// AI Inventory Control Tower — a control tower, not a report. // Every screen answers: what's happening, why, the impact, what to do now, // and what happens if you don't. Decisions persist via the App State Store. import React, { useEffect, useMemo, useState } from "react"; import { ResponsiveContainer, BarChart, Bar, Cell, XAxis, YAxis, Tooltip, ComposedChart, Line, Legend } from "recharts"; import { obl, loadState, saveState } from "./obl"; import { C, S } from "./ui"; import { ACTIVE_PLANTS, ACTIVE_SUPPLIERS, generateParts, generateOpenPOs, adaptRealItems } from "./data"; import { enrich, kpis, forecastPart, replenishment, alerts as buildAlerts, savingsOpportunities, simulate, copilot, MATRIX_STRATEGY, money, plantName } from "./engine"; const NS = "inventory-control-tower"; const tt = { background: C.card, border: `1px solid ${C.line}`, color: C.text, fontSize: 12, borderRadius: 8 }; const STATUS_COLOR = { Active: C.green, "Slow Moving": C.dim, Excess: C.amber, Obsolete: C.purple, Critical: C.red, "At Risk": C.amber }; const SEV_COLOR = { critical: C.red, high: C.amber, medium: C.sky, low: C.dim }; const NAV_SECTIONS = [ { label: "Overview", color: "#3B82F6", items: [ ["dashboard", "📊", "Dashboard"], ["alerts", "🔔", "Alerts & Exceptions"], ["reports", "📋", "Reports"] ]}, { label: "Inventory", color: "#10B981", items: [ ["master", "📦", "Inventory Master"], ["excess", "📈", "Excess & Obsolete"], ["matrix", "🔢", "ABC / XYZ Matrix"] ]}, { label: "Planning", color: "#F59E0B", items: [ ["optimize", "🎯", "Safety Stock & ROP"], ["replenish", "🛒", "Replenishment"], ["stockout", "⚠️", "Stockout Prediction"], ["forecast", "📉", "Demand Forecast"] ]}, { label: "Supply Chain", color: "#8B5CF6", items: [ ["suppliers", "🏭", "Suppliers"], ]}, { label: "AI Tools", color: "#0EA5E9", items: [ ["simulate", "🧪", "What-If Simulator"], ["copilot", "🤖", "AI Copilot"] ]}, { label: "Admin & Settings", color: "#EC4899", items: [ ["settings", "⚙️", "Planning Parameters"], ["thresholds", "📐", "Thresholds & Rules"], ["approvals", "✋", "Approval Matrix"], ["audit", "📜", "Activity Log"] ]}, { label: "Data Management", color: "#6B7280", items: [ ["wizard", "🧭", "Setup Wizard"], ["objects", "📁", "Data Objects"], ["data", "✅", "Data Quality"], ["readiness", "🚦", "Readiness Score"], ["integrations", "🔗", "Integrations"] ]} ]; export default function App() { const [view, setView] = useState("dashboard"); const [plantF, setPlantF] = useState("all"); const [catF, setCatF] = useState("all"); const [supF, setSupF] = useState("all"); const [decisions, setDecisions] = useState({}); const [alertMeta, setAlertMeta] = useState({}); const [version, setVersion] = useState(0); const [save, setSave] = useState("idle"); const [drill, setDrill] = useState(null); const [showWelcome, setShowWelcome] = useState(true); const [showHelp, setShowHelp] = useState(false); const [showChecklist, setShowChecklist] = useState(false); const [baseParts, setBaseParts] = useState(null); const [dataMode, setDataMode] = useState("loading"); // "real" | "sample" useEffect(() => { (async () => { try { const [items, plant] = await Promise.all([ obl("/inventory/items").catch(() => null), obl("/plant").catch(() => null) ]); // heuristic: our demo endpoint returns the engineered 40-item set; real data has its own ids const looksReal = Array.isArray(items) && items.length > 0 && !items.some(i => /^(RM|WIP|FG|MRO)-\d{3}$/.test(i.id ?? "")); if (looksReal) { setBaseParts(adaptRealItems(items, plant)); setDataMode("real"); return; } } catch {} setBaseParts(generateParts()); setDataMode("sample"); })(); }, []); const pos = useMemo(() => baseParts ? generateOpenPOs(baseParts) : [], [baseParts]); const allRows = useMemo(() => baseParts ? enrich(baseParts, decisions) : [], [baseParts, decisions]); const rows = useMemo(() => allRows.filter(p => (plantF === "all" || p.plantId === plantF) && (catF === "all" || p.category === catF) && (supF === "all" || p.supplierId === supF)), [allRows, plantF, catF, supF]); const k = useMemo(() => kpis(rows, pos), [rows, pos]); const cats = useMemo(() => Array.from(new Set((baseParts ?? []).map(p => p.category))).sort(), [baseParts]); useEffect(() => { loadState(NS).then(st => { if (st?.data) { setDecisions(st.data.decisions ?? {}); setAlertMeta(st.data.alertMeta ?? {}); setVersion(st.version); setSave("saved"); } else if (st === null) setSave("demo"); }).catch(() => setSave("offline")); }, []); const persist = async (d, a) => { if (save === "demo") { setDecisions(d); setAlertMeta(a); return; } setDecisions(d); setAlertMeta(a); setSave("saving"); const res = await saveState(NS, { decisions: d, alertMeta: a }, version); if (res.ok) { setVersion(res.version); setSave("saved"); } else if (res.conflict) { if (res.data) { setDecisions(res.data.decisions ?? {}); setAlertMeta(res.data.alertMeta ?? {}); setVersion(res.version); } setSave("saved"); } else setSave(res.demo ? "demo" : "offline"); }; const decide = (partId, patch) => persist({ ...decisions, [partId]: { ...(decisions[partId] ?? {}), ...patch } }, alertMeta); const csv = (name, header, rowsOut) => { const text = [header.join(","), ...rowsOut.map(r => r.map(c => `"${String(c ?? "").replace(/"/g, '""')}"`).join(","))].join("\n"); const a = document.createElement("a"); a.href = "data:text/csv;charset=utf-8," + encodeURIComponent(text); a.download = name + ".csv"; a.click(); }; if (!baseParts) return

