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<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width,initial-scale=1">
  <meta name="theme-color" content="#080d19">
  <meta name="description" content="FlowPilot: an open-source, reproducible operations digital twin for simulating staffing decisions, queue risk and total cost.">
  <title>FlowPilot | Operations Digital Twin</title>
  <style>
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4px;line-height:1.15;font-variant-numeric:tabular-nums}.kpi-sub{font-size:11px;color:var(--muted);line-height:1.45}.section-heading.smaller{margin-top:39px}.section-heading.smaller p{color:var(--muted);font-size:11px;max-width:250px;text-align:right;margin:0}.chart-grid{display:grid;grid-template-columns:1fr 1fr;gap:15px}.panel{background:var(--panel);border:1px solid var(--stroke);border-radius:11px;min-width:0;overflow:hidden}.chart-grid .panel{padding:22px 20px 17px}.panel-head{display:flex;justify-content:space-between;align-items:start;gap:10px}.panel-overline{font-size:10px;letter-spacing:1.6px;color:#84a7bb;font-weight:800}.panel h3{margin:5px 0 0;font-size:16px;letter-spacing:-.25px;font-weight:760}.info-dot{color:#69879c;font-size:13px}.legend-chip{font-size:9px;font-weight:790;letter-spacing:.6px;color:#9db0c7;white-space:nowrap;margin-top:13px}.legend-chip 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#375069;color:#bed3e6;background:#152438}.action-secondary:hover{border-color:#7995aa}.risk-rows{min-height:172px;padding-top:15px}.risk-item{padding:12px 0;border-bottom:1px solid var(--stroke)}.risk-item:last-child{border:0}.risk-label{display:flex;justify-content:space-between;font-size:11px;color:var(--sub);margin-bottom:8px}.risk-label strong{font-size:12px;color:var(--text)}.risk-track{height:7px;border-radius:8px;background:#2a3a4b;position:relative}.risk-track .range{position:absolute;top:0;bottom:0;background:#6f73bb;border-radius:6px}.risk-track .median{position:absolute;top:-4px;width:3px;height:15px;background:var(--accent);border-radius:3px}.risk-footnote{font-size:10px;line-height:1.55;color:var(--muted);border-top:1px solid var(--stroke);padding-top:11px}.muted{color:var(--muted)}.table-panel{margin-top:15px}.table-panel .panel-head{padding:21px 22px 17px}.action-secondary.compact{padding:8px 11px;margin-top:2px}.table-wrap{overflow-x:auto}table{border-collapse:collapse;width:100%;font-variant-numeric:tabular-nums;white-space:nowrap}thead{background:#152133}th{text-align:left;font-size:10px;color:#8da3bc;letter-spacing:.75px;font-weight:850;padding:13px 19px}td{font-size:12px;padding:13px 19px;border-top:1px solid var(--stroke);color:#c3cce0}td:first-child{font-weight:850;color:var(--text)}tbody tr{transition:background .2s}tbody tr:hover{background:#19273b}tbody tr.baseline{background:#1a273b}tbody tr.recommended{background:#122c2b}.table-empty{text-align:center;padding:36px;color:var(--muted)}.row-tag{display:inline-block;font-size:9px;font-weight:800;letter-spacing:.4px;border-radius:4px;padding:3px 6px;color:#97bcf6;background:#1a3551}.row-tag.best{color:var(--accent);background:#1b3933}.row-tag.miss{color:#a2a9b8;background:#242a37}.method{display:grid;grid-template-columns:1.6fr 1fr;gap:50px;margin:48px 0 37px;border-top:1px solid var(--stroke);padding:30px 0 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        <a href="#workspace" class="brand" aria-label="FlowPilot dashboard"><span class="brand-mark">◈</span><span class="brand-name">flow<span>pilot</span><small>OPERATIONS INTELLIGENCE</small></span></a>
        <span class="badge beta">PROJECT 23</span>
      </div>
      <div class="side-scroll">
        <div class="sidebar-section">
          <div class="side-label">01 / WORKSPACE</div>
          <label for="preset">Scenario template</label>
          <select id="preset">
            <option value="retail">Retail & customer service</option>
            <option value="health">Healthcare reception</option>
            <option value="support">Technical support desk</option>
            <option value="custom">Imported / custom scenario</option>
          </select>
          <div class="side-help">Choose a real-world operations case, then customize assumptions.</div>
        </div>
        <div class="sidebar-section">
          <div class="side-label">02 / SERVICE MODEL</div>
          <div class="field-grid">
            <div class="field"><label for="agents">Staff members</label><input id="agents" type="number" min="1" max="20" step="1"></div>
            <div class="field"><label for="patience">Patience · min</label><input id="patience" type="number" min="1" max="180" step="1"></div>
            <div class="field"><label for="serviceMin">Service min · min</label><input id="serviceMin" type="number" min="0.2" max="90" step="0.5"></div>
            <div class="field"><label for="serviceMode">Most likely · min</label><input id="serviceMode" type="number" min="0.2" max="90" step="0.5"></div>
