Market Simulation Engine
Quantifying Sales Risk Under Complex Contract Scenarios
The Challenge: Forecasting Without Precedent
The on-premise market is highly sensitive to contractual structures, but reliable forecasting was hindered by:
- Sparse and noisy historical data with very few instances of structural change.
- Strong regional differences in consumer and venue behaviour.
- A lack of precedent for some contract arrangements, making spreadsheet-based forecasts impossible.
"How do you quantify long-run sales risk when there is no clean historical analogue?"
Our FDE Approach: Engineering a Multi-Layer Simulation
Rather than relying on a single econometric model, our Forward Deployed Engineers designed a hybrid forecasting engine that blended multiple approaches:
- Bayesian Structural Models: Incorporated prior knowledge of market behaviour while handling uncertainty and missing data.
- Agent-Based Simulation: Modelled venue-level decision-making and consumer substitution dynamics under different contract rules.
- System Dynamics Layer: Captured feedback loops and cascading effects (e.g., regional growth shifts, competitive entry).
- Scenario Generation: Ran a structured set of "what-if" futures to test contract adjustments at both national and state levels.
Visualised Insights: A Quantified Risk Profile
- Moderate Impact: A sales decline of approximately 5-7% was forecast under scenarios with constrained contracts.
- Severe Impact: A decline of 20%+ was predicted under scenarios involving complete contract withdrawal.
- Regional Variation: Some large states absorbed shocks with little effect, while smaller, more volatile markets showed potential declines exceeding 30%.
- Competitive Dynamics: Emerging brands were predicted to gain market share more rapidly in scenarios with fewer contractual protections for incumbent brands.
National Sales Impact by Scenario
State-Level Variation (Full Removal)
Market Evolution in Contract-Light Scenario
The Outcome: From Uncertainty to Strategic Confidence
By combining statistical forecasting, agent-based modelling, and system dynamics, the client gained a risk profile that was both granular and robust. The engine transformed uncertainty into clear, scenario-based evidence, enabling:
- Confidence: Transparent forecasts validated against historic baselines.
- Clarity: Side-by-side comparison of contracting strategies.
- Capacity to Plan: A future-proof tool to support negotiations, compliance, and long-term strategy.
Result: Mapping Possible Futures
A digital twin of the on-premise market, built not to predict a single future, but to map the range of possible outcomes. This animation illustrates the core principle of our simulation engine.
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