Why this decision matters
"Forecast" sounds like a math exercise; it's actually a coordination problem. Sales wants high numbers so they're not constrained. Operations wants low numbers so they're not stuck with stock. Finance wants the one that makes the quarter. S&OP is the monthly ritual where those three sit at the same table and agree on one number — and forecasting gives them the rigor to argue about it instead of guessing.
By the end of this topic you'll be able to
Choose the right forecasting method for a time series (level, trend, seasonal, intermittent). Calculate moving averages and exponential smoothing by hand, and tune the alpha to your data. Detect bias with a tracking signal. Run a chase-vs-level production plan. Explain the S&OP cycle and why misalignment between sales and operations is so expensive.
Materials
Key concepts to know
- Naive forecast — tomorrow = today. The benchmark every model has to beat.
- Moving average — average of last n periods. Smooths noise, lags trend.
- Weighted moving average — recent periods weighted heavier.
- Exponential smoothing — Ft+1 = α · At + (1 − α) · Ft. Alpha ≈ 0.1–0.3 for stable demand, higher for reactive.
- Trend & seasonality — double exponential smoothing (Holt) for trend, Holt-Winters for both.
- Forecast error — MAD, MAPE, MSE. Pick one and stick with it.
- Tracking signal — cumulative error ÷ MAD; if >±4, the model is biased.
- Chase vs. level production — chase changes capacity to match demand; level keeps capacity steady and buffers with inventory.
- S&OP cycle — data → demand review → supply review → pre-S&OP → executive S&OP. Monthly.
Class notes & cheat sheets
- Operations Cheat HandbookZone 3 has all the forecasting and S&OP formulas.
- Forecasting & S&OP — Industry GuideHow real companies run their monthly S&OP cycle.
Hands-on activity — Tim Hortons demand
Two weeks of work in one dataset: the Tim Hortons weekly sales file lets you build moving averages, fit exponential smoothing, detect seasonality, and propose a chase-vs-level production plan. The EireGreenWorks file is the S&OP companion.
- Tim Hortons Weekly Sales52 weeks, four product categories, weather and event columns.
- Forecasting Examples (Excel)Side-by-side worked examples for every method.
- FRED Macro DemoLive macro time series in Excel — useful for trend-seasonality intuition.
- EireGreenWorks S&OP DatasetThree years of seasonal demand for the chase-vs-level decision.
Practice with games · Forecasting
- Forecasting DemoDrag a model over a real time series and watch it fit.
- Alpha Slider SimulatorFeel what alpha does. The single best intuition tool for exponential smoothing.
- Forecasting Method MatcherMatch the right method to each data pattern.
- Forecast or Bust CalculatorSpeed practice on the core formulas.
- Tracking Signal DetectiveFind the biased forecast among the unbiased ones.
- Forecasting Flashcards RaceTimed terminology drill.
- Forecasting JeopardyMixed retrieval game.
- Starbucks — OSCM ChallengeForecasting & S&OP applied to Starbucks demand.
- Six Flags — OSCM Thrill RideHighly seasonal demand — classic Holt-Winters case.
- Forecasting & S&OP ReviewQ&A drilling for Ch 18–19.
- Forecasting & S&OP in Pure ServicesHow the cycle changes when "inventory" is appointments and seats.
Using AI on this decision
Modern AI is genuinely strong at time-series classification (level vs. trend vs. seasonal vs. intermittent) — paste a column and ask. It's reasonable at suggesting methods, weaker at picking alpha, and unreliable at point forecasts unless you make it write the formula and run it in a spreadsheet. The S&OP cycle itself is where AI shines: summarize the demand review for the supply meeting, draft the executive deck, surface assumptions worth challenging.
The AI Lab walks through forecasting the Tim Hortons dataset with an AI assistant.