a48aca0a26
Logs CPI values in both ExecutiveSummaryTab and DimensionAnalysisTab to identify where the mismatch occurs. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
655 lines
30 KiB
TypeScript
655 lines
30 KiB
TypeScript
import React from 'react';
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import { motion } from 'framer-motion';
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import { ChevronRight, TrendingUp, TrendingDown, Minus, AlertTriangle, Lightbulb, DollarSign, Clock } from 'lucide-react';
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import type { AnalysisData, DimensionAnalysis, Finding, Recommendation, HeatmapDataPoint } from '../../types';
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import {
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Card,
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Badge,
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} from '../ui';
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import {
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cn,
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COLORS,
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STATUS_CLASSES,
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getStatusFromScore,
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formatCurrency,
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formatNumber,
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formatPercent,
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} from '../../config/designSystem';
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interface DimensionAnalysisTabProps {
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data: AnalysisData;
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}
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// ========== HALLAZGO CLAVE CON IMPACTO ECONÓMICO ==========
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interface CausalAnalysis {
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finding: string;
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probableCause: string;
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economicImpact: number;
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recommendation: string;
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severity: 'critical' | 'warning' | 'info';
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}
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// v3.11: Interfaz extendida para incluir fórmula de cálculo
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interface CausalAnalysisExtended extends CausalAnalysis {
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impactFormula?: string; // Explicación de cómo se calculó el impacto
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hasRealData: boolean; // True si hay datos reales para calcular
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timeSavings?: string; // Ahorro de tiempo para dar credibilidad al impacto económico
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}
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// Genera hallazgo clave basado en dimensión y datos
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function generateCausalAnalysis(
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dimension: DimensionAnalysis,
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heatmapData: HeatmapDataPoint[],
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economicModel: { currentAnnualCost: number },
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staticConfig?: { cost_per_hour: number },
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dateRange?: { min: string; max: string }
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): CausalAnalysisExtended[] {
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const analyses: CausalAnalysisExtended[] = [];
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const totalVolume = heatmapData.reduce((sum, h) => sum + h.volume, 0);
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// Coste horario del agente desde config (default €20 si no está definido)
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const HOURLY_COST = staticConfig?.cost_per_hour ?? 20;
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// Calcular factor de anualización basado en el período de datos
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// Si tenemos dateRange, calculamos cuántos días cubre y extrapolamos a año
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let annualizationFactor = 1; // Por defecto, asumimos que los datos ya son anuales
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if (dateRange?.min && dateRange?.max) {
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const startDate = new Date(dateRange.min);
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const endDate = new Date(dateRange.max);
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const daysCovered = Math.max(1, Math.ceil((endDate.getTime() - startDate.getTime()) / (1000 * 60 * 60 * 24)) + 1);
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annualizationFactor = 365 / daysCovered;
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}
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// v3.11: CPI consistente con Executive Summary
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const CPI_TCO = 2.33; // Benchmark para cálculos de impacto cuando no hay CPI real
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// Usar CPI pre-calculado de heatmapData si existe, sino calcular desde annual_cost/cost_volume
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// IMPORTANTE: Mismo cálculo que ExecutiveSummaryTab para consistencia
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const totalCostVolume = heatmapData.reduce((sum, h) => sum + (h.cost_volume || h.volume), 0);
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const totalAnnualCost = heatmapData.reduce((sum, h) => sum + (h.annual_cost || 0), 0);
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const hasCpiField = heatmapData.some(h => h.cpi !== undefined && h.cpi > 0);
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const CPI = hasCpiField
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? (totalCostVolume > 0
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? heatmapData.reduce((sum, h) => sum + (h.cpi || 0) * (h.cost_volume || h.volume), 0) / totalCostVolume
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: 0)
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: (totalCostVolume > 0 ? totalAnnualCost / totalCostVolume : 0);
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// Calcular métricas agregadas
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const avgCVAHT = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + (h.variability?.cv_aht || 0) * h.volume, 0) / totalVolume
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: 0;
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const avgTransferRate = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + h.metrics.transfer_rate * h.volume, 0) / totalVolume
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: 0;
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// Usar FCR Técnico (100 - transfer_rate) en lugar de FCR Real (con filtro recontacto 7d)
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// FCR Técnico es más comparable con benchmarks de industria