Opening the tower…

; const V = { dashboard: Dashboard, master: Master, optimize: Optimize, replenish: Replenish, stockout: Stockout, excess: Excess, matrix: Matrix, forecast: Forecast, suppliers: Suppliers, simulate: Simulator, copilot: Copilot, alerts: Alerts, data: DataQ, reports: Reports, wizard: SetupWizard, objects: DataObjects, readiness: Readiness, integrations: Integrations, settings: Settings, thresholds: Thresholds, approvals: ApprovalMatrix, audit: AuditLog }[view]; return (

{/* left nav — dark anchor */}

Inventory

Control Tower

{save === "saving" ? "● saving…" : save === "saved" ? "✓ saved" : save === "demo" ? "demo mode" : save === "offline" ? "⚠ offline" : ""}

{NAV_SECTIONS.map(section => (

{section.label}

{section.items.map(([id, icon, label]) => { const active = view === id; return ( ); })}

))}

{(() => { const cur = NAV_SECTIONS.flatMap(sc => sc.items).find(i => i[0] === view); return (

{cur?.[2] ?? ""}

AI Inventory Control Tower · Executive command center

{dataMode === "real" ? "● LIVE DATA" : dataMode === "empty" ? "AWAITING DATA" : "SAMPLE DATA"}

); })()} {/* filter bar */}

{rows.length > 0 && <> [p.id, p.name])]} /> [c, c])]} /> [s.id, s.name])]} /> } {rows.length} parts · {dataMode === "real" ? "✓ running on your data" : "sample data — connect yours anytime"}

{dataMode === "sample" && (

You're exploring with sample data

3 plants, 100 parts, 20 suppliers, 12 months of history — every number is simulated. When you're ready, connect your real inventory and the sample data disappears completely.

)} {dataMode === "empty" && (

Ready for your data

Go to the Data page in the main nav, drop in your inventory workbook (part master with stock levels, costs, usage, and lead times), and the tower lights up on your plant — no sample data mixed in.

Changed your mind?

