# GOALS ACHIEVEMENT CHECKLIST
## AstraZeneca Cash Flow Forecasting - Datathon 2025

**Date:** December 19, 2025  
**Status:** Comprehensive Review

---

## ✅ **CORE FORECASTING GOALS**

### ✅ **1. Select Model**
**Status:** ✅ **COMPLETE**

**What We Did:**
- Compared 3 models: Moving Average, Exponential Smoothing, Prophet
- Tested on weeks 40-44 (holdout set)
- **Selected: Prophet** (Lowest MAE: $2.24M vs $3.72M vs $4.80M)

**Evidence:**
- ✅ `00_model_comparison.py` - Model comparison script
- ✅ `model_comparison_results.csv` - Comparison results
- ✅ `dashboard/00_model_comparison.png` - Visualization
- ✅ `EXECUTIVE_SUMMARY.md` - Documents Prophet selection

**Rationale Documented:**
- Lowest MAE
- Provides confidence intervals
- Automatic seasonality detection
- Business-friendly decomposition

---

### ✅ **2. Find Key Drivers Behind Forecast Trend**
**Status:** ✅ **COMPLETE**

**What We Found:**

**Primary Inflow Driver:**
- **AR (Accounts Receivable):** $107.2M (85% of all inflows)
- Correlation with total cash flow: 0.67 (Strong)
- 26-week forecast: +$77.5M

**Primary Outflow Drivers:**
- **Netting AP:** -$64.2M (52% of all outflows)
- **AP (Accounts Payable):** -$38.6M (31% of all outflows)
- **Payroll:** -$9.4M (7.6% of outflows)
- **Tax:** -$6.5M (5.3% of outflows)

**Evidence:**
- ✅ `06_category_deep_dive.py` - Category analysis
- ✅ `category_analysis_summary.csv` - Category breakdown
- ✅ `dashboard/06_category_deep_dive.png` - Visualization
- ✅ `EXECUTIVE_SUMMARY.md` - Documents key drivers

**Key Insight:** AR collection timing and Netting operations timing are the primary drivers of cash flow variability.

---

### ✅ **3. How to Extend to 6-Month Weekly Forecast**
**Status:** ✅ **COMPLETE**

**What We Did:**
1. **Started with 1-month forecast** (Option A validation)
2. **Extended to 4-month** (Option B1 rolling windows)
3. **Validated robustness** (Option B2 monthly cross-validation)
4. **Extended to 6-month** (Option C production forecast)

**Methodology:**
- Trained Prophet on ALL 44 weeks of historical data
- Forecasted 26 weeks ahead (Nov 2025 - Apr 2026)
- Used same model configuration validated in shorter horizons
- Applied degradation pattern: Expected MAE ~$2.0-2.5M for 6-month

**Evidence:**
- ✅ `04_forecast_option_c_production.py` - Production forecast script
- ✅ `FORECAST_OUTPUT_FINAL.csv` - 26-week forecast ⭐
- ✅ `FORECAST_OUTPUT_REPORT.md` - Methodology documented
- ✅ `ACCURACY_BY_HORIZON.md` - Degradation analysis

**Extension Strategy Documented:**
- Degradation pattern: 1M ($1.36M) → 4M ($1.91M) → 6M (~$2.0-2.5M)
- Confidence intervals widen appropriately
- Monthly updates recommended to maintain accuracy

---

### ✅ **4. Compare Forecasted vs Actual Cash Flow**
**Status:** ✅ **COMPLETE**

**What We Did:**

**Validation Approaches:**
1. **Option A:** Train on weeks 1-39, test on weeks 40-44
   - Actual vs Predicted comparison
   - Week-by-week error analysis

2. **Option B1:** Rolling window validation
   - Multiple train/test splits
   - Degradation pattern analysis

3. **Option B2:** Monthly cross-validation
   - 10 months tested independently
   - Consistency across time periods

**Comparison Results:**
- Week 40: Actual -$0.17M, Predicted +$4.90M (Direction: Wrong)
- Week 41: Actual -$0.43M, Predicted +$0.76M (Direction: Wrong)
- Week 42: Actual +$0.37M, Predicted +$0.24M (Direction: Correct, Error: $0.13M)
- Week 43: Actual -$7.86M, Predicted -$5.49M (Direction: Correct, Error: $2.37M)
- Week 44: Actual +$7.81M, Predicted +$5.36M (Direction: Correct, Error: $2.45M)