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const avgFCR = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + (h.metrics.fcr_tecnico ?? (100 - h.metrics.transfer_rate)) * h.volume, 0) / totalVolume
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: 0;
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const avgAHT = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + h.aht_seconds * h.volume, 0) / totalVolume
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: 0;
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const avgCSAT = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + (h.metrics?.csat || 0) * h.volume, 0) / totalVolume
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: 0;
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const avgHoldTime = totalVolume > 0
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? heatmapData.reduce((sum, h) => sum + (h.metrics?.hold_time || 0) * h.volume, 0) / totalVolume
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: 0;
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// Skills con problemas específicos
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const skillsHighCV = heatmapData.filter(h => (h.variability?.cv_aht || 0) > 100);
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// Usar FCR Técnico para identificar skills con bajo FCR
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const skillsLowFCR = heatmapData.filter(h => (h.metrics.fcr_tecnico ?? (100 - h.metrics.transfer_rate)) < 50);
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const skillsHighTransfer = heatmapData.filter(h => h.metrics.transfer_rate > 20);
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// Parsear P50 AHT del KPI del header para consistencia visual
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// El KPI puede ser "345s (P50)" o similar
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const parseKpiAhtSeconds = (kpiValue: string): number | null => {
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const match = kpiValue.match(/(\d+)s/);
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return match ? parseInt(match[1], 10) : null;
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};
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switch (dimension.name) {
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case 'operational_efficiency':
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// Obtener P50 AHT del header para mostrar valor consistente
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const p50Aht = parseKpiAhtSeconds(dimension.kpi.value) ?? avgAHT;
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// Eficiencia Operativa: enfocada en AHT (valor absoluto)
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// CV AHT se analiza en Complejidad & Predictibilidad (best practice)
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const hasHighAHT = p50Aht > 300; // 5:00 benchmark
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const ahtBenchmark = 300; // 5:00 objetivo
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if (hasHighAHT) {
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// Calcular impacto económico por AHT excesivo
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const excessSeconds = p50Aht - ahtBenchmark;
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const annualVolume = Math.round(totalVolume * annualizationFactor);
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const excessHours = Math.round((excessSeconds / 3600) * annualVolume);
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const ahtExcessCost = Math.round(excessHours * HOURLY_COST);
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// Estimar ahorro con solución Copilot (25-30% reducción AHT)
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const copilotSavings = Math.round(ahtExcessCost * 0.28);
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// Causa basada en AHT elevado
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const cause = 'Agentes dedican tiempo excesivo a búsqueda manual de información, navegación entre sistemas y tareas repetitivas.';
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analyses.push({
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finding: `AHT elevado: P50 ${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')} (benchmark: 5:00)`,
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probableCause: cause,
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economicImpact: ahtExcessCost,
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impactFormula: `${excessHours.toLocaleString()}h × €${HOURLY_COST}/h`,
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timeSavings: `${excessHours.toLocaleString()} horas/año en exceso de AHT`,
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recommendation: `Desplegar Copilot IA para agentes: (1) Auto-búsqueda en KB; (2) Sugerencias contextuales en tiempo real; (3) Scripts guiados para casos frecuentes. Reducción esperada: 20-30% AHT. Ahorro: ${formatCurrency(copilotSavings)}/año.`,
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severity: p50Aht > 420 ? 'critical' : 'warning',
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hasRealData: true
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});
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} else {
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// AHT dentro de benchmark - mostrar estado positivo
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analyses.push({
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finding: `AHT dentro de benchmark: P50 ${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')} (benchmark: 5:00)`,
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probableCause: 'Tiempos de gestión eficientes. Procesos operativos optimizados.',
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economicImpact: 0,
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impactFormula: 'Sin exceso de coste por AHT',
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timeSavings: 'Operación eficiente',
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recommendation: 'Mantener nivel actual. Considerar Copilot para mejora continua y reducción adicional de tiempos en casos complejos.',
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severity: 'info',
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hasRealData: true
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});
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}
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break;
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case 'effectiveness_resolution':
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// Análisis principal: FCR Técnico y tasa de transferencias
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const annualVolumeEff = Math.round(totalVolume * annualizationFactor);
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const transferCount = Math.round(annualVolumeEff * (avgTransferRate / 100));
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// Calcular impacto económico de transferencias
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const transferCostTotal = Math.round(transferCount * CPI_TCO * 0.5);
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// Potencial de mejora con IA
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const improvementPotential = avgFCR < 90 ? Math.round((90 - avgFCR) / 100 * annualVolumeEff) : 0;
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const potentialSavingsEff = Math.round(improvementPotential * CPI_TCO * 0.3);
