29d ago

1 Views

0 Downloads

282.74 KB

19 Pages

Transcription

USING QUANTITATIVE RISK ANALYSIS TO SUPPORT STRATEGICDECISIONS1David T. Hulett, Ph.D.President, Hulett & Associates, LLC,www.projectrisk.com, [email protected], 1-310-476-7699ABSTRACTMany see statistical simulation techniques as low-level technical tools relevant only todetailed risk analysis. The approach offers much more however, when the results areused at the strategic level to support better decision-making. This briefing makes thecase for using the results of quantitative risk analysis at this level, highlighting thebenefits and outlining potential shortfalls. It also describes what senior managementshould expect, or demand, of the practitioners of risk analysis and what, in turn, seniormanagement must do to establish an environment conducive to successful riskanalysis.INTRODUCTION – STRATEGIC AND BUSINESS RISKPlanning for organisational success involves dealing with many kinds of risk.Without risk there would be no challenge in business. Threats, if they materialise, canmake a seemingly-successful strategy fail and opportunities, if captured, can enhancethe results from an otherwise marginal strategy.Strategic planning involves balancing many factors that are risky. Yet businessesoften do not understand the way risk contributes to results from competing strategiesor how to identify and analyse strategic risk. Risk analysis of strategy may lead anorganisation to choose an alternative that has lower return if it has less risk than analternative with higher nominal returns but more risk.Whether an organisation is risk-averse, risk-seeking or risk-neutral, it should evaluatethe risk associated with its prospective strategies before making important decisions.this briefing discusses how quantitative risk analysis can contribute to understandingrisk exposure and making better strategic decisions.THE ROLE OF MONTE CARLO SIMULATION IN ANALYZING THESTRATEGIC PLANWe will define business risk as including uncertain events and conditions that, if theyoccur, have a positive or negative effect on the organisation’s objectives. Factorsboth external and internal to the organisation are involved in making good strategydecisions, and uncertainty attaches to each of these factors.Strategy development requires looking forward in the business. Since there are nofacts about the future, the future is best described in statistical terms such as:1. “Sales may be as low as X or as high as Y but are most likely to be in theneighbourhood of Z.”1Published in Consult GEE Executive Briefings in Business Risk Management, Thomson GEE,London, UK, December 2004.

2. “Competitors may respond to our price reduction by keeping their prices even orby lowering theirs to A, or in the extreme to B.”These parameters translate easily into probability distributions of uncertain sales orcompetitor prices respectively. It is clear that these uncertain variables will have amajor influence over whether the strategy is successful or not.Often the strategic plan will be expressed in a spreadsheet model that representsobjectives such as sales, internal rate of return, net present value or return on assetsemployed. Organisations usually build the models with the assumption that theyknow the models’ components, structures and parameters with certainty. Thisassumption is dangerous. It can lead to less-than-optimal or even quite wrongdecisions. Understanding the uncertainty in the factors that contribute to thestrategy’s success or failure is crucial to making good decisions. We sometimes callthese “risk-adjusted decisions”.Including that uncertainty in the model of the strategic plan of the organisation canprovide concrete and calibrated information that executives need to make decisions.Monte Carlo simulation offers one powerful and well-understood way to evaluate theimpact of uncertainties on the key measures of success that attach to a strategic plan.Using Monte Carlo simulation, we can quantify several issues that are important inmaking the decision, for example:1. How likely are we to achieve the desired target, e.g., a return on investment or netpresent value of the plan? This might tell us whether to pursue the strategy at all.2. How different are the results of two (or more) competing strategic options that areavailable to us, when we take into account the uncertainty inherent in them? Thiscomparison will help to choose between competing strategies when resources arelimited.3. Which risk drivers or other environmental factors should be targeted to enhancethe strategic plan’s chance of success? The answer to this question may make itpossible for us to improve the strategy’s projected risk-adjusted results, bymitigating threats and enhancing opportunities.These are questions that cannot be answered by simple deterministic models, whichassume the inputs are known with certainty. Once we admit uncertainty in ourassumptions or input calculations we venture into the world of realism and can start tomake risk-based decisions.QUANTITATIVE RISK ANALYSIS USING MONTE CARLOWhen the future is uncertain and several uncertainties may be important in theoutcome, a method of analysis is needed that can:1. Encompass all risks simultaneously, and2. Provide a picture of the effect of risk on strategic outcomes.Application of Monte Carlo simulation to a strategic planning model will meet both ofthese requirements. Monte Carlo simulation is a method of examining the impact on astrategy of the main risks, including technical, external, competitive and regulatoryfactors, as they may act simultaneously to modify the result found in the nominal ordeterministic strategy model.2

