feat: Add Law 10/2025 compliance analysis tab
- Add new Law10Tab with compliance analysis for Spanish Law 10/2025 - Sections: LAW-01 (Response Speed), LAW-02 (Resolution Quality), LAW-07 (Time Coverage) - Add Data Maturity Summary showing available/estimable/missing data - Add Validation Questionnaire for manual data input - Add Dimension Connections linking to other analysis tabs - Fix KPI consistency: use correct field names (abandonment_rate, aht_seconds) - Fix cache directory path for Windows compatibility - Update economic calculations to use actual economicModel data Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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sujucu70
@@ -99,6 +99,15 @@ class EconomyCostMetrics:
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+ df["wrap_up_time"].fillna(0)
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) # segundos
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# Filtrar por record_status para cálculos de AHT/CPI
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# Solo incluir registros VALID (excluir NOISE, ZOMBIE, ABANDON)
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if "record_status" in df.columns:
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df["record_status"] = df["record_status"].astype(str).str.strip().str.upper()
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df["_is_valid_for_cost"] = df["record_status"] == "VALID"
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else:
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# Legacy data sin record_status: incluir todo
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df["_is_valid_for_cost"] = True
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self.df = df
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@property
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@@ -115,12 +124,19 @@ class EconomyCostMetrics:
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"""
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CPI (Coste Por Interacción) por skill/canal.
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CPI = Labor_cost_per_interaction + Overhead_variable
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CPI = (Labor_cost_per_interaction + Overhead_variable) / EFFECTIVE_PRODUCTIVITY
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- Labor_cost_per_interaction = (labor_cost_per_hour * AHT_hours)
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- Overhead_variable = overhead_rate * Labor_cost_per_interaction
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- EFFECTIVE_PRODUCTIVITY = 0.70 (70% - accounts for non-productive time)
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Excluye registros abandonados del cálculo de costes para consistencia
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con el path del frontend (fresh CSV).
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Si no hay config de costes -> devuelve DataFrame vacío.
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Incluye queue_skill y channel como columnas (no solo índice) para que
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el frontend pueda hacer lookup por nombre de skill.
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"""
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if not self._has_cost_config():
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return pd.DataFrame()
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@@ -132,8 +148,22 @@ class EconomyCostMetrics:
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if df.empty:
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return pd.DataFrame()
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# AHT por skill/canal (en segundos)
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grouped = df.groupby(["queue_skill", "channel"])["handle_time"].mean()
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# Filter out abandonments for cost calculation (consistency with frontend)
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if "is_abandoned" in df.columns:
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df_cost = df[df["is_abandoned"] != True]
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else:
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df_cost = df
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# Filtrar por record_status: solo VALID para cálculo de AHT
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# Excluye NOISE, ZOMBIE, ABANDON
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if "_is_valid_for_cost" in df_cost.columns:
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df_cost = df_cost[df_cost["_is_valid_for_cost"] == True]
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if df_cost.empty:
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return pd.DataFrame()
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# AHT por skill/canal (en segundos) - solo registros VALID
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grouped = df_cost.groupby(["queue_skill", "channel"])["handle_time"].mean()
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if grouped.empty:
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return pd.DataFrame()
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@@ -141,9 +171,14 @@ class EconomyCostMetrics:
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aht_sec = grouped
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aht_hours = aht_sec / 3600.0
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# Apply productivity factor (70% effectiveness)
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# This accounts for non-productive agent time (breaks, training, etc.)
