{"cells":[{"cell_type":"markdown","id":"d91fd011","metadata":{"id":"d91fd011"},"source":["# Switching in 24 ore — simulazione su 30 milioni di POD\n","\n","### Cosa fa questo notebook\n","\n","Simula **un anno intero** di cambi fornitore di energia elettrica su **30 milioni di punti di prelievo**,\n","applicando le regole del nuovo processo ARERA (Deliberazione **58/2026/R/eel**, in vigore dal **1° dicembre 2026**).\n","\n","Il riferimento di scala è reale: secondo la Relazione Annuale ARERA, nel 2025 i **punti di prelievo domestici\n","in Italia sono poco più di 30,5 milioni**. Con 30 milioni di POD si simula l'intero mercato.\n","\n","| | |\n","|---|---|\n","| **Tempo di esecuzione** | ~10 secondi per 10 milioni di POD, ~20 secondi per 30,5 milioni |\n","| **Requisiti** | nessuna installazione: bastano NumPy, pandas e Matplotlib, già presenti su Colab |\n","| **Autore** | Fabio Mencio — Energy \\| Data \\| AI — https://fabio-mencio.vercel.app/ |\n","\n","### Cosa sono (e cosa non sono) questi numeri\n","\n","I dati sono **totalmente sintetici**. La *scala* è quella del mercato italiano, i *comportamenti* no: nessun\n","dato reale di clienti, venditori, Acquirente Unico o SII è stato usato. Non sono previsioni del mercato né\n","misure delle prestazioni del SII: sono la conseguenza logica di regole vere applicate a ipotesi dichiarate.\n","\n","Le regole **REGULATORY** (dal testo ufficiale) sono: 1 giorno lavorativo per la richiesta veloce del cliente\n","domestico in regola, 10 giorni lavorativi per tutti gli altri, preliminary check obbligatorio con esito entro\n","2 giorni lavorativi, blocco del POD per 7 giorni lavorativi, verifica SII entro 1 giorno lavorativo.\n","Tutto il resto (capacità di elaborazione, tassi di errore, comportamenti dei clienti, costi commerciali)\n","è **SIMULATION**: ipotesi mie, modificabili nella cella dei parametri."]},{"cell_type":"markdown","id":"4701d9a4","metadata":{"id":"4701d9a4"},"source":["## 1. Il calendario dei giorni lavorativi\n","\n","Il processo ragiona in **giorni lavorativi**, quindi servono le festività italiane, compreso il 4 ottobre, che dal 2026 torna festa nazionale."]},{"cell_type":"code","execution_count":1,"id":"aa22ab76","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:11:50.439370Z","iopub.status.busy":"2026-09-20T06:11:50.439125Z","iopub.status.idle":"2026-09-20T06:11:50.665847Z","shell.execute_reply":"2026-09-20T06:11:50.664917Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"aa22ab76","executionInfo":{"status":"ok","timestamp":1790538459843,"user_tz":-120,"elapsed":590,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"9aee9ddd-6b3a-4b28-8080-71b196039265"},"outputs":[{"output_type":"stream","name":"stdout","text":["Calendario pronto. Festivita' 2027: 13 giorni\n"]}],"source":["# =============================================================================\n","# Calendario dei giorni lavorativi italiani + costanti regolatorie\n","# (estratto da src/switching_engine.py del progetto switching-24h-energy)\n","# =============================================================================\n","from datetime import date, timedelta\n","from functools import lru_cache\n","\n","import numpy as np\n","import pandas as pd\n","\n","# --- REGULATORY: ARERA 58/2026/R/eel, Allegato A1 ---------------------------\n","FAST_SWITCH_MIN_BDAYS = 1       # art. 6: richiesta veloce, da 1 giorno lavorativo\n","ORDINARY_SWITCH_MIN_BDAYS = 10  # art. 7: richiesta ordinaria, da 10 giorni lavorativi\n","PRELIMINARY_CHECK_MAX_BDAYS = 2 # art. 5.3: esito del preliminary check\n","POD_LOCK_BDAYS = 7              # art. 5.12: blocco del POD dopo una richiesta\n","\n","\n","def easter_sunday(year: int) -> date:\n","    \"\"\"Domenica di Pasqua (algoritmo gregoriano anonimo).\"\"\"\n","    a = year % 19\n","    b, c = divmod(year, 100)\n","    d, e = divmod(b, 4)\n","    f = (b + 8) // 25\n","    g = (b - f + 1) // 3\n","    h = (19 * a + b - d - g + 15) % 30\n","    i, k = divmod(c, 4)\n","    l = (32 + 2 * e + 2 * i - h - k) % 7\n","    m = (a + 11 * h + 22 * l) // 451\n","    month = (h + l - 7 * m + 114) // 31\n","    day = ((h + l - 7 * m + 114) % 31) + 1\n","    return date(year, month, day)\n","\n","\n","@lru_cache(maxsize=None)\n","def italian_holidays(year: int) -> frozenset:\n","    \"\"\"\n","    Festivita' nazionali italiane. Dal 2026 c'e' anche il 4 ottobre\n","    (legge 151/2025, San Francesco patrono d'Italia).\n","    \"\"\"\n","    fixed = [(1, 1), (1, 6), (4, 25), (5, 1), (6, 2), (8, 15), (11, 1), (12, 8), (12, 25), (12, 26)]\n","    days = {date(year, m, d) for m, d in fixed}\n","    if year >= 2026:\n","        days.add(date(year, 10, 4))\n","    easter = easter_sunday(year)\n","    days.add(easter)\n","    days.add(easter + timedelta(days=1))\n","    return frozenset(days)\n","\n","\n","def holiday_array(start_year: int, end_year: int) -> np.ndarray:\n","    days = sorted(h for y in range(start_year, end_year + 1) for h in italian_holidays(y))\n","    return np.array(days, dtype=\"datetime64[D]\")\n","\n","\n","print(\"Calendario pronto. Festivita' 2027:\", len(italian_holidays(2027)), \"giorni\")"]},{"cell_type":"markdown","id":"e526a6c5","metadata":{"id":"e526a6c5"},"source":["## 2. Il motore di simulazione\n","\n","Una cella sola: genera i POD, la domanda di cambio, applica le regole, risolve le code e calcola i KPI. È tutto vettoriale con NumPy, per questo 10 milioni di POD girano in pochi secondi invece che in ore."]},{"cell_type":"code","execution_count":2,"id":"f574507c","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:11:50.668374Z","iopub.status.busy":"2026-09-20T06:11:50.668069Z","iopub.status.idle":"2026-09-20T06:11:50.719563Z","shell.execute_reply":"2026-09-20T06:11:50.718618Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"f574507c","executionInfo":{"status":"ok","timestamp":1790538459998,"user_tz":-120,"elapsed":156,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"c60add61-ebd9-4ab5-c94d-39b5096468d7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Motore caricato. POD domestici in Italia (ARERA, dati 2025): 30.500.000\n"]}],"source":["# =============================================================================\n","# Motore vettoriale (identico a src/large_scale.py del repository)\n","# =============================================================================\n","\"\"\"\n","large_scale.py\n","==============\n","\n","Motore VETTORIALE per simulare il cambio fornitore su scala di mercato reale\n","(10-30 milioni di POD), pensato per girare su Google Colab in pochi minuti.\n","\n","Perché un secondo motore\n","------------------------\n","`simulation.run_scenario` percorre ogni richiesta una per una in Python: è\n","leggibile e produce l'event log completo, ma a 10 milioni di POD sarebbero\n","~4 milioni di richieste e ~30 milioni di eventi, fuori portata per Colab.\n","Qui le stesse regole sono applicate con operazioni NumPy su interi array, senza\n","event log: si perde il dettaglio per richiesta, si guadagnano due ordini di\n","grandezza in scala.\n","\n","Le regole REGULATORY sono le stesse di `switching_engine.py`\n","(ARERA 58/2026/R/eel, Allegato A1): 1 giorno lavorativo per la richiesta veloce,\n","10 per l'ordinaria, preliminary check entro 2 giorni, blocco del POD per 7 giorni\n","lavorativi, verifica SII entro 1 giorno lavorativo.\n","\n","Semplificazioni rispetto al motore per-richiesta (tutte SIMULATION):\n","- nessun event log, nessuna cronologia per singola richiesta;\n","- il blocco dei 7 giorni lavorativi è valutato rispetto alla richiesta\n","  precedente sullo stesso POD, non rispetto all'ultima *ammessa*: cambia\n","  qualcosa solo con 3+ richieste sullo stesso POD nella stessa quindicina;\n","- niente \"richiesta superata da una successiva\", niente ripensamento tardivo\n","  con passaggio ai servizi di ultima istanza;\n","- la coda del back-office dei venditori è simulata, quella del SII pure, ma non\n","  le code intermedie di distributori e UdD.\n","\n","Riferimento reale utile per leggere i numeri: secondo la Relazione Annuale ARERA\n","(dati 2025) i punti di prelievo domestici in Italia sono poco più di 30,5 milioni.\n","Questo resta comunque un modello con dati sintetici: la scala è realistica, i\n","comportamenti no.