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Sales – Strategy @ Risk

Strategy @ Risk

Category: Sales

  • Forecasting sales and forecasting uncertainty

    Forecasting sales and forecasting uncertainty

    This entry is part 1 of 4 in the series Predictive Analytics

     

    Introduction

    There are a large number of methods used for forecasting ranging from judgmental (expert forecasting etc.) thru expert systems and time series to causal methods (regression analysis etc.).

    Most are used to give single point forecast or at most single point forecasts for a limited number of scenarios.  We will in the following take a look at the un-usefulness of such single point forecasts.

    As example we will use a simple forecast ‘model’ for net sales for a large multinational company. It turns out that there is a good linear relation between the company’s yearly net sales in million euro and growth rates (%) in world GDP:

    with a correlation coefficient R= 0.995. The relation thus accounts for almost 99% of the variation in the sales data. The observed data is given as green dots in the graph below, and the regression as the green line. The ‘model’ explains expected sales as constant equal 1638M and with 53M in increased or decreased sales per percent increase or decrease in world GDP:

    The International Monetary Fund (IMF) that kindly provided the historical GDP growth rates also gives forecasts for expected future change in the World GDP growth rate (WEO, April 2012) – for the next five years. When we put these forecasts into the ‘model’ we ends up with forecasts for net sales for 2012 to 2016 as depicted by the yellow dots in the graph above.

    So mission accomplished!  …  Or is it really?

    We know that the probability for getting a single-point forecast right is zero even when assuming that the forecast of the GDP growth rate is correct – so the forecasts we so far have will certainly be wrong, but how wrong?

    “Some even persist in using forecasts that are manifestly unreliable, an attitude encountered by the future Nobel laureate Kenneth Arrow when he was a young statistician during the Second World War. When Arrow discovered that month-long weather forecasts used by the army were worthless, he warned his superiors against using them. He was rebuffed. “The Commanding General is well aware the forecasts are no good,” he was told. “However, he needs them for planning purposes.” (Gardner & Tetlock, 2011)

    Maybe we should take a closer look at possible forecast errors, input data and the final forecast.

    The prediction band

    Given the regression we can calculate a forecast band for future observations of sales given forecasts of the future GDP growth rate. That is the region where we with a certain probability will expect new values of net sales to fall. In the graph below the green area give the 95% forecast band:

    Since the variance of the predictions increases the further new forecasts for the GDP growth rate lies from the mean of the sample values (used to compute the regression), the band will widen as we move to either side of this mean. The band will also widen with decreasing correlation (R) and sample size (the number of observations the regression is based on).

    So even if the fit to the data is good, our regression is based on a very small sample giving plenty of room for prediction errors. In fact a 95% confidence interval for 2012, with an expected GDP growth rate of 3.5%, is net sales 1824M plus/minus 82M. Even so the interval is still only approx. 9% of the expected value.

    Now we have shown that the model gives good forecasts, calculated the confidence interval(s) and shown that the expected relative error(s) with high probability will be small!

    So the mission is finally accomplished!  …  Or is it really?

    The forecasts we have made is based on forecasts of future world GDP growth rates, but how certain are they?

    The GDP forecasts

    Forecasting the future growth in GDP for any country is at best difficult and much more so for the GDP growth for the entire world. The IMF has therefore supplied the baseline forecasts with a fan chart ((  The Inflation Report Projections: Understanding the Fan Chart By Erik Britton, Paul Fisher and John Whitley, BoE Quarterly Bulletin, February 1998, pages 30-37.)) picturing the uncertainty in their estimates.

    This fan chart ((Figure 1.12. from:, World Economic Outlook (April 2012), International Monetary Fund, Isbn  9781616352462))  shows as blue colored bands the uncertainty around the WEO baseline forecast with 50, 70, and 90 percent confidence intervals ((As shown, the 70 percent confidence interval includes the 50 percent interval, and the 90 percent confidence interval includes the 50 and 70 percent intervals. See Appendix 1.2 in the April 2009 World Economic Outlook for details.)) :

    There is also another band on the chart, implied but un-seen, indicating a 10% chance of something “unpredictable”. The fan chart thus covers only 90% of the IMF’s estimates of the future probable growth rates.