**Evidence:**
- ✅ `option_a_predictions.csv` - Detailed comparison
- ✅ `dashboard/01_option_a_validation.png` - Visualization
- ✅ `05_accuracy_analysis.py` - Comprehensive comparison
- ✅ `dashboard/05_accuracy_analysis.png` - Actual vs Predicted charts

---

### ✅ **5. Appropriate Metrics**
**Status:** ✅ **COMPLETE**

**Metrics Used:**

**Primary Metrics (Reliable):**
- ✅ **MAE:** $1.84M average (primary metric)
- ✅ **RMSE:** $2.18M average
- ✅ **Direction Accuracy:** 60% (inflow vs outflow)
- ✅ **CI Coverage:** 86% (uncertainty calibration)

**Improved Percentage Metrics:**
- ✅ **Threshold MAPE ($1M):** 30.7% (excludes small values)
- ✅ **Direction-Aware MAPE:** 32.3% (same-sign weeks only)
- ✅ **Scaled MAPE:** 67.4% (scaled by average magnitude)

**Why These Metrics:**
- MAE: Not affected by zero-crossing issues
- Direction Accuracy: Business-meaningful for cash flow
- Threshold MAPE: Avoids explosion near zero
- CI Coverage: Validates uncertainty estimates

**Evidence:**
- ✅ `accuracy_summary.csv` - All metrics
- ✅ `improved_metrics_summary.csv` - Improved MAPE/SMAPE
- ✅ `ACCURACY_REPORT.md` - Comprehensive metrics documentation
- ✅ `METRIC_EXPLANATIONS.md` - Detailed metric explanations

---

### ✅ **6. Forecast Performance and Limitations to Finance Stakeholder**
**Status:** ✅ **COMPLETE**

**Performance Documented:**
- ✅ Direction Accuracy: 60% (better than random 50%)
- ✅ Average Error: $1.84M per week
- ✅ 1-Month: 75% direction, $1.36M error (Excellent)
- ✅ 6-Month: ~60% direction, ~$2.0-2.5M error (Acceptable)

**Limitations Documented:**
- ⚠️ **Accuracy degrades over time:** 1-month (75%) → 6-month (60%)
- ⚠️ **Direction accuracy:** 60% means 40% of weeks may have wrong sign
- ⚠️ **Large errors possible:** Worst case scenario shows -$83.3M balance
- ⚠️ **Small values problematic:** MAPE unreliable when |actual| < $1M
- ⚠️ **Opposite signs:** Model struggles when predicting inflow but actual is outflow

**Recommendations for Finance:**
- Use for 1-4 month planning with high confidence
- For 6-month, use confidence intervals for scenario planning
- Update forecasts monthly as new data arrives
- Monitor actuals vs forecast weekly and adjust as needed

**Evidence:**
- ✅ `EXECUTIVE_SUMMARY.md` - Performance summary
- ✅ `ACCURACY_REPORT.md` - Detailed performance analysis
- ✅ `ACCURACY_BY_HORIZON.md` - Horizon-specific performance
- ✅ `business_recommendations.csv` - Stakeholder recommendations

---

## ✅ **SHORT-TERM CASH FLOW GOALS**

### ✅ **7. Expected Net Cash Flow**
**Status:** ✅ **COMPLETE**

**Short-Term Forecast (1-3 months):**
- **1-Month:** Expected net cash flow varies by week
- **Average Weekly:** ~$0.21M (based on forecast)
- **3-Month Total:** ~$2.5-3.0M (estimated)

**6-Month Forecast:**
- **Total Net Cash Flow:** +$5.45M (most likely)
- **Best Case (95%):** +$98.4M
- **Worst Case (95%):** -$89.0M

**Evidence:**
- ✅ `FORECAST_OUTPUT_FINAL.csv` - Weekly forecasts
- ✅ `FORECAST_OUTPUT_REPORT.md` - Forecast summary
- ✅ `dashboard/04_option_c_production_forecast.png` - Visualization

---

### ✅ **8. Primary Drivers of Short-Term Liquidity Risk and Stability**
**Status:** ✅ **COMPLETE**

**Liquidity Risk Drivers Identified:**

**1. AR Collection Timing (Risk Factor)**
- AR drives 85% of inflows
- Delayed collections → immediate liquidity impact
- **Recommendation:** Optimize AR collection timing

**2. Netting Operations (Risk Factor)**
- Netting AP drives 52% of outflows
- Large, unpredictable timing
- **Recommendation:** Review netting operations timing

**3. Entity Concentration (Risk Factor)**
- KR10: -$1.1M (largest negative)
- MY10: -$1.6M (needs monitoring)
- **Recommendation:** Monitor these entities closely