            <div class="field"><label for="serviceMax">Service max · min</label><input id="serviceMax" type="number" min="0.2" max="90" step="0.5"></div>
            <div class="field"><label for="slaMinutes">SLA within · min</label><input id="slaMinutes" type="number" min="0" max="120" step="1"></div>
          </div>
        </div>
        <div class="sidebar-section">
          <div class="row-split"><div class="side-label">03 / HOURLY ARRIVALS</div><button id="downloadCsvTemplate" class="text-button" type="button">CSV template ↗</button></div>
          <div class="side-help profile-hint">Expected arrivals in each hour of an 8-hour shift.</div>
          <div id="hourlyInputs" class="hourly-inputs" aria-label="Expected arrivals per hour"></div>
          <label class="upload-inline" for="uploadCsv">↑ Import 8-hour demand CSV <input id="uploadCsv" type="file" accept=".csv,text/csv" hidden></label>
        </div>
        <div class="sidebar-section">
          <div class="side-label">04 / GOALS & COSTS</div>
          <div class="field-grid">
            <div class="field"><label for="slaGoal">SLA goal · %</label><input id="slaGoal" type="number" min="1" max="100" step="1"></div>
            <div class="field"><label for="wage">Wage · $/hr</label><input id="wage" type="number" min="0" max="500" step="1"></div>
            <div class="field"><label for="abandonmentCost">Lost customer · $</label><input id="abandonmentCost" type="number" min="0" max="500" step="1"></div>
            <div class="field"><label for="waitingCost">Wait cost · $/min</label><input id="waitingCost" type="number" min="0" max="20" step="0.01"></div>
          </div>
        </div>
        <div class="sidebar-section">
          <div class="side-label">05 / SIMULATION SETTINGS</div>
          <div class="field-grid">
            <div class="field"><label for="trials">Replications</label><input id="trials" type="number" min="10" max="300" step="10"></div>
            <div class="field"><label for="seed">Random seed</label><input id="seed" type="number" min="0" max="4294967295" step="1"></div>
            <div class="field full"><label for="optimizerMaxAgents">Staffing search maximum</label><input id="optimizerMaxAgents" type="number" min="2" max="20" step="1"></div>
          </div>
        </div>
        <button id="run" class="run-button" type="button"><span>▶</span> Run simulation <span class="run-arrow">↗</span></button>
        <p id="formError" class="form-error" role="alert" hidden></p>
        <div class="privacy-box"><span>♧</span><div><strong>Local by design</strong><p>No backend, tracking, API keys or uploads. Data stays in your browser.</p></div></div>
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    <main id="workspace" class="main">
      <header class="topbar"><div class="breadcrumbs">WORKSPACE <span>/</span> <b>OPERATIONS DIGITAL TWIN</b></div><div class="top-actions"><span class="status"><i></i> BROWSER COMPUTE</span><button id="themeToggle" class="mini-button" type="button" aria-label="Toggle light or dark theme">☼</button></div></header>
      <div class="content">
        <div class="hero">
          <div class="hero-eyebrow"><span class="eyebrow-line"></span> DECISION SCIENCE / 001</div>
          <div class="hero-grid"><div><h1>Turn operational uncertainty<br><em>into better decisions.</em></h1><p>Simulate demand, test staffing strategies and understand the trade-offs between service quality, customer abandonment and cost.</p></div><div class="hero-symbol" aria-hidden="true"><div class="hero-ring r1"></div><div class="hero-ring r2"></div><div class="hero-diamond">◇</div></div></div>
          <div class="hero-bottom"><span>DISCRETE-EVENT SIMULATION</span><span>SEEDED MONTE CARLO</span><span>REPRODUCIBLE ANALYSIS</span></div>
        </div>
        <div class="section-heading"><div><div class="section-kicker">01 — OVERVIEW</div><h2>Performance snapshot</h2></div><div class="heading-right"><span id="freshness" class="freshness">● Ready to analyze</span><span id="runMeta" class="tiny">80 runs · 8-hour shift</span></div></div>
        <div class="kpis">
          <div class="kpi"><div class="kpi-top"><span>SERVICE LEVEL</span><span class="kpi-icon">◎</span></div><strong id="kpiSla">—</strong><div id="kpiSlaSub" class="kpi-sub">Customers served within SLA</div></div>
          <div class="kpi"><div class="kpi-top"><span>TOTAL EXPECTED COST</span><span class="kpi-icon">$</span></div><strong id="kpiCost">—</strong><div id="kpiCostSub" class="kpi-sub">Across an 8-hour shift</div></div>
          <div class="kpi"><div class="kpi-top"><span>AVERAGE WAIT</span><span class="kpi-icon">◷</span></div><strong id="kpiWait">—</strong><div id="kpiWaitSub" class="kpi-sub">Among served customers</div></div>
          <div class="kpi"><div class="kpi-top"><span>CUSTOMER ABANDONMENT</span><span class="kpi-icon">↘</span></div><strong id="kpiAbandon">—</strong><div id="kpiAbandonSub" class="kpi-sub">Expected customers lost</div></div>
        </div>
