The Art of the Big Decision

The Art of the Big Decision A new approach to analyzing capital-intensive resource investments Dynamic Decision Management views projects as adjustable sequences of decisions. Our approach increases the quality, flexibility, and control of decision making. 1

This paper is an update to Dynamic Models for Managing Big Decisions, which first appeared in Strategy & Leadership, volume 40, issue 5, 2012. Executive Agenda

Let s face it. Despite the increasing risk and complexity around major investment decisions, decision making is often overly simplistic and static. The various possibilities are limited to best case, worst case, and most likely case scenarios, followed by a two- or three-year implementation plan. The net present (NPV) is calculated, capital budgets are allocated, and the investment or long-term contract is underway. This wildly popular approach to decision making has its shortcomings. For one, the possibilities are often based on assumptions about the future rather than any real testing of expected and unexpected futures over time. The consequences of making reversals, full stops, and go-slow adjustments are rarely accounted for, and the potential risks are often calculated on a qualitative rather than a quantitative level. Then there are the unpredictable events and trends that could devastate the entire investment if not prepared for in advance this includes the impact of one-time events and how different events might interact. Future flexibility is sometimes absent from the crucial resource investment plans, leaving decision makers without the needed flexibility to reassess a decision in light of new information. With this in mind, we came up with a new approach to investment decisions. Called Dynamic Decision Management (DDM), it consists of five components: 1. Model the future Use future market analysis in a more sophisticated way than traditional analysis. Instead of considering a best case, worst case, and most likely case set of possibilities, a range of possibilities can be considered using a stochastic model. Future flexibility is sometimes absent from crucial resource investment plans. 2. Improve the understanding and evaluation of risk When trying to anticipate the dynamics of uncertain environments, different managers will hold different assumptions about how markets, revenues, and costs will evolve. And they may not actively share their assumptions about uncertainty with colleagues. Unshared or hidden assumptions about uncertainty can lead to poor risk analysis, weak management of provisional actions, and a general lack of preparation for rapid decision making when events occur. As a quick and dirty way of surfacing these assumptions and expectations, managers often propose a best case, most likely case, and worst case analysis. The danger is that what everyone wants, expects, or believes could be the worst case does not evaluate the full range of ways in which the business environment might unfold. The first step of the DDM approach involves a comprehensive understanding of the main risks that may affect strategic investment decisions changes in market prices, commodity prices, or labor costs, for example. For each risk factor, a risk profile is created and the risk potential estimated. For example, we might consider the risk of future upward or downward change or volatilities. Feeding this information into the probabilistic DDM model allows for the modeling of an almost infinite number of potential futures. Multiple runs for each of the key variables, such as using Monte Carlo techniques, creates an overall risk profile and set of project outcomes. It is now possible to see how frequently extreme events in several 1

variables interact and create results that otherwise would neither be considered nor quantified (see figure 1). We can use our work for a utility company to illustrate this. The company wanted to know how to best deliver renewable energy across national borders over a 10-year period. An early step was to understand and make explicit the sources of risk in the project, allowing the management team an opportunity to decide what level of risk was acceptable. Unlike traditional risk models, the risks were asymmetric, so more was placed on avoiding losses than on the opportunity to make gains. At the beginning of the project, we identified three main sources of risk: 1. Change in electricity costs over the 10-year life of the project 2. Change in transnational electricity transmission costs 3. The cost of green certificates Figure 1 The DDM approach allows for an almost infinite number of potential scenarios 110 Cash flow 110 50 5=55 Market price Average case: 100 Volatility 90 70 Commodity price Average case: 60 Volatility 50 15 Operating cost Average case: 10 Volatility 90 70 15=5 Increase Decrease 5 Set of all possible cash flow s Source: A.T. Kearney analysis 2

