV stations or newspapers want to hear. That would be dangerous. No, they strictly publish what their algorithms and forecasting processes show them. We can therefore rely on them (except for the obvious and inherent forecast errors that can occur).

2. Forecast discussions should look forward not backward: Huh? Well, weather forecasts focus on the future. Have you ever seen a weather person spend 75% of his time explaining past variances, apologizing and arguing about assumptions? No. Weather forecasts are strictly forward looking. The focus is on what lies ahead and not on what happened in the past.

3. Forecasts should be flexible: How often do we we get an updated weather forecast? Once per quarter? Once per month? No, the weather is too volatile for that. The forecast would be outdated within a few hours. People might be unprepared for a snowstorm, for example. Instead, weather forcasters continuously update their models when new information arrives. That way we can all rely on the most current and accurate forecast. We don’t have to worry too much about being caught in dangerous weather.

4. Forecasts should speak a clear language: Weather forecasts are being presented in a simple and concise manner: “Heavy winter storms expected with up to 20cm of fresh snow.” This type of presentation allows us to quickly make decisions (stay at home). The message is not hidden in hundreds of lines of technical details.

Today's forecast is detailed. The further out we look the less detail we have.

5. Detail is adjusted based on the predictive ability: What is easier to forecast – the weather tomorrow or the weather in two weeks? Stupid question: the weather tomorrow. Weather forecasts acknowledge that they cannot predict weather much further out than a few days. And they adjust the level of detail based on that insight. Today’s forecast shows detail by the hour. The forecast for next week is just a general trend (‘rising temperatures expected’). This approach obviously reduces the effort involved in creating the forecast. Most importantly, this approach avoids the trap of setting wrong expectations (“I thought it would be sunny in three weeks from now!”) More detail does not mean higher accuracy.

6. Forecasts are compiled with the help of modern technology:

Technology drives efficiencies and increases effectiveness

What type of instruments and tools do weather forecasters leverage? Weather frogs, old fashioned thermometers, wet fingers, flight patterns of birds? No, they rely on modern technology. They continuously push the envelope and upgrade their equipment. This tremendously speeds up their work while also reducing mistakes and increasing the accuracy. They actively look for new ways to improve their processes and techniques.

YOUR FORECASTS?

Think about your forecasts. How do they stack up when compared to these six characteristics? Are there areas where your forecasts can improve? If you are interested, join of of our Rolling Forecast workshops to learn more.

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The ultimate rolling forecast workshop

Having fun at the workshop

Forecasting is a critical topic for many companies these days. No big surprise: the volatility and the speed in the world requires organizations to stay agile. About four years ago, my team and I started working with several customers and thought-leaders (David Axson, Steve Morlidge) to collect best practices for forecasting in these turbulent times. The results of the countless hours of talking, brainstorming, analyzing and reading are captured in the IBM Cognos ‘Best Practices in Rolling Forecasts’ workshop. This workshop ended up being way more successful than any one of us would have ever imagined. I have personally delivered over 100 of these events in the past three years.

THE WORKSHOP FORMAT

Forecasting is a complex topic and we were able to collect a full library worth of experiences. But simplicity rules and we selected the most interesting aspects

David Axson is showing the way!

to fill the agenda for a half-day workshop. That creates more focus and the attendees leave with just enough ideas to drive change in their organizations and without feeling overwhelmed. The overall focus is on the business process and not software. While we share a lot of best practices, the workshops are very interactive. We usually have extended and very fruitful discussions amongst the participants. Many attendees stay after the official event ends to continue their idea exchange. This is one of my favorite parts. There are always many things to learn.

BEST PRACTICES AND MORE

Static vs rolling forecasts?

So, what do we cover? A lot! The focus is clearly on proven practices that were identified by our customers. But it is also important to look beyond those things. We therefore injected some thought-provoking ideas from our thought-leaders. And each workshop we run typically provides new ideas, stories and experiences that we leverage to enhance the materials.  It would be too much detail to cover in this post but here are some of the things we discuss:

  • Is a rolling forecast right for your organization?
  • What’s the right time horizon? 90-day? Four quarter? Six quarter? Three year?
  • How often should you update the forecast?
  • How do you use a rolling forecast as an early alert of threats and opportunities?
  • What is the role of scenarios?
  • What role can driver-based modeling and tools play in the forecast process?
  • How do you sell the need for a rolling forecast?
  • What does the business case look like?
  • How can you measure the efficiency and effectiveness of your process?

IT’S YOUR TURN NOW!

