I recently read the book review of "Unlocking Opportunities for Growth" By Alexander B. van Putten and Ian C MacMillan and surmised if opportunity engineering methods and tools can really help in plugging the downside. The author argues that Opportunity engineering can help in breaking the typical symmetry on the downside of the opportunity. If for a moment we assume a normal opportunity return curve as illustrated in Fig 1 below where the mean is the break-even point for an opportunity, the author mentions that opportunity engineering can help in breaking the symmetry of the curve by eliminating the likelihood of an opportunity outcome falling on the left side of the curve. This effectively means we have an asymmetric curve (that is a curve with different functional drivers; obviously the whole curve is no longer normal then!)... Please note the author never mentions that the opportunity is a normal curve, all he mentions is that there is a symmetry between the risk and reward. These are typical stochastic models. i've used a normal curve to help illustrate the gaps that I see in what is proposed by the author. Now note that at the time of planning or forecasting for an opportunity we usually use DCF and NPV to make crucial business decisions, however in reality the actual outcome is influenced by several real-life factors and parameters. This means at any point in time we may land up anywhere in the green rectangular box at any point in time in reality assuming we fore casted and planned for a normal-curved based returns for an opportunity.... Now there are underlying assumptions in the argument the author makes that can be challenged:
1. The existence of symmetric relationship or equal likelihood nature of opportunity return curves. This is seldom a case where we have equal likelihoods on either side of a break-even point for an opportunity curve....there are countless product curves and innovations curves that can be plotted with products in the market that have a asymmetric opportunity return curves...
2. Assumption that any propensity to reduce the risk will impact only the left side of the opportunity return curve without having a holistic impact on the right side of the return curve...look at this this way have we ever seen a car or a computer that meets every customer need out there....if that is the case then we would not have various product categories and customer segments :-)....
Having noted 2 such fallacies, let us also look at what are all the options or scenarios we can generate at the time of forecasting to ensure that we are in a position to manage the outcome always on the right side of the normal curve. (For a moment I've now assumed the normal curve remains and the authors inherent assumptions of changing the curve only for the left side to break symmetry is not feasible in real-life!). Let us assume that we are simplifying the forecasting model for business decision by only factoring a finite set of known parameters or factors that could affect an opportunity (Making that assumption is theoretical it is never practically feasible...I'm just attributing a zero probability for the occurrence of a "force majure" event during a planning horizon!). Lets now look at 3 ideal scenarios:
1. "Conservative Scenario": Reducing the skew or variance of an opportunity outcome (Fig 2) . In this scenario we make certain choices for the opportunity that limits the failure points at the same time also limits the success. Classic example is that of Nokia Mobile phones, they keep releasing model after model with incremental feature having now got a base mobile phone ecosystem right, they just add a camera, stereo phone, touch screens etc. as added feature on mobile phone. So every new model that Nokia makes can now leverage the evolutionary scenario forecast model. This model usually works in oligopolistic markets with few competitors: Microsoft, Nokia, etc. The have shaped their market as oligopolistic through differentiated strategy and reached where they are over the years by decimating competition. Basically what this means is that the opportunity curve for every model they release in the market have an equal likelihood of success and failure!
2. "Normal Scenario": ": Increasing the expected return for the opportunity curve while reducing the loss (Fig 3). This is just shifting the normal opportunity curve (Fig 1) by increasing the expected value of returns on positive side. Most businesses try to make sure that their forecasted model always has yields a positive return and reduces the likelihood of failure i.e. falling into the left side of the expected value. To achieve this forecast model look at an opportunity holistically from different functional perspectives: Engineering, Design, production, Sales, and marketing. There are tactical strategies and lobbying made at conception stage of the opportunity to make it successful. Typically we see this in commoditized market where opportunities are shaped to meet a specific customer need. The customer need is well understood through market research. Good examples of such products are in the Consumer goods industry: Colgate tooth paste with salt. J…
3. "Aggressive scenario": Increasing the expected return for the opportunity curve while reducing the loss at the same time increasing the likelihood of the outcome falling within the forecasted opportunity curve (Fig 4). This is not just about shifting the curve by its mean and reducing risk, but also about increasing the likelihood that the outcome will fall within the opportunity curve that is forecasted. Good example of this model is that of an iPhone, iPod, etc, here Apple has done something magical in reducing the returns from the opportunity curve through innovative design to create an successful market for its product, while at the same time limiting the left side by offering complementary services such as iTunes, tie up with telecom operators who are ready to offer 3G services, and so on. They may not have necessarily addressed the immediate customer need, but created a new customer need. These products are revolutionary in its nature and it is often difficult to forecast such model without tipping off competition about such an entry into the market. There are few companies that can create such opportunities.
Having discussed all this, we cannot say with certainty that our forecasted model will always predict a positive outcome or we have a mind and hand of God to plug the downside of an opportunity without commensurately impacting the upside!
But we can definitely analyze scenarios based on range of possible outcome to satisfy ourselves at the time of making business decision!
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