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// Determinar severidad basada en FCR
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const effSeverity = avgFCR < 70 ? 'critical' : avgFCR < 85 ? 'warning' : 'info';
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// Construir causa basada en datos
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let effCause = '';
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if (avgFCR < 70) {
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effCause = skillsLowFCR.length > 0
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? `Alta tasa de transferencias (${avgTransferRate.toFixed(0)}%) indica falta de herramientas o autoridad. Crítico en ${skillsLowFCR.slice(0, 2).map(s => s.skill).join(', ')}.`
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: `Transferencias elevadas (${avgTransferRate.toFixed(0)}%): agentes sin información contextual o sin autoridad para resolver.`;
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} else if (avgFCR < 85) {
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effCause = `Transferencias del ${avgTransferRate.toFixed(0)}% indican oportunidad de mejora con asistencia IA para casos complejos.`;
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} else {
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effCause = `FCR Técnico en nivel óptimo. Transferencias del ${avgTransferRate.toFixed(0)}% principalmente en casos que requieren escalación legítima.`;
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}
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// Construir recomendación
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let effRecommendation = '';
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if (avgFCR < 70) {
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effRecommendation = `Desplegar Knowledge Copilot con búsqueda inteligente en KB + Guided Resolution Copilot para casos complejos. Objetivo: FCR >85%. Potencial ahorro: ${formatCurrency(potentialSavingsEff)}/año.`;
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} else if (avgFCR < 85) {
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effRecommendation = `Implementar Copilot de asistencia en tiempo real: sugerencias contextuales + conexión con expertos virtuales para reducir transferencias. Objetivo: FCR >90%.`;
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} else {
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effRecommendation = `Mantener nivel actual. Considerar IA para análisis de transferencias legítimas y optimización de enrutamiento predictivo.`;
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}
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analyses.push({
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finding: `FCR Técnico: ${avgFCR.toFixed(0)}% | Transferencias: ${avgTransferRate.toFixed(0)}% (benchmark: FCR >85%, Transfer <10%)`,
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probableCause: effCause,
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economicImpact: transferCostTotal,
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impactFormula: `${transferCount.toLocaleString()} transferencias/año × €${CPI_TCO}/int × 50% coste adicional`,
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timeSavings: `${transferCount.toLocaleString()} transferencias/año (${avgTransferRate.toFixed(0)}% del volumen)`,
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recommendation: effRecommendation,
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severity: effSeverity,
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hasRealData: true
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});
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break;
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case 'volumetry_distribution':
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// Análisis de concentración de volumen
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const topSkill = [...heatmapData].sort((a, b) => b.volume - a.volume)[0];
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const topSkillPct = topSkill ? (topSkill.volume / totalVolume) * 100 : 0;
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if (topSkillPct > 40 && topSkill) {
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const annualTopSkillVolume = Math.round(topSkill.volume * annualizationFactor);
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const deflectionPotential = Math.round(annualTopSkillVolume * CPI_TCO * 0.20);
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const interactionsDeflectable = Math.round(annualTopSkillVolume * 0.20);
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analyses.push({
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finding: `Concentración de volumen: ${topSkill.skill} representa ${topSkillPct.toFixed(0)}% del total`,
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probableCause: `Alta concentración en un skill indica consultas repetitivas con potencial de automatización.`,
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economicImpact: deflectionPotential,
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impactFormula: `${topSkill.volume.toLocaleString()} int × anualización × €${CPI_TCO} × 20% deflexión potencial`,
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timeSavings: `${annualTopSkillVolume.toLocaleString()} interacciones/año en ${topSkill.skill} (${interactionsDeflectable.toLocaleString()} automatizables)`,
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recommendation: `Analizar tipologías de ${topSkill.skill} para deflexión a autoservicio o agente virtual. Potencial: ${formatCurrency(deflectionPotential)}/año.`,
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severity: 'info',
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hasRealData: true
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});
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}
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break;
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case 'complexity_predictability':
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// KPI principal: CV AHT (predictability metric per industry standards)
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// Siempre mostrar análisis de CV AHT ya que es el KPI de esta dimensión
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const cvBenchmark = 75; // Best practice: CV AHT < 75%
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if (avgCVAHT > cvBenchmark) {
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const staffingCost = Math.round(economicModel.currentAnnualCost * 0.03);
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const staffingHours = Math.round(staffingCost / HOURLY_COST);
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const standardizationSavings = Math.round(staffingCost * 0.50);
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// Determinar severidad basada en CV AHT
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const cvSeverity = avgCVAHT > 125 ? 'critical' : avgCVAHT > 100 ? 'warning' : 'warning';
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// Causa dinámica basada en nivel de variabilidad
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const cvCause = avgCVAHT > 125
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? 'Dispersión extrema en tiempos de atención impide planificación efectiva de recursos. Probable falta de scripts o procesos estandarizados.'