Effective risk management of business strategies requires several processes, each ofwhich can be performed at a summary or detailed level:1. Specify the objectives of the business. The business objective may be expressed asinternal rate of return (IRR), net present value (NPV), market share or some otherquantifiable measure of success.2. Develop a model relating objective(s) to influencing factors. For instance, marketpenetration may be a factor of prices, competitor response, market forces,regulation and like factors. The model, often built in a spreadsheet, must bequantitative and may use assumed or estimated parameters linking causes andeffects. There are two examples of these models later in this briefing.3. Determine the various risks that contribute to or may deflect from strategicsuccess. This is usually done in a brainstorming session that looks explicitly atrisks. Technical, organisational, external and other risks should be considered andplaced in the model. A technique that may assist is the Risk Breakdown Structure(RBS) that catalogues the sources of risks in major categories and detailed subcategories.4. Express the risky factors in the model using probability concepts such as aprobability distribution, which reflects not only the alternative values possible buttheir relative probability of occurring. Other statistical concepts may beemployed, such as the correlation of various uncertain factors, or the use ofstochastic branches.5. Perform a Monte Carlo simulation of the model, varying all the uncertain inputssimultaneously. The result is a probability distribution of the result variable, e.g.,market penetration or internal rate of return (IRR). Since some of the inputs areuncertain, the output is a probability distribution reflecting the uncertainty in thebusiness environment. This presents the range of possible outcomes (best to worstcase), and the expected value within that range.6. Compare the probability distribution of the objective to the target level. Forinstance, an analysis may indicate that the expected IRR is 28%. The minimumacceptable IRR (often called a “hurdle rate”) may be 20%, and the outputdistribution may show that there is a one-in-three chance that the actual IRR willbe below that level. The organisation then needs to balance that information withother factors, such as strategic direction, to see if one-in-three is too much of achance of failure.7. Focus on the main drivers of the uncertain result to identify options to enhanceopportunities or mitigate threats. The simulation will provide indications of whichuncertain factors are important in driving the uncertainty in the objective, in thisexample the IRR. A sensitivity graph or “tornado chart” will highlight thoseelements that are closely related to the result.COLLECTING REALISTIC DATA ABOUT STRATEGIC RISKSAn essential underpinning of understanding the risk in business strategy is an honestand searching inquiry into the risks that may affect the strategy. This would seem tobe what businesses do all the time. Yet many businesses (and governmentalorganisations) do not consider risk in their strategic decisions. This lack of3