The risk profile of each source was quite different. There was an active market in selling electricity forward so the company was able to fix prices and thus minimize its risk exposure. But transmission costs could not be locked in (at least not very easily) unless one was able to find a contract partner willing to take the risk and offer a custom contract with fixed prices (typically in return for a premium as part of the electrical contract). And, with little history, green certificate costs were difficult to estimate, but the company believed that today s price was likely to rise over time. Figure 2 illustrates the process of assessing the risk and return of strategies that have different exposures to these risks. For our utility a public company with limited risk appetite the strategy of choice was one with lower variance rather than a strategy with higher risks and also higher upside potential. The decision was made despite the fact that the expected NPV of the low-risk strategy exceeded the expected results of the higher-risk strategy. Figure 2 Decisions are made after assessing the risk and return of strategies with different risk exposures 100% 90% 76 270 80% Confidence level 70% 60% 50% 40% 30% Reduced chance to source energy at lower cost Estimated risk of price increase in the market Illustrative example: With a probability of 50% the NPV will be in the range of X and Y 20% 10% Bidder A EEX 50/50 0 0 300 400 500 600 700 800 900 1,000 1,100 1,200 Decreasing cost NPV* total cost in (millions) 1,300 1,400 1,500 Increasing cost Notes: NPV is net present. EEX refers to European Electricity Exchange. Sources: Project team analysis: comparison of hedging alternatives; A.T. Kearney analysis 3. Design a dynamic strategy A key concept in decision management is the idea of seeing a strategy or capital investment decision as a series of choices over time rather than as one unalterable path no matter how the world changes. Each step in the DDM decision-making process reveals contingencies and opportunities to react to a competitor, a policy, or an environmental change and to modify the initial plan accordingly. In an uncertain world, having the flexibility to choose between different paths has significant. We call it flex and it can be calculated using the DDM approach (see sidebar: Capitalizing on Flex Value on page 4). 3

Capitalizing on Flex Value Our work for an iron ore company will help illustrate the attributes of flex. The company, which we will call Iron-Ore, Inc. for the purposes of this article, owns land that can be used to increase production capacity by 50 million tons. The company will still need to acquire land to increase iron ore production capacity by an additional 5 million tons. In order to later develop this mine, Iron-Ore requires transshipping capacity from a port that is not yet constructed. And port construction cannot be postponed as the government requires a decision within a year and alternatives, such as shipping by sea, are more expensive. Iron-Ore s management team has a big decision to make. Investment in the standalone port (without any business) will result in a negative net present (NPV) as there are limited amounts of alternative cargo to ship. Also, based on the current market price of iron ore, investment in the port along with Phase I of the mining business results in a negative NPV. But, if the port is constructed, the company has the flexibility to develop the site later and profit from potentially rising iron ore prices. As such, there is in flexibility (flex ) to either develop or not develop the site that clearly outweighs the negative NPV of the port investment. The management team decides to capitalize on flex. Basically, there are three ways to increase flexibility of any given strategy: Flexibility in choice. Decide among different strategic options at a certain point in time. For example, a utility planning to expand its business might consider entering either the e-mobility business or the smart home business, depending on how each market develops. Flexibility in timing. Once a certain project has been chosen, deciding when to carry out the project can create additional flexibility. For example, postponing an investment until it is clear how the market will develop can create. In certain industries, staged investments are common ways to mitigate risk. Flexibility in scope. Adapting the size or modularity of a project creates more possibilities to increase or decrease project scope or to abandon a project altogether. This approach is often used in large construction or infrastructure projects. Consider a manufacturer that wants to build a new factory. The management team has two options: (1) adopt a static strategy in which a decision is made today to construct a large factory able to meet the forecasted demand of the next 10 years or (2) adopt a dynamic, staged-investment strategy in which market conditions in the near future are observed and decisions are made to scale up the project or not. In the first case, construction of the factory is slightly cheaper on a per unit basis than in option 2. Also, the company immediately benefits from the scale effects if the production facility is properly utilized. However, there is the risk of having significant overcapacity in the first years if demand does not reach expectations. In the second case, total construction costs over the course of the project are slightly higher than in option 1 since the company is essentially managing two separate construction events. But capacity requirements can be adjusted much more flexibly to market conditions, including deciding to postpone the next stage or to abandon it altogether. In a non-risky environment facing increased demand in the coming year, the obvious decision is to immediately build the large factory (see figure 3 on page 5). In a more volatile 4