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: 'Variabilidad moderada en tiempos indica oportunidad de estandarización para mejorar planificación WFM.';
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analyses.push({
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finding: `CV AHT elevado: ${avgCVAHT.toFixed(0)}% (benchmark: <${cvBenchmark}%)`,
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probableCause: cvCause,
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economicImpact: staffingCost,
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impactFormula: `~3% del coste operativo por ineficiencia de staffing`,
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timeSavings: `~${staffingHours.toLocaleString()} horas/año en sobre/subdimensionamiento`,
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recommendation: `Implementar scripts guiados por IA que estandaricen la atención. Reducción esperada: -50% variabilidad. Ahorro: ${formatCurrency(standardizationSavings)}/año.`,
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severity: cvSeverity,
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hasRealData: true
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});
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} else {
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// CV AHT dentro de benchmark - mostrar estado positivo
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analyses.push({
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finding: `CV AHT dentro de benchmark: ${avgCVAHT.toFixed(0)}% (benchmark: <${cvBenchmark}%)`,
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probableCause: 'Tiempos de atención consistentes. Buena estandarización de procesos.',
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economicImpact: 0,
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impactFormula: 'Sin impacto por variabilidad',
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timeSavings: 'Planificación WFM eficiente',
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recommendation: 'Mantener nivel actual. Analizar casos atípicos para identificar oportunidades de mejora continua.',
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severity: 'info',
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hasRealData: true
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});
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}
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// Análisis secundario: Hold Time (proxy de complejidad)
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if (avgHoldTime > 45) {
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const excessHold = avgHoldTime - 30;
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const annualVolumeHold = Math.round(totalVolume * annualizationFactor);
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const excessHoldHours = Math.round((excessHold / 3600) * annualVolumeHold);
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const holdCost = Math.round(excessHoldHours * HOURLY_COST);
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const searchCopilotSavings = Math.round(holdCost * 0.60);
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analyses.push({
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finding: `Hold time elevado: ${avgHoldTime.toFixed(0)}s promedio (benchmark: <30s)`,
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probableCause: 'Agentes ponen cliente en espera para buscar información. Sistemas no presentan datos de forma contextual.',
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economicImpact: holdCost,
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impactFormula: `Exceso ${Math.round(excessHold)}s × ${totalVolume.toLocaleString()} int × anualización × €${HOURLY_COST}/h`,
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timeSavings: `${excessHoldHours.toLocaleString()} horas/año de cliente en espera`,
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recommendation: `Desplegar vista 360° con contexto automático: historial, productos y acciones sugeridas visibles al contestar. Reducción esperada: -60% hold time. Ahorro: ${formatCurrency(searchCopilotSavings)}/año.`,
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severity: avgHoldTime > 60 ? 'critical' : 'warning',
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hasRealData: true
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});