appreciation of the role of risk in making organisational strategy may be based onseveral factors:1. Organisations typically are “success oriented” in their planning and execution.Planning for alternative outcomes is not always practiced, tolerated or oftenrewarded. Most plans assume success of the activities and certainty of theassumptions underlying that success. One way to address this issue is to requirestaff to be honest about “the good, the bad and the ugly” in the business situation.Emphasising that “we cannot get where we want to go unless we know honestlywhere we are today” underlies the seriousness of this approach.2. Risk is viewed as a bad thing, causing the organisation to miss its objectives. Forthis reason, many organisations’ cultures do not allow them to deal with riskclearly and in a straightforward manner. Put succinctly, “risk” is often associatedwith failure and unpleasant outcomes, and discussions about risk are oftendiscouraged within the business environment and culture. One way to combat thisperception is to include opportunities for improvement, if they are uncertain, inthe definition of risk.3. Some risks may be unspeakable – for example, a software company may fear thatit is squandering hundreds of programmer-years on failed software, or telling thepublic that an announced product will not be on time or have the featurespromised. Many organisations are just postponing the inevitable and have tobecome more mature in their practices.4. Organisations do not understand quantitative risk analysis, with techniques suchas Monte Carlo simulation or other analytical tools that can help them analyse therisk, to put risk in the strategic picture. University courses may not put muchemphasis on risk in dealing with strategy. People in leadership have learnedleadership from others who do not understand risk, so they may not have abackground in the subject.Unquestionably, the factors included in any strategic model are not known withcertainty. There are three different types of risk to consider:1. Estimating uncertainty. As the strategy becomes better-known, the accuracy ofthe estimates of the inputs and their underlying assumptions should becomegreater. Estimating uncertainty may be viewed as symmetrical around the value,often reported as “plus or minus 10%.”2. Biases of individual or organisational nature in making the estimate. Thesebiases often have the effect of making the strategy look better than it shouldrealistically be – seldom does an organisation want to make a strategy look bad forfear of angering someone in power or losing a bid to another company. It haslong been observed that strategies’ results look like a hockey stick, with cash flowstarting off negative and then expanding exponentially for the foreseeable future –though this is clearly unrealistic.3. Uncertainty in the assumptions underlying the plan. A much more variable andpotentially disruptive problem is that the assumptions made, either implicitly orexplicitly, either do not actually lead to the estimates of the factors underlying thestrategy or are not solid and may themselves change, calling into question theentire basis of the strategy. These assumptions may be about internal (e.g.,4

organisational) or external (e.g., the economic or political environment) factors.Whatever the cause, there seem to be more ways that the assumptions can causestrategies to fall short of, than to exceed, expectations. Strategic plans seem toadopt optimistic assumptions, making the plan appear to be more successful thanit is likely to be. This phenomenon probably stems from the fact that the plannersare advocates for their plans and see mostly success.Interviewing individuals knowledgeable about this strategy and with experience inpast similar strategy development exercises can help us understand the uncertainty inthe estimates.EXAMPLE: MONTE CARLO ANALYSIS OF A SALES STRATEGYOne common example of uncertainty is the reaction of competitors. If we try tocapture market share by reducing prices, they may change their prices in response.What are the prices they may choose?The Model: A simple corporate strategy can be illustrated with a sales model.Suppose that a business wants to increase its sales. The factors that contribute to thesuccess of an overall sales strategy may be simple or complex. For illustration of therole of risk analysis using Monte Carlo simulation, let us make a simple model ofproduct sales as a function of a strategic decision including price and marketingbudget.The factors that influence sales may be as simple as marketing budget, price andcompetitor response. If the business can develop a model that combines these factors,leading to a sales calculation, then it might have the following model:Sales a * (marketing budget) – b * (our price) c * (competitor’s price)This simplified model can be estimated using econometric techniques with marketdata. If that estimate is shown to be somewhat able to explain the past, it could bepredictive of the future. There are, however, several uncertainties in this model:1. The competitor’s response, represented by competitor’s price, is uncertain.Will they lower price to try to offset an increase in our marketing or loweringof our price?2. How well does the model explain reality, as represented in the confidence wehave in the overall equation and the parameters a, b and c?The original situation vis a vis the competitor might look like this:Model ComponentMarketing BudgetOur PriceCompetitor's PriceSalesSales ModelValue Parameter500a 901,000b -1001,000c 100Contribution45,000-100,000100,00045,000The Strategy: The business wants to develop a strategy that will increase sales. If thebusiness wants to increase sales beyond 45,000, say to 50,000, it can either increasethe marketing budget, reduce the price or both. Suppose the plan is to reduce ourprice by 50, which will increase sales if the competitor does not respond. Assuming5

the competitor does nothing, this action could lead to an increase in sales that mightlook like the following, with the company’s strategy variable shown in bold:Model ComponentMarketing BudgetOur PriceCompetitor'

used at the strategic level to support better decision-making. This briefing makes the case for using the results of quantitative risk analysis at this level, highlighting the benefits and outlining potential shortfalls. It also describes what senior management should expect, or demand, of the practitioners of risk analysis and what, in turn ...