Warning: define(): Argument #3 ($case_insensitive) is ignored since declaration of case-insensitive constants is no longer supported in /home/u742613510/domains/strategy-at-risk.com/public_html/wp-content/plugins/wpmathpub/wpmathpub.php on line 65
Modeling – Page 2 – Strategy @ Risk

Strategy @ Risk

Category: Modeling

  • Inventory management – Stochastic supply

    Inventory management – Stochastic supply

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

     

    Introduction

    We will now return to the newsvendor who was facing a onetime purchasing decision; where to set the inventory level to maximize expected profit – given his knowledge of the demand distribution.  It turned out that even if we did not know the closed form (( In mathematics, an expression is said to be a closed-form expression if it can be expressed analytically in terms of a finite number of certain “well-known” functions.)) of the demand distribution, we could find the inventory level that maximized profit and how this affected the vendor’s risk – assuming that his supply with certainty could be fixed to that level. But what if that is not the case? What if the supply his supply is uncertain? Can we still optimize his inventory level?

    We will look at to slightly different cases:

    1.  one where supply is uniformly distributed, with actual delivery from 80% to 100% of his ordered volume and
    2. the other where the supply have a triangular distribution, with actual delivery from 80% to 105% of his ordered volume, but with most likely delivery at 100%.

    The demand distribution is as shown below (as before):

    Maximizing profit – uniformly distributed supply

    The figure below indicates what happens as we change the inventory level – given fixed supply (blue line). We can see as we successively move to higher inventory levels (from left to right on the x-axis) that expected profit will increase to a point of maximum.

    If we let the actual delivery follow the uniform distribution described above, and successively changes the order point expected profit will follow the red line in the graph below. We can see that the new order point is to the right and further out on the inventory axis (order point). The vendor is forced to order more newspapers to ‘outweigh’ the supply uncertainty:

    At the point of maximum profit the actual deliveries spans from 2300 to 2900 units with a mean close to the inventory level giving maximum profit for the fixed supply case:

    The realized profits are as shown in the frequency graph below:

    Average profit has to some extent been reduced compared with the non-stochastic supply case, but more important is the increase in profit variability. Measured by the quartile variation, this variability has increased by almost 13%, and this is mainly caused by an increased negative skewness – the down side has been raised.

    Maximizing profit – triangular distributed supply

    Again we compare the expected profit with delivery following the triangular distribution as described above (red line) with the expected profit created by known and fixed supply (blue line).  We can see as we successively move to higher inventory levels (from left to right on the x-axis) that expected profits will increase to a point of maximum. However the order point for the stochastic supply is to the right and further out on the inventory axis than for the non-stochastic case:

    The uncertain supply again forces the vendor to order more newspapers to ‘outweigh’ the supply uncertainty:

    At the point of maximum profit the actual deliveries spans from 2250 to 2900 units with a mean again close to the inventory level giving maximum profit for the fixed supply case ((This is not necessarily true for other combinations of demand and supply distributions.)) .

    The realized profits are as shown in the frequency graph below:

    Average profit has somewhat been reduced compared with the non-stochastic supply case, but more important is the increase in profit variability. Measured by the quartile variation this variability has increased by 10%, and this is again mainly caused by an increased negative skewness – again have the down side been raised.

    The introduction of uncertain supply has shown that profit can still be maximized however the profit will be reduced by increased costs both in lost sales and in excess inventory. But most important, profit variability will increase raising issues of possible other strategies.

    Summary

    We have shown through Monte-Carlo simulations, that the ‘order point’ when the actual delivered amount is uncertain can be calculated without knowing the closed form of the demand distribution. We actually do not need the closed form for the distribution describing delivery, only historic data for the supplier’s performance (reliability).

    Since we do not need the closed form of the demand distribution or supply, we are not limited to such distributions, but can use historic data to describe the uncertainty as frequency distributions. Expanding the scope of analysis to include supply disruptions, localization of inventory etc. is thus a natural extension of this method.

    This opens for use of robust and efficient methods and techniques for solving problems in inventory management unrestricted by the form of the demand distribution and best of all, the results given as graphs will be more easily communicated to all parties than pure mathematical descriptions of the solutions.

    Average profit has to some extent been reduced compared with the non-stochastic supply case, but more important is the increase in profit variability. Measured by the quartile variation, this variability has increased by almost 13%, and this is mainly caused by an increased negative skewness – the down side has been raised.