        <div class="section-heading smaller"><div><div class="section-kicker">02 — INTELLIGENCE</div><h2>Staffing frontier</h2></div><p>Each point uses the same simulated arrival cohorts for fair comparison.</p></div>
        <div class="chart-grid">
          <section class="panel"><div class="panel-head"><div><div class="panel-overline">ECONOMIC TRADE-OFF</div><h3>Expected shift cost <span class="info-dot" title="Wages + overtime work + waiting minutes + customer abandonment">ⓘ</span></h3></div><span class="legend-chip"><i class="cyan"></i> TOTAL COST</span></div><div id="costChart" class="chart" role="img" aria-label="Expected total shift cost by staffing level"></div><div id="costSummary" class="chart-note">Run simulation to view the optimization curve.</div></section>
          <section class="panel"><div class="panel-head"><div><div class="panel-overline">CUSTOMER EXPERIENCE</div><h3>Service-level performance <span class="info-dot" title="Share of all arrivals who begin service within the SLA threshold">ⓘ</span></h3></div><span class="legend-chip"><i class="purple"></i> SLA ATTAINMENT</span></div><div id="slaChart" class="chart" role="img" aria-label="Service-level attainment by staffing level, with SLA goal"></div><div id="slaSummary" class="chart-note">Choose a service-level target and compare options.</div></section>
        </div>
        <div class="lower-grid">
          <section class="panel decision-panel"><div class="panel-head"><div><div class="panel-overline">03 — DECISION SUPPORT</div><h3>Recommended strategy</h3></div><span id="recBadge" class="rec-badge">WAITING FOR RUN</span></div><div id="recommendation" class="recommendation"><p>Use <strong>Run simulation</strong> to find the cheapest staffing level that achieves your SLA target.</p></div><div class="decision-actions"><button type="button" id="applyRecommended" class="action-primary" disabled>Apply recommendation <span>↗</span></button><button type="button" id="exportJson" class="action-secondary" disabled>↓ Export report</button></div></section>
          <section class="panel risk-panel"><div class="panel-head"><div><div class="panel-overline">04 — UNCERTAINTY</div><h3>Variability across simulations</h3></div><span class="legend-chip">P10–P90 BAND</span></div><div id="riskRows" class="risk-rows"><p class="muted">Replicated simulation output will appear here.</p></div><div class="risk-footnote">P10–P90 is the middle 80% of simulated outcomes. It is not a guarantee or a confidence interval for future performance.</div></section>
        </div>
        <section class="panel table-panel"><div class="panel-head"><div><div class="panel-overline">05 — ALL ALTERNATIVES</div><h3>Staffing comparison</h3></div><button type="button" id="exportCsv" class="action-secondary compact" disabled>↓ Download CSV</button></div><div class="table-wrap"><table><thead><tr><th>AGENTS</th><th>SLA</th><th>AVG WAIT</th><th>LOST / SHIFT</th><th>UTILIZATION</th><th>TOTAL COST</th><th>FLAG</th></tr></thead><tbody id="comparisonBody"><tr><td colspan="7" class="table-empty">Run the simulation to generate comparison data.</td></tr></tbody></table></div></section>
        <section class="method"><div><div class="section-kicker">HOW IT WORKS</div><h2>Transparent by design.</h2><p>This model assumes hourly Poisson arrivals, triangular service duration, first-come-first-served queues, fixed customer patience and fixed staffing. It simulates an 8-hour shift with optional overtime, compares identical sampled cohorts across staffing levels and reports cost in illustrative USD.</p><p class="method-note"><strong>Important:</strong> This is a planning sandbox with synthetic demand, not an AI predictor or a validated production forecast. Check assumptions against actual operations before making decisions. For zero-arrival shifts the modeled SLA is defined as 100%.</p></div><div class="method-links"><div><span>SIMULATION ENGINE</span><strong>Deterministic seeded Monte Carlo</strong></div><div><span>SERVICE DISCIPLINE</span><strong>FCFS + fixed abandonment patience</strong></div><div><span>MODEL VERSION</span><strong>v1.0 · MIT · local execution</strong></div><button id="exportScenario" type="button">↓ Save scenario</button><label for="importScenario" class="import-scenario">↑ Load scenario <input id="importScenario" type="file" accept=".json,application/json" hidden></label></div></section>
        <footer><span>FLOWPILOT © 2026 · BUILT FOR TRANSPARENT OPERATIONS RESEARCH</span><span>PROJECT 23 / OPEN SOURCE PORTFOLIO</span></footer>
      </div>
    </main>
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<script>
/* CORE: deterministic simulations */
/** FlowPilot 1.0: deterministic browser-native operations simulation. MIT. */
const SHIFT_HOURS = 8;
const MODEL_VERSION = '1.0.0';
const EPS = 1e-10;

function makeRng(seed) {
  let state = (Number(seed) >>> 0);
  return () => {
    state = (state + 0x6D2B79F5) >>> 0;
    let t = state;
    t = Math.imul(t ^ (t >>> 15), t | 1);
    t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
  };