    The table below shows the actual figures for the forecasted GDP growth (%) and the limits of the confidence intervals:

    Lower

    Baseline

    Upper

    90%

    70%

    50%

    50%

    70%

    90%

    2012

    2.5

    2.9

    3.1

    .5

    3.8

    4.0

    4.3

    2013

    2.1

    2.8

    3.3

    4.1

    4.8

    5.2

    5.9

    The IMF has the following comments to the figures:

    “Risks around the WEO projections have diminished, consistent with market indicators, but they remain large and tilted to the downside. The various indicators do not point in a consistent direction. Inflation and oil price indicators suggest downside risks to growth. The term spread and S&P 500 options prices, however, point to upside risks.”

    Our approximation of the distribution that can have produced the fan chart for 2012 as given in the World Economic Outlook for April 2012 is shown below:

    This distribution has:  mean 3.43%, standard deviation 0.54, minimum 1.22 and maximum 4.70 – it is skewed with a left tail. The distribution thus also encompasses the implied but un-seen band in the chart.

    Now we are ready for serious forecasting!

    The final sales forecasts

    By employing the same technique that we used to calculate the forecast band we can by Monte Carlo simulation compute the 2012 distribution of net sales forecasts, given the distribution of GDP growth rates and by using the expected variance for the differences between forecasts using the regression and new observations. The figure below describes the forecast process:

    We however are not only using the 90% interval for The GDP growth rate or the 95% forecast band, but the full range of the distributions. The final forecasts of net sales are given as a histogram in the graph below:

    This distribution of forecasted net sales has:  mean sales 1820M, standard deviation 81, minimum sales 1590M and maximum sales 2055M – and it is slightly skewed with a left tail.

    So what added information have we got from the added effort?

    Well, we now know that there is only a 20% probability for net sales to be lower than 1755 or above 1890. The interval from 1755M to 1890M in net sales will then with 60% probability contain the actual sales in 2012 – see graph below giving the cumulative sales distribution:

    We also know that we with 90% probability will see actual net sales in 2012 between 1720M and 1955M.But most important is that we have visualized the uncertainty in the sales forecasts and that contingency planning for both low and high sales should be performed.

    An uncertain past

    The Bank of England’s fan chart from 2008 showed a wide range of possible futures, but it also showed the uncertainty about where we were then – see that the black line showing National Statistics data for the past has probability bands around it:

    This indicates that the values for past GDP growth rates are uncertain (stochastic) or contains measurement errors. This of course also holds for the IMF historic growth rates, but they are not supplying this type of information.

    If the growth rates can be considered stochastic the results above will still hold, if the conditional distribution for net sales given the GDP growth rate still fulfills the standard assumptions for using regression methods. If not other methods of estimation must be considered.

    Black Swans

    But all this uncertainty was still not enough to contain what was to become reality – shown by the red line in the graph above.

    How wrong can we be? Often more wrong than we like to think. This is good – as in useful – to know.

    “As Donald Rumsfeld once said: it’s not only what we don’t know – the known unknowns – it’s what we don’t know we don’t know.”

    While statistic methods may lead us to a reasonably understanding of some phenomenon that does not always translate into an accurate practical prediction capability. When that is the case, we find ourselves talking about risk, the likelihood that some unfavorable or favorable event will take place. Risk assessment is then necessitated and we are left only with probabilities.

    A final word

    Sales forecast models are an integrated part of our enterprise simulation models – as parts of the models predictive analytics. Predictive analytics can be described as statistic modeling enabling the prediction of future events or results ((in this case the probability distribution of future net sales)) , using present and past information and data.

    In today’s fast moving and highly uncertain markets, forecasting have become the single most important element of the management process. The ability to quickly and accurately detect changes in key external and internal variables and adjust tactics accordingly can make all the difference between success and failure:

    1. Forecasts must integrate both external and internal drivers of business and the financial results.
    2. Absolute forecast accuracy (i.e. small confidence intervals) is less important than the insight about how current decisions and likely future events will interact to form the result.
    3. Detail does not equal accuracy with respect to forecasts.
    4. The forecast is often less important than the assumptions and variables that underpin it – those are the things that should be traced to provide advance warning.
    5. Never relay on single point or scenario forecasting.