If you are considering to make changes to your forecasting processes or if you are working in the IT department supporting Finance, you should join one of these workshops. It is a great opportunity to meet other finance & IT professionals and to get solid ideas. Believe it or not, but we have had several customers attend multiple events. They simply liked the interaction with the other professionals so much and they felt that they got a lot of value out of each workshop. Check out my events page to find out about upcoming dates or simply drop me a note. Hope to see you soon!

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A discussion about forecast errors

Forecasting continues to be a hot topic. My recent interviews with Steve Morlidge continues to be very popular. Also, ‘Franz the Frog’ sparked some interesting discussions behind the scenes. Given the strong interest in these topics, I reached out to a friend who has spent a lot of time and effort driving solid forecasting processes.

Please meet Ulrich Pilsl. He provides a different perspective. Ulrich currently works as an Interim Manager in Munich. He spent over 14 years at Softlab / BMW Group (later Cirquent / NTT Data Group). As a member of the executive board, he held different senior executive positions including CFO of a consulting subsidiary and as the Head of Controlling & Business Administration.

Christoph Papenfuss: Forecasting is a key focus area for many finance professionals. But many organizations are struggling to obtain an objective view of the future. What are some of the key problems?

Ulrich Pilsl: The biggest problem I see is complexity. Many companies have bloated processes that are too detailed. It simply takes too much time and people have a hard time differentiating between what is important and what is not. There is no clear focus. Also, management tends to have a hard time managing the process. My advice is to simplify and to get rid of excessive detail. More detail does not create more accurate forecasts. On the contrary: the more detail, the less accurate forecasts tend to be for the above mentioned reasons.

Christoph Papenfuss: What is the main problem with inaccurate forecasts?

Ulrich Pilsl: Inaccurate forecasts lead to a serious confidence problem. Shareholders don’t like surprises. It gets worse when surprises are caused by poor forecasting efforts.

Christoph Papenfuss: Are positive and negative errors equally problematic? Let’s take a look at a typical sales or business forecast. Some people tend to create very conservative forecasts and often end up outperforming. Isn’t this better than creating a very ambitious forecast and then coming in lower?

Ulrich Pilsl: This is an interesting but common situation. First of all, positive and negative errors are equally problematic. Both type of errors can create serious management challenges apart from the already discussed confidence problems. In regards to this specific situation, one might be tempted to say that it is a good thing for a sales person to continuously beat his or her forecast. However, this can create some serious challenges. Let’s take a look at a consulting company. Low sales forecasts indicated low resource requirements. Hiring efforts might be slowed down and the business might quickly end up in a situation where they do not have enough talent available. Business is lost. Customers might loose confidence in us as a trust-worthy business partner. I therefore strongly believe that both negative and positive errors require serious attention.

Christoph Papenfuss: What should the Controller do to help minimize forecast errors?

Ulrich Pilsl: The Controlling department should show some ‘tough love’. They have to challenge the departments to deliver realistic forecasts. We found that it is critical to provide suggestions and to jointly develop scenarios with the business managers. Finance basically acts as a tough but fair coach in the process. This continues in the the monthly and weekly management meetings: We openly discussed the forecast results and challenged the numbers. It is obviously the job of the Business Controller to moderate this process. Last but not least, we found that it sometimes makes sense to create top-down adjustments that reflect upside and downside risk.

Christoph Papenfuss: Based on your experience, does it make sense to measure forecast accuracy? If yes, how often and at what level did you measure accuracy?

Ulrich Pilsl: It depends on the organization. This reminds me of a quote by my former manager who said: “Most companies are over-controlled but under-managed.” A team that understands the value of a forecast will usually deliver solid forecasts. Measuring forecast accuracy won’t necessarily improve it. I do believe, though, that it makes sense to measure it if the organization has challenges with the forecast process. Especially in the case of a management team that does not see the value in the forecast. It might make sense to add an accuracy target to the annual objectives. We had a variable goal “internal quality”. This allowed us to substantially change the mindset of some managers. The goal was set once per year.

Christoph Papenfuss: How do you utilize forecast accuracy measures? Should you communicate the numbers to the organization or is this something that should stay within the walls of the finance department?

Ulrich Pilsl: In my opinion, it does make sense to communicate forecast accuracy to the management team. But it makes no sense to communicate it to the whole organization. The aim is to improve forecast quality and not to blame the management in the organization.

Christoph Papenfuss: What can Finance do to help create a culture where people are happy to create meaningful and objective forecasts?