}
function triangular(rng, low, mode, high) {
  if (low === high) return low;
  const u = rng();
  const p = (mode - low) / (high - low);
  return u < p ? low + Math.sqrt(u * (high - low) * (mode - low))
    : high - Math.sqrt((1 - u) * (high - low) * (high - mode));
}
function normalizeConfig(obj = {}) {
  if (!obj || typeof obj !== 'object' || Array.isArray(obj)) throw new Error('Scenario must be an object.');
  const num = (v, key, min, max, integer = false) => {
    const n = Number(v);
    if (v === '' || v === null || v === undefined || !Number.isFinite(n) || n < min || n > max || (integer && !Number.isInteger(n))) {
      throw new Error(`${key} must be ${integer ? 'an integer' : 'a number'} between ${min} and ${max}.`);
    }
    return n;
  };
  const profile = obj.hourlyArrivals;
  if (!Array.isArray(profile) || profile.length !== SHIFT_HOURS) throw new Error('Hourly demand needs exactly eight numbers.');
  const cfg = {
    name: typeof obj.name === 'string' ? obj.name.slice(0, 80) : 'Custom scenario',
    hourlyArrivals: profile.map((x, i) => num(x, `Hour ${i + 1} arrivals`, 0, 140)),
    agents: num(obj.agents, 'Agents', 1, 20, true),
    serviceMin: num(obj.serviceMin, 'Minimum service time', 0.2, 90),
    serviceMode: num(obj.serviceMode, 'Most likely service time', 0.2, 90),
    serviceMax: num(obj.serviceMax, 'Maximum service time', 0.2, 90),
    patience: num(obj.patience, 'Patience', 1, 180),
    slaMinutes: num(obj.slaMinutes, 'SLA wait threshold', 0, 120),
    slaGoal: num(obj.slaGoal, 'Target SLA', 1, 100),
    wage: num(obj.wage, 'Hourly wage', 0, 500),
    abandonmentCost: num(obj.abandonmentCost, 'Abandonment cost', 0, 500),
    waitingCost: num(obj.waitingCost, 'Waiting cost per minute', 0, 20),
    seed: num(obj.seed, 'Random seed', 0, 4294967295, true),
    trials: num(obj.trials, 'Simulation runs', 10, 300, true),
    optimizerMaxAgents: num(obj.optimizerMaxAgents ?? 12, 'Maximum staffing levels', 2, 20, true),
  };
  if (!(cfg.serviceMin <= cfg.serviceMode && cfg.serviceMode <= cfg.serviceMax)) {
    throw new Error('Service duration must follow minimum ≤ most likely ≤ maximum.');
  }
  return cfg;
}
// Piecewise-constant Poisson arrivals per hour; deterministic service draws per customer.
// All staffing alternatives use the SAME customers, arrival times and service durations.
function generateCohort(config, seed) {
  const cfg = normalizeConfig(config);
  const rng = makeRng(seed);
  const customers = [];
  for (let h = 0; h < SHIFT_HOURS; h++) {
    const rate = cfg.hourlyArrivals[h] / 60;
    if (rate === 0) continue;
    let minute = h * 60;
    const end = (h + 1) * 60;
    while (true) {
      minute += -Math.log(Math.max(EPS, 1 - rng())) / rate;
      if (minute >= end) break;
      customers.push({arrival: minute, service: triangular(rng, cfg.serviceMin, cfg.serviceMode, cfg.serviceMax)});
    }
  }
  return customers;
}
// O(n*c), simple and auditable; typical c <= 20, n <= 1120 per run.
// Fixed patience implies abandonment deadlines preserve arrival order.
function simulateCohort(customers, config, agents = config.agents) {
  const cfg = normalizeConfig(config);
  if (!Number.isInteger(agents) || agents < 1 || agents > 20) throw new Error('Agents must be between 1 and 20.');
  let previousArrival = -Infinity;
  const ready = Array(agents).fill(0);
  let served = 0, abandoned = 0, withinSla = 0;
  let waitMinutes = 0, abandonedWaitMinutes = 0, busyInShift = 0, busyOvertime = 0;
  const waits = [];
  let lastServiceFinish = 0;
  for (const customer of customers) {
    const t = customer.arrival, duration = customer.service;
    if (!Number.isFinite(t) || t < previousArrival || t < 0 || t >= 480 || !Number.isFinite(duration) || duration <= 0) {
      throw new Error('Customer data is invalid or not sorted.');
    }
    previousArrival = t;
    let best = 0;
    for (let i = 1; i < ready.length; i++) if (ready[i] < ready[best]) best = i;
    const start = Math.max(t, ready[best]);
    const delay = start - t;
    if (delay > cfg.patience + EPS) {
      abandoned++;
      abandonedWaitMinutes += cfg.patience;
      continue;
    }
    served++;
    waitMinutes += delay;
    waits.push(delay);
    if (delay <= cfg.slaMinutes + EPS) withinSla++;
    const finish = start + duration;
    ready[best] = finish;
    lastServiceFinish = Math.max(lastServiceFinish, finish);
    busyInShift += Math.max(0, Math.min(finish, 480) - Math.min(Math.max(start, 0), 480));
    busyOvertime += Math.max(0, finish - Math.max(start, 480));
  }
  waits.sort((a, b) => a - b);
  const n = customers.length;
  const meanWait = served ? waitMinutes / served : 0;
  const staffCost = agents * 8 * cfg.wage + busyOvertime / 60 * cfg.wage * 1.5;
  const delayCost = (waitMinutes + abandonedWaitMinutes) * cfg.waitingCost;
  const churnCost = abandoned * cfg.abandonmentCost;
  const totalCost = staffCost + delayCost + churnCost;
  return {
    agents, arrivals: n, served, abandoned, withinSla,
    serviceLevel: n ? withinSla / n * 100 : 100,