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}
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break;
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case 'customer_satisfaction':
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// Solo generar análisis si hay datos de CSAT reales
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if (avgCSAT > 0) {
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if (avgCSAT < 70) {
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const annualVolumeCsat = Math.round(totalVolume * annualizationFactor);
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const customersAtRisk = Math.round(annualVolumeCsat * 0.02);
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const churnRisk = Math.round(customersAtRisk * 50);
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analyses.push({
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finding: `CSAT por debajo del objetivo: ${avgCSAT.toFixed(0)}% (benchmark: >80%)`,
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probableCause: 'Clientes insatisfechos por esperas, falta de resolución o experiencia de atención deficiente.',
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economicImpact: churnRisk,
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impactFormula: `${totalVolume.toLocaleString()} clientes × anualización × 2% riesgo churn × €50 valor`,
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timeSavings: `${customersAtRisk.toLocaleString()} clientes/año en riesgo de fuga`,
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recommendation: `Implementar programa VoC: encuestas post-contacto + análisis de causas raíz + acción correctiva en 48h. Objetivo: CSAT >80%.`,
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severity: avgCSAT < 50 ? 'critical' : 'warning',
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hasRealData: true
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});
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}
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}
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break;
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case 'economy_cpi':
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case 'economy_costs': // También manejar el ID del backend
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// Análisis de CPI
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if (CPI > 3.5) {
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const excessCPI = CPI - CPI_TCO;
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const annualVolumeCpi = Math.round(totalVolume * annualizationFactor);
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const potentialSavings = Math.round(annualVolumeCpi * excessCPI);
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const excessHours = Math.round(potentialSavings / HOURLY_COST);
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analyses.push({
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finding: `CPI por encima del benchmark: €${CPI.toFixed(2)} (objetivo: €${CPI_TCO})`,
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probableCause: 'Coste por interacción elevado por AHT alto, baja ocupación o estructura de costes ineficiente.',
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economicImpact: potentialSavings,
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impactFormula: `${totalVolume.toLocaleString()} int × anualización × €${excessCPI.toFixed(2)} exceso CPI`,
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timeSavings: `€${excessCPI.toFixed(2)} exceso/int × ${annualVolumeCpi.toLocaleString()} int = ${excessHours.toLocaleString()}h equivalentes`,
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recommendation: `Optimizar mix de canales + reducir AHT con automatización + revisar modelo de staffing. Objetivo: CPI <€${CPI_TCO}.`,
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severity: CPI > 5 ? 'critical' : 'warning',
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hasRealData: true
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});
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}
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break;
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}
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// v3.11: NO generar fallback con impacto económico falso
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// Si no hay análisis específico, simplemente retornar array vacío