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EFFECTIVE_PRODUCTIVITY = 0.70
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labor_cost = cfg.labor_cost_per_hour * aht_hours
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overhead = labor_cost * cfg.overhead_rate
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cpi = labor_cost + overhead
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raw_cpi = labor_cost + overhead
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cpi = raw_cpi / EFFECTIVE_PRODUCTIVITY
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out = pd.DataFrame(
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{
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@@ -154,7 +189,8 @@ class EconomyCostMetrics:
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}
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)
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return out.sort_index()
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# Reset index to include queue_skill and channel as columns for frontend lookup
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return out.sort_index().reset_index()
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# ------------------------------------------------------------------ #
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# KPI 2: coste anual por skill/canal
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@@ -180,7 +216,9 @@ class EconomyCostMetrics:
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.rename("volume")
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)
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joined = cpi_table.join(volume, how="left").fillna({"volume": 0})
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# Set index on cpi_table to match volume's MultiIndex for join
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cpi_indexed = cpi_table.set_index(["queue_skill", "channel"])
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joined = cpi_indexed.join(volume, how="left").fillna({"volume": 0})
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joined["annual_cost"] = (joined["cpi_total"] * joined["volume"]).round(2)
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return joined
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@@ -216,7 +254,9 @@ class EconomyCostMetrics:
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.rename("volume")
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)
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joined = cpi_table.join(volume, how="left").fillna({"volume": 0})
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# Set index on cpi_table to match volume's MultiIndex for join
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cpi_indexed = cpi_table.set_index(["queue_skill", "channel"])
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joined = cpi_indexed.join(volume, how="left").fillna({"volume": 0})
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# Costes anuales de labor y overhead
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annual_labor = (joined["labor_cost"] * joined["volume"]).sum()
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@@ -252,7 +292,7 @@ class EconomyCostMetrics:
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- Ineff_seconds = Delta * volume * 0.4
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- Ineff_cost = LaborCPI_per_second * Ineff_seconds
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⚠️ Es un modelo aproximado para cuantificar "orden de magnitud".
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NOTA: Es un modelo aproximado para cuantificar "orden de magnitud".
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"""
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if not self._has_cost_config():
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return pd.DataFrame()
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@@ -261,6 +301,12 @@ class EconomyCostMetrics:
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assert cfg is not None
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df = self.df.copy()
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# Filtrar por record_status: solo VALID para cálculo de AHT
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# Excluye NOISE, ZOMBIE, ABANDON
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if "_is_valid_for_cost" in df.columns:
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df = df[df["_is_valid_for_cost"] == True]
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grouped = df.groupby(["queue_skill", "channel"])
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stats = grouped["handle_time"].agg(
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@@ -273,10 +319,14 @@ class EconomyCostMetrics:
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return pd.DataFrame()
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# CPI para obtener coste/segundo de labor
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cpi_table = self.cpi_by_skill_channel()
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if cpi_table.empty:
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# cpi_by_skill_channel now returns with reset_index, so we need to set index for join
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cpi_table_raw = self.cpi_by_skill_channel()
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if cpi_table_raw.empty:
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return pd.DataFrame()
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# Set queue_skill+channel as index for the join
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cpi_table = cpi_table_raw.set_index(["queue_skill", "channel"])
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merged = stats.join(cpi_table[["labor_cost"]], how="left")
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merged = merged.fillna(0.0)
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@@ -297,7 +347,8 @@ class EconomyCostMetrics:
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merged["ineff_seconds"] = ineff_seconds.round(2)
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merged["ineff_cost"] = ineff_cost
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return merged[["aht_p50", "aht_p90", "volume", "ineff_seconds", "ineff_cost"]]
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# Reset index to include queue_skill and channel as columns for frontend lookup
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return merged[["aht_p50", "aht_p90", "volume", "ineff_seconds", "ineff_cost"]].reset_index()
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# ------------------------------------------------------------------ #
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# KPI 5: ahorro potencial anual por automatización
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@@ -419,7 +470,9 @@ class EconomyCostMetrics:
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.rename("volume")
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)
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joined = cpi_table.join(volume, how="left").fillna({"volume": 0})
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# Set index on cpi_table to match volume's MultiIndex for join
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cpi_indexed = cpi_table.set_index(["queue_skill", "channel"])
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joined = cpi_indexed.join(volume, how="left").fillna({"volume": 0})
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# CPI medio ponderado por canal
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per_channel = (
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