  • Budgeting Revisited

    Budgeting Revisited

    This entry is part 2 of 2 in the series Budgeting

     

    Introduction

    Budgeting is one area that is well suited for Monte Carlo Simulation. Budgeting involves personal judgments about future values of large number of variables like; sales, prices, wages, down- time, error rates, exchange rates etc. – variables that describes the nature of the business.

    Everyone that has been involved in a budgeting process knows that it is an exercise in uncertainty; however it is seldom described in this way and even more seldom is uncertainty actually calculated as an integrated part of the budget.

    Good budgeting practices are structured to minimize errors and inconsistencies, drawing in all the necessary participants to contribute their business experience and the perspective of each department. Best practice in budgeting entails a mixture of top-down guidelines and standards, combined with bottom-up individual knowledge and experience.

    Excel, the de facto tool for budgeting, is a powerful personal productivity tool. Its current capabilities, however, are often inadequate to support the critical nature of budgeting and forecasting. There will come a point when a company’s reliance on spreadsheets for budgeting leads to severely ineffective decision-making, lost productivity and lost opportunities.

    Spreadsheets can accommodate many tasks – but, over time, some of the models running in Excel may grow too big for the spreadsheet application. Programming in a spreadsheet model often requires embedded assumptions, complex macros, creating opportunities for formula errors and broken links between workbooks.

    It is common for spreadsheet budget models and their intricacies to be known and maintained by a single person who becomes a vulnerability point with no backup. And there are other maintenance and usage issues:

    A.    Spreadsheet budget models are difficult to distribute and even more difficult to collect and consolidate.
    B.    Data confidentiality is almost impossible to maintain in spreadsheets, which are not designed to hide or expose data based upon each user’s role.
    C.    Financial statements are usually not fully integrated leaving little basis for decision making.

    These are serious drawbacks for corporate governance and make the audit process more difficult.

    This is a few of many reasons why we use a dedicated simulation language for our models that specifically do not mix data and code.

    The budget model

    In practice budgeting can be performed on different levels:
    1.    Cash Flow
    2.    EBITDA
    3.    EBIT
    4.    Profit or
    5.    Company value.

    The most efficient is on EBITDA level, since taxes, depreciation and amortization on the short-term is mostly given. This is also the level where consolidation of daughter companies easiest is achieved. An EBITDA model describing the firm’s operations can again be used as a subroutine for more detailed and encompassing analysis thru P&L and Balance simulation.

    The aim will then to estimate of the firm’s equity value and is probability distribution. This can again be used for strategy selection etc.

    Forecasting

    In today’s fast moving and highly uncertain markets, forecasting have become the single most important element of the budget process.

    Forecasting or predictive analytics can best be described as statistic modeling enabling prediction of future events or results, using present and past information and data.

    1. Forecasts must integrate both external and internal cost and value drivers of the business.
    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.

    All uncertainty about the market sizes, market shares, cost and prices, interest rates, exchange rates and taxes etc. – and their correlation will finally end up contributing to the uncertainty in the firm’s budget forecasts.

    The EBITDA model

    The EBITDA model have to be detailed enough to capture all important cost and value drivers, but simple enough to be easy to update with new data and assumptions.

    Input to the model can come from different sources; any internal reporting system or spread sheet. The easiest way to communicate with the model is by using Excel  spread sheet – templates.

    Such templates will be pre-defined in the sense that the information the model needs is on a pre-determined place in the workbook.  This makes it easy if the budgets for daughter companies is reported (and consolidated) in a common system (e.g. SAP) and can ‘dump’ onto an excel spread sheet. If the budgets are communicated directly to head office or the mother company then they can be read directly by the model.

    Standalone models and dedicated subroutines

    We usually construct our EBITDA models so that they can be used both as a standalone model and as a subroutine for balance simulation. The model can then be used both for short term budgeting and long-term EBITDA forecasting and simulation and for short/long term balance forecasting and simulation. This means that the same model can be efficiently reused in different contexts.
    Rolling budgets and forecast

    The EBITDA model can be constructed to give rolling forecast based on updated monthly or quarterly values, taking into consideration the seasonality of the operations. This will give new forecasts (new budget) for the remaining of the year and/or the next twelve month. By forecasts we again mean the probability distributions for the budget variables.

    Even if the variables have not changed, the fact that we move towards the end of the year will reduce the uncertainty of if the end year results and also for the forecast for the next twelve month.

