refactor: implement i18n in ExecutiveSummary and DimensionAnalysis tabs (phase 2)
Successfully refactored two major tab components to use react-i18next: - ExecutiveSummaryTab: All metrics, benchmarks, findings, tooltips, industry names - DimensionAnalysisTab: All dimension analyses, findings, causes, recommendations Added 140+ comprehensive translation keys to es.json and en.json: - executiveSummary section: metrics, benchmarks, tooltips, percentiles - dimensionAnalysis section: findings, causes, recommendations for all 6 dimensions - industries section: all industry names - agenticReadiness section: extensive keys for future use (400+ keys) Note: AgenticReadinessTab refactoring deferred due to file complexity (3721 lines). Translation keys prepared for future implementation. Build verified successfully. https://claude.ai/code/session_4f888c33-8937-4db8-8a9d-ddc9ac51a725
This commit is contained in:
Claude
@@ -1,4 +1,5 @@
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import React from 'react';
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import { useTranslation } from 'react-i18next';
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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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@@ -42,6 +43,7 @@ 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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t: (key: string, options?: any) => string,
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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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@@ -129,28 +131,29 @@ function generateCausalAnalysis(
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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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const ahtFormatted = `${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')}`;
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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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finding: t('dimensionAnalysis.operationalEfficiency.highAHTFinding', { aht: ahtFormatted }),
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probableCause: t('dimensionAnalysis.operationalEfficiency.highAHTCause'),
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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: ${formatCurrency(copilotSavings)}/año.`,
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recommendation: t('dimensionAnalysis.operationalEfficiency.highAHTRecommendation', { savings: formatCurrency(copilotSavings) }),
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severity: p50Aht > 420 ? 'critical' : 'warning',
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hasRealData: true
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});
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} else {
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// AHT dentro de benchmark - mostrar estado positivo
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const ahtFormatted = `${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')}`;
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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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finding: t('dimensionAnalysis.operationalEfficiency.goodAHTFinding', { aht: ahtFormatted }),
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probableCause: t('dimensionAnalysis.operationalEfficiency.goodAHTCause'),
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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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impactFormula: t('dimensionAnalysis.operationalEfficiency.goodAHTImpact'),
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timeSavings: t('dimensionAnalysis.operationalEfficiency.goodAHTTimeSavings'),
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recommendation: t('dimensionAnalysis.operationalEfficiency.goodAHTRecommendation'),
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severity: 'info',
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hasRealData: true
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});
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@@ -176,30 +179,42 @@ function generateCausalAnalysis(
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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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? t('dimensionAnalysis.effectiveness.criticalCause', {
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transfer: avgTransferRate.toFixed(0),
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skills: skillsLowFCR.slice(0, 2).map(s => s.skill).join(', ')
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})
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: t('dimensionAnalysis.effectiveness.criticalCauseGeneric', { transfer: avgTransferRate.toFixed(0) });
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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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effCause = t('dimensionAnalysis.effectiveness.warningCause', { transfer: avgTransferRate.toFixed(0) });
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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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effCause = t('dimensionAnalysis.effectiveness.goodCause', { transfer: avgTransferRate.toFixed(0) });
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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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effRecommendation = t('dimensionAnalysis.effectiveness.criticalRecommendation', { savings: formatCurrency(potentialSavingsEff) });
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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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effRecommendation = t('dimensionAnalysis.effectiveness.warningRecommendation');
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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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effRecommendation = t('dimensionAnalysis.effectiveness.goodRecommendation');
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}
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analyses.push({
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finding: `FCR Técnico: ${avgFCR.toFixed(0)}% | Transferencias: ${avgTransferRate.toFixed(0)}% (benchmark: FCR >85%, Transfer <10%)`,
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finding: t('dimensionAnalysis.effectiveness.finding', {
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fcr: avgFCR.toFixed(0),
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transfer: avgTransferRate.toFixed(0)
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}),
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probableCause: effCause,
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economicImpact: transferCostTotal,
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impactFormula: `${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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impactFormula: t('dimensionAnalysis.effectiveness.impactFormula', {
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count: transferCount.toLocaleString(),
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cpi: CPI_TCO
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}),
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timeSavings: t('dimensionAnalysis.effectiveness.timeSavings', {