    abandonmentRate: n ? abandoned / n * 100 : 0,
    meanWait, p90Wait: percentile(waits, 90),
    utilization: busyInShift / (agents * 480) * 100,
    overtimeMinutes: Math.max(0, lastServiceFinish - 480),
    staffCost, delayCost, churnCost, totalCost,
  };
}
function mean(values) { return values.length ? values.reduce((a,b) => a+b,0) / values.length : 0; }
function percentile(sortedAscending, p) {
  if (!sortedAscending.length) return 0;
  const point = (sortedAscending.length - 1) * p / 100;
  const a = Math.floor(point), b = Math.ceil(point);
  return sortedAscending[a] + (sortedAscending[b] - sortedAscending[a]) * (point - a);
}
const MEASURES = ['arrivals','served','abandoned','withinSla','serviceLevel','abandonmentRate','meanWait','p90Wait','utilization','overtimeMinutes','staffCost','delayCost','churnCost','totalCost'];
function summarize(rows, agents) {
  const out = { agents, trials: rows.length };
  for (const key of MEASURES) {
    const values = rows.map(r => r[key]);
    out[key] = mean(values);
    const sorted = values.slice().sort((a,b) => a-b);
    out[key + 'P10'] = percentile(sorted, 10);
    out[key + 'P90'] = percentile(sorted, 90);
  }
  return out;
}
function analyzeScenario(rawConfig) {
  const cfg = normalizeConfig(rawConfig);
  const options = [...new Set([...Array.from({length:cfg.optimizerMaxAgents},(_,i)=>i+1),cfg.agents])].sort((a,b)=>a-b);
  const rowsByAgents = new Map(options.map(n => [n, []]));
  for (let i = 0; i < cfg.trials; i++) {
    const trialSeed = (cfg.seed + Math.imul(i, 0x9E3779B9)) >>> 0;
    const cohort = generateCohort(cfg, trialSeed);
    for (const agents of options) rowsByAgents.get(agents).push(simulateCohort(cohort, cfg, agents));
  }
  const curve = options.map(agents => summarize(rowsByAgents.get(agents), agents));
  const baseline = curve.find(x=>x.agents===cfg.agents);
  // Cheapest expected total cost satisfying SLA. Mean SLA target is a planning assumption,
  // not a probabilistic service-level guarantee.
  const feasible = curve.filter(x=>x.serviceLevel + EPS >= cfg.slaGoal);
  feasible.sort((a,b)=>a.totalCost-b.totalCost || a.agents-b.agents);
  const recommended = feasible[0] || null;
  const cheapest = curve.reduce((a,b)=>b.totalCost<a.totalCost ? b:a);
  return {version: MODEL_VERSION, config: cfg, baseline, recommended, cheapest, curve, feasibleCount: feasible.length, generatedAt: new Date().toISOString()};
}
function parseHourlyCsv(csv) {
  if (typeof csv !== 'string' || csv.length > 50000) throw new Error('The CSV is missing or too large.');
  const lines = csv.replace(/^\uFEFF/, '').trim().split(/\r?\n/).filter(Boolean).map(x=>x.split(',').map(v=>v.trim()));
  if (lines.length !== 9) throw new Error('CSV needs a header and eight rows.');
  if (lines[0].join(',').toLowerCase() !== 'hour,arrivals') throw new Error('CSV header must be: hour,arrivals');
  const hours = lines.slice(1).map(x=>Number(x[0]));
  if (!hours.every(Number.isInteger) || new Set(hours).size !== 8) throw new Error('Hours must be unique integers.');
  const zeroBased = hours.every(x=>x>=0&&x<=7);
  const oneBased = hours.every(x=>x>=1&&x<=8);
  if (!zeroBased && !oneBased) throw new Error('Hours must be 1–8 or 0–7.');
  const result = Array(8).fill(null);
  for(const cells of lines.slice(1)) {
    if(cells.length !== 2 || cells[1] === '') throw new Error('Every row needs an hour and arrival count.');
    const h = Number(cells[0]) - (zeroBased ? 0 : 1);
    const n = Number(cells[1]);
    if (!Number.isFinite(n) || n < 0 || n > 140) throw new Error('Hourly arrivals must be between 0 and 140.');
    result[h] = n;
  }
  if (result.some(x=>x===null)) throw new Error('All eight hours are required.');
  return result;
}
function toCsv(analysis) {
  const columns = ['agents','serviceLevel','meanWait','p90Wait','abandonmentRate','utilization','arrivals','abandoned','staffCost','delayCost','churnCost','totalCost'];
  return [columns.join(','), ...analysis.curve.map(x=>columns.map(k=>Number.isFinite(x[k]) ? x[k].toFixed(4) : x[k]).join(','))].join('\n');
}

/* DEMONSTRATION PRESETS */
const PRESETS = {
  retail: {
    name: 'Retail & customer service', hourlyArrivals: [12,16,24,40,33,26,18,10],
    agents: 4, serviceMin: 3, serviceMode: 6, serviceMax: 11,
    patience: 18, slaMinutes: 8, slaGoal: 85,
    wage: 19, abandonmentCost: 35, waitingCost: 0.10,
    seed: 23061, trials: 80, optimizerMaxAgents: 12,
  },
  health: {
    name: 'Healthcare reception', hourlyArrivals: [8,12,19,22,28,22,14,10],
    agents: 4, serviceMin: 6, serviceMode: 9, serviceMax: 16,
    patience: 30, slaMinutes: 12, slaGoal: 90,
    wage: 25, abandonmentCost: 65, waitingCost: 0.22,
    seed: 23062, trials: 80, optimizerMaxAgents: 12,
  },
  support: {
    name: 'Technical support desk', hourlyArrivals: [10,12,14,17,16,23,25,15],
    agents: 5, serviceMin: 5, serviceMode: 9, serviceMax: 20,
    patience: 25, slaMinutes: 10, slaGoal: 85,
    wage: 24, abandonmentCost: 50, waitingCost: 0.15,
