File size: 53,452 Bytes
0d66d30 3a9a28e 0d66d30 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 | <!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>
:root{color-scheme:dark;--bg:#080d19;--surface:#0d1423;--panel:#101827;--panel2:#111b2b;--stroke:#243148;--muted:#8695b1;--text:#f1f5ff;--sub:#b5bfd5;--accent:#50e3bd;--violet:#a79af8;--orange:#ffb57b;--danger:#fa929e;--sans:Inter,ui-sans-serif,-apple-system,BlinkMacSystemFont,"Segoe UI",Arial,sans-serif;--shadow:0 18px 50px #00000021}
*{box-sizing:border-box}html{scroll-behavior:smooth}body{margin:0;background:var(--bg);color:var(--text);font:14px/1.55 var(--sans)}button,input,select{font:inherit}button{cursor:pointer}button:disabled{opacity:.45;cursor:not-allowed}a{color:inherit;text-decoration:none}.skip-link{position:absolute;top:-60px;left:15px;padding:12px;background:var(--accent);z-index:30;color:#06151a}.skip-link:focus{top:10px}:focus-visible{outline:2px solid var(--accent);outline-offset:3px}.app-shell{display:flex;min-height:100vh;max-width:1900px;margin:auto}.sidebar{width:306px;min-width:306px;background:#0b1220;border-right:1px solid var(--stroke);height:100vh;position:sticky;top:0;display:flex;flex-direction:column;z-index:3}.side-head{min-height:91px;border-bottom:1px solid var(--stroke);display:flex;align-items:center;justify-content:space-between;padding:24px 20px}.brand{display:flex;align-items:center;gap:11px}.brand-mark{font-size:41px;line-height:1;color:var(--accent);transform:rotate(22deg)}.brand-name{font-size:22px;font-weight:820;letter-spacing:-1.1px;line-height:1}.brand-name>span{color:var(--accent)}.brand-name small{display:block;font-size:7px;letter-spacing:2.0px;font-weight:750;color:var(--muted);margin-top:7px}.badge{display:inline-flex;align-items:center;border:1px solid #35514e;background:#102d2b;color:var(--accent);font-size:9px;font-weight:800;letter-spacing:1px;border-radius:4px;padding:6px}.side-scroll{padding:22px 20px 35px;overflow:auto;scrollbar-width:thin;scrollbar-color:#35445a transparent}.sidebar-section{padding-bottom:22px;margin-bottom:22px;border-bottom:1px solid var(--stroke)}.side-label{font-size:10px;letter-spacing:1.4px;color:#84a5af;font-weight:750;margin-bottom:13px}.sidebar label{display:block;color:#b8c3d8;font-size:11px;font-weight:700;margin-bottom:7px}.sidebar select,.sidebar input:not([type=file]){width:100%;border:1px solid #34425a;background:#101a2b;color:var(--text);border-radius:7px;padding:10px 11px;min-height:39px;outline:none}.sidebar select:focus,.sidebar input:focus{border-color:var(--accent)}.side-help{color:var(--muted);font-size:11px;margin:10px 0 0}.field-grid{display:grid;grid-template-columns:1fr 1fr;gap:12px 11px}.field.full{grid-column:1/-1}.field label{font-size:10.5px;margin-bottom:6px}.field input{padding:8px 9px!important;font-size:13px}.row-split{display:flex;align-items:center;justify-content:space-between}.row-split .side-label{margin-bottom:6px}.text-button{border:0;background:none;color:var(--accent);font-size:11px;padding:2px 0}.profile-hint{margin:3px 0 12px}.hourly-inputs{display:grid;grid-template-columns:repeat(4,1fr);gap:8px}.hourly-item label{text-align:center;color:var(--muted);font-size:10px;margin:0 0 4px}.hourly-item input{text-align:center;padding:7px 2px!important;min-height:34px!important}.upload-inline{display:block;margin-top:13px!important;padding:10px;text-align:center;border-radius:7px;border:1px dashed #3e5364;cursor:pointer;color:var(--accent)!important}.upload-inline:hover{border-color:var(--accent)}.run-button{width:100%;border:0;display:flex;align-items:center;gap:9px;background:var(--accent);color:#062723;font-size:13px;font-weight:850;border-radius:8px;padding:13px 16px;box-shadow:0 10px 25px #48e1ba1c}.run-button:hover{filter:brightness(1.06)}.run-arrow{margin-left:auto}.form-error{background:#452332;border:1px