)} {dataMode === "real" && (

✓ Running on your inventory data Assumptions are labeled where history is derived from average usage. Load monthly consumption history for full forecast and variability math.

)} {/* Welcome modal — first visit only */} {showWelcome && dataMode === "sample" && (

setShowWelcome(false)}>

e.stopPropagation()} style={{ background: C.card, borderRadius: 20, padding: "32px 36px", maxWidth: 520, width: "92%", boxShadow: "0 24px 60px rgba(0,0,0,0.2)" }}>

Welcome to your
Inventory Control Tower

You're looking at a fully working inventory management system loaded with realistic sample data — 100 parts, 3 plants, 20 suppliers, 12 months of history. Explore everything. When you're ready, connect your own data and the sample disappears.

{[ ["1", "Explore", "Click through the dashboard, drill into parts, ask the AI copilot questions — everything works."], ["2", "Connect your data", "Go to Data Management → Setup Wizard. Download CSV templates, fill from your ERP, upload. The AI maps your columns."], ["3", "Configure & go live", "Set your planning parameters, thresholds, and approval rules under Admin. Share with your team."] ].map(([n, title, desc]) => (

{n}

{title}

{desc}

))}

Free to explore · Your data stays yours · No credit card needed

)} {/* Onboarding checklist */} {showChecklist && (

🚀 Setup checklist

{[ { label: "Explore the dashboard", done: true, hint: "You're here — done!" }, { label: "Review planning parameters", done: !!decisions.__config, hint: "Admin → Planning Parameters", action: () => setView("settings") }, { label: "Set service levels by criticality", done: !!decisions.__config?.serviceByCriticality, hint: "Admin → Thresholds & Rules", action: () => setView("thresholds") }, { label: "Configure approval matrix", done: !!decisions.__config?.approvalRequired, hint: "Admin → Approval Matrix", action: () => setView("approvals") }, { label: "Download data templates", done: false, hint: "Data Management → Data Objects", action: () => setView("objects") }, { label: "Upload your inventory data", done: dataMode === "real", hint: "Data Management → Setup Wizard", action: () => setView("wizard") }, { label: "Approve first safety stock recommendations", done: Object.values(decisions).some(d => d.ssStatus), hint: "Planning → Safety Stock & ROP", action: () => setView("optimize") } ].map((step, i) => { const done = step.done; return (

{done ? "✓" : i + 1}

{step.label}

{step.hint}

{step.action && !done && Go →}

); })}

Steps checked off automatically based on your actions. You can use the tower at any stage — each step makes it more accurate.

)} {/* Contextual help panel */} {showHelp && (

❓ What am I looking at?