**Stability Drivers:**

**1. Payroll (Stable)**
- Predictable recurring outflow
- -$9.4M over 44 weeks
- Low volatility

**2. Tax (Stable)**
- Predictable recurring outflow
- -$6.5M over 44 weeks
- Regular timing

**Current Liquidity Status:**
- ✅ **HEALTHY:** No negative balance weeks in forecast
- ✅ **Minimum Balance:** $2.16M (above zero)
- ⚠️ **Risk:** Worst case shows -$83.3M (use credit line buffer)

**Evidence:**
- ✅ `06_category_deep_dive.py` - Category analysis
- ✅ `09_analysis_recommendations.py` - Risk assessment
- ✅ `dashboard/04_liquidity_risk_calendar.png` - Risk visualization
- ✅ `business_recommendations.csv` - Risk mitigation recommendations

---

## ⚠️ **LONG-TERM CASH FLOW GOALS** (Needs Enhancement)

### ⚠️ **9. How Sustainable is the Cash Flow**
**Status:** ⚠️ **PARTIALLY COMPLETE** - Needs Enhancement

**What We Have:**
- ✅ Historical trend analysis (slight increasing trend: +$0.01M/week)
- ✅ Volatility analysis ($3.58M weekly volatility)
- ✅ 6-month forecast showing positive net cash flow (+$5.45M)
- ✅ Ending balance projection ($11.19M from $9.65M)

**What's Missing:**
- ⚠️ Long-term sustainability metrics (beyond 6 months)
- ⚠️ Structural pattern analysis (operating vs investing vs financing)
- ⚠️ Cash flow sustainability ratios
- ⚠️ Break-even analysis

**Action Needed:** Create sustainability analysis script

---

### ⚠️ **10. Structural Cash Flow Patterns Finance Leadership Should Monitor**
**Status:** ⚠️ **PARTIALLY COMPLETE** - Needs Enhancement

**What We Have:**
- ✅ Monthly patterns identified
- ✅ Category-level patterns (AR, AP, Netting)
- ✅ Entity-level patterns
- ✅ Seasonal patterns (monthly cycles)

**What's Missing:**
- ⚠️ Operating vs Investing vs Financing breakdown
- ⚠️ Structural trend analysis (long-term direction)
- ⚠️ Cash conversion cycle analysis
- ⚠️ Working capital patterns

**Action Needed:** Create structural pattern analysis

---

## ✅ **ANOMALY DETECTION GOALS**

### ✅ **11. Which Transactions/Weeks Show Unusual Inflows/Outflows**
**Status:** ✅ **COMPLETE**

**Anomalies Detected:**

**Transaction-Level:**
- ✅ **18,368 transactions** flagged for review
- ✅ **Duplicate transactions** identified
- ✅ **Out-of-pattern transactions** flagged

**Week-Level:**
- ✅ **Unusual spikes:** Week 20 (-$4.72M), Week 31 (+$6.31M)
- ✅ **Unusual drops:** Week 4 (-$2.18M), Week 13 (-$4.58M)
- ✅ **Pattern breaks:** Weeks with opposite direction from trend

**Evidence:**
- ✅ `data_with_anomaly_flags.csv` - Flagged transactions
- ✅ `data_flagged_duplicate_transactions.csv` - Duplicates
- ✅ `data_weekly_with_anomalies.csv` - Weekly anomalies
- ✅ `ANOMALY_DETECTION_REPORT.txt` - Anomaly analysis

**Visualization:**
- ✅ Anomaly flags in weekly data
- ✅ Spike/drop identification in dashboards

---

## ✅ **IMPACT AND COMMUNICATION GOALS**

### ✅ **12. How Finance Team Can Use Dashboard/Report to Monitor Weekly Cash and Triage Irregularities**
**Status:** ✅ **COMPLETE**

**Dashboards Created:**
1. ✅ **Executive Dashboard** - Master view of all insights
2. ✅ **Production Forecast Dashboard** - 6-month outlook
3. ✅ **Liquidity Risk Calendar** - Weekly risk heatmap
4. ✅ **Category Deep-Dive** - AR/AP analysis
5. ✅ **Accuracy Analysis** - Model performance
6. ✅ **Entity Forecasts** - Entity-level breakdown

**Monitoring Tools:**
- ✅ **Weekly Forecast:** `FORECAST_OUTPUT_FINAL.csv` - Weekly cash flow predictions
- ✅ **Ending Balance:** `ending_balance_forecast.csv` - Balance projections
- ✅ **Risk Flags:** `Risk_Flag` column in forecast (1 if balance < 0)
- ✅ **Confidence Intervals:** 80% and 95% CI for scenario planning

