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Building and Testing Business Models – the role of scenarios and modelling

August 13, 2014

One of the most interesting recent advances in the art of business model innovation and testing has been the creation of the Business Model Canvas, a language capable of describing and manipulating business models to create new strategic alternatives*

There are nine basic building blocks in this structure covering the four areas of customer, offer, infrastructure and financial viability. The design process itself is broken out into five phases – mobilize, understand, design, implement, and manage.

Within that, design is the place where prototype models are developed, options are generated, and the best selected. This is where techniques such as scenarios and simulation are proving to be increasingly valued.

Although these were once seen as the province of two distinct (and rarely overlapping) communities, advances in simulation modelling have significantly increased the potential of an integrated approach. In fact, we see their integration as vital to the development of a modern strategy capability in any enterprise of scale. But just how might a more integrated approach be made to work and what would be the benefits?

Business simulation modeling has been applied to every possible complex decision in every conceivable industry, most recently featured in the popular movie Moneyball. In practice, simulations seek to create replicas of businesses or operating assets that allow the users of those models to experiment with them under a wide variety of real or imagined conditions. Scenario planning, on the other hand, is the well ordered, systematic discussion and development of a range of plausible futures facing that business based on analysis of the key drivers of future change. Putting these pieces together, we see that a business simulation that faithfully captures the essence of the business and the way it will develop in response to the many challenges it faces, could be placed in the hands of a scenario planning team as the centerpiece of the strategy dialogue. Doing so would offer the following advantages:

  1. The simulation ensures that the scenarios and their implications are also data-rich, not simply qualitative.
  2. A simulation model can very quickly calculate the implications of a scenario – that allows the team to consider a wider range of scenarios, even those at the “tails” of the distribution (extreme conditions).
  3. A simulation model can provide dynamic imagery that can ‘bring the scenarios to life.’
  4. Running many more scenarios would show the teams where there are “inflection points” in circumstances – i.e. a small change in the market may double (or half) the value of an asset.
  5. A more complex set of interventions/insurance against “perfect storm” scenarios might be developed, given the speed at which a model may identify and assess such conditions.
  6. Combining simulation with scenario planning respects the bare economic fact that computing power is cheap and human talent is expensive. An integrated approach provides the maximum leverage of expensive human talent.
  7. By codifying the scenario information in the form of a model, more stakeholders – suppliers, regulators, the public – can be both participants in its design as well as consumers of the final result.

Decisions about the future are naturally fraught with uncertainty. Some aspects can be modeled because the underlying assumptions and ‘rules’ remain valid. There is also increasing uncertainty as one moves further into the future. Therefore, for some aspects, data driven approaches provide rapid and powerful explorations of some possible futures. For others, uncertainty cannot be reduced to quantitative values; judgement is needed based on experience and a host of unquantifiable elements. But bringing these two capabilities together in a mutually reinforcing way will help both today’s decion makers and provide a powerful learning environment in which decion making steadily improves over time to the benefit of all stakeholders.

Scenario analysis can help determine the variable to be explored in the models. The model can be used to test, visualize and quantify a range of futures. It is not possible to remove the uncertainty of the future but a combination of scenarios and modeling will enable these uncertainties to be better understood and managed. This will lead to more robust decisions, lower risk and potential competitive advantage.

It is also important to recognize that to varying degrees we can influence the future. Those that have a better understanding of the future are better placed to influence it and be leading events, rather than reacting to them.

*Business Model Generation: Osterwalder and Pigneur Wiley 2010

Written by David Peregrine Jones of TorusBW and John Reynolds of TorusBW.

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