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count: transferCount.toLocaleString(),
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pct: avgTransferRate.toFixed(0)
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}),
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recommendation: effRecommendation,
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severity: effSeverity,
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hasRealData: true
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@@ -215,12 +230,25 @@ function generateCausalAnalysis(
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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 ${topSkillPct.toFixed(0)}% del total`,
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probableCause: `Alta concentración en un skill indica consultas repetitivas con potencial de automatización.`,
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finding: t('dimensionAnalysis.volumetry.concentrationFinding', {
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skill: topSkill.skill,
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pct: topSkillPct.toFixed(0)
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}),
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probableCause: t('dimensionAnalysis.volumetry.concentrationCause'),
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economicImpact: deflectionPotential,
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impactFormula: `${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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impactFormula: t('dimensionAnalysis.volumetry.impactFormula', {
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volume: topSkill.volume.toLocaleString(),
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cpi: CPI_TCO
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}),
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timeSavings: t('dimensionAnalysis.volumetry.timeSavings', {
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volume: annualTopSkillVolume.toLocaleString(),
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skill: topSkill.skill,
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deflectable: interactionsDeflectable.toLocaleString()
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}),
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recommendation: t('dimensionAnalysis.volumetry.concentrationRecommendation', {
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skill: topSkill.skill,
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savings: formatCurrency(deflectionPotential)
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}),
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severity: 'info',
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hasRealData: true
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});
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@@ -242,28 +270,34 @@ function generateCausalAnalysis(
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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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? t('dimensionAnalysis.complexity.highCVCauseCritical')
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: t('dimensionAnalysis.complexity.highCVCauseWarning');
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analyses.push({
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finding: `CV AHT elevado: ${avgCVAHT.toFixed(0)}% (benchmark: <${cvBenchmark}%)`,
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finding: t('dimensionAnalysis.complexity.highCVFinding', {
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cv: avgCVAHT.toFixed(0),
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benchmark: cvBenchmark
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}),
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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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impactFormula: t('dimensionAnalysis.complexity.highCVImpactFormula'),
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timeSavings: t('dimensionAnalysis.complexity.highCVTimeSavings', { hours: staffingHours.toLocaleString() }),
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recommendation: t('dimensionAnalysis.complexity.highCVRecommendation', { savings: formatCurrency(standardizationSavings) }),
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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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finding: t('dimensionAnalysis.complexity.goodCVFinding', {
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cv: avgCVAHT.toFixed(0),
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benchmark: cvBenchmark
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}),
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probableCause: t('dimensionAnalysis.complexity.goodCVCause'),
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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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impactFormula: t('dimensionAnalysis.complexity.goodCVImpactFormula'),
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timeSavings: t('dimensionAnalysis.complexity.goodCVTimeSavings'),
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recommendation: t('dimensionAnalysis.complexity.goodCVRecommendation'),
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severity: 'info',
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hasRealData: true
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});
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@@ -277,12 +311,16 @@ function generateCausalAnalysis(
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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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finding: t('dimensionAnalysis.complexity.holdTimeFinding', { holdTime: avgHoldTime.toFixed(0) }),
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probableCause: t('dimensionAnalysis.complexity.holdTimeCause'),
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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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impactFormula: t('dimensionAnalysis.complexity.holdTimeImpactFormula', {
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excess: Math.round(excessHold),
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volume: totalVolume.toLocaleString(),
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cost: HOURLY_COST
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}),
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timeSavings: t('dimensionAnalysis.complexity.holdTimeTimeSavings', { hours: excessHoldHours.toLocaleString() }),
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recommendation: t('dimensionAnalysis.complexity.holdTimeRecommendation', { savings: formatCurrency(searchCopilotSavings) }),
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severity: avgHoldTime > 60 ? 'critical' : 'warning',
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hasRealData: true
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});
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@@ -297,12 +335,12 @@ function generateCausalAnalysis(
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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: ${avgCSAT.toFixed(0)}% (benchmark: >80%)`,
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probableCause: 'Clientes insatisfechos por esperas, falta de resolución o experiencia de atención deficiente.',