    seed: 23063, trials: 80, optimizerMaxAgents: 12,
  },
};

/* USER INTERFACE */


const $ = id => document.getElementById(id);
const FIELDS = ['agents','serviceMin','serviceMode','serviceMax','patience','slaMinutes','slaGoal','wage','abandonmentCost','waitingCost','seed','trials','optimizerMaxAgents'];
const fmtNum = (n, d=1) => Number(n).toLocaleString('en-US',{maximumFractionDigits:d,minimumFractionDigits:d});
const usd = n => '$' + Number(n).toLocaleString('en-US',{maximumFractionDigits:0,minimumFractionDigits:0});
const pct = n => fmtNum(n,1) + '%';
let analysis = null;
let toastTimer = null;
let isStale = false;

function notify(message, error=false) {
  const toast=$('toast');toast.textContent=message;toast.classList.toggle('error',error);toast.classList.add('show');
  clearTimeout(toastTimer);toastTimer=setTimeout(()=>toast.classList.remove('show'),3500);
}
function demandInputs(){
  const box=$('hourlyInputs');
  for(let i=0;i<8;i++){
    const div=document.createElement('div');div.className='hourly-item';
    const label=document.createElement('label');label.htmlFor=`h${i}`;label.textContent=`H${i+1}`;
    const input=document.createElement('input');input.id=`h${i}`;input.type='number';input.min='0';input.max='140';input.step='1';input.setAttribute('aria-label',`Expected arrivals, hour ${i+1}`);
    input.addEventListener('input',markStale);
    div.append(label,input);box.append(div);
  }
}
function applyConfig(config,presetKey='custom'){
  for(const field of FIELDS) $(field).value = config[field];
  for(let i=0;i<8;i++) $(`h${i}`).value=config.hourlyArrivals[i];
  $('preset').value=presetKey;
  markStale();
}
function readConfig(){
  const raw={name:$('preset').selectedOptions[0]?.textContent || 'Custom scenario'};
  for(const field of FIELDS) raw[field]=$(field).value;
  raw.hourlyArrivals=Array.from({length:8},(_,i)=>$(`h${i}`).value);
  return normalizeConfig(raw);
}
function markStale(){
  if(!analysis)return;
  isStale=true;$('freshness').textContent='● Settings changed · run again';$('freshness').classList.add('stale');
  $('applyRecommended').disabled=true;$('exportJson').disabled=true;$('exportCsv').disabled=true;
}
function renderKpis(a){
  $('kpiSla').textContent=pct(a.serviceLevel);
  $('kpiSlaSub').textContent=`Goal ${pct(analysis.config.slaGoal)} · ${fmtNum(a.withinSla,0)} of ${fmtNum(a.arrivals,0)} arrivals`;
  $('kpiCost').textContent=usd(a.totalCost);
  $('kpiCostSub').textContent=`P10–P90 ${usd(a.totalCostP10)} to ${usd(a.totalCostP90)}`;
  $('kpiWait').textContent=fmtNum(a.meanWait,1)+' min';
  $('kpiWaitSub').textContent=`90th percentile ≈ ${fmtNum(a.p90Wait,1)} min (mean)`;
  $('kpiAbandon').textContent=pct(a.abandonmentRate);
  $('kpiAbandonSub').textContent=`${fmtNum(a.abandoned,1)} lost / shift on average`;
}
function graphic(curve,key,{color,goal=null,money=false}){
  const W=550,H=238,L=60,R=21,T=21,B=37,innerW=W-L-R,innerH=H-T-B;
  const maxVal=Math.max(...curve.map(r=>r[key]),goal||0, 1);
  const yMax=key==='serviceLevel'?100:Math.ceil(maxVal*1.14 / 25)*25;
  const y=v=>T+(1-v/yMax)*innerH;
  const x=i=>L+(curve.length===1?innerW/2:i*innerW/(curve.length-1));
  const axes=[0,.25,.5,.75,1].map(k=>{
    const n=yMax*(1-k);
    return `<line class="gridline" x1="${L}" x2="${W-R}" y1="${y(n)}" y2="${y(n)}"/><text x="${L-9}" y="${y(n)+4}" text-anchor="end">${money ? ('$'+(n>=1000?(n/1000).toFixed(1)+'k':Math.round(n))) : Math.round(n)+'%'}</text>`;
  }).join('');
  const xs=curve.map((r,i)=>`<text x="${x(i)}" y="${H-13}" text-anchor="middle">${r.agents}</text>`).join('');
  const points=curve.map((r,i)=>`${x(i)},${y(r[key])}`).join(' ');
  const area=`${L},${T+innerH} ${points} ${x(curve.length-1)},${T+innerH}`;
  const circles=curve.map((r,i)=>{
    const isBase=r.agents===analysis.config.agents;
    const isRec=analysis.recommended&&r.agents===analysis.recommended.agents;
    return `<circle cx="${x(i)}" cy="${y(r[key])}" r="${isRec?6:isBase?5:3}" fill="${isRec?'#f9c87a':color}" stroke="var(--panel)" stroke-width="${isRec||isBase?2:1}"><title>${r.agents} agents: ${money?usd(r[key]):pct(r[key])}${isRec?' · recommended':''}${isBase?' · current':''}</title></circle>`;
  }).join('');
  const goalLine=goal===null?'':`<line class="goal" x1="${L}" x2="${W-R}" y1="${y(goal)}" y2="${y(goal)}"/><text x="${W-R-3}" y="${Math.max(T+9,y(goal)-5)}" text-anchor="end" style="fill:var(--orange)">GOAL ${fmtNum(goal,0)}%</text>`;
  const baseIdx=curve.findIndex(r=>r.agents===analysis.config.agents);
  const baseLine=baseIdx<0?'':`<line x1="${x(baseIdx)}" x2="${x(baseIdx)}" y1="${T}" y2="${H-B}" stroke="#6e8ca5" opacity=".6" stroke-dasharray="3 5"/>`;
  return `<svg viewBox="0 0 ${W} ${H}" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><defs><linearGradient id="fill-${key}" x1="0" x2="0" y1="0" y2="1"><stop offset="0" stop-color="${color}" stop-opacity=".22"/><stop offset="1" stop-color="${color}" stop-opacity="0"/></linearGradient></defs>${axes}${baseLine}${goalLine}<polygon points="${area}" fill="url(#fill-${key})"/><polyline points="${points}" fill="none" stroke="${color}" stroke-width="3" stroke-linejoin="round" stroke-linecap="round"/>${circles}${xs}<text x="${W/2}" y="${H-1}" text-anchor="middle">STAFF MEMBERS</text></svg>`;