Figure 3 Investment decisions allow for flexibility Simplified example Postpone investment Build small factory phase 1 Abandon project Build small factory phase 1 Build small factory phase 2 Postpone investment Build small factory phase 2 Abandon phase 2 Build small factory phase 2 Abandon phase 2 Time Source: A.T. Kearney analysis Figure 4 Our total methodology builds on the traditional NPV approach Traditional Dynamic Decision Management (DDM) NPV with lump sum probabilities NPV with specific probabilities Total NPV S1 30% S2 55% S3 15% Total Probability (P) Probability to achieve project Probability (P) Value enhancement through investment decision Scenarios Discrete future scenarios with estimated probabilities Probability weighted NPV (static) Limited number of output scenarios NPV Assessment of probabilities of all input parameters Analytical methodology used to determine completeness of all cash flow points Risk considered through probabilities Total Consideration of active management (flexibility) in the future Optimal determined via active management of decision alternatives Total determined by NPV plus of flexibility Note: NPV is net present. Source: A.T. Kearney analysis 5

environment, where a more cautious approach is needed, option 2 provides valuable flexibility as decision points are built into the future to allow the company to adjust to market conditions. 4. Calculate total of dynamic strategies and corresponding probability distribution Once risks are understood and the potential paths have been identified, it is time to select the right path. In our manufacturer example, there are three questions on the table: Should the firm build a large factory now or take a phased investment approach? What market scenario should be considered? And will the flex of the staged investment approach outweigh the cost advantage of the large factory? Traditionally, the NPV approach is used to evaluate strategic choices. However, NPV neither considers the risks nor the of flexibility. Our total methodology allows consideration of both the risks and the of flexibility (see figure 4 on page 5). It builds on the traditional NPV approach by using the risk assessment from step one to generate a probability distribution allowing assessment of both the outcome and the probability of each scenario. While introducing a probabilistic approach is already a major improvement in decision making, the total methodology additionally considers the of flexibility by modeling the strategic decision paths outlined in step 2. The result: a probability distribution that identifies which of the strategic paths offers the best choice given the uncertainties and the total of the decision path. To summarize, total consists of three main components: Net present : the current of future discounted cash flow for a defined state Risk : a strategy s potential range of resulting from a probabilistic (also known as a stochastic or Monte Carlo) analysis Flex : the of flexibility generated by actively adapting to an uncertain business environment How does this improve the quality of decision making? Let s go back to our manufacturer s investment decision. When calculating a simple NPV in an average market scenario, the static strategy is more advantageous than the dynamic strategy (see figure 5). However, when applying Figure 5 A dynamic decision results in higher total Static strategy Risk Dynamic strategy Risk Flex Total Traditional valuation Flex Total Traditional valuation NPV Risk Flexibility Total NPV Risk Flexibility Total Note: NPV is net present. Source: A.T. Kearney analysis 6

the total- approach, we find that the increased flexibility of the dynamic strategy outweighs the cost advantage of the static strategy for example, resulting in total that is 15 percent higher. In addition, the likelihood of realizing a positive would be 78 percent for the dynamic strategy, but only 63 percent for the static strategy. Clearly, relying on the static NPV approach would have been the wrong decision. 5. Formalize the decision-making process Finally, when using DDM in practice, instead of assigning fixed budgets to certain projects at the beginning of the year, heuristic rules are agreed upon for making strategic investment decisions. Market events trigger strategic decisions in the sense of if event x takes place, funds should be shifted toward project y. By having the rules in place, decisions are made faster and reactions to risks or evolving opportunities are quicker, even if the projected chain of events shifts in an unanticipated or more extreme way. Managers who think about managing in a variety of futures and who are empowered to think about more adaptive and flexible strategies will adjust to black swan events more quickly and more successfully. Imagine the Possibilities Thinking of projects as adjustable sequences of decisions changes everything. Managers can become more entrepreneurial in reacting to uncertainty and more able to seize unanticipated opportunities. The exact impact of risk and of strategic flexibility can be quantified, as well as the probability of ultimately achieving total. Imagine basing decisions on real market events rather than on planning and budgeting cycles. Or, better yet, imagine having control and adjustability over every big uncertain decision. Authors Hanjo Arms, partner, Berlin hanjo.arms@atkearney.com Christian Loy, consultant, Düsseldorf christian.loy@atkearney.com The authors wish to thank Sebastian Reinartz for his valuable assistance in writing this paper. References Jochen Gerber, Hanjo Arms, Mathias Wiecher, Christian Danner, Leveraging Flexibility Win the Race with Dynamic Decision Management; Springer, June 2014 Jochen Gerber, Hanjo Arms, Matthias Cord, Mathias Wiecher, Kalkulierte Flexibilität; Gabler Verlag, 2010 Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Random House, Second Edition, 2010 Federal Reserve Press Release on Capital Adequacy: http://federalreserve.gov/newsevents/press/ bcreg/20111122a.htm, November 22, 2011

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