    Uncertainty

    The most important part of budgeting with Monte Carlo simulation is assessment of the uncertainty in the budgeted (forecasted) cost and value drivers. This uncertainty is given as the most likely value (usually the budget figure) and the interval where it is assessed with a high degree of confidence (approx. 95%) to fall.

    We will then use these lower and upper limits (5% and 95%) for sales, prices and other budget items and the budget values as indicators of the shape of the probability distributions for the individual budget items. Together they described the range and uncertainty in the EBITDA forecasts.

    This gives us the opportunity to simulate (Monte Carlo) a number of possible outcomes – by a large number of runs of the model, usually 1000 – of net revenue, operating expenses and finally EBITDA. This again will give us their probability distributions

    Most managers and their staff have, based on experience, a good grasp of the range in which the values of their variables will fall. It is not based on any precise computation but is a reasonable assessment by knowledgeable persons. Selecting the budget value however is more difficult. Should it be the “mean”
    or the “most likely value” or should the manager just delegate fixing of the values to the responsible departments?

    Now we know that the budget values might be biased by a number of reasons – simplest by bonus schemes etc. – and that budgets based on average assumptions are wrong on average .

    This is therefore where the individual mangers intent and culture will be manifested, and it is here the greatest learning effect for both the managers and the mother company will be, as under-budgeting  and overconfidence  will stand out as excessive large deviations from the model calculated expected value (probability weighted average over the interval).

    Output

    The output from the Monte Carlo simulation will be in the form of graphs that puts all run’s in the simulation together to form the cumulative distribution for the operating expenses (red line):

    In the figure we have computed the frequencies of observed (simulated) values for operating expenses (blue frequency plot) – the x-axis gives the operating expenses and the left y-axis the frequency. By summing up from left to right we can compute the cumulative probability curve. The s-shaped curve (red) gives for every point the probability (on the right y-axis) for having an operating expenses less than the corresponding point on the x-axis. The shape of this curve and its range on the x-axis gives us the uncertainty in the forecasts.

    A steep curve indicates little uncertainty and a flat curve indicates greater uncertainty.  The curve is calculated from the uncertainties reported in the reporting package or templates.

    Large uncertainties in the reported variables will contribute to the overall uncertainty in the EBITDA forecast and thus to a flatter curve and contrariwise. If the reported uncertainty in sales and prices has a marked downside and the costs a marked upside the resulting EBITDA distribution might very well have a portion on the negative side on the x-axis – that is, with some probability the EBITDA might end up negative.

    In the figure below the lines give the expected EBITDA and the budget value. The expected EBIT can be found by drawing a horizontal line from the 0.5 (50%) point on the y-axis to the curve and a vertical line from this point on the curve to the x-axis. This point gives us the expected EBITDA value – the point where it is 50% probability of having a value of EBITDA below and 100%-50%=50% of having it above.

    The second set of lines give the budget figure and the probability that it will end up lower than budget. In this case it is almost a 100% probability that it will be much lower than the management have expected.

    This distributions location on the EBITDA axis (x-axis) and its shape gives a large amount of information of what we can expect of possible results and their probability.

    The following figure that gives the EBIT distributions for a number of subsidiaries exemplifies this. One wills most probable never earn money (grey), three is cash cows (blue, green and brown) and the last (red) can earn a lot of money:

    Budget revisions and follow up

    Normally – if something extraordinary does not happen – we would expect both the budget and the actual EBITDA to fall somewhere in the region of the expected value. We have however to expect some deviation both from budget and expected value due to the nature of the industry.  Having in mind the possibility of unanticipated events or events “outside” the subsidiary’s budget responsibilities, but affecting the outcome this implies that:

    • Having the actual result deviating from budget is not necessary a sign of bad budgeting.
    • Having the result close to or on budget is not necessary a sign of good budgeting.

    However:

    •  Large deviations between budget and actual result needs looking into – especially if the deviation to expected value also is large.
    • Large deviation between budget and expected value can imply either that the limits are set “wrong” or that the budget EBITDA is not reflecting the downside risk or upside opportunity expressed by the limits.

    Another way of looking at the distributions is by the probabilities of having the actual result below budget that is how far off line the budget ended up. In the graph below, country #1’s budget came out with a probability of 72% of having the actual result below budget.  It turned out that the actual figure with only 36% probability would have been lower. The length of the bars thus indicates the budget discrepancies.

    For country# 2 it is the other way around: the probability of having had a result lower than the final result is 88% while the budgeted figure had a 63% probability of having been too low. In this case the market was seriously misjudged.

