Demand Forecasting Techniques

Why This Matters

Demand forecasting sits at the heart of every operations decision you'll encounter on the exam. Whether a question asks about inventory management, capacity planning, or supply chain coordination, the underlying assumption is that managers have some method for predicting what customers will want and when. You're being tested on your ability to match the right forecasting technique to the right situation—understanding when quantitative data is abundant versus when you need qualitative judgment, and recognizing how factors like seasonality, trend patterns, and causal relationships shape which method works best.

The techniques in this guide fall into two broad camps: quantitative methods that crunch historical numbers, and qualitative methods that leverage human expertise. But the real exam skill is knowing the trade-offs—simple methods are fast but miss complexity; sophisticated methods capture nuance but require more data and computation. Don't just memorize technique names—know what data conditions and business contexts make each one the smart choice.

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Quantitative Methods: Smoothing and Averaging

These techniques work best when you have reliable historical data and want to filter out random noise to reveal underlying patterns. The core principle: past demand behavior, properly processed, predicts future demand.

Moving Average Method

• Averages a fixed number of past periods to smooth out random fluctuations and reveal the underlying demand pattern
• Lag effect—this method responds slowly to changes because it weights all included periods equally
• Best for short-term forecasting when demand is relatively stable without strong trends or seasonality

Exponential Smoothing

• Assigns decreasing weights to older data—recent observations matter more, calculated as $F_t = \alpha D_{t-1} + (1-\alpha)F_{t-1}$
• Smoothing constant ($\alpha$) controls responsiveness: higher values react faster to changes, lower values produce smoother forecasts
• Variants exist for complexity—single (level only), double (level + trend), and triple/Holt-Winters (level + trend + seasonality)

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Quantitative Methods: Pattern Recognition

These techniques go beyond simple smoothing to explicitly model the components driving demand variation. The core principle: demand can be decomposed into identifiable patterns—trend, seasonality, cycles—each requiring specific treatment.

Time Series Analysis

• Decomposes historical data into four components—trend (long-term direction), seasonality (predictable calendar patterns), cyclical (economic fluctuations), and irregular (random noise)
• Foundation for other techniques—understanding these components determines which forecasting method fits your data
• Requires substantial historical data to reliably separate signal from noise across multiple time periods

Trend Projection

• Extends identified trends into the future using linear ($Y = a + bX$) or nonlinear equations fitted to historical data
• Assumes past patterns continue—dangerous when market conditions or technology disrupt established trajectories
• Best for long-term forecasting when you've confirmed a stable, persistent trend through time series analysis

Seasonal Adjustments

• Isolates predictable calendar-based fluctuations by calculating seasonal indices that quantify how each period deviates from the average
• Deseasonalizing data allows you to see underlying trends without seasonal distortion, then reapply seasonal factors to final forecasts
• Critical for retail, agriculture, and tourism where demand swings predictably with holidays, weather, or school schedules

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Quantitative Methods: Causal Relationships

These techniques model why demand changes by linking it to explanatory variables. The core principle: demand doesn't exist in isolation—it responds to prices, economic conditions, marketing spend, and other measurable factors.

Regression Analysis

• Establishes mathematical relationships between demand (dependent variable) and factors like price, advertising, or income (independent variables)
• Multiple regression handles several predictors simultaneously, showing the isolated effect of each while controlling for others
• Provides explanatory power—not just predicting demand but understanding which levers managers can pull to influence it

Causal Models

• Broader category including regression, econometric models, and input-output analysis that explicitly model cause-and-effect relationships
• Requires identifying leading indicators—variables that change before demand changes, enabling proactive forecasting
• Higher accuracy potential when causal relationships are stable, but models break down if underlying relationships shift

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Qualitative Methods: Expert Judgment

When historical data is scarce, unreliable, or irrelevant—think new product launches or unprecedented market disruptions—these techniques harness human insight. The core principle: structured expert opinion beats unstructured guessing.

Delphi Method

• Iterative expert consensus process—anonymous questionnaires, feedback on group responses, repeated rounds until convergence
• Reduces bias from dominant personalities by keeping responses anonymous and preventing groupthink
• Best for long-range forecasting of new technologies, markets, or situations where no historical precedent exists

Market Research

• Collects primary data on customer intentions through surveys, focus groups, test markets, and competitive analysis
• Captures demand drivers that historical data misses—changing preferences, unmet needs, reactions to proposed features
• Essential for new product forecasting where past sales data simply doesn't exist

Judgmental Forecasting

• Leverages sales force composites, executive opinion, and industry expertise for rapid, intuition-based predictions
• Fills gaps in quantitative methods—adjusting statistical forecasts for known events like competitor launches or supply disruptions
• Risk of bias—optimism, anchoring, and politics can distort forecasts without structured safeguards

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Quick Reference Table

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Self-Check Questions

1. A retailer has three years of weekly sales data showing consistent December spikes. Which two techniques should they combine for next year's forecast, and why?

2. Compare exponential smoothing and moving average: under what data conditions would exponential smoothing with a high $\alpha$ value significantly outperform a 12-period moving average?

3. Your company is launching a product category that didn't exist two years ago. Which forecasting approaches are viable, and what are the trade-offs between them?

4. An FRQ presents demand data alongside advertising spend and competitor pricing. Which forecasting technique allows you to isolate the effect of each variable, and what's the key assumption this method requires?

5. A manager's exponential smoothing forecast consistently lags behind actual demand during a growth period. What adjustment to the forecasting approach would you recommend, and what's the technical term for this problem?