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intermediateAnalysis40 minutes
Diagnose Forecast Accuracy Problems
Analyze historical forecast data to identify patterns of inaccuracy and recommend fixes.
Learning Objectives
- Calculate forecast accuracy metrics
- Identify patterns in forecast errors
- Diagnose root causes
- Recommend process improvements
Data
{
"months": [
{
"month": "October",
"commit": {
"forecast": 180000,
"actual": 142000
},
"bestCase": {
"forecast": 95000,
"actual": 38000
},
"pipeline": {
"forecast": 220000,
"actual": 85000
},
"byRep": [
{
"rep": "Sarah",
"commit": 60000,
"actual": 58000
},
{
"rep": "Mike",
"commit": 45000,
"actual": 42000
},
{
"rep": "Jessica",
"commit": 40000,
"actual": 28000
},
{
"rep": "Tom",
"commit": 35000,
"actual": 14000
}
]
},
{
"month": "November",
"commit": {
"forecast": 195000,
"actual": 156000
},
"bestCase": {
"forecast": 110000,
"actual": 42000
},
"pipeline": {
"forecast": 240000,
"actual": 78000
},
"byRep": [
{
"rep": "Sarah",
"commit": 65000,
"actual": 62000
},
{
"rep": "Mike",
"commit": 50000,
"actual": 48000
},
{
"rep": "Jessica",
"commit": 45000,
"actual": 30000
},
{
"rep": "Tom",
"commit": 35000,
"actual": 16000
}
]
},
{
"month": "December",
"commit": {
"forecast": 210000,
"actual": 168000
},
"bestCase": {
"forecast": 125000,
"actual": 35000
},
"pipeline": {
"forecast": 260000,
"actual": 92000
},
"byRep": [
{
"rep": "Sarah",
"commit": 70000,
"actual": 68000
},
{
"rep": "Mike",
"commit": 55000,
"actual": 52000
},
{
"rep": "Jessica",
"commit": 50000,
"actual": 32000
},
{
"rep": "Tom",
"commit": 35000,
"actual": 16000
}
]
}
]
}Instructions
- 1Review the 6-month forecast vs. actual data
- 2Calculate accuracy for each category
- 3Identify which categories are most problematic
- 4Analyze rep-level patterns
- 5Diagnose likely root causes
- 6Recommend 3-5 specific process changes
Deliverables
- Accuracy analysis by forecast category
- Rep-level accuracy breakdown
- Root cause analysis document
- Process improvement recommendations
Evaluation Criteria
- Calculations are accurate
- Patterns are correctly identified
- Root causes are plausible and specific
- Recommendations address identified causes
Hints
- Commit should be 85%+ accurate, Best Case much lower
- Look for consistent over/under forecasting by rep
- Pipeline accuracy issues often indicate stage definition problems