\n","\"\"\"\n","\n","\n","import time\n","from dataclasses import dataclass, field\n","\n","import numpy as np\n","import pandas as pd\n","\n","\n","# --- costanti di mercato ---------------------------------------------------\n","POD_DOMESTICI_ITALIA = 30_500_000   # ARERA, Relazione Annuale (dati 2025)\n","\n","TIPI = [\"domestico_residente\", \"domestico_non_residente\", \"microbusiness\", \"altri_bt\"]\n","TIPI_P = [0.62, 0.16, 0.17, 0.05]\n","SEGMENTI = [\"fedele\", \"standard\", \"attivo\", \"alta_frequenza\"]\n","CANALI = [\"Web\", \"Teleselling\", \"Store\", \"Partner\", \"Comparison Website\", \"App\", \"Referral\"]\n","CAC_CANALE = np.array([45, 115, 95, 125, 75, 35, 40], dtype=np.float32)\n","\n","VENDITORI = [\"Energia Alfa\", \"Energia Beta\", \"Energia Gamma\", \"Nova Power\", \"Energia Verde\",\n","             \"PowerOne\", \"SmartEnergy\", \"Energia Italia X\", \"Luce Delta\", \"VoltaCasa\"]\n","QUOTE = np.array([0.20, 0.15, 0.12, 0.10, 0.09, 0.09, 0.08, 0.07, 0.05, 0.05])\n","PREZZO_IDX = np.array([1.02, 1.00, 0.98, 0.94, 1.01, 0.99, 0.92, 0.97, 0.96, 0.97])\n","ONBOARDING = np.array([1, 1, 2, 0, 1, 1, 0, 2, 1, 1])          # giorni lavorativi\n","DIGITAL = np.array([0.55, 0.60, 0.40, 0.90, 0.75, 0.50, 0.95, 0.35, 0.65, 0.70])\n","ERR_MULT = np.array([1.0, 0.9, 1.3, 0.8, 0.9, 1.1, 0.7, 1.4, 1.0, 1.0])\n","DATAQ_MULT = np.array([1.0, 0.9, 1.3, 0.6, 0.8, 1.1, 0.5, 1.4, 1.0, 0.9])\n","MIX_CANALI = np.array([\n","    [.25, .25, .15, .15, .10, .05, .05], [.30, .20, .20, .10, .10, .05, .05],\n","    [.15, .30, .10, .30, .10, .02, .03], [.40, .05, .00, .05, .25, .20, .05],\n","    [.35, .10, .05, .10, .20, .10, .10], [.20, .30, .15, .20, .10, .02, .03],\n","    [.35, .00, .00, .05, .25, .30, .05], [.10, .35, .20, .25, .05, .02, .03],\n","    [.30, .15, .10, .15, .20, .05, .05], [.30, .10, .15, .10, .15, .10, .10]])\n","\n","\n","@dataclass\n","class Parametri:\n","    \"\"\"Tutti i parametri della simulazione su larga scala (tutti SIMULATION).\"\"\"\n","    n_pods: int = 10_000_000\n","    anno_inizio: str = \"2026-12-01\"\n","    anno_fine: str = \"2027-11-30\"\n","    seed: int = 42\n","    # capacità di elaborazione sintetica, espressa per milione di POD\n","    capacita_per_milione: int = 2_000\n","    capacita_giorno: int | None = None        # se valorizzata, ha la precedenza\n","    tasso_switch_base: float = 0.40           # contratti attesi per POD all'anno\n","    quota_veloce_scelta: float = 0.90         # quota di idonei per cui il venditore sceglie la corsia veloce\n","    error_rate: float = 0.020\n","    retry_rate: float = 0.70\n","    cancel_rate: float = 0.035\n","    duplicate_rate: float = 0.015\n","    concurrent_rate: float = 0.020\n","    crescita: float = 0.0                     # crescita lineare della domanda sull'anno\n","    campagne: tuple = ()                      # ((data_inizio, giorni, moltiplicatore), ...)\n","    alta_frequenza: float = 0.02              # quota di clienti che cambiano spesso\n","    giorno_incidente: str | None = \"2027-02-15\"   # giorno con capacità ridotta\n","    fattore_incidente: float = 0.30\n","    moltiplicatore_ripetuti: float = 1.0\n","    verbose: bool = True\n","\n","    def capacita(self) -> float:\n","        if self.capacita_giorno:\n","            return float(self.capacita_giorno)\n","        return self.capacita_per_milione * self.n_pods / 1e6\n","\n","\n","SCENARI = {\n","    \"NORMALE\": dict(),\n","    \"FORTE_CRESCITA\": dict(crescita=0.60),\n","    \"GUERRA_COMMERCIALE\": dict(campagne=((\"2027-01-11\", 14, 1.8), (\"2027-04-12\", 10, 1.6),\n","                                         (\"2027-05-17\", 14, 2.0), (\"2027-09-13\", 14, 2.0)),\n","                               concurrent_rate=0.06, duplicate_rate=0.02, error_rate=0.026),\n","    \"CHURN_ELEVATO\": dict(moltiplicatore_ripetuti=1.6, alta_frequenza=0.04),\n","    \"STRESS\": dict(crescita=0.25, capacita_per_milione=1_600, error_rate=0.035, concurrent_rate=0.06,\n","                   campagne=((\"2027-01-11\", 14, 1.8), (\"2027-04-12\", 10, 1.6),\n","                             (\"2027-05-17\", 14, 2.0), (\"2027-09-13\", 14, 2.0))),\n","}\n","\n","\n","def _log(p: Parametri, t0: float, msg: str):\n","    if p.verbose:\n","        print(f\"[{time.time() - t0:6.1f}s] {msg}\", flush=True)\n","\n","\n","def _coda_fifo(arrivo_idx: np.ndarray, capacita: np.ndarray, pesi: np.ndarray | None = None):\n","    \"\"\"Coda FIFO a capacità giornaliera: restituisce (giorno di servizio, arrivi, serviti, coda).\"\"\"\n","    n = len(capacita)\n","    pesi = np.ones(len(arrivo_idx), np.float32) if pesi is None else pesi\n","    arrivi = np.bincount(arrivo_idx, weights=pesi, minlength=n)[:n]\n","    serviti = np.empty(n)\n","    coda = np.empty(n)\n","    resto = 0.0\n","    for t in range(n):                       # ciclo sui ~260 giorni, non sulle richieste\n","        lavoro = resto + arrivi[t]\n","        serviti[t] = min(lavoro, capacita[t])\n","        resto = lavoro - serviti[t]\n","        coda[t] = resto\n","    cum = np.cumsum(serviti)\n","    ordine = np.argsort(arrivo_idx, kind=\"stable\")\n","    carico = np.cumsum(pesi[ordine])\n","    servito_ord = np.searchsorted(cum, carico - 1e-6, side=\"left\")\n","    servizio = np.empty(len(arrivo_idx), np.int32)\n","    servizio[ordine] = np.minimum(servito_ord, n - 1)\n","    return np.maximum(servizio, arrivo_idx), arrivi, serviti, coda\n","\n","\n","def simula_mercato(p: Parametri | None = None, scenario: str = \"NORMALE\", **kw) -> dict:\n","    \"\"\"\n","    Simula un anno di cambi fornitore su `p.n_pods` punti di prelievo.\n","\n","    Restituisce un dizionario con: kpi (dict), giornaliero (DataFrame),\n","    venditori (DataFrame), distribuzione dei cambi per POD, parametri usati.\n","    \"\"\"\n","    p = p or Parametri()\n","    for k, v in {**SCENARI.get(scenario, {}), **kw}.items():\n","        setattr(p, k, v)\n","    rng = np.random.default_rng(p.seed)\n","    t0 = time.time()\n","    n = p.n_pods\n","\n","    # ---------------------------------------------------------------- calendario\n","    giorni = pd.date_range(p.anno_inizio, p.anno_fine, freq=\"D\")\n","    n_giorni = len(giorni)\n","    festivi = holiday_array(2026, 2029)\n","    tutti = np.arange(np.datetime64(p.anno_inizio), np.datetime64(\"2028-06-30\"), dtype=\"datetime64[D]\")\n","    bdays = tutti[np.is_busday(tutti, holidays=festivi)]\n","    # per ogni giorno solare: indice del primo giorno lavorativo utile\n","    giorno_to_bday = np.searchsorted(bdays, giorni.values.astype(\"datetime64[D]\"))\n","    n_bdays_anno = int(np.searchsorted(bdays, np.datetime64(p.anno_fine))) + 1\n","\n","    # ---------------------------------------------------------------- clienti\n","    _log(p, t0, f\"genero {n:,} POD sintetici\".replace(\",\", \".\"))\n","    tipo = rng.choice(len(TIPI), size=n, p=TIPI_P).astype(np.int8)\n","    seg_p = np.array([0.45, 0.42 - p.alta_frequenza, 0.13, p.alta_frequenza])\n","    segmento = rng.choice(len(SEGMENTI), size=n, p=seg_p / seg_p.sum()).astype(np.int8)\n","    venditore = rng.choice(len(VENDITORI), size=n, p=QUOTE / QUOTE.sum()).astype(np.int8)\n","    moroso = rng.random(n) < 0.06\n","    cmor = moroso & (rng.random(n) < 0.20)\n","    sospeso = moroso & (rng.random(n) < 0.065)\n","    pod_inattivo = rng.random(n) < 0.006\n","    consumo = np.empty(n, np.float32)\n","    for i, (med, sig) in enumerate([(2200, .35), (900, .6), (8000, .7), (25000, .6)]):\n","        m = tipo == i\n","        consumo[m] = rng.lognormal(np.log(med), sig, int(m.sum())).astype(np.float32)\n","    margine_unit = np.array([0.018, 0.022, 0.015, 0.010], np.float32)[tipo]\n","    margine = (consumo * margine_unit + 36).astype(np.float32)\n","    domestico = tipo <= 1\n","    _log(p, t0, \"clienti pronti\")\n","\n","    # ---------------------------------------------------------------- domanda\n","    mult_seg = np.array([0.10, 1.00, 2.50, 8.00])[segmento]\n","    if p.moltiplicatore_ripetuti != 1.0:\n","        mult_seg = np.where(segmento >= 2, mult_seg * p.moltiplicatore_ripetuti, mult_seg)\n","    mult_tipo = np.array([1.00, 0.80, 1.10, 0.70])[tipo]\n","    lam = (p.tasso_switch_base * mult_seg * mult_tipo).astype(np.float32)\n","\n","    dow = np.array([1.15, 1.10, 1.05, 1.00, 0.95, 0.65, 0.30])[giorni.dayofweek]\n","    mese = np.array([0, 1.15, 1.05, 1.00, .95, .95, .85, .80, .55, 1.10, 1.10, 1.05, .85])[giorni.month]\n","    fest = np.isin(giorni.values.astype(\"datetime64[D]\"), festivi)\n","    peso = dow * mese * np.where(fest, 0.3, 1.0)\n","    peso = peso * (1 + p.crescita * np.arange(n_giorni) / max(n_giorni - 1, 1))\n","    campagna = np.zeros(n_giorni, bool)\n","    for inizio, durata, molt in p.campagne:\n","        m = (giorni >= pd.Timestamp(inizio)) & (giorni < pd.Timestamp(inizio) + pd.Timedelta(days=durata))\n","        peso = np.where(m, peso * molt, peso)\n","        campagna |= np.asarray(m)\n","    lam = lam * float(peso.sum() / (dow * mese * np.where(fest, .3, 1.)).sum())\n","\n","    quanti = rng.poisson(lam).astype(np.int32)\n","    del lam, mult_seg, mult_tipo\n","    idx_pod = np.repeat(np.arange(n, dtype=np.int32), quanti)\n","    R = len(idx_pod)\n","    _log(p, t0, f\"{R:,} richieste di cambio generate\".replace(\",\", \".