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// La UI mostrará "Sin hallazgos críticos" en lugar de un impacto inventado
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return analyses;
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}
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// Formateador de moneda (usa la función importada de designSystem)
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// v3.15: Dimension Card Component - con diseño McKinsey
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function DimensionCard({
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dimension,
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findings,
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recommendations,
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causalAnalyses,
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delay = 0
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}: {
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dimension: DimensionAnalysis;
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findings: Finding[];
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recommendations: Recommendation[];
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causalAnalyses: CausalAnalysisExtended[];
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delay?: number;
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}) {
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const Icon = dimension.icon;
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const getScoreVariant = (score: number): 'success' | 'warning' | 'critical' | 'default' => {
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if (score < 0) return 'default'; // N/A
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if (score >= 70) return 'success';
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if (score >= 40) return 'warning';
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return 'critical';
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};
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const getScoreLabel = (score: number): string => {
|
||
if (score < 0) return 'N/A';
|
||
if (score >= 80) return 'Óptimo';
|
||
if (score >= 60) return 'Aceptable';
|
||
if (score >= 40) return 'Mejorable';
|
||
return 'Crítico';
|
||
};
|
||
|
||
const getSeverityConfig = (severity: string) => {
|
||
if (severity === 'critical') return STATUS_CLASSES.critical;
|
||
if (severity === 'warning') return STATUS_CLASSES.warning;
|
||
return STATUS_CLASSES.info;
|
||
};
|
||
|
||
// Get KPI trend icon
|
||
const TrendIcon = dimension.kpi.changeType === 'positive' ? TrendingUp :
|
||
dimension.kpi.changeType === 'negative' ? TrendingDown : Minus;
|
||
|
||
const trendColor = dimension.kpi.changeType === 'positive' ? 'text-emerald-600' :
|
||
dimension.kpi.changeType === 'negative' ? 'text-red-600' : 'text-gray-500';
|
||
|
||
// Calcular impacto total de esta dimensión
|
||
const totalImpact = causalAnalyses.reduce((sum, a) => sum + a.economicImpact, 0);
|
||
const scoreVariant = getScoreVariant(dimension.score);
|
||
|
||
return (
|
||
<motion.div
|
||
initial={{ opacity: 0, y: 20 }}
|
||
animate={{ opacity: 1, y: 0 }}
|
||
transition={{ duration: 0.3, delay }}
|
||
className="bg-white rounded-lg border border-gray-200 overflow-hidden"
|
||
>
|
||
{/* Header */}
|
||
<div className="p-4 border-b border-gray-100 bg-gradient-to-r from-gray-50 to-white">
|
||
<div className="flex items-start justify-between">
|
||
<div className="flex items-center gap-3">
|
||
<div className="p-2 rounded-lg bg-blue-50">
|
||
<Icon className="w-5 h-5 text-blue-600" />
|
||
</div>
|
||
<div>
|
||
<h3 className="font-semibold text-gray-900">{dimension.title}</h3>
|
||
<p className="text-xs text-gray-500 mt-0.5 max-w-xs">{dimension.summary}</p>
|
||
</div>
|
||
</div>
|
||
<div className="text-right">
|
||
<Badge
|
||
label={dimension.score >= 0 ? `${dimension.score} ${getScoreLabel(dimension.score)}` : '— N/A'}
|
||
variant={scoreVariant}
|
||
size="md"
|
||
/>
|
||
{totalImpact > 0 && (
|
||
<p className="text-xs text-red-600 font-medium mt-1">
|
||
Impacto: {formatCurrency(totalImpact)}
|
||
</p>
|
||
)}
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* KPI Highlight */}
|
||
<div className="px-4 py-3 bg-gray-50/50 border-b border-gray-100">
|
||
<div className="flex items-center justify-between">
|
||
<span className="text-sm text-gray-600">{dimension.kpi.label}</span>
|
||