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finding: t('dimensionAnalysis.satisfaction.lowCSATFinding', { csat: avgCSAT.toFixed(0) }),
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probableCause: t('dimensionAnalysis.satisfaction.lowCSATCause'),
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economicImpact: churnRisk,
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impactFormula: `${totalVolume.toLocaleString()} clientes × anualización × 2% riesgo churn × €50 valor`,
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timeSavings: `${customersAtRisk.toLocaleString()} clientes/año en riesgo de fuga`,
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recommendation: `Implementar programa VoC: encuestas post-contacto + análisis de causas raíz + acción correctiva en 48h. Objetivo: CSAT >80%.`,
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impactFormula: t('dimensionAnalysis.satisfaction.lowCSATImpactFormula', { volume: totalVolume.toLocaleString() }),
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timeSavings: t('dimensionAnalysis.satisfaction.lowCSATTimeSavings', { customers: customersAtRisk.toLocaleString() }),
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recommendation: t('dimensionAnalysis.satisfaction.lowCSATRecommendation'),
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severity: avgCSAT < 50 ? 'critical' : 'warning',
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hasRealData: true
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});
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@@ -319,12 +357,22 @@ function generateCausalAnalysis(
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const potentialSavings = Math.round(annualVolumeCpi * excessCPI);
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const excessHours = Math.round(potentialSavings / HOURLY_COST);
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analyses.push({
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finding: `CPI por encima del benchmark: €${CPI.toFixed(2)} (objetivo: €${CPI_TCO})`,
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probableCause: 'Coste por interacción elevado por AHT alto, baja ocupación o estructura de costes ineficiente.',
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finding: t('dimensionAnalysis.economy.highCPIFinding', {
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cpi: CPI.toFixed(2),
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target: CPI_TCO
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}),
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probableCause: t('dimensionAnalysis.economy.highCPICause'),
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economicImpact: potentialSavings,
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impactFormula: `${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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impactFormula: t('dimensionAnalysis.economy.highCPIImpactFormula', {
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volume: totalVolume.toLocaleString(),
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excess: excessCPI.toFixed(2)
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}),
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timeSavings: t('dimensionAnalysis.economy.highCPITimeSavings', {
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excess: excessCPI.toFixed(2),
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volume: annualVolumeCpi.toLocaleString(),
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hours: excessHours.toLocaleString()
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}),
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recommendation: t('dimensionAnalysis.economy.highCPIRecommendation', { target: 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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@@ -347,13 +395,15 @@ function DimensionCard({
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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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delay = 0,
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t
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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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t: (key: string, options?: any) => string;
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}) {
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const Icon = dimension.icon;
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@@ -365,11 +415,11 @@ function DimensionCard({
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};
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const getScoreLabel = (score: number): string => {
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if (score < 0) return 'N/A';
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if (score >= 80) return 'Óptimo';
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if (score >= 60) return 'Aceptable';
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if (score >= 40) return 'Mejorable';
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return 'Crítico';
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if (score < 0) return t('common.na');
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if (score >= 80) return t('common.optimal');
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if (score >= 60) return t('common.acceptable');
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if (score >= 40) return t('common.improvable');
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return t('common.critical');
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};
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const getSeverityConfig = (severity: string) => {
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@@ -410,13 +460,13 @@ function DimensionCard({
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</div>
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<div className="text-right">
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<Badge
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label={dimension.score >= 0 ? `${dimension.score} ${getScoreLabel(dimension.score)}` : '— N/A'}
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label={dimension.score >= 0 ? `${dimension.score} ${getScoreLabel(dimension.score)}` : `— ${t('common.na')}`}
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variant={scoreVariant}
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size="md"
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/>
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{totalImpact > 0 && (
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<p className="text-xs text-red-600 font-medium mt-1">
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Impacto: {formatCurrency(totalImpact)}
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{t('dimensionAnalysis.impact')} {formatCurrency(totalImpact)}
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</p>
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)}
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</div>
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@@ -459,7 +509,7 @@ function DimensionCard({
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<div className="p-3 bg-gray-50 rounded-lg border border-gray-200">
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<p className="text-sm text-gray-500 italic flex items-center gap-2">
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<Minus className="w-4 h-4" />
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Sin datos disponibles para esta dimensión.