solid #894256;border-radius:6px;color:#ffe1e4;padding:10px;font-size:12px}.privacy-box{margin-top:22px;display:flex;gap:11px;border:1px solid #2c4b48;border-radius:10px;padding:14px;background:#0c2426}.privacy-box>span{color:var(--accent);font-size:23px}.privacy-box strong{color:#c3f0e4;font-size:12px}.privacy-box p{font-size:11px;color:#a0bcba;margin:3px 0 0}.main{flex:1;min-width:0}.topbar{display:flex;align-items:center;justify-content:space-between;padding:24px 41px;border-bottom:1px solid var(--stroke);height:91px}.breadcrumbs{font-size:10px;letter-spacing:1.35px;font-weight:750;color:#7f8da6}.breadcrumbs span{padding:0 12px;color:#4c5c73}.breadcrumbs b{color:#c4cee0}.top-actions{display:flex;align-items:center;gap:20px}.status{color:#a7b4c9;font-size:10px;letter-spacing:.8px;font-weight:700}.status i{display:inline-block;height:7px;width:7px;background:var(--accent);border-radius:50%;box-shadow:0 0 8px #40e6b6;margin-right:5px}.mini-button{border:1px solid var(--stroke);background:transparent;color:var(--sub);font-size:19px;border-radius:7px;width:34px;height:34px}.content{max-width:1440px;margin:auto;padding:33px 40px 0}.hero{background:radial-gradient(circle at 83% 42%,#23544e50,transparent 37%),linear-gradient(119deg,#152538,#0e1c2a 65%,#16273b);border:1px solid #365565;border-radius:13px;position:relative;overflow:hidden;padding:32px 33px 23px;box-shadow:var(--shadow)}.hero:before{content:"";position:absolute;inset:0;opacity:.16;background-image:linear-gradient(90deg,#b4ddff11 1px,transparent 1px),linear-gradient(#b4ddff11 1px,transparent 1px);background-size:32px 32px;pointer-events:none}.hero-eyebrow{font-size:10px;letter-spacing:2.2px;font-weight:850;color:var(--accent);display:flex;align-items:center;gap:8px}.eyebrow-line{width:20px;height:2px;background:var(--accent)}.hero-grid{display:flex;justify-content:space-between;align-items:center;position:relative;gap:25px}.hero h1{font-size:clamp(27px,3.2vw,48px);line-height:1.13;letter-spacing:-1.65px;margin:18px 0 14px;font-weight:780}.hero h1 em{font-style:normal;color:var(--accent)}.hero p{color:#b6cbd7;max-width:620px;font-size:13px;line-height:1.75;margin:0 0 25px}.hero-symbol{position:relative;width:194px;height:156px;flex:0 0 194px;display:flex;justify-content:center;align-items:center}.hero-ring{position:absolute;border:1px solid #51e3bb47;border-radius:50%;transform:rotate(-28deg)}.r1{width:170px;height:90px}.r2{width:125px;height:147px;border-color:#57d8b42f;transform:rotate(55deg)}.hero-diamond{font-size:118px;font-weight:100;color:var(--accent);text-shadow:0 0 45px #4ceabb45;line-height:1}.hero-bottom{display:flex;gap:26px;border-top:1px solid #36526082;padding-top:18px;color:#a6c3ca;font-size:9px;font-weight:800;letter-spacing:1px;position:relative;flex-wrap:wrap}.hero-bottom span:before{content:"•";color:var(--accent);margin-right:9px}.section-heading{display:flex;align-items:end;justify-content:space-between;gap:15px;margin:35px 0 17px}.section-kicker{font-size:10px;letter-spacing:1.7px;color:#6eceba;font-weight:800;margin-bottom:6px}.section-heading h2,.method h2{font-size:22px;letter-spacing:-.65px;margin:0;font-weight:780}.heading-right{display:flex;gap:14px;align-items:center}.freshness{background:#12312e;border:1px solid #306257;border-radius:20px;padding:5px 10px;font-size:10px;color:var(--accent);font-weight:720}.freshness.stale{color:var(--orange);background:#31251e;border-color:#745039}.tiny{color:var(--muted);font-size:11px}.kpis{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:14px}.kpi{background:var(--panel);border:1px solid var(--stroke);border-radius:11px;padding:20px 19px;min-height:139px}.kpi-top{display:flex;align-items:center;justify-content:space-between;gap:6px;color:#9cacc4;font-size:10px;letter-spacing:.9px;font-weight:780}.kpi-icon{font-size:19px;color:var(--accent)}.kpi