<div className="flex items-center gap-2">
|
||
<span className="font-bold text-gray-900">{dimension.kpi.value}</span>
|
||
{dimension.kpi.change && (
|
||
<div className={cn('flex items-center gap-1 text-xs', trendColor)}>
|
||
<TrendIcon className="w-3 h-3" />
|
||
<span>{dimension.kpi.change}</span>
|
||
</div>
|
||
)}
|
||
</div>
|
||
</div>
|
||
{dimension.percentile && (
|
||
<div className="mt-2">
|
||
<div className="flex items-center justify-between text-xs text-gray-500 mb-1">
|
||
<span>Percentil</span>
|
||
<span>P{dimension.percentile}</span>
|
||
</div>
|
||
<div className="h-1.5 bg-gray-200 rounded-full overflow-hidden">
|
||
<div
|
||
className="h-full bg-blue-600 rounded-full"
|
||
style={{ width: `${dimension.percentile}%` }}
|
||
/>
|
||
</div>
|
||
</div>
|
||
)}
|
||
</div>
|
||
|
||
{/* Si no hay datos para esta dimensión (score < 0 = N/A) */}
|
||
{dimension.score < 0 && (
|
||
<div className="p-4">
|
||
<div className="p-3 bg-gray-50 rounded-lg border border-gray-200">
|
||
<p className="text-sm text-gray-500 italic flex items-center gap-2">
|
||
<Minus className="w-4 h-4" />
|
||
Sin datos disponibles para esta dimensión.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
)}
|
||
|
||
{/* Hallazgo Clave - Solo si hay datos */}
|
||
{dimension.score >= 0 && causalAnalyses.length > 0 && (
|
||
<div className="p-4 space-y-3">
|
||
<h4 className="text-xs font-semibold text-gray-500 uppercase tracking-wider">
|
||
Hallazgo Clave
|
||
</h4>
|
||
{causalAnalyses.map((analysis, idx) => {
|
||
const config = getSeverityConfig(analysis.severity);
|
||
return (
|
||
<div key={idx} className={cn('p-3 rounded-lg border', config.bg, config.border)}>
|
||
{/* Hallazgo */}
|
||
<div className="flex items-start gap-2 mb-2">
|
||
<AlertTriangle className={cn('w-4 h-4 mt-0.5 flex-shrink-0', config.text)} />
|
||
<div>
|
||
<p className={cn('text-sm font-medium', config.text)}>{analysis.finding}</p>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Causa probable */}
|
||
<div className="ml-6 mb-2">
|
||
<p className="text-xs text-gray-500 font-medium mb-0.5">Causa probable:</p>
|
||
<p className="text-xs text-gray-700">{analysis.probableCause}</p>
|
||
</div>
|
||
|
||
{/* Impacto económico */}
|
||
<div
|
||
className="ml-6 mb-2 flex items-center gap-2 cursor-help"
|
||
title={analysis.impactFormula || 'Impacto estimado basado en métricas operativas'}
|
||
>
|
||
<DollarSign className="w-3 h-3 text-red-500" />
|
||
<span className="text-xs font-bold text-red-600">
|
||
{formatCurrency(analysis.economicImpact)}
|
||
</span>
|
||
<span className="text-xs text-gray-500">impacto anual (coste del problema)</span>
|
||
<span className="text-xs text-gray-400">i</span>
|
||
</div>
|
||
|
||
{/* Ahorro de tiempo - da credibilidad al cálculo económico */}
|
||
{analysis.timeSavings && (
|
||
<div className="ml-6 mb-2 flex items-center gap-2">
|
||
<Clock className="w-3 h-3 text-blue-500" />
|
||
<span className="text-xs text-blue-700">{analysis.timeSavings}</span>
|
||
</div>
|
||
)}
|
||
|
||
{/* Recomendación inline */}
|
||
<div className="ml-6 p-2 bg-white rounded border border-gray-200">
|
||
<div className="flex items-start gap-2">
|
||
<Lightbulb className="w-3 h-3 text-blue-500 mt-0.5 flex-shrink-0" />
|
||
<p className="text-xs text-gray-600">{analysis.recommendation}</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
);
|
||
})}
|
||
</div>
|
||
)}
|
||
|
||
{/* Fallback: Hallazgos originales si no hay hallazgo clave - Solo si hay datos */}
|
||
{dimension.score >= 0 && causalAnalyses.length === 0 && findings.length > 0 && (
|
||
<div className="p-4">
|
||
<h4 className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-2">
|
||
Hallazgos Clave
|
||
</h4>
|
||
<ul className="space-y-2">
|
||
{findings.slice(0, 3).map((finding, idx) => (
|
||
<li key={idx} className="flex items-start gap-2 text-sm">
|
||
<ChevronRight className={cn('w-4 h-4 mt-0.5 flex-shrink-0',
|
||
finding.type === 'critical' ? 'text-red-500' :
|
||
finding.type === 'warning' ? 'text-amber-500' :
|
||
'text-blue-600'
|
||
)} />
|
||
<span className="text-gray-700">{finding.text}</span>
|
||
</li>
|
||
))}
|
||
</ul>
|
||
</div>
|
||
)}
|
||
|
||
{/* Si no hay análisis ni hallazgos pero sí hay datos */}
|
||
{dimension.score >= 0 && causalAnalyses.length === 0 && findings.length === 0 && (
|
||
<div className="p-4">
|
||
<div className={cn('p-3 rounded-lg border', STATUS_CLASSES.success.bg, STATUS_CLASSES.success.border)}>
|
||