\"))\n","\n","    giorno = rng.choice(n_giorni, size=R, p=peso / peso.sum()).astype(np.int32)\n","    # duplicati e richieste concorrenti: copie ravvicinate sullo stesso POD\n","    dup = rng.random(R) < p.duplicate_rate\n","    conc = rng.random(R) < p.concurrent_rate\n","    extra = dup | conc\n","    idx_pod = np.concatenate([idx_pod, idx_pod[extra]])\n","    giorno = np.concatenate([giorno, np.minimum(giorno[extra] + rng.integers(0, 5, int(extra.sum())),\n","                                                n_giorni - 1)]).astype(np.int32)\n","    e_dup = np.concatenate([np.zeros(R, bool), dup[extra]])\n","    R = len(idx_pod)\n","    del dup, conc, extra\n","    _log(p, t0, f\"{R:,} richieste totali (inclusi duplicati e concorrenti)\".replace(\",\", \".\"))\n","\n","    # ---------------------------------------------------------------- venditore scelto e canale\n","    attratt = QUOTE / PREZZO_IDX ** 4\n","    to_sup = rng.choice(len(VENDITORI), size=R, p=attratt / attratt.sum()).astype(np.int8)\n","    canale = np.empty(R, np.int8)\n","    for s in range(len(VENDITORI)):\n","        m = to_sup == s\n","        canale[m] = rng.choice(len(CANALI), size=int(m.sum()), p=MIX_CANALI[s] / MIX_CANALI[s].sum())\n","\n","    # ---------------------------------------------------------------- back-office del venditore\n","    ricev = giorno_to_bday[giorno]\n","    pc_bday = np.empty(R, np.int32)\n","    rif = n * p.tasso_switch_base * 0.95 / 250\n","    cap_bd = len(bdays)\n","    for s in range(len(VENDITORI)):\n","        m = to_sup == s\n","        cap = np.full(cap_bd, (1.6 + 1.5 * DIGITAL[s]) * QUOTE[s] * rif)\n","        serv, *_ = _coda_fifo(ricev[m], cap)\n","        pc_bday[m] = serv + rng.integers(0, ONBOARDING[s] + 1, int(m.sum()))\n","    attesa_bo = (pc_bday - ricev).astype(np.int16)\n","    _log(p, t0, \"coda dei back-office risolta\")\n","\n","    # ---------------------------------------------------------------- blocco del POD (art. 5.12)\n","    ordine = np.lexsort((pc_bday, idx_pod))\n","    pod_ord, pc_ord = idx_pod[ordine], pc_bday[ordine]\n","    stesso_pod = np.zeros(R, bool)\n","    stesso_pod[1:] = pod_ord[1:] == pod_ord[:-1]\n","    gap = np.full(R, POD_LOCK_BDAYS + 1, np.int32)\n","    gap[1:] = np.where(stesso_pod[1:], pc_ord[1:] - pc_ord[:-1], POD_LOCK_BDAYS + 1)\n","    bloccata_ord = gap < POD_LOCK_BDAYS\n","    bloccata = np.empty(R, bool)\n","    bloccata[ordine] = bloccata_ord\n","    # rango della richiesta sullo stesso POD (serve per la catena dei fornitori)\n","\n","    nuovo_gruppo = ~stesso_pod\n","    gruppo_id = np.cumsum(nuovo_gruppo) - 1\n","    inizio_gruppo = np.zeros(gruppo_id[-1] + 1, np.int64)\n","    inizio_gruppo[gruppo_id[nuovo_gruppo]] = np.flatnonzero(nuovo_gruppo)\n","    rango_ord = (np.arange(R) - inizio_gruppo[gruppo_id]).astype(np.int16)\n","    rango = np.empty(R, np.int16)\n","    rango[ordine] = rango_ord\n","\n","    # ---------------------------------------------------------------- preliminary check\n","    dq = DATAQ_MULT[to_sup]\n","    ch_err = np.array([0.8, 1.8, 0.9, 1.5, 1.0, 0.5, 0.9], np.float32)[canale]\n","    u = rng.random(R)\n","    p_bad = 0.025 * dq * ch_err\n","    dati_errati = u < p_bad                                   # > 2 caratteri sbagliati\n","    pod_sbagliato = rng.random(R) < 0.008 * dq\n","    cliente_ignoto = rng.random(R) < 0.003\n","    pod_non_attivo = pod_inattivo[idx_pod]\n","    pc_ko = dati_errati | pod_sbagliato | cliente_ignoto | pod_non_attivo\n","    motivo = np.zeros(R, np.int8)             # 0 = ok\n","    motivo[pod_non_attivo] = 4\n","    motivo[cliente_ignoto] = 3\n","    motivo[dati_errati] = 2\n","    motivo[pod_sbagliato] = 1\n","    motivo[bloccata] = 5                      # il blocco prevale: richiesta inammissibile\n","    pc_ko = pc_ko | bloccata\n","    del dq, ch_err, u, p_bad\n","\n","    pc_risposta = pc_bday + rng.integers(0, 3, R).astype(np.int32)      # entro 2 gg lav. (art. 5.3)\n","    cambia_udd = rng.random(R) < 0.85\n","    pronta = pc_risposta + np.where(cambia_udd, rng.integers(0, 3, R) + 1, 0).astype(np.int32)\n","    udd_nega = rng.random(R) < 0.004\n","    dso_nega = rng.random(R) < 0.004\n","\n","    # ---------------------------------------------------------------- eleggibilità (art. 6.2)\n","    idonea = domestico[idx_pod] & ~sospeso[idx_pod] & ~cmor[idx_pod]\n","    # il percorso si assegna solo alle richieste che superano il preliminary check\n","    veloce = idonea & ~pc_ko & (rng.random(R) < p.quota_veloce_scelta)\n","\n","    # ---------------------------------------------------------------- richiesta al SII\n","    ritardo = rng.integers(0, 1 + np.round(3 * (1 - DIGITAL[to_sup])).astype(int) + 1).astype(np.int32)\n","    sii_bday = pronta + ritardo\n","    data_richiesta = np.where(veloce, sii_bday + FAST_SWITCH_MIN_BDAYS,\n","                              sii_bday + ORDINARY_SWITCH_MIN_BDAYS).astype(np.int32)\n","\n","    cancellata = rng.random(R) < p.cancel_rate * np.array(\n","        [0.8, 2.0, 0.9, 1.6, 1.0, 0.7, 0.6], np.float32)[canale]\n","    err_tec = rng.random(R) < p.error_rate * ERR_MULT[to_sup]\n","    ritentata = err_tec & (rng.random(R) < p.retry_rate)\n","    persa_tec = err_tec & ~ritentata\n","    sii_bday = sii_bday + ritentata.astype(np.int32)           # il retry costa un giorno\n","    data_richiesta = np.maximum(data_richiesta, sii_bday + np.where(veloce, FAST_SWITCH_MIN_BDAYS,\n","                                                                   ORDINARY_SWITCH_MIN_BDAYS))\n","\n","    arriva_al_sii = ~pc_ko & ~udd_nega & ~dso_nega & ~cancellata\n","    _log(p, t0, f\"{int(arriva_al_sii.sum()):,} richieste arrivano al SII\".replace(\",\", \".\"))\n","\n","    # ---------------------------------------------------------------- coda del SII\n","    cap = np.full(cap_bd, p.capacita())\n","    if p.giorno_incidente:\n","        gi = int(np.searchsorted(bdays, np.datetime64(p.giorno_incidente)))\n","        if gi < cap_bd:\n","            cap[gi] *= p.fattore_incidente\n","    idx_sii = np.flatnonzero(arriva_al_sii)\n","    pesi = 1.0 + ritentata[idx_sii]\n","    servizio, arrivi, serviti, coda = _coda_fifo(sii_bday[idx_sii], cap, pesi.astype(np.float32))\n","    attesa = servizio - sii_bday[idx_sii]\n","    _log(p, t0, \"coda del SII risolta\")\n","\n","    effettiva = np.full(R, -1, np.int32)\n","    effettiva[idx_sii] = np.maximum(data_richiesta[idx_sii], servizio + 1)\n","    completata = arriva_al_sii & ~persa_tec\n","    fuori_sla = np.zeros(R, bool)\n","    fuori_sla[idx_sii] = attesa > 1\n","    in_target = np.zeros(R, bool)\n","    in_target[idx_sii] = veloce[idx_sii] & (effettiva[idx_sii] - sii_bday[idx_sii] <= 1)\n","\n","    # entro la fine dell'anno simulato?\n","    entro_anno = effettiva <= n_bdays_anno\n","    conclusa = completata & entro_anno\n","\n","    # ---------------------------------------------------------------- catena dei fornitori\n","    from_sup = venditore[idx_pod].copy()\n","    max_rango = int(rango.max()) if R else 0\n","    for r in range(1, max_rango + 1):\n","        prec = ordine[np.flatnonzero(rango_ord == r) - 1]\n","        cur = ordine[np.flatnonzero(rango_ord == r)]\n","        from_sup[cur] = np.where(conclusa[prec], to_sup[prec], from_sup[prec])\n","    gia_associato = from_sup == to_sup\n","    conclusa &= ~gia_associato\n","\n","    # ---------------------------------------------------------------- tempi\n","    giorni_bday = bdays.astype(\"datetime64[D]\")\n","    data_firma = giorni.values.astype(\"datetime64[D]\")[giorno]\n","    data_cambio = np.full(R, np.datetime64(\"NaT\", \"D\"), dtype=\"datetime64[D]\")\n","    ok = conclusa & (effettiva >= 0) & (effettiva < len(bdays))\n","    data_cambio[ok] = giorni_bday[effettiva[ok]]\n","    lead = (data_cambio - data_firma).astype(\"timedelta64[D]\").astype(float)\n","    tec = np.full(R, np.nan)\n","    tec[ok] = (effettiva[ok] - sii_bday[ok]).astype(float)\n","\n","    # ---------------------------------------------------------------- KPI\n","    n_conclusi = int(conclusa.sum())\n","    cambi_per_pod = np.bincount(idx_pod[conclusa], minlength=n)\n","    pod_che_cambiano = int((cambi_per_pod > 0).sum())\n","    cac = CAC_CANALE[canale] * np.array([1.10, 1.00, 0.95, 0.85, 0.95, 1.05, 0.80, 1.00, 0.90, 0.90],\n","                                        np.float32)[to_sup]\n","    lead_ok = lead[ok]\n","    veloci = veloce      # tutte quelle instradate sulla corsia veloce\n","    kpi = {\n","        \"POD simulati\": n,\n","        \"Richieste di cambio\": R,\n","        \"Richieste concluse\": n_conclusi,\n","        \"Tasso di completamento\": n_conclusi / R,\n","        \"POD che cambiano almeno una volta\": pod_che_cambiano,\n","        \"Tasso di switching annuo (% POD)\": pod_che_cambiano / n,\n","        \"POD con 2+ cambi\": int((cambi_per_pod >= 2).sum()),\n","        \"Quota di chi cambia che lo fa 2+ volte\": float((cambi_per_pod >= 2).sum() / max(pod_che_cambiano, 1)),\n","        \"Quota idonea alla corsia veloce\": float(idonea[~pc_ko].mean()) if (~pc_ko).any() else 0.0,\n","        \"Richieste veloci concluse entro 1 giorno lavorativo\": float(in_target[veloci].mean()) if veloci.any() else 0.0,\n","        \"Cambi completati nell'anno\": n_conclusi,\n","        \"Cambi completati ogni 1.000 POD\": 1000 * n_conclusi / n,\n","        \"Attesa mediana dalla firma al cambio (giorni)\": float(np.median(lead_ok)) if len(lead_ok) else np.nan,\n","        \"Attesa P95 dalla firma al cambio (giorni)\": float(np.percentile(lead_ok, 95)) if len(lead_ok) else np.nan,\n","        \"Preliminary check KO\": float(pc_ko.mean()),\n","        \"Richieste fuori SLA (verifica > 1 gg lav.)