Commercial Area Holiday Demand Forecasting
Please note: Specific program names, internal tool references, and other potentially sensitive details mentioned in the original project document have been generalized or masked in this summary for confidentiality.
When Standard Models Meet Unusual Reality
Here's a problem that sounds simple until you try to solve it: How do you forecast delivery volume for areas where most customers disappear on holidays?
Our delivery network serves two distinct worlds. In the residential world—about 90% of our volume—packages flow seven days a week. People expect deliveries on weekends, holidays, even during snowstorms. It's the Amazon effect: constant availability has become the baseline expectation.
But in the commercial world—that remaining 10%—different rules apply. Offices close. Retail stores observe holidays. Entire business districts go quiet on three-day weekends. When businesses close, deliveries don't happen.
The challenge: Our forecasting models treated both worlds the same. They applied network-level holiday patterns to every delivery station, regardless of whether that station served residential neighborhoods or commercial districts.
The result: Forecasting accuracy for commercial-heavy areas during holidays was... suboptimal. Actually, it was terrible.
The Holiday Forecasting Trap
Not all holidays are created equal. Some holidays crush commercial deliveries. Others barely register.
Through data analysis, we discovered three distinct holiday patterns:
Group A: The Shutdown Holidays. Think Christmas, Thanksgiving, New Year's Day. Both our network and most businesses close. Forecasting becomes meaningless because the whole system pauses.
Group B: The Business Holidays. Presidents Day, Memorial Day, Labor Day. We deliver, but businesses close. These holidays create predictable patterns: light volume on the holiday itself, heavy catch-up volume the following days.
Group C: The Phantom Holidays. MLK Day, Juneteenth. We deliver, businesses stay open. Minimal disruption to normal patterns.
Group B holidays presented the clearest opportunity. The pattern was consistent and predictable: commercial-heavy areas would see significant volume lightness on the holiday, followed by heavier-than-normal volume as businesses reopened and catch-up deliveries occurred.
But our models couldn't see this pattern because they treated all stations identically.
The Breakthrough: Weighted Reality
The solution wasn't to build separate models for commercial and residential areas. It was to recognize that most stations serve both, just in different proportions.
Picture a station that serves 40% commercial volume and 60% residential. On Memorial Day, the commercial portion effectively disappears while the residential portion continues normally. The overall volume pattern should reflect this weighted reality.
We developed a methodology that creates station-specific Day-of-Week curves by blending two patterns:
- The Network DoW Curve: Representing typical residential-heavy delivery patterns
- The Pseudo Commercial DoW Curve: Representing pure commercial delivery patterns
The magic happens in the weighting. A station with 41.2% commercial volume gets a forecast that's 41.2% weighted toward commercial patterns and 58.8% weighted toward network patterns.
Building the Commercial Pattern
Here's where the technical work gets interesting: How do you model the delivery pattern for a purely commercial area when none exist in your network?
We constructed a "pseudo commercial" curve based on logical assumptions and historical data analysis. Commercial areas show dramatically reduced activity on weekends and holidays—we set these at 7% for holidays and 8% for Saturdays, compared to normal weekday levels.
But volume doesn't vanish—it shifts. The remaining weekdays get proportionally higher percentages to maintain the same total weekly volume. It's conservation of energy, applied to package delivery.
These parameters weren't arbitrary. They emerged from analyzing historical patterns at our most commercial-heavy stations and understanding how businesses actually operate.
The Validation: Memorial Day 2023
Numbers don't lie, but they can mislead. In forecasting, the only validation that matters is performance on real data.
We tested our model against historical data from Memorial Day and Labor Day 2023, focusing on stations with 15% or more commercial volume. The results were striking:
Memorial Day: Our custom model achieved 7.4% MAPE versus 11.4% for the standard network curve. That's a 395 basis point improvement.
Labor Day: 5.9% MAPE versus 8.7% for the standard curve. A 278 basis point improvement.
These weren't marginal gains. They were substantial improvements in forecasting accuracy for a specific but important segment of our network.
The Real-World Test: Memorial Day 2024
Laboratory results are one thing. Production performance is another.
When we deployed the model for Memorial Day 2024, we tracked eleven stations using the custom approach. The results exceeded our expectations:
Capacity Ask WAPE improvements:
- W-3 forecast: 397 basis points better (9.8% to 5.8%)
- W-1 forecast: 355 basis points better (9.1% to 5.6%)
Input metric MAPE: 5.3% versus 11.1% for the network curve—a 573 basis point improvement.
These improvements came from accurately predicting lower volume on the holiday itself and higher volume on subsequent days, proportionate to each station's commercial mix.
The Compound Effect of Specificity
This project illustrates a fundamental principle: Generic solutions optimize for average cases, but real value often lies in handling the exceptions well.
Our commercial-heavy stations represented a small percentage of total volume but a significant percentage of forecasting errors during holidays. By solving for this specific use case, we didn't just improve accuracy—we improved operational confidence.
Better forecasts mean better capacity planning. Better capacity planning means fewer stockouts and less wasted capacity. The economic impact compounds across the entire network.
Implementation Philosophy: Central Intelligence
The temptation with custom models is to let individual planners apply them locally. That's a mistake.
We implemented the commercial model centrally, through enhanced forecasting scripts that automatically identify commercial-heavy stations and apply the appropriate blended curves during holiday weeks.
This approach ensures consistency, enables performance tracking, and reduces the cognitive load on individual planners. They don't need to remember which stations need special treatment—the system knows and applies the logic automatically.
Centralization also enables continuous improvement. As we gather more data and refine our understanding of commercial patterns, we can update the model parameters once and improve forecasting across all affected stations simultaneously.
The Future of Tailored Forecasting
This project opened our eyes to a broader opportunity: How many other "special cases" exist in our network that could benefit from tailored forecasting approaches?
Rural stations with different seasonality patterns? Urban stations affected by local events? Stations serving specific industries with unique operational calendars?
The methodology we developed—identifying distinct patterns, creating weighted models, and implementing centrally—becomes a template for handling any forecasting challenge where one-size-fits-all approaches fall short.
The Lesson: Precision Over Perfection
The commercial holiday model teaches us that forecasting excellence isn't about creating perfect models. It's about creating precise models that handle specific situations well.
Perfect models are impossible. The future is uncertain, demand is variable, and external factors constantly change. But precise models—models that understand the nuances of specific operational contexts—can deliver substantial value.
In our case, recognizing that commercial and residential delivery patterns differ during holidays led to a 300-500 basis point improvement in forecasting accuracy for affected stations. That's not incremental improvement—that's transformation.
And transformation happens one specific problem at a time, solved with precision and implemented with discipline.