In recent years, market uncertainty has reached extreme levels due to countless factors including unprecedented pandemics, rapidly changing supply chains, and unpredictable raw material price fluctuations. Traditional demand forecasting methods that rely solely on historical data have become increasingly inadequate for accurately predicting a rapidly changing future.
In this environment, scenario-based demand forecasting is no longer optional—it's essential. We've entered an era where companies can only survive by considering multiple possibilities simultaneously rather than relying on single forecast results. For resource-constrained startups and SMEs especially, incorrect forecasting can directly determine survival, requiring even more careful approaches.
Traditional demand forecasting primarily relies on quantitative methods that analyze historical data patterns to predict the future. This works extremely well when market conditions are stable and predictable. However, in today's world of increasing market volatility where historical patterns break down, this approach alone has clear limitations.
In contrast, scenario-based demand forecasting doesn't pin the future to a single forecast value. Instead, it divides possible situations into multiple scenarios and develops response strategies for each. This empowers companies to respond proactively even amid uncertainty.
Companies that have adopted scenario forecasting are quickly adapting to unexpected market changes and securing competitive advantages. Rather than simply repeating the past, they minimize risk and maximize opportunities by preparing in advance for multiple future possibilities.
Where traditional point forecasting definitively states 'next year's sales will be 1 million units,' scenario forecasting presents situation-specific alternatives: '1.2 million units in economic boom, 1 million maintaining status quo, 800,000 in recession.' This approach enables companies to prepare concrete action plans for each scenario in advance.
This preparation is especially critical for resource-constrained companies like startups. Incorrect inventory investments or production plans can have devastating impacts on a company's cash flow. Scenario forecasting allows you to establish minimum safety nets for surviving worst-case scenarios while simultaneously preparing to scale quickly when opportunities arise.
The first step in scenario forecasting is designing actionable scenarios. Rather than abstract assumptions, scenarios should be built around key variables that directly impact your business.
First, identify the key variables that most significantly affect your business. For manufacturers, this might be raw material prices; for retail, consumer purchasing power or promotional effectiveness; for IT solution companies, competitive market conditions or technology trends could be primary variables.
These variables should be identified through comprehensive analysis of both external and internal data. You need to consider external factors like economic indicators and competitor activities alongside internal data such as sales records and marketing campaign results to find realistic variables.
When developing optimistic, conservative, and pessimistic scenarios based on identified variables, each scenario should be structured like a logical story. For example, an optimistic scenario might weave together related variables: 'Key raw material prices drop while successful marketing campaigns drive consumer demand.'
Scenario design isn't something data analysts or executives can do alone. You must gather input from stakeholders across sales, marketing, production, purchasing, and other departments to complete realistic and concrete scenarios. Field insights provide invaluable information about which variables actually impact the business significantly and how these variables interact.
Sales teams can detect shifts in customer reactions fastest, production teams identify supply chain instabilities earliest, and marketing teams spot changing market trends most quickly. Only by incorporating this field intelligence into scenarios can you improve forecast accuracy.
When forecasting demand for each scenario, input each scenario's variables into existing forecast models to derive predicted values. For instance, input the variable 'raw material price increase of 10%' to predict expected demand reduction when production costs rise.
What's crucial here isn't just calculating numbers but interpreting what those results mean for your business. You need to analyze how key metrics like inventory turnover, stockout rates, profitability, and marketing ROI will fluctuate based on predicted demand volumes.
For example, if stockout rates rise to 20% in a pessimistic scenario, you can recognize this as a serious risk requiring immediate response. Conversely, if inventory turnover in an optimistic scenario exceeds expectations, you'll know it's time to consider increasing production or entering new markets.
Based on your analysis, develop response strategies for each scenario in advance. If an optimistic scenario materializes, prepare plans to expand production lines or increase marketing budgets; if a pessimistic scenario occurs, have ready-to-execute responses like reducing inventory levels or temporarily suspending investment in certain product lines.
These pre-plans mean having the readiness to execute swiftly without confusion when actual situations arise. Especially in rapidly changing markets where response speed often determines survival, pre-prepared scenario-specific action plans become a major competitive advantage.
Scenario forecasting only becomes meaningful when it leads to final decisions, not just forecasting itself. The key is reinterpreting complex analytical results into business language to support executive decision-making.
First, data must be visualized intuitively. Present forecast results and key metrics for each scenario in dashboard or graph formats for at-a-glance understanding. Particularly important is clearly showing how each scenario impacts profitability or inventory risk.
Visualization is essential for helping executives easily understand complex data, compare various possibilities, and increase decision-making confidence. Materials that visually demonstrate clear differences are far more effective for actual decision-making than number-only reports.
Clearly present the risks and opportunities inherent in each scenario. For example, provide specific figures like 'Expected loss in pessimistic scenario is 500 million won; minimum defensive response is 30% inventory reduction.'
This analysis goes beyond simple forecasting to provide answers to the essential management challenges of risk management and opportunity exploration. By understanding the potential impact of each scenario in advance, executives can make more balanced decisions.
Because market conditions constantly change, you can't keep forecasting with scenarios created once. Continue monitoring key market variables and update scenarios as needed.
By reviewing scenarios quarterly or semi-annually and reflecting new variables, you maintain forecast accuracy and enhance corporate agility. This allows scenario forecasting to establish itself as an ongoing management tool rather than a one-time project.
While scenario forecasting's effectiveness is proven, actual implementation requires considerable expertise and time. Particularly, using 224 advanced machine learning and deep learning models to perform accurate predictions for each scenario can be burdensome for most companies.
ImpactiveAI's Deepflow solution automates this complex process, enabling companies to easily leverage scenario-based demand forecasting. From cutting-edge transformer-based time series forecasting models like I-transformer and TFT to proven deep learning models including GRU, DilatedRNN, TCN, and LSTM, diverse AI models are integrated to automatically select optimal forecast models suited to each scenario's characteristics.
Deepflow's AutoML functionality particularly handles model training and deployment processes automatically whenever data is updated, enabling continuously accurate scenario forecasting without specialized personnel. Companies that have implemented it have achieved an average 33.4% reduction in inventory shortages and overages, with some realizing monthly inventory cost savings of 24.8 billion won.
Beyond providing simple forecast numbers, Deepflow highlights products where shortages or excess are anticipated, enabling proactive responses. This is a practical feature that implements scenario forecasting's core principle of 'preparing response strategies in advance' in actual operations.
Scenario forecasting doesn't completely eliminate uncertainty, but it gives you the power to prepare for it. Moving beyond traditional approaches that view the future as an extension of the past, simultaneously considering multiple possibilities and preparing for each has become an essential capability in modern business.
Risk management through scenario forecasting is especially important for resource-constrained startups and SMEs, where incorrect forecasting can be fatal. But you also need preparedness to respond quickly when opportunities arise in order to grow.
Scenario forecasting satisfies both requirements simultaneously. It establishes safety nets for surviving worst-case scenarios while enabling expansion plans that won't miss opportunities.
The future business environment will become even more complex and difficult to predict. To survive and grow in such environments, you need scenario forecasting capabilities that prepare for multiple future possibilities rather than simply repeating the past. This will become core competitiveness that goes beyond merely predicting the future, enabling companies to respond proactively and drive innovation.
.svg)
.svg)