\": float(fuori_sla[idx_sii].mean()) if len(idx_sii) else 0.0,\n","        \"Coda massima (richieste)\": float(coda[:n_bdays_anno].max()),\n","        \"Giorni con coda\": int((coda[:n_bdays_anno] > 0).sum()),\n","        \"Richieste medie per giorno lavorativo\": float(arrivi[:n_bdays_anno].mean()),\n","        \"Picco giornaliero\": float(arrivi[:n_bdays_anno].max()),\n","        \"Capacità giornaliera sintetica\": p.capacita(),\n","        \"Utilizzo medio della capacità\": float(serviti[:n_bdays_anno].mean() / p.capacita()),\n","        \"CAC medio (€)\": float(cac[conclusa].mean()) if n_conclusi else np.nan,\n","        \"Margine annuo medio del cliente acquisito (€)\": float(margine[idx_pod[conclusa]].mean()) if n_conclusi else np.nan,\n","        \"Costo di acquisizione totale (mln €)\": float(cac[conclusa].sum() / 1e6) if n_conclusi else np.nan,\n","    }\n","\n","    # ---------------------------------------------------------------- serie giornaliera\n","    giorni_lav = giorni_bday[:n_bdays_anno]\n","    giornaliero = pd.DataFrame({\n","        \"data\": giorni_lav,\n","        \"richieste_al_sii\": arrivi[:n_bdays_anno],\n","        \"elaborate\": serviti[:n_bdays_anno],\n","        \"coda\": coda[:n_bdays_anno],\n","        \"capacita\": cap[:n_bdays_anno],\n","    })\n","    giornaliero[\"utilizzo\"] = giornaliero[\"elaborate\"] / giornaliero[\"capacita\"]\n","\n","    # ---------------------------------------------------------------- venditori\n","    persi = np.bincount(from_sup[conclusa], minlength=len(VENDITORI))\n","    vinti = np.bincount(to_sup[conclusa], minlength=len(VENDITORI))\n","    base = np.bincount(venditore, minlength=len(VENDITORI))\n","    venditori = pd.DataFrame({\n","        \"venditore\": VENDITORI, \"base_iniziale\": base,\n","        \"acquisizioni\": vinti, \"clienti_persi\": persi,\n","        \"saldo_netto\": vinti - persi,\n","        \"churn_%\": 100 * persi / np.maximum(base, 1),\n","    })\n","\n","    distribuzione = pd.Series(\n","        np.bincount(np.minimum(cambi_per_pod, 4), minlength=5) / n,\n","        index=[\"0\", \"1\", \"2\", \"3\", \"4+\"], name=\"quota_pod\")\n","\n","    _log(p, t0, f\"fatto in {time.time() - t0:.0f}s\")\n","    return {\"kpi\": kpi, \"giornaliero\": giornaliero, \"venditori\": venditori,\n","            \"distribuzione_cambi\": distribuzione, \"parametri\": p, \"scenario\": scenario,\n","            \"secondi\": round(time.time() - t0, 1),\n","            \"motivi_ko\": pd.Series(np.bincount(motivo[pc_ko], minlength=6)[[1, 2, 3, 4, 5]],\n","                                   index=[\"POD inesistente\", \"Dati cliente errati\", \"Cliente inesistente\",\n","                                          \"POD non attivo\", \"POD già impegnato (blocco 7 gg lav.)\"])}\n","\n","\n","def tabella_kpi(ris: dict) -> pd.DataFrame:\n","    \"\"\"KPI in una tabella leggibile, con i numeri formattati all'italiana.\"\"\"\n","    righe = []\n","    for k, v in ris[\"kpi\"].items():\n","        if isinstance(v, float) and (\"%\" in k or \"Quota\" in k or \"Tasso\" in k or \"Utilizzo\" in k\n","                                     or \"entro\" in k or \"KO\" in k or \"SLA\" in k):\n","            testo = f\"{v:.1%}\".replace(\".\", \",\")\n","        elif isinstance(v, float):\n","            testo = f\"{v:,.1f}\".replace(\",\", \"X\").replace(\".\", \",\").replace(\"X\", \".\")\n","        else:\n","            testo = f\"{v:,}\".replace(\",\", \".\")\n","        righe.append({\"KPI\": k, \"Valore\": testo})\n","    return pd.DataFrame(righe)\n","\n","\n","def confronta_scenari(n_pods: int = 10_000_000, scenari: tuple = (\"NORMALE\", \"FORTE_CRESCITA\",\n","                                                                  \"GUERRA_COMMERCIALE\", \"CHURN_ELEVATO\",\n","                                                                  \"STRESS\"), **kw) -> pd.DataFrame:\n","    \"\"\"Esegue più scenari sulla stessa scala e mette i risultati a confronto.\"\"\"\n","    righe = []\n","    for s in scenari:\n","        r = simula_mercato(Parametri(n_pods=n_pods, verbose=False), scenario=s, **kw)\n","        k = r[\"kpi\"]\n","        righe.append({\n","            \"Scenario\": s.replace(\"_\", \" \").title(),\n","            \"Richieste\": k[\"Richieste di cambio\"],\n","            \"Cambi completati\": k[\"Cambi completati nell'anno\"],\n","            \"Completamento %\": round(100 * k[\"Tasso di completamento\"], 1),\n","            \"Switching annuo % POD\": round(100 * k[\"Tasso di switching annuo (% POD)\"], 1),\n","            \"Veloci entro 1 gg lav. %\": round(100 * k[\"Richieste veloci concluse entro 1 giorno lavorativo\"], 1),\n","            \"Attesa mediana (gg)\": k[\"Attesa mediana dalla firma al cambio (giorni)\"],\n","            \"Attesa P95 (gg)\": k[\"Attesa P95 dalla firma al cambio (giorni)\"],\n","            \"Richieste/giorno\": round(k[\"Richieste medie per giorno lavorativo\"]),\n","            \"Picco/giorno\": round(k[\"Picco giornaliero\"]),\n","            \"Coda massima\": round(k[\"Coda massima (richieste)\"]),\n","            \"Fuori SLA %\": round(100 * k[\"Richieste fuori SLA (verifica > 1 gg lav.)\"], 1),\n","        })\n","    return pd.DataFrame(righe)\n","\n","\n","CIANO, AMBRA, VERDE, ROSSO = \"#0FA3C7\", \"#E8951A\", \"#2E9E5B\", \"#D03B3B\"\n","VIOLA, GRIGIO, INK, INK2 = \"#7C6BD6\", \"#98A2B3\", \"#101722\", \"#48556B\"\n","SFONDO, GRIGLIA = \"#FAFAF8\", \"#E7E4DD\"\n","NOTA = \"Dati sintetici · simulazione su scala di mercato, non dati reali\"\n","\n","\n","def _stile(ax, titolo=None, sottotitolo=None):\n","    ax.set_facecolor(SFONDO)\n","    ax.grid(color=GRIGLIA, lw=0.7)\n","    ax.set_axisbelow(True)\n","    for lato in (\"top\", \"right\", \"left\"):\n","        ax.spines[lato].set_visible(False)\n","    ax.spines[\"bottom\"].set_color(\"#CFCBC2\")\n","    ax.tick_params(colors=\"#8A93A5\", labelsize=9)\n","    if titolo:\n","        ax.set_title(titolo, fontsize=12, loc=\"left\", weight=\"bold\", color=INK, pad=18 if sottotitolo else 10)\n","    if sottotitolo:\n","        ax.text(0, 1.02, sottotitolo, transform=ax.transAxes, fontsize=9.5, color=INK2, va=\"bottom\")\n","    return ax\n","\n","\n","def _mila(x, _=None):\n","    return f\"{x:,.0f}\".replace(\",\", \".\")\n","\n","\n","def grafici(ris: dict, figsize=(13.5, 8.6)):\n","    \"\"\"\n","    Quattro grafici di sintesi, pensati per essere letti senza spiegazioni:\n","    ognuno ha nel titolo la risposta che dà, non solo l'argomento.\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","    from matplotlib.ticker import FuncFormatter\n","\n","    g = ris[\"giornaliero\"].copy()\n","    k = ris[\"kpi\"]\n","    cap = k[\"Capacità giornaliera sintetica\"]\n","    fig, axes = plt.subplots(2, 2, figsize=figsize)\n","    fig.patch.set_facecolor(SFONDO)\n","\n","    # ---- 1. domanda contro capacità ---------------------------------------\n","    ax = _stile(axes[0, 0],\n","                \"Il sistema regge la domanda?