    In the following we have measured the deviation of the actual result both from the budget values and from the expected values. In the figures the left axis give the deviation from expected value and the bottom axis the deviation from budget value.

    1.  If the deviation for a country falls in the upper right quadrant the deviation are positive for both budget and expected value – and the country is overachieving.
    2. If the deviation falls in the lower left quadrant the deviation are negative for both budget and expected value – and the country is underachieving.
    3. If the deviation falls in the upper left quadrant the deviation are negative for budget and positive for expected value – and the country is overachieving but has had a to high budget.

    With a left skewed EBITDA distribution there should not be any observations in the lower right quadrant that will only happen when the distribution is skewed to the right – and then there will not be any observations in the upper left quadrant:

    As the manager’s gets more experienced in assessing the uncertainty they face, we see that the budget figures are more in line with the expected values and that the interval’s given is shorter and better oriented.

    If the budget is in line with expected value given the described uncertainty, the upside potential ratio should be approx. one. A high value should indicate a potential for higher EBITDA and vice versa. Using this measure we can numerically describe the managements budgeting behavior:

    Rolling budgets

    If the model is set up to give rolling forecasts of the budget EBITDA as new and in this case monthly data, we will get successive forecast as in the figure below:

    As data for new month are received, the curve is getting steeper since the uncertainty is reduced. From the squares on the lines indicating expected value we see that the value is moving slowly to the right and higher EBITDA values.

    We can of course also use this for long term forecasting as in the figure below:

    As should now be evident; the EBITDA Monte Carlo model have multiple fields of use and all of them will increases the managements possibilities of control and foresight giving ample opportunity for prudent planning for the future.

     

     

  • “How can you be better than us understand our business risk?”

    “How can you be better than us understand our business risk?”

    This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can use your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.

    The main reason for this lies in how we can treat uncertainties ((Variance is used as measure of uncertainty or risk.)) in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.

    Risk is usually compartmentalized in “silos” and regarded as proprietary to the department and – not as a risk correlated or co-moving with other risks in the company caused by common underlying events influencing their outcome:

    When Queen Elizabeth visited the London School of Economics in autumn 2008 she asked why no one had foreseen the crisis. The British Academy Forum replied to the Queen in a letter six months later. Included in the letter was the following:

    One of our major banks, now mainly in public ownership, reputedly had 4000 risk managers. But the difficulty was seeing the risk to the system as a whole rather than to any specific financial instrument or loan (…) they frequently lost sight of the bigger picture ((The letter from the British Academy to the Queen is available at: http://media.ft.com/cms/3e3b6ca8-7a08-11de-b86f-00144feabdc0.pdf)).

    To be precise we are actually not simulating risk in and of itself, risk just is a bi-product from simulation of a company’s financial and operational (economic) activities. Since the variables describing these activities is of stochastic nature, which is to say contains uncertainty, all variables in the P&L and Balance sheet will contain uncertainty. They can as such best be described by the shape of their frequency distribution – found after thousands of simulations. And it is the shape of these distributions that describes the uncertainty in the variables.

    Most ERM activities are focused on changing the left or downside tail – the tail that describes what normally is called risk.

    We however are also interested in the right tail or upside tail, the tail that describes possible outcomes increasing company value. Together they depict the uncertainty the company faces:

    S@R thus treats company risk holistic by modeling risks (uncertainty) as parts of the overall operational and financial activities. We are thus able to “add up” the risks – to a consolidated level.

    Having the probability distribution for e.g. the company’s equity value gives us the opportunity to apply risk measures to describe the risk facing the shareholders or the risk added or subtracted by different strategies like investments or risk mitigation tools.

    Since this can’t be done with ordinary addition (( The variance of the sum of two stochastic variables is the sum of their variance plus the covariance between them.)) (or subtraction) we have to use Monte Carlo simulation.

    The value added by this are:

    1.  A method for assessing changes in strategy; investments, new markets, new products etc.
    2. A heightening of risk awareness in management across an organization’s diverse businesses.
    3. A consistent measure of risk allowing executive management and board reporting and response across a diverse organization.
    4. A measure of risk (including credit and market risk) for the organization that can be compared with capital required by regulators, rating agencies and investors.
    5. A measure of risk by organization unit, product, channel and customer segment which allows risk adjusted returns to be assessed, and scarce capital to be rationally allocated.
    6.  A framework from which the organization can decide its risk mitigation requirements rationally.
    7. A measure of risk versus return that allows businesses and in particular new businesses (including mergers and acquisitions) to be assessed in terms of contribution to growth in shareholder value.