    The forecasts are usually done in three stages, first by forecasting the market for that particular product(s), then the firm’s market share(s) ending up with a sales forecast. If the firm has activities in different geographic markets then the exercise has to be repeated in each market, having in mind the correlation between markets:

    1. All uncertainty about the different market sizes, market shares and their correlation will finally end up contributing to the uncertainty in the forecast for the firm’s total sales.
    2. This uncertainty combined with the uncertainty from other forecasted variables like interest rates, exchange rates, taxes etc. will eventually be manifested in the probability distribution for the firm’s equity value.

    The ‘model’ we have been using in the example have never been tested out of sample. Its usefulness as a forecast model is therefore still debatable.

    References

    Gardner, D & Tetlock, P., (2011), Overcoming Our Aversion to Acknowledging Our Ignorance, http://www.cato-unbound.org/2011/07/11/dan-gardner-and-philip-tetlock/overcoming-our-aversion-to-acknowledging-our-ignorance/

    World Economic Outlook Database, April 2012 Edition; http://www.imf.org/external/pubs/ft/weo/2012/01/weodata/index.aspx

    Endnotes

     

     

  • Uncertainty modeling

    Uncertainty modeling

    This entry is part 2 of 3 in the series What We Do

    Prediction is very difficult, especially about the future.
    Niels Bohr. Danish physicist (1885 – 1962)

    Strategy @ Risks models provide the possibility to study risk and uncertainties related to operational activities;  cost, prices, suppliers,  markets, sales channels etc. financial issues like; interest rates risk, exchange rates risks, translation risk , taxes etc., strategic issues like investments in new or existing activities, valuation and M&As’ etc and for a wide range of budgeting purposes.

    All economic activities have an inherent volatility that is an integrated part of its operations. This means that whatever you do some uncertainty will always remain.

    The aim is to estimate the economic impact that such critical uncertainty may have on corporate earnings at risk. This will add a third dimension – probability – to all forecasts, give new insight: the ability to deal with uncertainties in an informed way and thus benefits above ordinary spread-sheet exercises.

    The results from these analyzes can be presented in form of B/S and P&L looking at the coming one to five (short term) or five to fifteen years (long term); showing the impacts to e.g. equity value, company value, operating income etc. With the purpose of:

    • Improve predictability in operating earnings and its’ expected volatility
    • Improve budgeting processes, predicting budget deviations and its’ probabilities
    • Evaluate alternative strategic investment options at risk
    • Identify and benchmark investment portfolios and their uncertainty
    • Identify and benchmark individual business units’ risk profiles
    • Evaluate equity values and enterprise values and their uncertainty in M&A processes, etc.

    Methods

    To be able to add uncertainty to financial models, we also have to add more complexity. This complexity is inevitable, but in our case, it is desirable and it will be well managed inside our models.

    People say they want models that are simple, but what they really want is models with the necessary features – that are easy to use. If something is complex but well designed, it will be easy to use – and this holds for our models.

    Most companies have some sort of model describing the company’s operations. They are mostly used for budgeting, but in some cases also for forecasting cash flow and other important performance measures. Almost all are deterministic models based on expected or average values of input data; sales, cost, interest and currency rates etc.

    We know however that forecasts based on average values are on average wrong. In addition will deterministic models miss the important uncertainty dimension that gives both the different risks facing the company and the opportunities they bring forth.

    S@R has set out to create models that can give answers to both deterministic and stochastic questions, by linking dedicated Ebitda models to holistic balance simulation taking into account all important factors describing the company. The basis is a real balance simulation model – not a simple cash flow forecast model.

    Both the deterministic and stochastic balance simulation can be set about in two different alternatives:

    1. by a using a EBITDA model to describe the companies operations or
    2. by using coefficients of fabrications (e.g. kg flour pr 1000 bread etc.) as direct input to the balance model – the ‘short cut’ method.

    The first approach implies setting up a dedicated Ebitda subroutine to the balance model. This will give detailed answers to a broad range of questions about markets, capacity driven investments, operational performance and uncertainty, but entails a higher degree of effort from both the company and S@R. This is a tool for long term planning and strategy development.