\" if k[\"Giorni con coda\"] < 10 else \"La domanda supera la capacità\",\n","                f\"media {_mila(k['Richieste medie per giorno lavorativo'])} richieste al giorno, \"\n","                f\"picco {_mila(k['Picco giornaliero'])}, capacità {_mila(cap)}\")\n","    media = g[\"richieste_al_sii\"].rolling(10, center=True, min_periods=1).mean()\n","    ax.fill_between(g[\"data\"], media, color=CIANO, alpha=0.22, lw=0)\n","    ax.plot(g[\"data\"], media, color=CIANO, lw=2.2, label=\"richieste al giorno (media mobile)\")\n","    sopra = g[\"richieste_al_sii\"] > g[\"capacita\"]\n","    if sopra.any():\n","        ax.scatter(g.loc[sopra, \"data\"], g.loc[sopra, \"richieste_al_sii\"], s=14, color=AMBRA, zorder=3,\n","                   label=\"giorni sopra la capacità\")\n","    ax.axhline(cap, color=INK, lw=1.6, ls=\"--\")\n","    ax.annotate(\"capacità del sistema\", xy=(g[\"data\"].iloc[len(g) // 12], cap), xytext=(0, 7),\n","                textcoords=\"offset points\", fontsize=9.5, color=INK, weight=\"bold\")\n","    ax.set_ylim(0, max(cap, g[\"richieste_al_sii\"].max()) * 1.18)\n","    ax.yaxis.set_major_formatter(FuncFormatter(_mila))\n","    ax.set_ylabel(\"richieste al giorno lavorativo\", fontsize=9.5, color=INK2)\n","    ax.legend(frameon=False, fontsize=9, loc=\"lower left\")\n","\n","    # ---- 2. arretrato, in giorni di lavoro ---------------------------------\n","    arretrato = g[\"coda\"] / cap\n","    picco_gg = float(arretrato.max())\n","    ax = _stile(axes[0, 1],\n","                \"Nessun arretrato significativo\" if picco_gg < 0.5 else\n","                (f\"Fino a {picco_gg:.1f} giorni di lavoro arretrato\" if picco_gg < 5 else\n","                 f\"L'arretrato arriva a {picco_gg:.0f} giorni di lavoro\"),\n","                \"quante giornate di lavoro restano indietro rispetto alla capacità\")\n","    ax.fill_between(g[\"data\"], arretrato, color=ROSSO, alpha=0.85, lw=0)\n","    if picco_gg > 0:\n","        i_max = int(arretrato.to_numpy().argmax())\n","        ax.annotate(f\"{picco_gg:.1f} giorni\\n({_mila(g['coda'].iloc[i_max])} richieste)\",\n","                    xy=(g[\"data\"].iloc[i_max], picco_gg), xytext=(12, -6), textcoords=\"offset points\",\n","                    fontsize=9.5, color=INK, weight=\"bold\",\n","                    arrowprops=dict(arrowstyle=\"-\", color=INK2, lw=0.9))\n","    ax.set_ylim(0, max(picco_gg * 1.45, 0.6))\n","    ax.set_ylabel(\"giorni di lavoro arretrato\", fontsize=9.5, color=INK2)\n","\n","    # ---- 3. quanti cambi per POD -------------------------------------------\n","    d = ris[\"distribuzione_cambi\"] * 100\n","    mobili = 100 - d.iloc[0]\n","    ax = _stile(axes[1, 0], f\"Cambia fornitore il {mobili:.1f}% dei POD in un anno\".replace(\".\", \",\"),\n","                \"quasi tutti restano fermi; una minoranza cambia più volte\")\n","    colori = [GRIGIO, CIANO, AMBRA, VIOLA, ROSSO]\n","    ax.bar(d.index, d.values, color=colori, width=0.62)\n","    for x, v in zip(d.index, d.values):\n","        ax.text(x, v + 1.5, f\"{v:.2f}%\".replace(\".\", \",\"), ha=\"center\", fontsize=10, color=INK, weight=\"bold\")\n","    ax.set_ylim(0, max(d.values) * 1.22)\n","    ax.set_ylabel(\"% dei POD\", fontsize=9.5, color=INK2)\n","    ax.set_xticks(range(5), [\"nessun\\ncambio\", \"1 cambio\", \"2 cambi\", \"3 cambi\", \"4 o più\"], fontsize=9.5)\n","    ax.grid(axis=\"x\", visible=False)\n","\n","    # ---- 4. chi vince e chi perde clienti -----------------------------------\n","    v = ris[\"venditori\"].sort_values(\"saldo_netto\")\n","    ax = _stile(axes[1, 1], \"Chi guadagna e chi perde clienti\",\n","                \"saldo fra acquisizioni e clienti persi · venditori inventati\")\n","    col = [ROSSO if x < 0 else VERDE for x in v[\"saldo_netto\"]]\n","    ax.barh(v[\"venditore\"], v[\"saldo_netto\"], color=col, height=0.66)\n","    for i, val in enumerate(v[\"saldo_netto\"]):\n","        ax.text(val + (max(abs(v[\"saldo_netto\"])) * 0.02 * (1 if val >= 0 else -1)), i,\n","                f\"{val:+,.0f}\".replace(\",\", \".\"), va=\"center\", ha=\"left\" if val >= 0 else \"right\",\n","                fontsize=9, color=INK)\n","    ax.axvline(0, color=\"#CFCBC2\", lw=1)\n","    lim = max(abs(v[\"saldo_netto\"])) * 1.35\n","    ax.set_xlim(-lim, lim)\n","    ax.set_xticks([])\n","    ax.grid(visible=False)\n","    ax.tick_params(labelsize=9.5)\n","\n","    fig.text(0.006, 0.985, f\"Simulazione su {_mila(k['POD simulati'])} POD\",\n","             fontsize=13.5, weight=\"bold\", color=INK, va=\"top\")\n","    fig.text(0.006, 0.006, NOTA, fontsize=8.5, color=\"#8A93A5\")\n","    fig.tight_layout(rect=(0, 0.022, 1, 0.955))\n","    return fig\n","\n","\n","def grafico_capacita(sensibilita, figsize=(12, 5.2)):\n","    \"\"\"\n","    Il grafico piu' utile del notebook: quanta capacita' serve perche' la\n","    promessa del giorno lavorativo regga. `sensibilita` e' il DataFrame\n","    prodotto da `analisi_capacita`.\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","    from matplotlib.ticker import FuncFormatter\n","\n","    fig, ax1 = plt.subplots(figsize=figsize)\n","    fig.patch.set_facecolor(SFONDO)\n","    _stile(ax1, \"Quanta capacità serve perché la promessa regga\",\n","           \"ogni punto è un anno intero simulato con una capacità diversa\")\n","    x = sensibilita[\"capacita\"]\n","    ax1.plot(x, sensibilita[\"veloci_entro_1gg\"], marker=\"o\", ms=10, color=VERDE, lw=2.6)\n","    for xi, yi in zip(x, sensibilita[\"veloci_entro_1gg\"]):\n","        ax1.annotate(f\"{yi:.0f}%\", (xi, yi), xytext=(0, 12), textcoords=\"offset points\",\n","                     ha=\"center\", fontsize=10, color=VERDE, weight=\"bold\")\n","    ax1.axhline(90, color=GRIGIO, ls=\":\", lw=1.4)\n","    ax1.text(x.iloc[0], 91.5, \"soglia indicativa del 90%\", fontsize=9, color=INK2)\n","    ax1.set_ylabel(\"% richieste veloci nei tempi\", fontsize=10, color=INK2)\n","    ax1.set_xlabel(\"capacità di elaborazione (richieste verificabili per giorno lavorativo)\",\n","                   fontsize=10, color=INK2, labelpad=38)\n","    ax1.xaxis.set_major_formatter(FuncFormatter(_mila))\n","    ax1.set_ylim(0, 106)\n","    for xi, gg in zip(x, sensibilita[\"giorni_con_coda\"]):\n","        etichetta = \"1 giorno\\ncon coda\" if gg == 1 else f\"{gg} giorni\\ncon coda\"\n","        ax1.annotate(etichetta, (xi, -14), ha=\"center\", fontsize=9,\n","                     color=ROSSO if gg > 20 else INK2, annotation_clip=False)\n","    fig.text(0.006, 0.006, NOTA, fontsize=8.5, color=\"#8A93A5\")\n","    fig.tight_layout(rect=(0, 0.075, 1, 1))\n","    return fig\n","\n","\n","def grafico_scenari(confronto, figsize=(12.5, 5.4)):\n","    \"\"\"Confronto fra scenari: due metriche che si leggono al volo.\"\"\"\n","    import matplotlib.pyplot as plt\n","    import numpy as np\n","\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)\n","    fig.patch.set_facecolor(SFONDO)\n","    nomi = confronto[\"Scenario\"].str.replace(\" \", \"\\n\")\n","    colori = [CIANO, AMBRA, VERDE, VIOLA, ROSSO][: len(confronto)]\n","\n","    _stile(ax1, \"Promessa mantenuta?\", \"% di richieste veloci concluse entro 1 giorno lavorativo\")\n","    ax1.bar(nomi, confronto[\"Veloci entro 1 gg lav. %\"], color=colori, width=0.62)\n","    for i, v in enumerate(confronto[\"Veloci entro 1 gg lav. %\"]):\n","        ax1.text(i, v + 2.5, f\"{v:.0f}%\", ha=\"center\", fontsize=11, weight=\"bold\", color=INK)\n","    ax1.set_ylim(0, 112)\n","    ax1.tick_params(labelsize=9)\n","    ax1.grid(axis=\"x\", visible=False)\n","\n","    _stile(ax2, \"Quanto lavoro resta indietro\", \"coda massima raggiunta nell'anno, in migliaia di richieste\")\n","    valori = confronto[\"Coda massima\"] / 1000\n","    ax2.bar(nomi, valori, color=colori, width=0.62)\n","    for i, v in enumerate(valori):\n","        ax2.text(i, v + max(valori) * 0.03, f\"{v:,.0f} mila\".replace(\",\", \".\"), ha=\"center\",\n","                 fontsize=10.5, weight=\"bold\", color=INK)\n","    ax2.set_ylim(0, max(valori) * 1.2)\n","    ax2.tick_params(labelsize=9)\n","    ax2.grid(axis=\"x\", visible=False)\n","    ax2.set_yticks([])\n","\n","    fig.text(0.006, 0.006, NOTA, fontsize=8.5, color=\"#8A93A5\")\n","    fig.tight_layout(rect=(0, 0.03, 1, 1))\n","    return fig\n","\n","\n","def analisi_capacita(n_pods: int = 10_000_000, livelli=(1_000, 1_500, 2_000, 2_500, 3_000, 4_000),\n","                     **kw):\n","    \"\"\"Prova diversi livelli di capacita' e restituisce la tabella di sintesi.\"\"\"\n","    righe = []\n","    for per_milione in livelli:\n","        r = simula_mercato(Parametri(n_pods=n_pods, capacita_per_milione=per_milione, verbose=False), **kw)\n","        k = r[\"kpi\"]\n","        righe.append({\n","            \"capacita\": k[\"Capacità giornaliera sintetica\"],\n","            \"utilizzo_%\": round(100 * k[\"Utilizzo medio della capacità\"], 1),\n","            \"veloci_entro_1gg\": round(100 * k[\"Richieste veloci concluse entro 1 giorno lavorativo\"], 1),\n","            \"attesa_mediana_gg\": k[\"Attesa mediana dalla firma al cambio (giorni)\"],\n","            \"coda_massima\": round(k[\"Coda massima (richieste)\"]),\n","            \"giorni_con_coda\": k[\"Giorni con coda\"],\n","            \"fuori_sla_%\": round(100 * k[\"Richieste fuori SLA (verifica > 1 gg lav.)