Forecasting Demand

In today’s volatileglobal environment, organizations face unprecedented challenges in forecastingdemand due to persistent market disruptions, policy shifts, and macroeconomicvolatility. From pandemics to trade wars, and the looming risk of recession, decision-makersare increasingly required to anticipate and adapt to multiple potentialfutures. Traditional forecasting methods often fall short in such environments,being overly reliant on linear assumptions and susceptible to cognitive biases,especially when vivid but rare events dominate perception.Scenario simulation,or conditional forecasting, offers a powerful solution. By modeling a range ofplausible future scenarios and assigning probabilities to each, businesses canmake informed, risk-adjusted decisions grounded in data rather than speculation.This whitepaper outlines the strengths of scenario simulation in the face ofcomplex, uncertain conditions and demonstrates its application using pastcrises and current market risks.

Accurate demandforecasting is a cornerstone of effective supply chain and financial planning.However, in periods marked by high uncertainty—such as the COVID-19 pandemic,global trade tensions, or financial crises—linear models based on historicaldata can become unreliable. The solution lies in enhancing forecast models withconditional logic and simulations that explore how demand responds underdifferent circumstances.

Standard forecasting methods typically rely on historical trends, recent growth rates, and regression models. These methods:
- Struggle with structural breaks (e.g., post-pandemic consumer behavior).
- Overweight recent, vivid events while underestimating low-probability but high-impact risks.
- Fail to incorporate complex conditional relationships or feedback loops.

Scenario simulation,also known as conditional forecasting, involves the construction of multipledistinct futures based on varying inputs:
‍Scenario Design: Key variables (e.g., interest rates, tariffs, infection rates) are varied within plausible bounds.
‍Model Calibration: Each scenario is processed through a causal or statistical model that forecasts demand based on the conditions.
‍Probability Assignment: Scenarios are assigned likelihoods based on macroeconomic indicators, expert judgment, or market-implied signals.
‍Benefits: Generates a distribution of outcomes, not a single point forecast. Encourages planning under uncertainty     with clear risk exposure. Reduces bias from overreacting to vivid or recent events.

A. COVID-19 Pandemic Conditional forecasting allowed companies tomodel various waves of infection, differing levels of lockdown strictness, andconsumer behavior scenarios. For example, demand for durable goods surged inoptimistic reopening scenarios but collapsed under repeated lockdownassumptions.
‍B. 2008 Financial Crisis Retail and housingdemand models that integrated potential credit availability shocks and consumerconfidence metrics outperformed those relying on historical sales alone.
‍C. U.S.-China TradeTensions Firms usedtariff-triggered scenarios to evaluate cost pass-through, re-sourcing fromalternate suppliers, and market share losses. Those leveraging scenariosimulations planned better and adjusted pricing strategies more efficiently.
‍D. Anticipated Policy Shifts (2025 Onward) With climatelegislation and fiscal tightening expected in major economies, simulations arealready being used to test demand sensitivity under varying carbon pricing,green incentives, and consumer tax regimes.

Data Collection: Identify keyindicators and external drivers.
‍Model Building: Develop aflexible model that can ingest variable inputs.
‍Scenario Development: Collaborate with economists and strategists to build diverse yet plausiblefutures.
‍Simulation Execution: Run MonteCarlo or structured scenario simulations.For advanced users such as economists familiarwith MATLAB, the ECB's BEAR Toolbox remains a preferred solution for scenariomodeling. However, for the broader business audience, tools have historicallybeen inaccessible—until recently. Indicio has bridged this gap by launching anintuitive, automated scenario simulation interface. It delivers cutting-edgeforecasting capabilities without requiring deep statistical expertise.Indicio's platform also integrates seamlessly with internal data storage systemsand leading BI and planning tools, enabling cross-functional teams to makeinformed decisions using scenario-based insights.
‍Decision Integration: Presentforecasts as distributions with associated probabilities to enablerisk-weighted planning.

Scenario simulation represents a transformative shift in demand forecasting under uncertainty. By embracing a probabilistic mindset, businesses can better prepare for the unexpected, allocate resources wisely, and maintain agility. In an era where change is the only constant, conditional forecasting offers the clarity needed to navigate complexity with confidence.