    The independent risk experts are often essential for consistency and integrity. They can also add value to the process by sharing risk and risk management knowledge gained both externally and elsewhere in the organization. This is not just a measurement exercise, but an investment in risk management culture.

    Forecasting

    All business planning are built on forecasts of market sizes, market shares, prices and costs. They are usually given as low, mean and high scenarios without specifying the relationship between the variables. It is easy to show that when you combine such forecasts you can end up very wrong (( https://www.strategy-at-risk.com/2009/05/04/the-fallacies-of-scenario-analysis/)). However the 5 %, 50 % and 95 % values from the scenarios can be used to produce a probability distribution for the variable and the simultaneous effect of these distributions can be calculated using Monte Carlo simulation, giving for instance the probability distribution for profit or cash flow from that market. This can again be used to consolidate the company’s cash flow or profit etc.

    Controls and Mitigation

    Controls and mitigation play a significant part in reducing the likelihood of a risk event or the amount of loss should one occur. They however have a material cost. One of the drivers of measuring risk is to support a more rational analysis of the costs and benefits of controls and.
    The result after controls and mitigation becomes the final or residual risk distribution for the company.

    Distributing Diversification Benefits

    At each level of aggregation within a business diversification benefits accrue, representing the capacity to leverage the risk capital against a larger range of non-perfectly correlated risks. How should these diversification benefits be distributed to the various businesses?

    This is not an academic matter, as the residual risk capital ((Bodoff, N. M.,  Capital Allocation by Percentile Layer VOLUME 3/ISSUE 1 CASUALTY ACTUARIAL SOCIETY, pp 13-30, http://www.variancejournal.org/issues/03-01/13.pdf

    Erel, Isil, Myers, Stewart C. and Read, James, Capital Allocation (May 28, 2009). Fisher College of Business Working Paper No. 2009-03-010. Available at SSRN: http://ssrn.com/abstract=1411190 or fttp://dx.doi.org/10.2139/ssrn.1411190))  attributed to each business segment is critical in determining its shareholder value creation and thus its strategic worth to the enterprise. Getting this wrong could lead the organization to discourage its better value creating segments and encourage ones that dissipate shareholder value.

    The simplest is the pro-rata approach which distributes the diversification benefits on a pro-rata basis down the various segment hierarchies (organizational unit, product, customer segment etc.).

    A more right approach that can be built into the Monte Carlo simulation is the contributory method which takes into account the extent to which a segment of the organization’s business is correlated with or contrary to the major risks that make up the company’s overall risk. This rewards counter cyclical businesses and others that diversify the company’s risk profile.

    Aggregation with market & credit risk

    For many parts of an organization there may be no market or credit risk – for areas, such as sales and manufacturing, operational and business risk covers all of their risks.

    But at the company level the operational and business risk needs to be integrated with market and credit risk to establish the overall measure of risk being run by the company. And it is this combined risk capital measure that needs to be apportioned out to the various businesses or segments to form the basis for risk adjusted performance measures.

    It is not enough just to add the operational, credit and market risks together. This would over count the risk – the risk domains are by no means perfectly correlated, which a simple addition would imply. A sharp hit in one risk domain does not imply equally sharp hits in the others.

    Yet they are not independent either. A sharp economic downturn will affect credit and many operational risks and probably a number of market risks as well.

    The combination of these domains can be handled in a similar way to correlations within operational risk, provided aggregate risk distributions and correlation factors can be estimated for both credit and market risk.

    Correlation risk

    Markets that are part of the same sector or group are usually very highly correlated or move together. Correlation risk is the risk associated with having several positions in too many similar markets. By using Monte Carlo simulation as described above this risk can be calculated and added to the company’s risks distribution that will take part in forming the company’s yearly profit or equity value distribution. And this is the information that the management and board will need.

    Decision making

    The distribution for equity value (see above) can then be used for decision purposes. By making changes to the assumptions about the variables distributions (low, medium and high values) or production capacities etc. this new equity distribution can be compared with the old to find the changes created by the changes in assumptions etc.:

    A versatile tool

    This is not only a tool for C-level decision-making but also for controllers, treasury, budgeting etc.:

    The results from these analyses 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’ Evaluate alternative strategic investment options
    • 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.