\"], 1),\n","        })\n","    return pd.DataFrame(righe)\n","\n","\n","def salva_grafici(ris: dict, cartella: str = \"grafici\", sensibilita=None, confronto=None) -> list:\n","    \"\"\"Salva i grafici come PNG ad alta risoluzione (per slide, LinkedIn, report).\"\"\"\n","    import matplotlib.pyplot as plt\n","    from pathlib import Path as _Path\n","\n","    d = _Path(cartella)\n","    d.mkdir(parents=True, exist_ok=True)\n","    salvati = []\n","    for nome, fig in [(\"sintesi\", grafici(ris)),\n","                      (\"capacita\", grafico_capacita(sensibilita) if sensibilita is not None else None),\n","                      (\"scenari\", grafico_scenari(confronto) if confronto is not None else None)]:\n","        if fig is None:\n","            continue\n","        percorso = d / f\"switching_{nome}.png\"\n","        fig.savefig(percorso, dpi=200, facecolor=SFONDO)\n","        plt.close(fig)\n","        salvati.append(str(percorso))\n","    return salvati\n","\n","print('Motore caricato. POD domestici in Italia (ARERA, dati 2025):',\n","      f'{POD_DOMESTICI_ITALIA:,}'.replace(',', '.'))"]},{"cell_type":"markdown","id":"af5d7b37","metadata":{"id":"af5d7b37"},"source":["## 3. La simulazione base: 10 milioni di POD\n","\n","`Parametri` contiene tutte le manopole. Le più interessanti da toccare:\n","\n","| Parametro | Significato | Default |\n","|---|---|---|\n","| `n_pods` | quanti punti di prelievo simulare | 10.000.000 |\n","| `capacita_per_milione` | quante richieste al giorno lavorativo il sistema riesce a verificare, per milione di POD | 2.000 |\n","| `tasso_switch_base` | contratti attesi per POD in un anno (prima dei moltiplicatori di segmento) | 0,40 |\n","| `error_rate` | probabilità di errore tecnico alla verifica SII | 2,0% |\n","| `crescita` | crescita lineare della domanda nell'arco dell'anno | 0 |\n","| `alta_frequenza` | quota di clienti che cambiano molto spesso | 2% |"]},{"cell_type":"code","execution_count":3,"id":"e1d15b99","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:11:50.721497Z","iopub.status.busy":"2026-09-20T06:11:50.721251Z","iopub.status.idle":"2026-09-20T06:11:55.211234Z","shell.execute_reply":"2026-09-20T06:11:55.210234Z"},"colab":{"base_uri":"https://localhost:8080/","height":979},"id":"e1d15b99","executionInfo":{"status":"ok","timestamp":1790538467653,"user_tz":-120,"elapsed":7654,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"9a6c7a8e-e00a-489a-a2ea-dd0cf3efb7b5"},"outputs":[{"output_type":"stream","name":"stdout","text":["[   0.0s] genero 10.000.000 POD sintetici\n","[   2.1s] clienti pronti\n","[   3.0s] 3.611.526 richieste di cambio generate\n","[   3.6s] 3.736.345 richieste totali (inclusi duplicati e concorrenti)\n","[   4.7s] coda dei back-office risolta\n","[   6.3s] 3.200.787 richieste arrivano al SII\n","[   6.8s] coda del SII risolta\n","[   7.8s] fatto in 8s\n"]},{"output_type":"execute_result","data":{"text/plain":["                                                  KPI      Valore\n","0                                        POD simulati  10.000.000\n","1                                 Richieste di cambio   3.736.345\n","2                                  Richieste concluse   2.729.429\n","3                              Tasso di completamento       73,1%\n","4                   POD che cambiano almeno una volta   2.031.496\n","5                    Tasso di switching annuo (% POD)       20,3%\n","6                                    POD con 2+ cambi     496.159\n","7              Quota di chi cambia che lo fa 2+ volte       24,4%\n","8                     Quota idonea alla corsia veloce       76,0%\n","9   Richieste veloci concluse entro 1 giorno lavor...       94,9%\n","10                         Cambi completati nell'anno   2.729.429\n","11                    Cambi completati ogni 1.000 POD       272,9\n","12      Attesa mediana dalla firma al cambio (giorni)         9,0\n","13          Attesa P95 dalla firma al cambio (giorni)        24,0\n","14                               Preliminary check KO       10,0%\n","15         Richieste fuori SLA (verifica > 1 gg lav.)        0,0%\n","16                           Coda massima (richieste)     8.284,0\n","17                                    Giorni con coda           2\n","18              Richieste medie per giorno lavorativo    12.581,6\n","19                                  Picco giornaliero    16.969,0\n","20                     Capacità giornaliera sintetica    20.000,0\n","21                      Utilizzo medio della capacità       62,9%\n","22                                      CAC medio (€)        74,8\n","23      Margine annuo medio del cliente acquisito (€)       105,6\n","24               Costo di acquisizione totale (mln €)       204,1"],"text/html":["\n","  <div id=\"df-98172483-aa02-47d7-baf4-4f32b219ffdb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>KPI</th>\n","      <th>Valore</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>POD simulati</td>\n","      <td>10.000.000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Richieste di cambio</td>\n","      <td>3.736.345</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Richieste concluse</td>\n","      <td>2.729.429</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Tasso di completamento</td>\n","      <td>73,1%</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>POD che cambiano almeno una volta</td>\n","      <td>2.031.496</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Tasso di switching annuo (% POD)</td>\n","      <td>20,3%</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>POD con 2+ cambi</td>\n","      <td>496.159</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Quota di chi cambia che lo fa 2+ volte</td>\n","      <td>24,4%</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Quota idonea alla corsia veloce</td>\n","      <td>76,0%</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Richieste veloci concluse entro 1 giorno lavor...</td>\n","      <td>94,9%</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Cambi completati nell'anno</td>\n","      <td>2.729.429</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Cambi completati ogni 1.000 POD</td>\n","      <td>272,9</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Attesa mediana dalla firma al cambio (giorni)</td>\n","      <td>9,0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Attesa P95 dalla firma al cambio (giorni)</td>\n","      <td>24,0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Preliminary check KO</td>\n","      <td>10,0%</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Richieste fuori SLA (verifica &gt; 1 gg lav.)</td>\n","      <td>0,0%</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Coda massima (richieste)</td>\n","      <td>8.284,0</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Giorni con coda</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Richieste medie per giorno lavorativo</td>\n","      <td>12.581,6</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Picco giornaliero</td>\n","      <td>16.969,0</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Capacità giornaliera sintetica</td>\n","      <td>20.000,0</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Utilizzo medio della capacità</td>\n","      <td>62,9%</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>CAC medio (€)</td>\n","      <td>74,8</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>Margine annuo medio del cliente acquisito (€)</td>\n","      <td>105,6</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Costo di acquisizione totale (mln €)</td>\n","      <td>204,1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98172483-aa02-47d7-baf4-4f32b219ffdb')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"tabella_kpi(ris)\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"KPI\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Quota idonea alla corsia veloce\",\n          \"Coda massima (richieste)\",\n          \"POD simulati\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valore\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"76,0%\",\n          \"2\",\n          \"10.000.000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":3}],"source":["p = Parametri(\n","    n_pods=10_000_000,          # prova 30_500_000 per il mercato domestico italiano intero\n","    capacita_per_milione=2_000, # capacità sintetica: 20.000 richieste/giorno lavorativo su 10 M di POD\n","    verbose=True,\n",")\n","ris = simula_mercato(p, scenario=\"NORMALE\")\n","tabella_kpi(ris)"]},{"cell_type":"markdown","id":"161947bd","metadata":{"id":"161947bd"},"source":["### Come leggere i numeri principali\n","\n","- **Tasso di switching annuo**: quota di POD che cambia fornitore almeno una volta nell'anno simulato.