{({ dashboard: "The Executive Dashboard is your morning read: 12 KPI cards showing inventory health at a glance, an aging chart showing how long stock has been sitting, and the AI's top savings recommendations. Click any KPI to drill into the relevant screen. Red numbers need action today; amber numbers need a plan this week.", master: "The Inventory Master is every part in your system with its full record: stock levels, costs, lead times, classifications, and status. Click any row to open the detail panel. Use the search bar and status filter to find specific items. This is your reference — the other screens are where you act.", optimize: "Safety Stock & ROP shows parts where the AI's recommended safety stock or reorder point differs materially from what's currently set. The formula is shown (z × σ × √lead), the dollar impact of each change is calculated, and you accept, reject, or override each one. Approved values flow immediately into all other screens.", replenish: "The Replenishment workbench lists every part below its reorder point, ranked by urgency. Each row shows the recommended order quantity (respecting MOQ and lot size), the supplier, confidence score, and open POs. Approve or adjust quantities, then export the approved set as a CSV for your ERP buyer.", stockout: "Stockout Prediction scores every part on a 0–100 risk scale based on how its pipeline (available + in-transit + on-order) compares to lead-time demand. Parts scoring 70+ are inside their lead time — those aren't purchase decisions anymore, they're expedites. The expected stockout date is printed for each.", excess: "Excess & Obsolescence shows parts above max stock (excess) and parts with zero consumption (obsolete). Each row has a disposition dropdown — stop buying, transfer, return to vendor, scrap — that persists so you can work through the list over multiple sessions.", matrix: "The ABC/XYZ matrix classifies every part by value (ABC) and demand variability (XYZ). Click any cell for the recommended inventory strategy — from 'tight safety stock, supplier VMI' for AX parts to 'keep a bag of them' for CZ.", forecast: "Demand Forecasting shows 12 months of actual consumption plus a 3-month forecast for any part. The forecast model is explained in plain English (trend direction, seasonality, backtest error). Use this to validate whether the tower's recommendations match your demand expectations.", suppliers: "Supplier Performance ranks every vendor by on-time delivery, average delay, and quality issues. The 'inventory at risk' column shows how much stock value depends on each supplier's reliability. Use this to prioritize escalation conversations.", simulate: "The What-If Simulator lets you test 'what if demand goes up 20%?' or 'what if lead times increase by 2 weeks?' with sliders. The KPIs recompute live, showing before/after deltas. This is how you stress-test your inventory strategy before reality does.", copilot: "The AI Copilot answers specific inventory questions using your data. Click any preset question or type your own. Answers are computed deterministically (not generated — no hallucinations), with tables and a recommendation. Try: 'How can I reduce inventory by 10%?'", alerts: "Alerts & Exceptions lists every item that needs attention, ranked by severity. Each alert has a root cause, business impact, and recommended action. Assign an owner and due date to track resolution — these persist across sessions.", data: "Data Quality checks your master data for gaps: missing lead times, costs, suppliers, stale records. Each check shows which tower features are degraded by the gap and which parts are affected.", reports: "Reports provides one-click CSV exports for eight standard inventory reports. Each export pulls live data from the tower's current filtered scope.", wizard: "The Setup Wizard walks you through connecting your data in plain English — what data the tower needs, why, and in what order. No technical knowledge required.", objects: "Data Objects lists every type of data the tower uses, with required and optional fields, sample rows, and downloadable CSV templates. Fill a template from your ERP export and upload it through the platform's Data page.", readiness: "The Readiness Score shows which tower features are fully functional with your current data and which need additional data objects to unlock.", integrations: "Integrations shows what data connection methods are live today (file upload, AI mapping) and what's on the roadmap (API, database connectors, scheduled refresh).", settings: "Planning Parameters control the fundamental assumptions behind every calculation — shift hours, working days, service levels, lead time defaults. Changes take effect immediately.", thresholds: "Thresholds define what 'excess', 'slow-moving', and 'obsolete' mean for your specific plant, and set service level targets by part criticality.", approvals: "The Approval Matrix controls which decisions need a second set of eyes. Toggle workflows on or off based on your team's governance needs.", audit: "The Activity Log shows every decision made in this tower: safety stock approvals, PO recommendations, dispositions, and configuration changes." })[view] ?? "Select a screen from the left navigation to see contextual help here."}

)}

); } /* ---------- shared bits ---------- */ const Sel = ({ value, onChange, options, w }) => ( ); const Kpi = ({ label, value, tone, sub, onClick }) => (

{label}

{value ?? "—"}

{sub &&

{sub}

}

); const Badge = ({ text, color }) => {text}; const Doctrine = ({ what, why, impact, doNow, ifNot }) => (

{[["What's happening", what, C.text], ["Why", why, C.sub], ["Business impact", impact, C.red], ["Do now", doNow, C.text], ["If nothing changes", ifNot, C.sub]].map(([h, t, c]) => (

{h}

{t}

))}

); const T = ({ head, children }) => (

{head.map(h => )}{children}

{h}

); const btn = (bg, fg) => ({ background: bg, color: fg ?? "#FFFFFF", border: "none", borderRadius: 8, padding: "7px 14px", fontWeight: 600, cursor: "pointer", fontSize: 12, transition: "opacity 0.15s", letterSpacing: "0.01em" }); /* ---------- 1. Executive Dashboard ---------- */ function Dashboard({ rows, pos, k, setView }) { const ops = savingsOpportunities(rows).slice(0, 5); return ( <>

setView("stockout")} /> 0 ? C.amber : C.green} onClick={() => setView("excess")} /> 0 ? "#B98CFF" : C.green} onClick={() => setView("excess")} /> setView("forecast")} /> setView("suppliers")} />