    If you always have a picture of what really can happen you are forewarned and thus forearmed to adverse events and better prepared to take advantage of favorable events.go-on-look-behind-the-curtainFrom Indexed: Go-on-look-behind-the-curtain ((From Indexed: http://thisisindexed.com/2012/02/go-on-look-behind-the-curtain/))

     Footnotes

  • Be prepared for a bumpy ride

    Be prepared for a bumpy ride

    Imagine you’re nicely settled down in your airline seat on a transatlantic flight – comfort-able, with a great feeling. Then the captain comes on and welcomes everybody on board and continues, “It’s the first time I fly this type of machine, so wish me luck!” Still feeling great? ((Inspired by an article from BTS: http://www.bts.com/news-insights/strategy-execution-blog/Why_are_Business_Simulations_so_Effective.aspx))

    Running a company in today’s interconnected and volatile world has become extremely complicated; surely far more than flying an airliner. You probably don’t have all the indicators, dashboard system and controls as on a flight deck. And business conditions are likely to change for more than flight conditions ever will. Today we live with an information overload. Data streaming at us almost everywhere we turn. How can we cope? How do we make smart decisions?

    Pilots train over and over again. They spend hour after hour in flight simulators before being allowed to sit as co-pilots on a real passenger flight. Fortunately, for us passengers, flight hours normally pass by, day after day, without much excitement. Time to hit the simulator again and train engine fires, damaged landing gear, landing on water, passenger evacuation etc. becoming both mentally and practically prepared to manage the worst.

    Why aren’t we running business simulations to the same extent? Accounting, financial models and budgeting is more an art than science, many times founded on theories from the last century. (Not to mention Pacioli’s Italian accounting from 1491.) While the theory of behavioural economics progresses we must use the best tools we can get to better understand financial risks and opportunities and how to improve and refine value creation. The true job we’re set to do.

    How is it done? Like Einstein – seeking simplicity, as far as it goes. Finding out which pieces of information that is most crucial to the success and survival of the business. For major corporations these can be drawn down from the hundreds to some twenty key variables. (These variables are not set in stone once and for all, but need to be redefined in accordance with the business situation we foresee in the near future.)

    At Allevo our focal point is on Risk Governance at large and helping organisations implement Enterprise Risk Management (ERM) frame¬works and processes, specifically assisting boards and executive management to exercise their Risk Oversight duties. Fundamental to good risk management practice is to understand end articulate the organisation’s (i.e. the Board’s) appetite for risk. Without understanding the appetite and tolerance levels for various risks it’s hard to measure, aggregate and prioritize them. How much are we willing to spend on new ventures and opportunities? How much can we afford to lose? How do we calculate the trade-offs?

    There are two essential elements of Risk Appetite: risk capacity and risk capability.

    By risk capacity we mean the financial ability to take on new opportunities with their inherent risks (i.e. availability of cash and funding across the strategy period). By risk capability is meant the non-financial resources of the organisation. Do we have the know¬ledge and resources to take on new ventures? Cash and funding is fundamental and comes first.

    Does executive management and the board really understand the strengths and vulnerabilities hiding in the balance sheet or in the P&L-account? Many may have a gut feeling, mostly the CFO and the treasury department. But shouldn’t the executive team and the board (including the Audit Committee, and the Risk Committee if there is one) also really know?

    At Allevo we have aligned with Strategy@Risk Ltd to do business simulations. They have experiences from all kinds of industries; especially process industries where they even helped optimize manufacturing processes. They have simulated airports and flight patterns for a whole country. For companies with high level of raw material and commodity risks they simulate optimum hedging strategies. But their main contribution, in our opinion, is their ability to simulate your organisation’s balance sheet and P&L accounts. They have created a simulation tool that can be applied to a whole corporation. It needs only to be adjusted to your specific operations and business environ¬ments, which is done through inter-views and a few workshops with your own people that have the best knowledge of your business (operations, finances, markets, strategy etc.).

    When the key variables have been identified, it’s time to run the first Monte Carlo simulations to find out if the model fits with recent actual experiences and otherwise feels reliable.

    No model can ever predict the future. What we want to do is to find the key strengths and weaknesses in your operations and in your balance sheet. By running sensitivity analysis we can first of all understand which the key variables are. We want to focus what’s important, and leave alone those variables that have little effect on outcomes.

    Now, it’s time for the most important part. Considering how the selected variables can vary and interact over time. The future contains an inconceivable amount of different outcomes ((There are probably more different futures than ways of dealing 52 playing cards. Don’t you think? Well there are only 80,658,175,170,943,878,571,660,636,856,403,766,975,289,505,440,883,277,824,000,000,000,000 ways to shuffle a deck of 52 cards (8.1 x 1067 ))). What does that say about budgeting with discrete numbers?)). The question is how can we achieve the outcomes that we desire and avoid the ones that we dread the most?