\n","  Per un confronto con la realtà: ARERA pubblica i tassi di switching effettivi nella Relazione Annuale.\n","- **Richieste veloci concluse entro 1 giorno lavorativo**: è la promessa della riforma, misurata su tutte le\n","  richieste incamminate sulla corsia veloce (comprese quelle che poi si perdono per errori o ripensamenti).\n","- **Attesa mediana dalla firma al cambio**: quello che sente il cliente. È più lunga del \"giorno lavorativo\"\n","  perché prima arrivano preliminary check, autorizzazioni e i tempi del venditore.\n","- **Coda massima**: quante richieste restano in attesa nel giorno peggiore. Se resta a zero, il sistema ha\n","  margine; se cresce per settimane, la promessa dei tempi salta."]},{"cell_type":"code","execution_count":4,"id":"324b67f4","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:11:55.213404Z","iopub.status.busy":"2026-09-20T06:11:55.213112Z","iopub.status.idle":"2026-09-20T06:11:56.123601Z","shell.execute_reply":"2026-09-20T06:11:56.122719Z"},"colab":{"base_uri":"https://localhost:8080/","height":881},"id":"324b67f4","executionInfo":{"status":"ok","timestamp":1790538469517,"user_tz":-120,"elapsed":1862,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"c55230dd-375b-4ca7-905c-465c373618c4"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1350x860 with 4 Axes>"],"image/png":"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= grafici(ris)   # 4 grafici: domanda vs capacità, arretrato, cambi per POD, saldo venditori"]},{"cell_type":"markdown","id":"a7ec7e36","metadata":{"id":"a7ec7e36"},"source":["## 4. Cosa succede negli scenari difficili\n","\n","Stessa scala, cinque mondi diversi: crescita della domanda, guerra commerciale a colpi di campagne,\n","clienti che cambiano più spesso, e lo scenario di stress che mette tutto insieme con meno capacità."]},{"cell_type":"code","execution_count":5,"id":"7880fb56","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:11:56.125390Z","iopub.status.busy":"2026-09-20T06:11:56.125118Z","iopub.status.idle":"2026-09-20T06:12:23.612077Z","shell.execute_reply":"2026-09-20T06:12:23.611016Z"},"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"7880fb56","executionInfo":{"status":"ok","timestamp":1790538519341,"user_tz":-120,"elapsed":49822,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"4c0ea099-d4e4-4c61-8202-bedebbb09b4a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["             Scenario  Richieste  Cambi completati  Completamento %  \\\n","0             Normale    3736345           2729429             73.1   \n","1      Forte Crescita    4859828           3424872             70.5   \n","2  Guerra Commerciale    4448941           3104785             69.8   \n","3       Churn Elevato    5854980           4057768             69.3   \n","4              Stress    4979391           3258111             65.4   \n","\n","   Switching annuo % POD  Veloci entro 1 gg lav. %  Attesa mediana (gg)  \\\n","0                   20.3                      94.9                  9.0   \n","1                   24.1                      67.9                 11.0   \n","2                   22.4                      67.9                 10.0   \n","3                   23.8                      20.5                 15.0   \n","4                   23.3                      12.1                 19.0   \n","\n","   Attesa P95 (gg)  Richieste/giorno  Picco/giorno  Coda massima  Fuori SLA %  \n","0             24.0             12582         16969          8284          0.0  \n","1             24.0             16150         23483        116466         22.1  \n","2             24.0             14376         28292         65927         16.3  \n","3             24.0             19030         25912        122544         70.3  \n","4             30.0             16163         31044        281996         84.0  "],"text/html":["\n","  <div id=\"df-4c12fbae-ca5e-4075-86cd-3b1f3aff3235\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Scenario</th>\n","      <th>Richieste</th>\n","      <th>Cambi completati</th>\n","      <th>Completamento %</th>\n","      <th>Switching 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<td>31044</td>\n","      <td>281996</td>\n","      <td>84.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c12fbae-ca5e-4075-86cd-3b1f3aff3235')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      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]\n}"}},"metadata":{},"execution_count":5}],"source":["confronto = confronta_scenari(n_pods=10_000_000)\n","confronto"]},{"cell_type":"code","execution_count":6,"id":"ff0f2ee1","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:12:23.614422Z","iopub.status.busy":"2026-09-20T06:12:23.614133Z","iopub.status.idle":"2026-09-20T06:12:23.847097Z","shell.execute_reply":"2026-09-20T06:12:23.846400Z"},"colab":{"base_uri":"https://localhost:8080/","height":561},"id":"ff0f2ee1","executionInfo":{"status":"ok","timestamp":1790538519675,"user_tz":-120,"elapsed":336,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"7afc2287-e62c-4fcd-99be-010b989b2d2c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1250x540 with 2 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\n"},"metadata":{}}],"source":["fig = grafico_scenari(confronto)"]},{"cell_type":"markdown","id":"f56507e1","metadata":{"id":"f56507e1"},"source":["Il confronto mostra il punto centrale del progetto: la **velocità** del processo non è ciò che rompe il\n","sistema. A rompere è il rapporto fra domanda e capacità di elaborazione. Nello scenario normale la coda resta\n","quasi sempre vuota; con la domanda che cresce, o con il 20% di capacità in meno, la coda cresce per mesi e il\n","\"giorno lavorativo\" diventa una settimana.\n","\n","> Nota di metodo: nel motore per-richiesta del repository tutti gli scenari condividono la stessa popolazione\n","> di clienti, per confrontarli a parità di clienti; qui invece la popolazione viene rigenerata a ogni scenario,\n","> quindi lo scenario a churn elevato cambia anche la quota di clienti \"alta frequenza\" e produce volumi un po'\n","> più alti. È una scelta diversa, non un errore: l'ho lasciata perché su scala di mercato interessa proprio\n","> l'effetto di una popolazione più mobile."]},{"cell_type":"markdown","id":"cbebe0d7","metadata":{"id":"cbebe0d7"},"source":["## 5. Il mercato domestico italiano intero: 30,5 milioni di POD\n","\n","Questa cella gira sulla dimensione reale del mercato domestico (ARERA, dati 2025). Su Colab base servono\n","alcuni GB di RAM: se l'ambiente si riavvia, abbassa `n_pods`."]},{"cell_type":"code","execution_count":11,"id":"ce539d37","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:12:23.848858Z","iopub.status.busy":"2026-09-20T06:12:23.848626Z","iopub.status.idle":"2026-09-20T06:12:39.279873Z","shell.execute_reply":"2026-09-20T06:12:39.278766Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"ce539d37","executionInfo":{"status":"ok","timestamp":1790538718562,"user_tz":-120,"elapsed":24101,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"73ed1fd5-b731-49f7-a91f-5b1a853ba1e4"},"outputs":[{"output_type":"stream","name":"stdout","text":["[   0.0s] genero 30.500.000 POD sintetici\n","[   5.9s] clienti pronti\n","[   7.9s] 11.001.864 richieste di cambio generate\n","[   9.1s] 11.383.096 richieste totali (inclusi duplicati e concorrenti)\n","[  12.8s] coda dei back-office risolta\n","[  19.1s] 9.752.419 richieste arrivano al SII\n","[  21.0s] coda del SII risolta\n","[  24.1s] fatto in 24s\n","\n","MERCATO DOMESTICO ITALIANO SIMULATO (dati sintetici, scala reale)\n","======================================================================\n","POD simulati................................. 30,50 milioni\n","Richieste di cambio in un anno............... 11,38 milioni\n","Cambi completati............................. 8,31 milioni\n","POD che cambiano almeno una volta............ 6,19 milioni  (20,3% dei POD)\n","Richieste medie per giorno lavorativo........ 38.335\n","Picco giornaliero............................ 51.937\n","Capacita giornaliera ipotizzata.............. 61.000\n","Coda massima nel giorno peggiore............. 25.002\n","Richieste veloci entro 1 giorno lavorativo... 94,9%\n","Attesa mediana dalla firma al cambio......... 9 giorni\n","Costo di acquisizione complessivo............ 622 milioni di euro\n"]}],"source":["ris_it = simula_mercato(Parametri(n_pods=POD_DOMESTICI_ITALIA, verbose=True), scenario=\"NORMALE\")\n","k = ris_it[\"kpi\"]\n","\n","def mil(x):\n","    return f\"{x/1e6:.2f} milioni\".replace(\".\", \",\")\n","\n","def num(x):\n","    return f\"{x:,.0f}\".replace(\",\", \".