Inventory aging — value by last-movement bucket

"$" + (v / 1000).toFixed(0) + "k"} /> money(v)} /> {k.aging.map((a, i) => )}

AI recommendations — top savings ({money(ops.reduce((s, o) => s + o.value, 0))})

{ops.map((o, i) => (

{o.part.id} {o.part.desc} — {o.action} {money(o.value)}

))}

); } /* ---------- 2. Inventory Master + drill ---------- */ function Master({ rows, drill, setDrill }) { const [q, setQ] = useState(""); const [st, setSt] = useState("all"); const list = rows.filter(p => (st === "all" || p.status === st) && (!q || (p.id + " " + p.desc + " " + p.category).toLowerCase().includes(q.toLowerCase()))); const p = drill ? rows.find(x => x.id === drill) : null; return ( <>

setQ(e.target.value)} placeholder="Search part, description, category…" style={{ flex: 1, maxWidth: 320, background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 8, padding: "7px 10px", fontSize: 12.5 }} /> [s, s])]} />

{p && (

{p.id} — {p.desc}

{[["Plant / WH", plantName(p.plantId) + " · " + p.warehouseId + " · " + p.location], ["Supplier", ACTIVE_SUPPLIERS.find(s => s.id === p.supplierId)?.name ?? "⚠ missing"], ["Stock / reserved / avail", `${p.stock} / ${p.reserved} / ${p.avail} ${p.uom}`], ["In transit / on order", `${p.inTransit} / ${p.onOrder}`], ["Safety stock (cur → rec)", `${p.currentSS} → ${p.recSS}`], ["Reorder point", p.rop], ["Min–max", `${p.recSS}–${p.maxStock}`], ["Cost / value", `${p.cost != null ? "$" + p.cost : "⚠"} · ${money(p.value)}`], ["Lead time", p.lead + "d" + (p.leadAssumed ? " (assumed)" : "") + (p.leadVarDays ? ` ±${p.leadVarDays}` : "")], ["MOQ / lot", `${p.moq} / ${p.lotSize}`], ["Shelf life / expiry", p.shelfLifeDays ? `${p.shelfLifeDays}d · ${p.expiryDate}` : "n/a"], ["ABC / XYZ / criticality", `${p.abc}${p.xyz} · ${p.criticality}/5`], ["Obsolescence risk", p.obsRisk + "/100"], ["Last issue / purchase", `${p.lastIssueDate} / ${p.lastPurchaseDate}`], ["Days on hand", p.doh ?? "—"], ["Status", p.status] ].map(([l, v]) =>

{l}: {v}

)}

)} {list.slice(0, 60).map(p2 => ( setDrill(p2.id)} style={{ cursor: "pointer" }}> {p2.id}

{p2.desc}

{p2.category}{plantName(p2.plantId)} {p2.avail.toLocaleString()} {p2.doh ?? "—"} {p2.rop}{p2.abc}{p2.xyz} {money(p2.value)} ))}

Showing {Math.min(list.length, 60)} of {list.length} — click a row for the full master record.

); } /* ---------- 3+4. Safety Stock & ROP optimization with workflow ---------- */ function Optimize({ rows, decisions, decide }) { const cands = rows.filter(p => Math.abs(p.recSS - p.currentSS) > Math.max(p.currentSS * 0.2, 5) && p.dailyD > 0) .sort((a, b) => Math.abs((b.recSS - b.currentSS) * (b.cost ?? 0)) - Math.abs((a.recSS - a.currentSS) * (a.cost ?? 0))); return ( <> {cands.slice(0, 25).map(p => { const d = decisions[p.id] ?? {}; const over = p.currentSS > p.recSS; return ( {p.id}

{p.desc} · {p.abc}{p.xyz}

{Math.round(p.svc * 100)}% {p.currentSS} {p.recSS} {p.rop} {p.recRop} {over ? `Demand variability doesn't justify the buffer — ${money((p.currentSS - p.recSS) * (p.cost ?? 0))} releasable.` : `σ=${Math.round(p.sdMonthly)}/mo, lead ${p.lead}d${p.leadAssumed ? " (assumed)" : ""} — under-buffered; stockout risk ${p.riskScore}.`} {d.ssStatus === "approved" ? : d.ssStatus === "rejected" ? : ( { if (e.key === "Enter" && Number(e.target.value) > 0) decide(p.id, { ssStatus: "approved", ssOverride: Number(e.target.value) }); }} /> )} ); })}

Approved values take effect immediately across the tower (ROP, alerts, replenishment). Overrides: type a value and press Enter.