    Running 10,000 simulations (i.e. closing each and every annual account over 10,000 years) we can stop the simulation when reaching a desired level of outcome and investigate the position of the key variables. Likewise when nasty results appear, we stop again and recording the underlying position of each variable.

    The simulations generate an 80-page standard report (which, once again, can feel like information overload). But once you’ve got a feeling for the sensitivity of the business you could instead do specific “what if?” analysis of scenarios of special interest to yourself, the executive team or to the board.

    Finally, the model equates the probability distribution of the organisation’s Enterprise Value going forward. The key for any business is to grow Enterprise Value.

    Simulations show how the likelihood of increasing or losing value varies with different strategies. This part of the simulation tool could be extremely important in strategy selection.

    If you wish to go into more depth on how simulations can support you and your organisation, please visit

    www.allevo.se or www.strategy-at-risk.com

    There you’ll find a great depth of material to chose from; or call us direct and we’ll schedule a quick on-site presentation.

    Have a good flight, and …

    Happy landing!

  • Introduction to Simulation Models

    Introduction to Simulation Models

    This entry is part 4 of 6 in the series Balance simulation

     

    Simulation models sets out to mimic real life company operations, that is describing the transformation of raw materials and labor to finished products in such a way that it can be used as support for strategic decision making.

    A full simulation model will usually consist of two separate models:

    1. an EBITDA model that describes the particular firm’s operations and
    2. an generic P&L and Balance simulation model (PL&B).

     

     

    The EBITDA model ladder

    Both the deterministic and stochastic balance simulation can be approached as a ladder with two steps, where the first is especially well suited as an introduction to risk simulation and the second gives a full blown risk analysis. In these successive steps the EBITDA calculations will be based on:

    1. financial information only, by using coefficients of fabrications and unit prices (e.g. kg flour per 1000 bread and cost of flour per kg, etc.) as direct input to the balance model – the direct method and
    2. EBITDA models to give a detailed technical description of the company’s operations.

    The first step uses coefficients of fabrications and their variations give a low effort (cost) alternative, usually using the internal accounting as basis. In many cases, this will often 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.

    This step is especially well suited for introduction to risk simulation and the art of communicating risk and uncertainty throughout the firm. It can also profitably be used in cases where time and data is limited and where one wishes to limit efforts in an initial stage. Data and assumptions can later be augmented to much more sophisticated analyses within the same framework. This way the analysis can be successively built in the direction the previous studies suggested.

    The second step implies setting up a dedicated EBITDA subroutine to the balance model. This can then 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 EBITDA model can both be a stand-alone model and a subroutine to the PL&B model. The stand-alone EBITDA model can be used to in detail study the firm’s operations and how different operational strategies will or can affect EBITDA outcomes and distribution.

    When connected to the PL&B model it will act as a subroutine giving the necessary information to produce the P&L and ultimately the Balance and the – outcomes distributions.

    This gives great flexibility in model formulations and the opportunity to fit models to different industries and accommodate for the data available.

    P&L and Balance simulation

    The generic PL&B model – based on the IFRS standard – can be used for a wide range of business activities both:

    1. describes the firm’s financial environment (taxes, interest rates, currency etc.) and
    2. acts as a testing bed for financial strategies (hedging, translation risk, etc.)

    Since S@R has set out to create models that can give answers to both deterministic and stochastic questions thus the PL&B model is a real balance simulation model – not a simple cash flow forecast model.

    All runs in the simulation produces a complete P&L and Balance it enables uncertainty curves (distributions) for any financial metric like ‘Yearly result’, ‘free cash flow’, economic profit’, ‘equity value’, ‘IRR’ or’ translation gain/loss’ etc. to be produced.

    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.

    The results from these analyses can be presented in different forms from detailed traditional financial reports to graphs describing the range of possible outcomes for all items in the P&L and Balance (+ much more) looking at the coming one to five (short term) or five to fifteen years (long term) and showing the impacts to e.g. equity value, company value, operating income etc.

    The goal is to find the distribution for the firm’s equity value which will incorporate all uncertainty facing the firm.

    This uncertainty gives both shape and location of the equity value distribution, and this is what we – if possible – are aiming to change:

    1. reducing downside risk by reducing the left tail (blue curve)
    2. increasing expected company value by moving the curve to the right (green curve)
    3. increasing the upside potential by  increasing the right tail (red curve) etc.