}
function renderCharts(){
  const {curve,baseline,recommended,config}=analysis;
  $('costChart').innerHTML=graphic(curve,'totalCost',{color:'#50e3bd',money:true});
  $('slaChart').innerHTML=graphic(curve,'serviceLevel',{color:'#a79af8',goal:config.slaGoal});
  const low=analysis.cheapest;
  $('costSummary').textContent=`Lowest modeled cost: ${usd(low.totalCost)} with ${low.agents} agents. Costs include staff, waiting and abandonment.`;
  $('slaSummary').textContent=recommended?`${analysis.feasibleCount} staffing levels meet the ${fmtNum(config.slaGoal,0)}% mean SLA goal.`:`No evaluated staffing level meets the ${fmtNum(config.slaGoal,0)}% mean SLA goal.`;
  $('runMeta').textContent=`${config.trials} runs · 8-hour shift · seed ${config.seed}`;
}
function recStat(label,value){return `<div class="rec-stat"><small>${label}</small><b>${value}</b></div>`;}
function renderRecommendation(){
  const {baseline,recommended,cheapest,config}=analysis;
  const recBox=$('recommendation'),tag=$('recBadge');
  if(!recommended){
    tag.className='rec-badge warn';tag.textContent='TARGET NOT REACHED';
    const bestSla=analysis.curve.reduce((a,b)=>a.serviceLevel>b.serviceLevel? a:b);
    recBox.innerHTML=`<div class="rec-main"><span class="rec-count">!</span><div class="rec-label"><strong>No feasible staffing level found</strong>The best tested mean SLA is ${pct(bestSla.serviceLevel)} with ${bestSla.agents} staff.</div></div><p class="rec-explainer">Try raising the maximum staffing count, reducing service time, or revisiting the target. Do not treat the cheapest scenario as SLA-compliant.</p>`;
    $('applyRecommended').disabled=true;
    return;
  }
  tag.className='rec-badge good';tag.textContent='MEETS AVERAGE SLA TARGET';
  const delta=recommended.totalCost-baseline.totalCost;
  const saved=baseline.totalCost-recommended.totalCost;
  const change= recommended.agents-baseline.agents;
  recBox.innerHTML=`<div class="rec-main"><span class="rec-count">${recommended.agents}</span><div class="rec-label"><strong>staff members</strong>Cheapest expected-cost option that meets your mean SLA goal.</div></div><div class="rec-stats">${recStat('EXPECTED SLA',pct(recommended.serviceLevel))}${recStat('COST / SHIFT',usd(recommended.totalCost))}${recStat('CHANGE VS NOW',`${delta<=0?'-':'+'}${usd(Math.abs(delta)).replace('$',' $')}`)}</div><p class="rec-explainer">${change===0?'Your current staffing level is already the least-cost SLA-compliant option in the tested range.':`Compared with your ${baseline.agents}-person baseline, this scenario uses ${Math.abs(change)} ${change>0?'more':'fewer'} staff and ${saved>0?`reduces expected shift cost by ${usd(saved)}`:`increases expected shift cost by ${usd(-saved)}`} while meeting the average SLA target.`}</p>`;
  $('applyRecommended').disabled = baseline.agents===recommended.agents;
}
function riskItem(label,key,format){
  const a=analysis.baseline;
  const lo=a[key+'P10'],hi=a[key+'P90'],value=a[key];
  const min=Math.min(0,lo*0.8),max=Math.max(hi*1.2,1),spread=max-min;
  const left=Math.max(0,(lo-min)/spread*100),right=Math.min(100,(hi-min)/spread*100),center=Math.max(0,Math.min(100,(value-min)/spread*100));
  return `<div class="risk-item"><div class="risk-label"><span>${label}</span><strong>${format(lo)} – ${format(hi)}</strong></div><div class="risk-track"><span class="range" style="left:${left}%;width:${Math.max(1,right-left)}%"></span><span class="median" style="left:${center}%" title="Mean result"></span></div></div>`;
}
function renderRisk(){
  $('riskRows').innerHTML=riskItem('SLA attainment','serviceLevel',pct)+riskItem('Total shift cost','totalCost',usd)+riskItem('Customer abandonment','abandoned',n=>fmtNum(n,1));
}
function renderTable(){
  const {curve,baseline,recommended}=analysis;
  $('comparisonBody').innerHTML=curve.map(row=>{
    const base=row.agents===baseline.agents, rec=recommended&&row.agents===recommended.agents;
    const tag=rec?'<span class="row-tag best">OPTIMAL SLA</span>':base?'<span class="row-tag">CURRENT</span>':row.serviceLevel+1e-10>=analysis.config.slaGoal?'<span class="row-tag">ON TARGET</span>':'<span class="row-tag miss">BELOW GOAL</span>';
    return `<tr class="${rec?'recommended':base?'baseline':''}"><td>${row.agents}</td><td>${pct(row.serviceLevel)}</td><td>${fmtNum(row.meanWait,1)} min</td><td>${fmtNum(row.abandoned,1)}</td><td>${pct(row.utilization)}</td><td>${usd(row.totalCost)}</td><td>${tag}</td></tr>`;
  }).join('');
}
function run(){
  const error=$('formError');error.hidden=true;
  let cfg;
  try{cfg=readConfig();}catch(e){error.textContent=e.message;error.hidden=false;notify(e.message,true);return;}
  $('run').disabled=true;$('run').firstElementChild.textContent='⋯';
  // Allow the UI to paint a working state before heavy deterministic computation.