); } /* ---------- 5. Replenishment workbench ---------- */ function Replenish({ rows, pos, decisions, decide, csv }) { const recs = replenishment(rows, pos); return ( <>

{recs.slice(0, 30).map(r => { const d = decisions[r.part.id] ?? {}; return ( {r.part.id}

{r.part.desc}

{r.part.avail} {r.part.stockoutDate ?? "—"} {r.openPO || "—"} {d.poQty ?? r.qty} {r.part.uom}

MOQ {r.part.moq}

{r.supplier}

{r.part.lead}d{r.part.leadAssumed ? " (assumed)" : ""}

= 70 ? C.green : C.amber }}>{r.confidence}% {d.poStatus === "approved" ? : d.poStatus === "rejected" ? : ( { if (e.key === "Enter" && Number(e.target.value) > 0) decide(r.part.id, { poQty: Number(e.target.value) }); }} /> )} ); })} ); } /* ---------- 6. Stockout prediction ---------- */ function Stockout({ rows }) { const list = rows.filter(p => p.riskScore >= 35 && !p.dead).sort((a, b) => b.riskScore - a.riskScore); return ( <> p.riskScore >= 70).length} parts are inside their lead-time window; ${list.length} carry material risk.`} why="Pipeline (available + in-transit + on-order) covers less than 1.5× lead-time demand." impact="A-item stockouts stop lines; the cost isn't the part, it's everything behind it." doNow="Expedite risk ≥70 today; raise reorder points on repeat offenders via the Optimization screen." ifNot="The stockout arrives on schedule — the date is printed in the table." /> {list.slice(0, 25).map(p => ( = 70 ? C.red : C.amber }}>{p.riskScore} {p.id}

{p.desc} · {plantName(p.plantId)}

{p.stockoutDate ?? "—"} {p.stockoutDays != null ? Math.round(p.stockoutDays) : "—"}d / {p.lead}d {p.criticality >= 4 ? "Production line — critical part" : "Order fulfillment"} {p.onOrder === 0 ? "Nothing on order" : p.currentSS < p.recSS ? "Safety stock set below demand math" : "Demand outran plan"} {p.riskScore >= 70 ? "Expedite today + interim allocation" : "Place PO this cycle; fix parameters"} ))} ); } /* ---------- 7+8. Excess & Obsolescence ---------- */ function Excess({ rows, decisions, decide }) { const ex = rows.filter(p => p.excessQty > 0 || p.dead).sort((a, b) => (b.excessValue + (b.dead ? (b.value ?? 0) : 0)) - (a.excessValue + (a.dead ? (a.value ?? 0) : 0))); const totalEx = ex.reduce((s, p) => s + p.excessValue, 0); const totalObs = rows.filter(p => p.dead).reduce((s, p) => s + (p.value ?? 0), 0); const ACTIONS = ["Stop buying", "Reduce future PO qty", "Transfer to another plant", "Return to supplier", "Use in alternate production", "Discount / scrap", "Hold for service demand", "Engineering review"]; return ( <> {ex.slice(0, 30).map(p => { const d = decisions[p.id] ?? {}; return ( {p.id}

{p.desc} · {plantName(p.plantId)}

{p.dead ? p.avail : p.excessQty} {p.uom} {money(p.dead ? p.value : p.excessValue)} {p.monthsNoIssue * 30}d {p.superseded ? `Superseded by ${p.superseded}` : p.eol ? "End-of-life program" : p.moq > p.meanMonthly * 2 ? `MOQ ${p.moq} vs ${Math.round(p.meanMonthly)}/mo demand` : p.profile === "declining" ? "Declining demand" : "Over-ordering"} ); })} ); } /* ---------- 9. ABC/XYZ matrix ---------- */ function Matrix({ rows }) { const [cell, setCell] = useState(null); const grid = ["A", "B", "C"].flatMap(a => ["X", "Y", "Z"].map(x => { const list = rows.filter(p => p.abc === a && p.xyz === x); return { key: a + x, count: list.length, value: list.reduce((s, p) => s + (p.value ?? 0), 0), list }; })); const sel = grid.find(g => g.key === cell); return ( <>