\")\n","\n","righe = [\n","    (\"POD simulati\", mil(k[\"POD simulati\"])),\n","    (\"Richieste di cambio in un anno\", mil(k[\"Richieste di cambio\"])),\n","    (\"Cambi completati\", mil(k[\"Cambi completati nell'anno\"])),\n","    (\"POD che cambiano almeno una volta\",\n","     mil(k[\"POD che cambiano almeno una volta\"])\n","     + f\"  ({k['Tasso di switching annuo (% POD)']:.1%} dei POD)\".replace(\".\", \",\")),\n","    (\"Richieste medie per giorno lavorativo\", num(k[\"Richieste medie per giorno lavorativo\"])),\n","    (\"Picco giornaliero\", num(k[\"Picco giornaliero\"])),\n","    (\"Capacita giornaliera ipotizzata\", num(k[\"Capacità giornaliera sintetica\"])),\n","    (\"Coda massima nel giorno peggiore\", num(k[\"Coda massima (richieste)\"])),\n","    (\"Richieste veloci entro 1 giorno lavorativo\",\n","     f\"{k['Richieste veloci concluse entro 1 giorno lavorativo']:.1%}\".replace(\".\", \",\")),\n","    (\"Attesa mediana dalla firma al cambio\", f\"{k['Attesa mediana dalla firma al cambio (giorni)']:.0f} giorni\"),\n","    (\"Costo di acquisizione complessivo\",\n","     num(k[\"Costo di acquisizione totale (mln €)\"]) + \" milioni di euro\"),\n","]\n","\n","print()\n","print(\"MERCATO DOMESTICO ITALIANO SIMULATO (dati sintetici, scala reale)\")\n","print(\"=\" * 70)\n","for etichetta, valore in righe:\n","    print(f\"{etichetta:.<45} {valore}\")"]},{"cell_type":"markdown","id":"330022ec","metadata":{"id":"330022ec"},"source":["## 6. Quanta capacità serve davvero?\n","\n","La domanda operativa più concreta di tutto il progetto: **con quante verifiche al giorno il sistema regge?**\n","La cella prova sei livelli di capacità sul mercato intero e misura cosa succede al cliente."]},{"cell_type":"code","execution_count":8,"id":"4a1c5fc5","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:12:39.282527Z","iopub.status.busy":"2026-09-20T06:12:39.282205Z","iopub.status.idle":"2026-09-20T06:14:07.132509Z","shell.execute_reply":"2026-09-20T06:14:07.124431Z"},"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"4a1c5fc5","executionInfo":{"status":"ok","timestamp":1790538692472,"user_tz":-120,"elapsed":147490,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"6134916d-fff8-4365-dd85-abcb775c16a0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   capacita  utilizzo_%  veloci_entro_1gg  attesa_mediana_gg  coda_massima  \\\n","0   30500.0        98.1               1.8               56.0       2127349   \n","1   45750.0        83.8              82.4                9.0         55713   \n","2   61000.0        62.8              94.9                9.0         25002   \n","3   76250.0        50.3              95.0                9.0         20427   \n","4   91500.0        41.9              95.1                9.0         15852   \n","5  122000.0        31.4              95.2                9.0          6702   \n","\n","   giorni_con_coda  fuori_sla_%  \n","0              246         96.2  \n","1               67          0.3  \n","2                2          0.0  \n","3                1          0.0  \n","4                1          0.0  \n","5                1          0.0  "],"text/html":["\n","  <div id=\"df-9dcd0b8d-53df-4734-9d97-f1b7c6a2eb04\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n"," 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<td>61000.0</td>\n","      <td>62.8</td>\n","      <td>94.9</td>\n","      <td>9.0</td>\n","      <td>25002</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>76250.0</td>\n","      <td>50.3</td>\n","      <td>95.0</td>\n","      <td>9.0</td>\n","      <td>20427</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>91500.0</td>\n","      <td>41.9</td>\n","      <td>95.1</td>\n","      <td>9.0</td>\n","      <td>15852</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>122000.0</td>\n","      <td>31.4</td>\n","      <td>95.2</td>\n","      <td>9.0</td>\n","      <td>6702</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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\"coda_massima\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 858547,\n        \"min\": 6702,\n        \"max\": 2127349,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2127349,\n          55713\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"giorni_con_coda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98,\n        \"min\": 1,\n        \"max\": 246,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          67,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuori_sla_%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.24917408897501,\n        \"min\": 0.0,\n        \"max\": 96.2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          96.2,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":8}],"source":["sensibilita = analisi_capacita(POD_DOMESTICI_ITALIA)\n","sensibilita"]},{"cell_type":"code","execution_count":9,"id":"240967d0","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:14:07.143225Z","iopub.status.busy":"2026-09-20T06:14:07.142354Z","iopub.status.idle":"2026-09-20T06:14:07.620654Z","shell.execute_reply":"2026-09-20T06:14:07.618927Z"},"colab":{"base_uri":"https://localhost:8080/","height":541},"id":"240967d0","executionInfo":{"status":"ok","timestamp":1790538692797,"user_tz":-120,"elapsed":327,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"f81ab921-28a3-4026-e7b8-3f3997895da7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x520 with 1 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\n"},"metadata":{}}],"source":["fig = grafico_capacita(sensibilita)"]},{"cell_type":"markdown","id":"b96db57c","metadata":{"id":"b96db57c"},"source":["Questo è il grafico che risponde alla domanda: sotto una certa capacità la promessa del giorno lavorativo\n","non regge più, sopra una certa soglia aggiungere capacità non serve quasi a niente. In mezzo c'è il punto\n","di equilibrio da cercare."]},{"cell_type":"markdown","id":"def9a802","metadata":{"id":"def9a802"},"source":["## 7. Portarsi via i grafici\n","\n","Una riga per salvare tutto in PNG ad alta risoluzione (utili per slide, report o LinkedIn).\n","Su Colab i file finiscono nel pannello a sinistra, sotto `grafici/`."]},{"cell_type":"code","execution_count":10,"id":"c755b731","metadata":{"execution":{"iopub.execute_input":"2026-09-20T06:14:07.622942Z","iopub.status.busy":"2026-09-20T06:14:07.622668Z","iopub.status.idle":"2026-09-20T06:14:08.606104Z","shell.execute_reply":"2026-09-20T06:14:08.605361Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"c755b731","executionInfo":{"status":"ok","timestamp":1790538694328,"user_tz":-120,"elapsed":1530,"user":{"displayName":"Fabio mencio","userId":"05860154059512044184"}},"outputId":"b3614ee5-afdc-40ba-f443-100ee5adafcc"},"outputs":[{"output_type":"stream","name":"stdout","text":["salvato: grafici/switching_sintesi.png\n","salvato: grafici/switching_capacita.png\n","salvato: grafici/switching_scenari.png\n"]}],"source":["file_salvati = salva_grafici(ris_it, cartella=\"grafici\",\n","                             sensibilita=sensibilita, confronto=confronto)\n","for f in file_salvati:\n","    print(\"salvato:\", f)"]},{"cell_type":"markdown","id":"9e329cd9","metadata":{"id":"9e329cd9"},"source":["## 8. Cosa portarsi a casa\n","\n","1. Su scala di mercato reale il nuovo processo produce **milioni di cambi all'anno** concentrati in poche\n","   decine di migliaia di richieste al giorno lavorativo: è un flusso continuo, non più un'onda mensile.\n","2. Finché la capacità di elaborazione sta sopra il picco, la promessa del giorno lavorativo regge; appena\n","   scende sotto, la coda si accumula e i tempi percepiti dal cliente si allungano di settimane.\n","3. Il cliente aspetta comunque più di 24 ore: la parte tecnica è veloce, ma prima ci sono preliminary check,\n","   autorizzazioni e i tempi del venditore.\n","4. Il blocco di 7 giorni lavorativi sul POD respinge da solo una quota rilevante di richieste: sono\n","   soprattutto duplicati e richieste concorrenti, cioè lavoro commerciale sprecato.\n","\n","### Limiti, detti chiaramente\n","\n","Dati sintetici: la scala è realistica, i comportamenti sono ipotesi. Le specifiche tecniche del SII\n","pubblicate da Acquirente Unico non sono state usate: i tempi interni derivano solo dalle scadenze scritte\n","nell'Allegato A1. La capacità di elaborazione è un parametro inventato, non una misura del SII reale.\n","\n","---\n","\n","**Fabio Mencio** — Energy | Data | AI — https://fabio-mencio.vercel.app/\n","*«Dalla rete elettrica all'AI: porto l'intelligenza artificiale dove i processi dell'energia succedono davvero.»*\n","\n","Progetto completo (motore per-richiesta con event log, notebook di analisi, report e presentazione):\n","repository `switching-24h-energy`."]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.13.15"}},"nbformat":4,"nbformat_minor":5}