ðŪ Game Overview
Welcome to the most intense week in retail! You're the inventory manager for "TechMart," a consumer electronics retailer. Your mission: predict demand for Black Friday week without drowning in excess inventory or facing angry customers with empty shelves.
ð Your Data Arsenal
- 3 years of daily sales history
- Product categories: TVs, Laptops, Gaming, Smart Home
- External factors: weather, competitor ads, social media buzz
- Historical Black Friday patterns
ðŊ Your Mission
- Forecast daily demand for Black Friday week
- Optimize inventory levels
- Minimize costs while maximizing revenue
- Handle surprise events in real-time
⥠The Challenges
- Storage costs: $5 per unit per day
- Stockout penalty: $50 per missed sale
- Rush shipping: $20 per unit
- Customer satisfaction metrics
ð Game Progression
Round 1
Data Exploration
(10 min)
Data Exploration
(10 min)
Round 2
Model Building
(15 min)
Model Building
(15 min)
Round 3
Initial Forecast
(10 min)
Initial Forecast
(10 min)
Round 4
Black Swan Event
(5 min)
Black Swan Event
(5 min)
Round 5
Final Results
(5 min)
Final Results
(5 min)
ðē Round Details
Round 1: Data Exploration (10 minutes)
Teams receive historical sales data and must identify:
- Overall trends (growing? declining?)
- Seasonal patterns (weekly, monthly, yearly)
- Previous Black Friday spikes
- Product correlations
# Sample Data Preview
Date | Product | Units_Sold | Avg_Price | Weather | Promo
------------|------------|------------|-----------|---------|--------
2022-11-25 | TV-55inch | 145 | $399 | Clear | Yes
2022-11-26 | TV-55inch | 98 | $449 | Clear | No
2022-11-27 | TV-55inch | 67 | $449 | Rain | No
...
Round 2: Model Building (15 minutes)
Choose your forecasting approach:
Available Methods:
- Simple: Moving averages, trend projection
- Seasonal: Decomposition, seasonal indices
- Advanced: ARIMA, exponential smoothing
- External: Include weather, promotions, social media
Round 3: Initial Forecast (10 minutes)
Submit predictions for Black Friday week:
- Daily forecasts for each product category
- Confidence intervals
- Inventory order quantities
- Contingency stock levels
Round 4: Black Swan Event! (5 minutes)
BREAKING: Your competitor just announced bankruptcy! Their customers are flooding to your store. Social media is going crazy. You have 5 minutes to adjust your forecasts!
Quick decisions needed:
- Rush order more inventory? (costs extra!)
- Raise prices to manage demand?
- Limit quantities per customer?
Round 5: Final Results (5 minutes)
See how your predictions performed against actual sales!
ð Scoring System
| Metric | Calculation | Points |
|---|---|---|
| Forecast Accuracy | 100 - MAPE (Mean Absolute Percentage Error) | Up to 400 points |
| Inventory Efficiency | Revenue - (Storage Costs + Stockout Penalties) | Up to 300 points |
| Customer Satisfaction | % of demand fulfilled without delays | Up to 200 points |
| Black Swan Response | Speed and effectiveness of adjustment | Up to 100 points |
| Total Possible | 1000 points |
Profit Calculation:
Profit = (Units_Sold à Price) - (Excess_Inventory à $5/day) - (Stockouts à $50) - (Rush_Orders à $20)
Profit = (Units_Sold à Price) - (Excess_Inventory à $5/day) - (Stockouts à $50) - (Rush_Orders à $20)
ð Leaderboard Categories
Awards to Win:
- ðŊ The Oracle: Highest forecast accuracy
- ð° The Profit Maximizer: Highest total profit
- ð Customer Champion: Best satisfaction score
- ðĶĒ Black Swan Survivor: Best crisis response
- ð Most Improved: Biggest gain from Round 1 to Round 5
ðĄ Strategy Tips
Rookie Mistakes to Avoid
- Ignoring day-of-week patterns
- Over-fitting to last year only
- Missing the post-Thanksgiving surge
- Not accounting for product cannibalization
Pro Strategies
- Weight recent Black Fridays more heavily
- Build separate models for different products
- Include leading indicators (search trends)
- Keep safety stock for top sellers
Data Patterns to Find
- Pre-Black Friday dip (people waiting)
- Cyber Monday online spike
- Weather impact on foot traffic
- Social media buzz correlation
ð Learning Objectives
By playing this game, students will:
- Experience the complexity of real-world forecasting with messy data
- Understand the trade-off between over-stocking and under-stocking
- Learn to identify and model multiple seasonalities
- Practice adjusting models when conditions change rapidly
- See how external factors affect time series predictions
- Develop intuition for when simple vs. complex models work best
ð Implementation Options
ð Debrief Questions
After the game, discuss these key points:
- What patterns did you notice first? Which ones did you miss?
- How did your forecast change when you added external variables?
- What would you do differently with more time or data?
- How did the Black Swan event expose weaknesses in your model?
- Which was more costly: over-forecasting or under-forecasting? Why?
- How would this apply to other industries (healthcare, airlines, restaurants)?