A nationwide coffee chain in the U.S. had a problem. Every morning, baristas brewed gallons of coffee based on nothing more than gut instinct and a passing glance at the weather. Some days, they ran out of cold brew by noon. Other days, enough unsold espresso shots were dumped to keep an insomniac awake for weeks.
They needed something better—something that could predict demand without relying on sheer luck. Enter time series forecasting, a machine learning approach designed for data that moves through time, capturing trends, seasonality, and sudden spikes that mere mortals might miss.
(Note: The following scenario is fictional but represents how real businesses use time series forecasting to optimize operations.)
How Time Series Forecasting Works
Step 1: Data—Lots of It
First, the AI model feasted on historical sales data. But it wasn’t just about “how many lattes sold yesterday.” The model needed to understand:
✔ Daily trends – Morning rushes vs. slow afternoons.
✔ Seasonality – Pumpkin spice frenzies in fall, iced drinks in summer.
✔ Spikes & anomalies – Unpredictable but real, like a sudden rush after a TikTok trend or a blizzard driving people inside for extra hot chocolate.
Step 2: Univariate Forecasting
Unlike complex models juggling multiple variables, this system was trained only on past sales data—a univariate time series model. No weather, no social media hype—just the pure rhythm of demand over time.
Why? Because coffee drinkers are remarkably habitual creatures. Their buying patterns tend to repeat daily, weekly, and seasonally. A Simple Exponential Smoothing (SES) model or an ARIMA (AutoRegressive Integrated Moving Average) model can pick up these patterns without distraction.
Step 3: Spikes and Seasonal Adjustments
Time series forecasting doesn’t just follow an upward or downward trend—it watches for those pesky spikes that break the pattern. Maybe a local football game drives a sudden spike in sales. Maybe Mondays are just universally caffeine-dependent.
With models like Holt-Winters Exponential Smoothing, the system factored in:
✔ Level – The baseline sales average.
✔ Trend – The slow, creeping increase or decrease in coffee sales over months.
✔ Seasonality – The predictable highs and lows based on time of day, week, or year.
Real-World Use Case: Starbucks Demand Forecasting
While our coffee chain example is fictional, Starbucks has used AI-powered demand forecasting for years. Their system predicts how much coffee, milk, and food each store needs based on historical sales, weather, and local events. This reduced waste, improved efficiency, and optimized labor scheduling across thousands of stores.
What Changed?
Once the AI was trained, baristas no longer guessed how much coffee to brew. Instead, each store got hourly demand predictions—not just for today, but for next week, next month, and beyond.
✔ Waste dropped by 25%—no more over-brewing.
✔ Stockouts fell by 18%—customers got what they wanted.
✔ Efficiency soared—because predicting demand meant baristas could focus on making great coffee, not on crisis-level bean math.
Why It Matters
Time series forecasting isn’t just for coffee. It’s used in energy demand prediction, hospital staffing, airline ticket pricing, and stock market trends. Wherever time influences data, AI helps us see the patterns we didn’t even know were there.
And in this case? It simply meant the right number of lattes at the right time—one well-timed espresso shot at a time.