    The second (‘the short cut’) uses coefficients of fabrications and their variations, and is a low effort (cost) alternative, usually using the internal accounting as basis. This will in many cases give a ‘good enough’ description of the company – its risks and opportunities. It can be based on existing investment and market plans.  The data needed for the company’s economic environment (taxes, interest rates etc) will be the same in both alternatives:

    The ‘short cut’ approach is especially suited for quick appraisals of M&A cases where time and data is limited and where one wishes to limit efforts in an initial stage. Later the data and assumptions can be augmented to much more sophisticated analysis within the same ‘short cut’ framework. In this way analysis can be successively built in the direction the previous studies suggested.

    This also makes it a good tool for short-term (3-5 years) analysis and even for budget assessment. Since it will use a limited number of variables – usually less than twenty – describing the operations, it is easy to maintain and operate. The variables describing financial strategy and the economic environment come in addition, but will be easy to obtain.

    Used in budgeting it will give the opportunity to evaluate budget targets, their probable deviation from expected result and the probable upside or down side given the budget target (Upside/downside ratio).

    Done this way analysis can be run for subsidiaries across countries translating the P&L and Balance to any currency for benchmarking, investment appraisals, risk and opportunity assessments etc. The final uncertainty distributions can then be “aggregated’ to show global risk for the mother company.

    An interesting feature is the models ability to start simulations with an empty opening balance. This can be used to assess divisions that do not have an independent balance since the model will call for equity/debt etc. based on a target ratio, according to the simulated production and sales and the necessary investments. Questions about further investment in divisions or product lines can be studied this way.

    Since all runs (500 to 1000) in the simulation produces a complete P&L and Balance the uncertainty curve (distribution) for any financial metric like ‘Yearly result’, ‘free cash flow’, economic profit’, ‘equity value’, ‘IRR’ or’ translation gain/loss’ etc. can be produced.

    In some cases we have used both approaches for the same client, using the last approach for smaller daughter companies with production structures differing from the main companies.
    The second approach can also be considered as an introduction and stepping stone to a more holistic Ebitda model.

    Time and effort

    The work load for the client is usually limited to a small team of people ( 1 to 3 persons) acting as project leaders and principal contacts, assuring that all necessary information, describing value and risks for the clients’ operations can be collected as basis for modeling and calculations. However the type of data will have to be agreed upon depending on the scope of analysis.

    Very often will key people from the controller group be adequate for this work and if they don’t have the direct knowledge they usually know who to ask. The work for this team, depending on the scope and choice of method (see above) can vary in effective time from a few days to a couple of weeks, but this can be stretched from three to four weeks to the same number of months.

    For S&R the time frame will depend on the availability of key personnel from the client and the availability of data. For the second alternative it can take from one to three weeks of normal work to three to six months for the first alternative for more complex models. The total time will also depend on the number of analysis that needs to be run and the type of reports that has to be delivered.

    S@R_ValueSim

    Selecting strategy

    Models like this are excellent for selection and assessment of strategies. Since we can find the probability distribution for equity value, changes in this brought by different strategies will form a basis for selection or adjustment of current strategy. Models including real option strategies are a natural extension of these simulation models:

    If there is a strategy with a curve to the right and under all other feasible strategies this will be the stochastic dominant one. If the curves crosses further calculations needs to be done before a stochastic dominant or preferable strategy can be found:

    Types of problems we aim to address:

    The effects of uncertainties on the P&L and Balance and the effects of the Boards strategies (market, hedging etc.) on future P&L and Balance sheets evaluating:

    • Market position and potential for growth
    • Effects of tax and capital cost
    • Strategies
    • Business units, country units or product lines –  capital allocation – compare risk, opportunity and expected profitability
    • Valuations, capital cost and debt requirements, individually and effect on company
    • The future cash-flow volatility of company and the individual BU’s
    • Investments, M&A actions, their individual value, necessary commitments and impact on company
    • Etc.

    The aim regardless of approach is to quantify not only the company’s single and aggregated risks, but also the potential, thus making the company capable to perform detailed planning and of executing earlier and more apt actions against uncertain factors.