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{t('dimensionAnalysis.noDataAvailable')}
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</p>
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</div>
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</div>
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@@ -469,7 +519,7 @@ function DimensionCard({
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{dimension.score >= 0 && causalAnalyses.length > 0 && (
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<div className="p-4 space-y-3">
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<h4 className="text-xs font-semibold text-gray-500 uppercase tracking-wider">
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Hallazgo Clave
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{t('dimensionAnalysis.keyFinding')}
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</h4>
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{causalAnalyses.map((analysis, idx) => {
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const config = getSeverityConfig(analysis.severity);
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||||
@@ -485,7 +535,7 @@ function DimensionCard({
|
||||
|
||||
{/* 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-500 font-medium mb-0.5">{t('dimensionAnalysis.probableCause')}</p>
|
||||
<p className="text-xs text-gray-700">{analysis.probableCause}</p>
|
||||
</div>
|
||||
|
||||
@@ -498,7 +548,7 @@ function DimensionCard({
|
||||
<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-500">{t('dimensionAnalysis.annualImpact')}</span>
|
||||
<span className="text-xs text-gray-400">i</span>
|
||||
</div>
|
||||
|
||||
@@ -527,7 +577,7 @@ function DimensionCard({
|
||||
{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
|
||||
{t('dimensionAnalysis.keyFindings')}
|
||||
</h4>
|
||||
<ul className="space-y-2">
|
||||
{findings.slice(0, 3).map((finding, idx) => (
|
||||
@@ -550,7 +600,7 @@ function DimensionCard({
|
||||
<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.
|
||||
{t('dimensionAnalysis.withinAcceptable')}
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
@@ -561,7 +611,7 @@ function DimensionCard({
|
||||
<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 font-semibold text-blue-600">{t('dimensionAnalysis.recommendation')}</span>
|
||||
<span className="text-xs text-gray-600">{recommendations[0].text}</span>
|
||||
</div>
|
||||
</div>
|
||||
@@ -574,6 +624,8 @@ function DimensionCard({
|
||||
// ========== v3.16: COMPONENTE PRINCIPAL ==========
|
||||
|
||||
export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
|
||||
const { t } = useTranslation();
|
||||
|
||||
// DEBUG: Verificar CPI en dimensión vs heatmapData
|
||||
const economyDim = data.dimensions.find(d =>
|
||||
d.id === 'economy_costs' || d.name === 'economy_costs' ||
|
||||
@@ -609,7 +661,7 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
|
||||
|
||||
// Generar hallazgo clave para cada dimensión
|
||||
const getCausalAnalysisForDimension = (dimension: DimensionAnalysis) =>
|
||||
generateCausalAnalysis(dimension, data.heatmapData, data.economicModel, data.staticConfig, data.dateRange);
|
||||
generateCausalAnalysis(dimension, data.heatmapData, data.economicModel, t, data.staticConfig, data.dateRange);
|
||||
|
||||
// Calcular impacto total de todas las dimensiones con datos
|
||||
const impactoTotal = coreDimensions
|
||||
@@ -627,10 +679,10 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
|
||||
<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>
|
||||
<h2 className="text-lg font-bold text-gray-900">{t('dimensionAnalysis.title')}</h2>
|
||||
<p className="text-sm text-gray-500">
|
||||
{coreDimensions.length} dimensiones analizadas
|
||||
{sinDatos.length > 0 && ` (${sinDatos.length} sin datos)`}
|
||||
{t('dimensionAnalysis.dimensionsAnalyzed', { count: coreDimensions.length })}
|
||||
{sinDatos.length > 0 && ` ${t('dimensionAnalysis.noData', { count: sinDatos.length })}`}
|
||||
</p>
|
||||
</div>
|
||||
|
||||
@@ -644,6 +696,7 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
|
||||
recommendations={getRecommendationsForDimension(dimension.id)}
|
||||
causalAnalyses={getCausalAnalysisForDimension(dimension)}
|
||||
delay={idx * 0.05}
|
||||
t={t}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user