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What We Do – Strategy @ Risk

Series: What We Do

  • What we do; Predictive and Prescriptive Analytics

    What we do; Predictive and Prescriptive Analytics

    This entry is part 1 of 3 in the series What We Do

     

    Analytics is the discovery and communication of meaningful patterns in data. It is especially valuable in areas rich with recorded information – as in all economic activities. Analytics relies on the simultaneous application of statistical methods, simulation modeling and operations research to quantify performance.

    Prescriptive analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.

    Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.

    Prescriptive analytics will also tell what probably will happen, but in addition:  when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.

    By employing simulation modeling (Monte Carlo methods) we can give answers – by probability statements – to the critical question at the top of the value staircase.

     

    Prescriptive-analytics

     

    This feature is a basic element of the S@R balance simulation model, where the Monte Carlo simulation can be stopped at any point on the probability distribution for company value  (i.e. very high or very low value of company) giving full set of reports: P&L and balance sheet etc. – enabling a full postmortem analysis: what it was that happened and why it did happen.

    Different courses of actions to repeat or avoid the result with high probability can then be researched and assessed. The EBITDA client specific model will capture relationships among many factors to allow simultaneous assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Even the language we use to write the models are specially developed for making decision support systems.

    Our methods will as well include data and information visualization to clearly and effectively communicate both information and acquired knowledge – to reinforce comprehension and cognition.

    Firms may thus fruitfully apply analytics to business data, to describe, predict, and improve its business performance.

     

  • 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

  • Big Issues Needs Big Tools

    Big Issues Needs Big Tools

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

     

    You can always amend a big plan, but you can never expand a little one. I don’t believe in little plans. I believe in plans big enough to meet a situation which we can’t possibly foresee now. Harry S. Truman : American statesman (33rd US president: 1945-53)

    We believe you know your business best and will in your capacity implement the necessary resources, competence, tools and methods for running a successful and efficient organization.

    Still issues related to uncertainty, whether it is finance, stakeholders, production , purchase or sale, has in most cases increased due to more complex business operational environment. Excellent systems for a range of processes; consolidation, Customer relationship, accounting has kept up with increasingly complex environments, and so has your most important tool – people.

    But we believe you do not possess the best available method and tool for bringing people – competence, experience, economic/financial facts, assumptions and economic/financial tools together.

    You know your budgets, valuations, projections and estimates, scenario analysis all are made and presented with valuable information regarding uncertainty left out on the way. Whether this is because human experience related to risk, or analyzing, understanding and projection macro or micro risks is hard to capture, or tools not designed to capture risk is the cause. It is a fact that most complex big issues important for companies are based on insufficient information, a portion of luck, gut feeling and believes in market turns /stability/cycles or other comforting assumptions shared by peers.

    Or you are restricted to giving guidelines, min/max orders, specifications and trust to third party experts that one hope are better capable of capturing risk and potential in a narrow area of expertise. Regardless of this risk spreading or differentiation works – you need the best assistance for setting your guidelines and road-map both for your internal as well as external resources.

    Systems and methods (( A Skeptic’s Guide to Computer Models (Pdf, pp 25) , by John D. Sterman. This paper is reprinted from Sterman, J. D. (1991). A Skeptic’s Guide to Computer Models. In Barney, G. O. et al., Managing a Nation: The Microcomputer Software Catalog. Boulder, CO: Westview Press, 209-229.)) are never better than human experience, knowledge and excellence, but if you want to look closer at a method/tool that can capture the best of your existing decision making process and bringing it to a new level. You should look closer at a stochastic complete p/L/balance simulation model for those big issues and big decisions.

    If you are not familiar with stochastic simulations and probability distributions, take a look at a report for the most likely outcome (Pdf, pp 32) from the simulations – similar reports could have been made for the outcomes you would not have liked to see, giving a heads up for the sources of downside risk, OR for outcomes you would have loved you see – explaining the generators of up-side possibilities.

    Endnotes