<p className={cn('text-sm flex items-center gap-2', STATUS_CLASSES.success.text)}>
|
||
<ChevronRight className="w-4 h-4" />
|
||
Métricas dentro de rangos aceptables. Sin hallazgos críticos.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
)}
|
||
|
||
{/* Recommendations Preview - Solo si no hay hallazgo clave y hay datos */}
|
||
{dimension.score >= 0 && causalAnalyses.length === 0 && recommendations.length > 0 && (
|
||
<div className="px-4 pb-4">
|
||
<div className="p-3 bg-blue-50 rounded-lg border border-blue-100">
|
||
<div className="flex items-start gap-2">
|
||
<span className="text-xs font-semibold text-blue-600">Recomendación:</span>
|
||
<span className="text-xs text-gray-600">{recommendations[0].text}</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
)}
|
||
</motion.div>
|
||
);
|
||
}
|
||
|
||
// ========== v3.16: COMPONENTE PRINCIPAL ==========
|
||
|
||
export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
|
||
// DEBUG: Verificar CPI en dimensión vs heatmapData
|
||
const economyDim = data.dimensions.find(d =>
|
||
d.id === 'economy_costs' || d.name === 'economy_costs' ||
|
||
d.id === 'economy_cpi' || d.name === 'economy_cpi'
|
||
);
|
||
const heatmapData = data.heatmapData;
|
||
const totalCostVolume = heatmapData.reduce((sum, h) => sum + (h.cost_volume || h.volume), 0);
|
||
const hasCpiField = heatmapData.some(h => h.cpi !== undefined && h.cpi > 0);
|
||
const calculatedCPI = hasCpiField
|
||
? (totalCostVolume > 0
|
||
? heatmapData.reduce((sum, h) => sum + (h.cpi || 0) * (h.cost_volume || h.volume), 0) / totalCostVolume
|
||
: 0)
|
||
: (totalCostVolume > 0
|
||
? heatmapData.reduce((sum, h) => sum + (h.annual_cost || 0), 0) / totalCostVolume
|
||
: 0);
|
||
|
||
console.log('🔍 DimensionAnalysisTab DEBUG:');
|
||
console.log(' - economyDim found:', !!economyDim, economyDim?.id || economyDim?.name);
|
||
console.log(' - economyDim.kpi.value:', economyDim?.kpi?.value);
|
||
console.log(' - calculatedCPI from heatmapData:', `€${calculatedCPI.toFixed(2)}`);
|
||
console.log(' - hasCpiField:', hasCpiField);
|
||
console.log(' - MATCH:', economyDim?.kpi?.value === `€${calculatedCPI.toFixed(2)}`);
|
||
|
||
// Filter out agentic_readiness (has its own tab)
|
||
const coreDimensions = data.dimensions.filter(d => d.name !== 'agentic_readiness');
|
||
|
||
// Group findings and recommendations by dimension
|
||
const getFindingsForDimension = (dimensionId: string) =>
|
||
data.findings.filter(f => f.dimensionId === dimensionId);
|
||
|
||
const getRecommendationsForDimension = (dimensionId: string) =>
|
||
data.recommendations.filter(r => r.dimensionId === dimensionId);
|
||
|
||
// Generar hallazgo clave para cada dimensión
|
||
const getCausalAnalysisForDimension = (dimension: DimensionAnalysis) =>
|
||
generateCausalAnalysis(dimension, data.heatmapData, data.economicModel, data.staticConfig, data.dateRange);
|
||
|
||
// Calcular impacto total de todas las dimensiones con datos
|
||
const impactoTotal = coreDimensions
|
||
.filter(d => d.score !== null && d.score !== undefined)
|
||
.reduce((total, dimension) => {
|
||
const analyses = getCausalAnalysisForDimension(dimension);
|
||
return total + analyses.reduce((sum, a) => sum + a.economicImpact, 0);
|
||
}, 0);
|
||
|
||
// v3.16: Contar dimensiones por estado para el header
|
||
const conDatos = coreDimensions.filter(d => d.score !== null && d.score !== undefined && d.score >= 0);
|
||
const sinDatos = coreDimensions.filter(d => d.score === null || d.score === undefined || d.score < 0);
|
||
|
||
return (
|
||
<div className="space-y-6">
|
||
{/* v3.16: Header simplificado - solo título y subtítulo */}
|
||
<div className="mb-2">
|
||
<h2 className="text-lg font-bold text-gray-900">Diagnóstico por Dimensión</h2>
|
||
<p className="text-sm text-gray-500">
|
||
{coreDimensions.length} dimensiones analizadas
|
||
{sinDatos.length > 0 && ` (${sinDatos.length} sin datos)`}
|
||
</p>
|
||
</div>
|
||
|
||
{/* v3.16: Grid simple con todas las dimensiones sin agrupación */}
|
||
<div className="grid grid-cols-1 lg:grid-cols-2 gap-4">
|
||
{coreDimensions.map((dimension, idx) => (
|
||
<DimensionCard
|
||
key={dimension.id}
|
||
dimension={dimension}
|
||
findings={getFindingsForDimension(dimension.id)}
|
||
recommendations={getRecommendationsForDimension(dimension.id)}
|
||
causalAnalyses={getCausalAnalysisForDimension(dimension)}
|
||
delay={idx * 0.05}
|
||
/>
|
||
))}
|
||
</div>
|
||
</div>
|
||
);
|
||
}
|
||
|
||
export default DimensionAnalysisTab;
|