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Strategic  planning  and scenario  simulation

Strategic planning and scenario simulation

Break through the noise.

Managers constantly make critical decisions that will have ripple effects well into the future. More often than not, these future-facing decisions are based on gut instincts and internal politics. Machine learning remains underused in strategy simulation, where horizon models are often based more on leaders’ individual points of view rather than actual data.

By mapping out the entire organization onto a mathematical model, and showing how it connects to the market and its consumers, machine learning can effortlessly simulate a plethora of futures that arise when any single variable of the business changes. 

 

If a firm pivots to electric vehicles, for example, the model can show the after-effects of that decision: the feasibility, the cost to suppliers, or the predicted sales increases in specific customer segments that value environmentally friendly products.

 

The mathematical model makes it possible to optimize strategy work: out of dozens of future scenarios, an optimization model can dynamically weigh each option in terms of impact, pointing towards the most favorable scenario.

 

Machine learning in strategy simulation is a completely new way of looking at business growth. The breadth of it allows leaders to expand their horizons well beyond simplistic models, while the enhanced precision ensures all decision-making is grounded in fact.

 

Fourkind, part of Thoughtworks, is at the cutting-edge of scenario work in its combining of data science and strategy simulation. The optimization model for scenarios is an innovation that remains rare in the field of management consulting. We’ve helped companies from media to textile service providers rethink their futures, and in the process, their entire organizations. For our clients, we deliver more robust decision making, a clearer vision, and better organizational alignment.

What we deliver

Co-created organizational model
Co-created organizational model

Leaders often make assumptions when discussing strategy, but they do not state those assumptions out loud, let alone model them. The co-created organizational model maps out all the necessary data and each changing variable of a firm, but also the hundreds of assumptions that covertly drive decision making. That creates clarity.

 

Clearer vision
Clearer vision

Mapping out an entire organization onto a mathematical model requires deeper, more meaningful strategy work. It brings up core questions of purpose and vision. That lucidity can help the business forge a better future path.

More robust decision making
More robust decision making

Horizon 2 and Horizon 3 -level scenarios can often get hazy and convoluted: these stretched out forecasts necessitate the type of systemic understanding of the business and its environment that only machine learning can provide.

Organizational alignment
Organizational alignment

Mathematical modeling makes strategy work open and transparent. It cuts through the internal noise, breaking silos, and helps organizations come together to make choices that benefit everyone.

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