  setTimeout(()=>{
    try{
      analysis=analyzeScenario(cfg);isStale=false;
      $('freshness').textContent='● Analysis up to date';$('freshness').classList.remove('stale');
      renderKpis(analysis.baseline);renderCharts();renderRecommendation();renderRisk();renderTable();
      $('exportJson').disabled=false;$('exportCsv').disabled=false;
      notify(`Analyzed ${cfg.trials} common-demand simulation runs`);
    }catch(e){error.textContent=e.message;error.hidden=false;notify('Could not run simulation: '+e.message,true);}
    finally{$('run').disabled=false;$('run').firstElementChild.textContent='▶';}
  },35);
}
function download(content,name,mime){
  const blob=new Blob([content],{type:mime});const url=URL.createObjectURL(blob);
  const link=document.createElement('a');link.href=url;link.download=name;document.body.append(link);link.click();link.remove();
  setTimeout(()=>URL.revokeObjectURL(url),1000);
}
async function loadJson(file){
  if(!file)return;
  try{
    if(file.size>100000)throw new Error('The scenario file exceeds 100 KB.');
    const obj=JSON.parse(await file.text());
    const raw=obj.scenario??obj;
    const cfg=normalizeConfig(raw);
    applyConfig(cfg);
    notify('Imported scenario successfully. Run simulation to refresh.');
  }catch(e){notify('Scenario import failed: '+e.message,true);}
  finally{$('importScenario').value='';}
}
async function loadCsv(file){
  if(!file)return;
  try{
    if(file.size>50000)throw new Error('CSV exceeds 50 KB.');
    const hourly=parseHourlyCsv(await file.text());
    hourly.forEach((v,i)=>$(`h${i}`).value=v);
    $('preset').value='custom';markStale();notify('Demand curve imported. Run simulation to refresh.');
  }catch(e){notify('CSV import failed: '+e.message,true);}
  finally{$('uploadCsv').value='';}
}
function boot(){
  demandInputs();applyConfig(PRESETS.retail,'retail');
  $('preset').addEventListener('change',e=>{
    if(PRESETS[e.target.value])applyConfig(PRESETS[e.target.value],e.target.value);
    else markStale();
  });
  FIELDS.forEach(id=>$(id).addEventListener('input',markStale));
  $('run').addEventListener('click',run);
  $('applyRecommended').addEventListener('click',()=>{
    if(!analysis || isStale || !analysis.recommended)return;
    $('agents').value=analysis.recommended.agents;markStale();run();
  });
  $('exportJson').addEventListener('click',()=>{if(!analysis||isStale)return;download(JSON.stringify(analysis,null,2),'flowpilot-simulation-report.json','application/json');notify('Report exported as JSON.');});
  $('exportCsv').addEventListener('click',()=>{if(!analysis||isStale)return;download(toCsv(analysis),'flowpilot-staffing-comparison.csv','text/csv;charset=utf-8');notify('Staffing comparison exported as CSV.');});
  $('exportScenario').addEventListener('click',()=>{try{download(JSON.stringify({format:'flowpilot-scenario-v1',scenario:readConfig()},null,2),'flowpilot-scenario.json','application/json');notify('Scenario saved.');}catch(e){notify(e.message,true);}});
  $('importScenario').addEventListener('change',e=>loadJson(e.target.files?.[0]));
  $('uploadCsv').addEventListener('change',e=>loadCsv(e.target.files?.[0]));
  $('downloadCsvTemplate').addEventListener('click',()=>download('hour,arrivals\n1,12\n2,16\n3,24\n4,40\n5,33\n6,26\n7,18\n8,10\n','flowpilot-hourly-demand-template.csv','text/csv;charset=utf-8'));
  $('themeToggle').addEventListener('click',()=>{document.body.classList.toggle('light');$('themeToggle').textContent=document.body.classList.contains('light')?'☾':'☼';});
  run();
}
boot();

</script>
</body>
</html>