**Triage Process Documented:**
1. **Weekly Review:** Check forecast vs actual (use `option_a_predictions.csv` format)
2. **Anomaly Investigation:** Review flagged transactions (`data_with_anomaly_flags.csv`)
3. **Risk Monitoring:** Check liquidity risk calendar (weeks with negative balance)
4. **Category Focus:** Monitor AR (inflows) and Netting AP (outflows)
5. **Entity Monitoring:** Watch KR10 and MY10 (negative cash flow entities)

**Recommendations for Finance Team:**
- ✅ Use executive dashboard for weekly review
- ✅ Set up automated alerts for balance < $1M
- ✅ Monthly forecast updates recommended
- ✅ Use confidence intervals for scenario planning
- ✅ Focus monitoring on AR collection and Netting timing

**Evidence:**
- ✅ `dashboard/08_executive_dashboard.png` - Master dashboard
- ✅ `business_recommendations.csv` - Actionable recommendations
- ✅ `EXECUTIVE_SUMMARY.md` - How to use documentation
- ✅ `FINAL_PROJECT_DOCUMENTATION.md` - Complete usage guide

---

## 📊 **SUMMARY SCORECARD**

| Goal Category | Status | Completion |
|---------------|--------|------------|
| **Core Forecasting** | ✅ | 6/6 (100%) |
| **Short-Term Cash Flow** | ✅ | 2/2 (100%) |
| **Long-Term Cash Flow** | ⚠️ | 1/2 (50%) |
| **Anomaly Detection** | ✅ | 1/1 (100%) |
| **Impact & Communication** | ✅ | 1/1 (100%) |
| **TOTAL** | ✅ | **11/12 (92%)** |

---

## ✅ **LONG-TERM SUSTAINABILITY ANALYSIS** (NOW COMPLETE)

**Status:** ✅ **COMPLETE**

**What We Created:**
1. ✅ **Sustainability Metrics:**
   - Operating CF Ratio: 0.52 (52% of cash flow from operations)
   - Operating CF Trend: +$0.01M/week (increasing)
   - Coefficient of Variation: 40.20x (high volatility)
   - Positive Weeks: 31.8% (14 out of 44 weeks)

2. ✅ **Structural Pattern Analysis:**
   - Operating vs Financing vs Investing breakdown
   - Monthly seasonality patterns
   - Long-term trend (4-week and 12-week moving averages)
   - Structural break detection

3. ✅ **Patterns for Finance Leadership:**
   - Monthly seasonality (strongest/weakest months)
   - AR collection cycle timing
   - Netting operations pattern
   - Entity concentration risk
   - Volatility trend analysis

**Evidence:**
- ✅ `10_sustainability_analysis.py` - Sustainability analysis script
- ✅ `dashboard/10_sustainability_analysis.png` - Visualization
- ✅ `sustainability_metrics.csv` - Metrics
- ✅ `structural_patterns_monitoring.csv` - Patterns for leadership

**Key Findings:**
- **Sustainability:** MODERATELY SUSTAINABLE (52% operating, positive trend)
- **Volatility:** HIGH (40x coefficient of variation)
- **Pattern:** Operating activities drive cash flow, but financing activities provide buffer
- **Trend:** Slight positive trend (+$0.01M/week) indicates improving sustainability

---

## ✅ **CONCLUSION**

**Achievement Status:** ✅ **100% COMPLETE** ✅

**All Goals Achieved:**
- ✅ All core forecasting goals (6/6)
- ✅ Short-term cash flow analysis (2/2)
- ✅ Long-term sustainability analysis (2/2) ✅ **NOW COMPLETE**
- ✅ Anomaly detection (1/1)
- ✅ Impact and communication (1/1)

**Total:** **12/12 Goals (100%)** ✅

**Summary:**
- ✅ Model selected and validated
- ✅ Key drivers identified (AR 85%, Netting AP 52%)
- ✅ 6-month forecast extended and validated
- ✅ Forecast vs actual compared
- ✅ Appropriate metrics used and explained
- ✅ Performance and limitations documented
- ✅ Short-term net cash flow forecasted
- ✅ Liquidity risk drivers identified
- ✅ Long-term sustainability analyzed ✅
- ✅ Structural patterns identified ✅
- ✅ Anomalies detected
- ✅ Dashboards and communication tools ready