    Used in budgeting, this will improve budget stability through higher insight in cost side risks and income-side potentials. This is achieved by an active budget-forecast process; the control-adjustment cycle will teach the company to better target realistic budgets – with better stability and increased company value as a result.

    This is most clearly seen when effort is put into correctly evaluating strategies-projects and investments effects on the enterprise. The best way to do this is by comparing and Choosing strategies by analyzing the individual strategies risks and potential – and select the alternative that is dominant (stochastic) given the company’s chosen risk-profile.

    A severe depression like that of 1920-1921 is outside the range of probability. –The Harvard Economic Society, 16 November 1929

  • The Uncertainty in Forecasting Airport Pax

    The Uncertainty in Forecasting Airport Pax

    This entry is part 3 of 4 in the series Airports

     

    When planning airport operations, investments both air- and land side or only making next years budget you need to make some forecasts of what traffic you can expect. Now, there are many ways of doing that most of them ending up with a single figure for the monthly or weekly traffic. However we do know that the probability for that figure to be correct is near zero, thus we end up with plans based on assumptions that most likely newer will happen.

    This is why we use Monte Carlo simulation to get a grasp of the uncertainty in our forecast and how this uncertainty develops as we go into the future. The following graph (from real life) shows how the passenger distribution changes as we go from year 2010 (blue) to 2017 (red). The distribution moves outwards showing an expected increase in Pax at the same time it spreads out on the x-axis (Pax) giving a good picture of the increased uncertainty we face.

    Pax-2010_2017This can also be seen from the yearly cumulative probability distributions given below. As we move out into the future the distributions are leaning more and more to the right while still being “anchored” on the left to approximately the same place – showing increased uncertainty in the future Pax forecasts. However our confidence in that the airport will reach at least 40M Pax during the next 5 years is bolstered.

    Pax_DistributionsIf we look at the fan-chart for the Pax forecasts below, the limits of the dark blue region give the lower (25%) and upper (75%) quartiles for the yearly Pax distributions i.e. the region where we expect with 50% probability the actual Pax figures to fall.

    Pax_Uncertainty

    The lower und upper limits give the 5% and 95% percentiles for the yearly Pax distributions i.e. we can expect with 90% probability that the actual Pax figures will fall somewhere inside these three regions.

    As shown the uncertainty about the future yearly Pax figures is quite high. With this as the backcloth for airport planning it is evident that the stochastic nature of the Pax forecasts has to be taken into account when investment decisions (under uncertainty) are to be made. (ref) Since the airport value will relay heavily on these forecasts it is also evident that this value will be stochastic and that methods from decision making under uncertainty have to be used for possible M&R.

    Major Airport Operation Disruptions

    Delays – the time lapse which occurs when a planned event does not happen at the planned time – are pretty common at most airports Eurocontrol  estimates it on average to approx 13 min on departure for 45%  of the flights and approx 12 min for arrivals in 42% of the flights (Guest, 2007). Nevertheless the airport costs of such delays are small; it can even give an increase in revenue (Cook, Tanner, & Anderson, 2004).

    We have lately in Europe experienced major disruptions in airport operations thru closing of airspace due to volcanic ash. Closed airspace give a direct effect on airport revenue and a higher effect the closer it is to an airport. Volcanic eruptions in some regions might be considered as Black Swan events to an airport, but there are a large number of volcanoes that might cause closing of airspace for shorter or longer time. The Smithsonian Global Volcanism Program lists more than 540 volcanoes with previous documented eruption.

    As there is little data for events like this it is difficult to include the probable effects of closed airspace due to volcanic eruptions in the simulation. However, the data includes effects of the 9/11 terrorist attack and the left tails of the yearly Pax distributions will be influenced by this.

    References

    Guest, Tim. (2007, September). A Matter of time: air traffic delay in Europe. , EUROCONTROL Trends in Air Traffic I, 2.

    Cook, A., Tanner, G., & Anderson, S. (2004). Evaluating the true cost to airlines of one minute of airborne or ground delay: final report. [University of Westminster]. Retrieved from, www.eurocontrol.int/prc/gallery/content/public/Docs/cost_of_delay.pdf