strong{display:block;font-size:32px;letter-spacing:-1.4px;font-weight:800;margin:9px 0 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 i{width:7px;height:7px;border-radius:50%;display:inline-block;margin-right:6px}.cyan{background:var(--accent)}.purple{background:var(--violet)}.chart{height:240px;margin:12px 0 0}.chart svg{width:100%;height:100%;overflow:visible}.chart svg text{font:10px var(--sans);fill:var(--muted)}.chart .gridline{stroke:var(--stroke);stroke-dasharray:3 5}.chart .goal{stroke:var(--orange);stroke-dasharray:5 4}.chart-note{border-top:1px solid var(--stroke);padding-top:12px;font-size:11px;color:#9caec5;min-height:30px}.lower-grid{display:grid;grid-template-columns:1.08fr 1fr;gap:15px;margin-top:15px}.decision-panel,.risk-panel{padding:22px}.rec-badge{font-size:9px;letter-spacing:.8px;color:var(--muted);border:1px solid var(--stroke);padding:5px 8px;border-radius:5px}.rec-badge.good{color:var(--accent);border-color:#326354;background:#12332b}.rec-badge.warn{color:var(--orange);border-color:#785236;background:#34251f}.recommendation{min-height:171px;padding:16px 0}.recommendation p{color:var(--muted);font-size:12px}.rec-main{display:flex;align-items:center;gap:16px}.rec-count{font-size:58px;letter-spacing:-4px;color:var(--accent);line-height:1;font-weight:850}.rec-label{font-size:12px;color:var(--muted)}.rec-label strong{display:block;color:var(--text);font-size:15px}.rec-stats{display:flex;gap:9px;flex-wrap:wrap;margin:16px 0}.rec-stat{border:1px solid var(--stroke);border-radius:7px;padding:8px 11px;min-width:110px}.rec-stat small{font-size:9px;color:var(--muted);display:block}.rec-stat b{font-size:16px;letter-spacing:-.3px}.rec-explainer{font-size:11px;color:var(--muted);line-height:1.5;margin:7px 0}.decision-actions{display:flex;gap:9px;flex-wrap:wrap}.action-primary,.action-secondary{border-radius:7px;padding:10px 13px;font-size:12px;font-weight:750}.action-primary{border:0;background:var(--accent);color:#082922}.action-primary span{margin-left:14px}.action-secondary{border:1px solid #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 0}.method h2{font-size:28px;margin:9px 0 14px}.method p{font-size:12px;color:#9fb0c8;max-width:730px;line-height:1.8}.method-note strong{color:var(--sub)}.method-links{padding-top:4px}.method-links>div{padding:10px 0;border-bottom:1px solid var(--stroke)}.method-links span{font-size:9px;letter-spacing:1.2px;font-weight:800;color:#73cbb7;display:block}.method-links strong{font-size:12px;font-weight:650;color:#c4d0e1}.method-links>button,.import-scenario{border:1px solid var(--stroke);background:var(--panel);color:var(--sub);padding:8px 11px;border-radius:6px;margin:15px 8px 0 0;font-weight:750;font-size:11px;display:inline-block;cursor:pointer}.import-scenario:hover,.method-links>button:hover{border-color:var(--accent)}footer{border-top:1px solid var(--stroke);padding:20px 0 30px;color:#667893;display:flex;justify-content:space-between;gap:10px;font-size:9px;letter-spacing:.7px;font-weight:800}.toast{position:fixed;bottom:25px;right:25px;padding:11px 16px;background:#193a37;color:#b7f5e3;border:1px solid #3c8b7d;z-index:30;border-radius:8px;box-shadow:var(--shadow);font-size:12px;opacity:0;pointer-events:none;transform:translateY(9px);transition:.2s}.toast.show{opacity:1;transform:translateY(0)}.toast.error{background:#3b2228;color:#ffe3e6;border-color:#914855}