Ulrich Pilsl: Finance simply has to be the role of a coach and consultant for the business. It is our role to educate and to support the business.

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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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Poor forecast accuracy

Over the last few weeks three separate clients have expressed their frustration with inaccurate forecasts delivered by certain members of the salesforce. Nothing new here. It happens all the time. However, what struck me about these three independent cases was the nature of the issues: The salesforce consistently forecast higher than actuals. This is not typical. Most sales people try to forecast lower to build up some buffer in case of bad news.

BOOKING INVENTORY

What happened here? Very simple: sales tried to utilize the forecast to ‘reserve” inventory of their extremely well-selling products. Their rational was that a higher sales forecast would inevitably lead to a higher availability of finished products ready for sale. In the past several sales people had encountered product shortages which affected their compensation negatively.

The sales forecast as an inventory 'reservation' mechanism

THE THREAT

This kind of poor forecast accuracy could lead to a precarious situation. In case of an unexpected economic downturn, the company could end up sitting on a ton of finished goods inventory. And not only that: average inventory could trend upward reducing liquidity as a result. As we all know, inventory is central to effective working capital management.

THE REMEDY

The controllers of these companies were very frustrated with the situation. Despite senior management discussing the resulting issues with the sales force, accuracy barely improved. But one controller had developed an interesting idea that he is about to implement: start compensating the sales teams based on Working Capital measures.

WORKING CAPITAL & FORECAST ACCURACY

The basic idea of this evolves around punishing sales for consistently producing these unacceptable variances. And the implementation of this does not necessarily have to be that hard. We can measure forecast accuracy. A series of negative variances (Actuals < Forecast) leads to a reduction in the sales bonus. The critical thing here will be to avoid punishing people for random variances. I could see using a rolling average to only punish consistent ‘offenders’.

This is an interesting idea. The actual compensation impact would obviously have to be worked out carefully. But the basic idea is quite interesting!

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The case for forecast accuracy

People always say that you get what you measure. And it is true. When I want to loose weight and I am serious about it, I do have to step on the scale frequently. The same thing is true for business. What gets measured gets done.

FORECAST ACCURACY

Forecasting has become a critical business process. Pretty much every company that I talk to is either improving or looking to improve this process. One of the measures that can be used to manage the forecast process is forecast accuracy. Forecast accuracy measures the percentage difference between Actuals and Forecast. Let’s say we forecast 100 units sold for next month and it turns out that we actually sold 95, the forecast accuracy value -5%. We can measure accuracy at different levels of an organization, let’s say at the Profit Center level or at the BU level.

EMOTIONS RUN HIGH

A few weeks ago, I had an interesting discussion with a group of consultants. They argued that forecast accuracy is not worth measuring. Their main arguments were:

  • Forecast accuracy cannot be influenced. The markets follow a random path and it can therefore not be expected to achieve accurate forecasts.
  • Forecast accuracy is a dangerous thing to measure and manage. People can start influencing the accuracy by managing their numbers according to expectations (for example sales managers can hold back deals for sake of influencing accuracy)
  • The quality of forecast accuracy is hard to define. Let’s say we beat our own forecast by performing really well. Forecast accuracy is off. Is that good or bad?

MY VIEW

Here is my personal view on this topic.

  • No single measure is perfect when looked at in isolation. Let’s say profits. What does the profit number for a certain quarter tell us? Nothing! We need to look at a mix of measures. Forecast accuracy is one measure that we can/ should look at.
  • Forecast accuracy provides us with the ability to identify potential bias. One of my clients, for example, found that their models were flawed. Forecast accuracy revealed this by highlighting a certain consistency.
  • Markets movements are difficult to anticipate. But it is the job of the forecaster to identify potential actions to make sure that targets are achieved. I should have a general clue about what is happening in my business. Once in a while, we encounter some surprises. Does that mean we should not measure forecast accuracy? I beg to differ. At the very least, a detailed analysis of the accuracy measurements can help us learn a lot about our organization and our environment.
  • Forecast accuracy is easy to measure. It can be automated. Cost are almost zero. Why not measure it and potentially learn something?

I could go on and on. The bottom-line is that forecast accuracy is easy to measure and that it allows us to get a good sense for our ability to forecast and manage our business. But we need to be careful about how we utilize the metric. A singular focus on managing just accuracy won’t do anybody any good. But that’s true for anything. If I want to loose weight, I should also look at muscle mass and water content – not just weight as measured in lbs or kg. But to start bashing a single metric is not a good way. I am all for looking at forecast accuracy – often.

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