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fix: Centralize CPI calculation for fresh data consistency

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- Calculate CPI once in main function from heatmapData
- Pass globalCPI to generateDimensionsFromRealData
- This ensures dimension.kpi.value matches ExecutiveSummaryTab's calculation
- Both now use identical formula: weighted avg of (cpi * cost_volume) / total_cost_volume

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
sujucu70
2026-01-23 11:32:40 +01:00
parent 728ba5772e
commit 820e8b4887
1 changed files with 22 additions and 16 deletions
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38
frontend/utils/realDataAnalysis.ts
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@@ -189,6 +189,17 @@ export function generateAnalysisFromRealData(
// Coste total
const totalCost = Math.round(skillMetrics.reduce((sum, s) => sum + s.total_cost, 0));
// === CPI CENTRALIZADO: Calcular UNA sola vez desde heatmapData ===
// Esta es la ÚNICA fuente de verdad para CPI, igual que ExecutiveSummaryTab
const totalCostVolume = heatmapData.reduce((sum, h) => sum + (h.cost_volume || h.volume), 0);
const totalAnnualCost = heatmapData.reduce((sum, h) => sum + (h.annual_cost || 0), 0);
const hasCpiField = heatmapData.some(h => h.cpi !== undefined && h.cpi > 0);
const globalCPI = hasCpiField
? (totalCostVolume > 0
? heatmapData.reduce((sum, h) => sum + (h.cpi || 0) * (h.cost_volume || h.volume), 0) / totalCostVolume
: 0)
: (totalCostVolume > 0 ? totalAnnualCost / totalCostVolume : 0);
// KPIs principales
const summaryKpis: Kpi[] = [
{ label: "Interacciones Totales", value: totalInteractions.toLocaleString('es-ES') },
@@ -196,17 +207,18 @@ export function generateAnalysisFromRealData(
{ label: "FCR Técnico", value: `${avgFCR}%` },
{ label: "CSAT", value: `${(avgCsat / 20).toFixed(1)}/5` }
];
// Health Score basado en métricas reales
const overallHealthScore = calculateHealthScore(heatmapData);
// Dimensiones (simplificadas para datos reales)
// Dimensiones (simplificadas para datos reales) - pasar CPI centralizado
const dimensions: DimensionAnalysis[] = generateDimensionsFromRealData(
interactions,
skillMetrics,
avgCsat,
avgAHT,
hourlyDistribution
hourlyDistribution,
globalCPI // CPI calculado desde heatmapData
);
// Agentic Readiness Score
@@ -1212,7 +1224,8 @@ function generateDimensionsFromRealData(
metrics: SkillMetrics[],
avgCsat: number,
avgAHT: number,
hourlyDistribution: { hourly: number[]; off_hours_pct: number; peak_hours: number[] }
hourlyDistribution: { hourly: number[]; off_hours_pct: number; peak_hours: number[] },
globalCPI: number // CPI calculado centralmente desde heatmapData
): DimensionAnalysis[] {
const totalVolume = interactions.length;
const avgCV = metrics.reduce((sum, m) => sum + m.cv_aht, 0) / metrics.length;
@@ -1270,17 +1283,10 @@ function generateDimensionsFromRealData(
volumetryScore = Math.max(0, Math.min(100, Math.round(volumetryScore)));
// === CPI: Coste por interacción (IDÉNTICO a Executive Summary) ===
// Usar cost_volume (non-abandon) como denominador
const totalCostVolume = metrics.reduce((sum, m) => sum + (m.cost_volume || m.volume), 0);
const totalAnnualCost = metrics.reduce((sum, m) => sum + (m.total_cost || 0), 0);
// Usar CPI pre-calculado si disponible, sino calcular desde total_cost / cost_volume
const hasCpiField = metrics.some(m => m.cpi !== undefined && m.cpi > 0);
const costPerInteraction = hasCpiField
? (totalCostVolume > 0
? metrics.reduce((sum, m) => sum + (m.cpi || 0) * (m.cost_volume || m.volume), 0) / totalCostVolume
: 0)
: (totalCostVolume > 0 ? totalAnnualCost / totalCostVolume : 0);
// === CPI: Usar el valor centralizado pasado como parámetro ===
// globalCPI ya fue calculado en generateAnalysisFromRealData desde heatmapData
// Esto garantiza consistencia con ExecutiveSummaryTab
const costPerInteraction = globalCPI;
// Calcular Agentic Score
const predictability = Math.max(0, Math.min(10, 10 - ((avgCV - 0.3) / 1.2 * 10)));