body.light{color-scheme:light;--bg:#f3f7fc;--surface:#fff;--panel:#fff;--panel2:#fff;--stroke:#dbe4ed;--muted:#61718a;--text:#101d31;--sub:#3f4c64;--accent:#178e77;--violet:#7062bd;--orange:#c47c25;--danger:#bb4055;--shadow:0 18px 50px #12334b12}body.light .sidebar{background:#f8fbff}body.light .sidebar input:not([type=file]),body.light .sidebar select{background:#fff;color:#26334c;border-color:#cad7e3}body.light .sidebar label{color:#35445a}body.light .side-label{color:#4b817e}body.light .privacy-box{background:#e0f7f0;border-color:#bee5d9}body.light .privacy-box strong{color:#135648}body.light .privacy-box p{color:#41665f}body.light .run-button,body.light .action-primary{color:#fff;background:#138c76}body.light .brand-name small{color:#71859e}body.light .kpi-top{color:#697b91}body.light .panel-overline{color:#55768a}body.light .legend-chip{color:#59708a}body.light .section-kicker{color:#178e77}body.light .hero{color:#fff}body.light .kpi-sub{color:#6c7c92}body.light .rec-label strong,body.light .rec-stat b{color:var(--text)}body.light .action-secondary{background:#eff5fa;color:#43566d}body.light thead{background:#edf3fa}body.light td{color:#42526a}body.light td:first-child{color:#101d31}body.light tbody tr:hover{background:#eff7f5}body.light tbody tr.baseline{background:#eff4fa}body.light tbody tr.recommended{background:#e3f5ec}body.light .row-tag{color:#22658e;background:#e6f1fa}body.light .row-tag.best{color:#147e65;background:#d8f5e9}body.light .method p{color:#687b92}body.light .method-links strong{color:#40536b}body.light .method-links span{color:#137d68}body.light .breadcrumbs b{color:#3c4d65}body.light .chart .gridline{stroke:#dbe4ed}body.light .badge{color:#137d68;background:#e5f9f1;border-color:#b3ddcf}body.light .freshness{background:#e6f7f0;color:#15745e;border-color:#c5e7da}body.light .freshness.stale{color:#9a6221;background:#fff0d9;border-color:#e7c596}
@media(max-width:1240px){.sidebar{width:283px;min-width:283px}.content{padding:26px 25px 0}.topbar{padding:24px 25px}.kpi{padding:17px 14px}.kpi strong{font-size:27px}.hero-symbol{width:135px;flex-basis:135px}.hero h1{font-size:32px}.chart-grid{gap:11px}.chart-grid .panel{padding:18px 13px}.legend-chip{font-size:8px}}
@media(max-width:1030px){.app-shell{display:block}.sidebar{width:100%;min-width:0;height:auto;position:static}.side-head{min-height:72px}.side-scroll{overflow:visible;display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:17px 22px;padding:18px 22px}.sidebar-section{margin:0}.sidebar-section:nth-child(3){grid-row:span 2}.run-button,.privacy-box,.form-error{align-self:center}.content{padding:25px 22px 0}.topbar{height:68px}.kpis{grid-template-columns:repeat(4,minmax(0,1fr))}}
@media(max-width:720px){.side-scroll{grid-template-columns:1fr}.sidebar-section:nth-child(3){grid-row:auto}.topbar{padding:14px 18px}.breadcrumbs{font-size:9px}.status{font-size:0}.status i{font-size:10px}.content{padding:20px 14px 0}.hero{padding:24px 20px 18px}.hero h1{font-size:29px;letter-spacing:-1.1px}.hero-symbol{display:none}.hero-bottom{gap:9px}.hero-bottom span{font-size:8px}.section-heading{align-items:start}.section-heading.smaller p{display:none}.heading-right{flex-direction:column;align-items:flex-end;gap:5px}.kpis{grid-template-columns:repeat(2,minmax(0,1fr));gap:9px}.kpi{min-height:122px}.kpi strong{font-size:27px}.chart-grid,.lower-grid{grid-template-columns:1fr}.chart{height:225px}.method{grid-template-columns:1fr;gap:14px}.method h2{font-size:24px}footer{flex-direction:column}.panel-head{flex-wrap:wrap}.section-heading h2{font-size:19px}.table-panel .panel-head{padding:17px}}
@media(prefers-reduced-motion:reduce){*{scroll-behavior:auto!important;transition:none!important}}
</style>
</head>
<body>
<a href="#workspace" class="skip-link">Skip to dashboard</a>
<div class="app-shell">
<aside class="sidebar">
<div class="side-head">
<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>
</div>
</aside>
<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>
</div>
<div id="toast" class="toast" role="status" aria-live="polite"></div>
<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>
|