     

    The Data

    To be able to simulate the operations we need to put into the model all variables that will affect the firm’s net yearly result. Most of these will be collected by S@R from outside sources like central banks, local authorities and others, but some will have to be collected from the firm.

    The production and firm specific variables are related to every day’s activities in the firm. Their historic values can be collected from internal accounts or from production reports.  Someone in the procurement-, production- or sales department will have their records and most always the controllers.  The rest will be variables inside the domain of the CEO and the company treasurer.

    The variables fall in five groups:

    i.      general  variables describing the firm’s financial environment ,
    ii.      variables describing the firms strategy,
    iii.      general variables used for forecasting purposes,
    iv.      direct problem related variables and
    v.      the firm specific:
    a.  production coefficients  and
    b.  cost of raw materials and labor related variables.

    The first group will contain – for all countries either delivering raw materials or buying the finished product (s) – variables like: taxes, spot exchange rates etc.  For the firm’s domestic country it will in addition contain variables like: Vat rates, taxes on investments and dividend income, depreciation rates and method, initial tax allowances, overdraft interest rates etc.

    The second group will contain variables like: minimum cash levels, debt distribution on short and long term loans and currencies, hedge ratios, targeted leverage, economic depreciation etc.

    The third group will contain variables needed for forecasting purposes: yield curves, inflation forecasts, GDP forecasts etc. The expected values and their 5 % and 95 % probability limits will be used to forecast exchange rates, interest rates, demand etc. They will be collected by S@R.

    The fourth group will contain variables related to sales forecasts: yearly air temperature profiles (and variation) for forecasting beer sales and yearly water temperature profiles (and variation) for forecasting increase in biomass in fish farming.

    The fifth group will contain variables that specify the production and costs of production. They will vary according to the type of operations e.g.: operating rate (%), max days of production, tools maintenance (h per 10.000 units) , error rate (errors per 1000 units), waste (% of weight of prod unit), cycle time (units per min), number of machines per shift (#), concession density (kg per m3), feed rates (%), mortality rates (%) etc., etc.. This variable specifies the production and will they be stochastic in the sense that they are not constant but will vary inside a given – theoretical or historic – range.

    To simulate costs of production we use the coefficients of fabrication and their unit costs. Both the coefficients and their unit costs will always be of stochastic nature and they can vary with capacity utilization:  energy per unit produced (kwh/unit) and energy price (cost per Kwh), malt use (kg per hectoliter), malt price (per kg), maximum takeoff weight (ton), takeoff charge (per ton), specific consumption of wood, (m3/Adt), cost of round wood (per m3), etc., etc.

    The uncertainty (and risk) stemming from all groups of variables will be propagated through the P&L and down to the Balance, ending up as volatility in the equity distribution.

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

    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.

    Most companies have some sort of model describing the company’s operations. They are used mostly for budgeting, but in some cases also for forecasting cash flow and other important performance measures.

    If the client already has spread sheet models describing the operations, we can build on this. There is no reason to reinvent what has already been done – thus saving time and resources that can be better utilized in other phases of the project.

    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.

    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.

    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 first approach can also be considered as an introduction and stepping-stone to a more complete EBITDA model and detailed simulations.

    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, key people from the controller group will be adequate for this work and if they do not have the direct knowledge, they usually know whom 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 – depending on the scope of the project.

    For S&R, the period 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 second alternative for more complex models. The total time will also depend on the number of analyses that needs to be run and the type of reports that has to be delivered.

    The team’s participation in the project also makes communication of the results up or down in the system simpler. Since the input data is collected by templates this gives the responsible departments and persons, ownership to assumptions, data and results. These templates thus visualize the flow of data thru the organization and the interdependence between the departments – facilitating the communication of risk and the different strategies both reviewed and selected.

    No knowledge or expertize on uncertainty calculations or statistical methods is required from the clients side. The team will thru ‘osmosis’ acquires the necessary knowledge. Usually the team finds this as an exciting experience.

  • Moddeling World 2011

    Moddeling World 2011

     

     

    S&R participated as a keynote speaker at the Modelling World conference held in London June 16. The theme was forecasting and decision making inn an uncertain world. The event was organized by Local Transport Today Ltd. and Modelling World 2011 © Local Transport Today and covered a broad range of issues in transport modeling (Conference programme as Pdf-file).

    The presentation can be found as a Pdf-file here.