{grid.map(g => ( ))}

{sel && (

{sel.key} — {MATRIX_STRATEGY[sel.key]}

{sel.list.slice(0, 12).map(p => p.id).join(" · ")}{sel.list.length > 12 ? ` · +${sel.list.length - 12} more` : ""}

)} ); } /* ---------- 10. Forecast ---------- */ function Forecast({ rows }) { const [pid, setPid] = useState(null); useEffect(() => { if (!pid && rows.length) setPid(rows[0].id); }, [rows, pid]); const p = rows.find(x => x.id === pid) ?? rows[0]; if (!p) return null; const f = forecastPart(p); const chart = [...p.history.map(h => ({ m: h.month.slice(2), actual: h.qty })), ...f.fc.map(x => ({ m: x.month, forecast: x.qty }))]; return ( <>

[x.id, x.id + " — " + x.desc])} w={320} /> accuracy {f.accuracy != null ? f.accuracy + "%" : "—"} · trend from 12 months of history

x.qty).join(" / ")} ${p.uom}.`} why={f.explanation} impact={`Forecast error directly sizes safety stock — this part's error implies ${p.xyz === "Z" ? "buffers, not precision" : "tight planning is achievable"}.`} doNow={p.xyz === "Z" ? "Don't chase this forecast — set service-level buffers and review monthly." : "Plan to the forecast; the pattern is stable enough to trust."} ifNot="Planning to a number without knowing its error is how excess and stockouts happen to the same part." /> ); } /* ---------- 11. Suppliers ---------- */ function Suppliers({ rows }) { const list = ACTIVE_SUPPLIERS.map(s => ({ ...s, parts: rows.filter(p => p.supplierId === s.id).length, exposure: rows.filter(p => p.supplierId === s.id && (p.belowRop || p.riskScore >= 45)).reduce((sum, p) => sum + (p.value ?? 0), 0) })).sort((a, b) => a.reliability - b.reliability); return ( <> s.otd < 82).length} suppliers run below 82% on-time delivery.`} why="Late suppliers force you to hold their unreliability as your safety stock." impact={`Inventory at risk from the bottom five: ${money(list.slice(0, 5).reduce((s, x) => s + x.exposure, 0))}.`} doNow="Escalate the bottom three with data; dual-source the A-parts they carry." ifNot="You keep paying their delays twice — once in buffers, once in expedites." /> {list.map(s => ( {s.name} {s.otd}% {s.avgDelayDays}d {s.leadTimeAccuracy}% 2 ? C.amber : C.text }}>{s.qualityIssues90d} {s.parts} {money(s.exposure)} ))} ); } /* ---------- 12. Simulator ---------- */ function Simulator({ baseParts, decisions, k }) { const [demandMult, setDemandMult] = useState(1); const [leadDelta, setLeadDelta] = useState(0); const [ssMult, setSsMult] = useState(1); const sim = useMemo(() => simulate(baseParts, decisions, { demandMult, leadDelta, ssMult }), [baseParts, decisions, demandMult, leadDelta, ssMult]); const Delta = ({ label, from, to, fmt = v => v, badUp }) => { const up = (to ?? 0) > (from ?? 0); const bad = badUp ? up : !up; return ; }; return ( <>

Demand: {Math.round(demandMult * 100)}% setDemandMult(Number(e.target.value))} style={{ width: "100%" }} /> Supplier lead time: {leadDelta >= 0 ? "+" : ""}{leadDelta}d setLeadDelta(Number(e.target.value))} style={{ width: "100%" }} /> Safety stock: {Math.round(ssMult * 100)}% setSsMult(Number(e.target.value))} style={{ width: "100%" }} />

v + "%"} />

Same math as the live tower, transformed assumptions — try “demand +20%” or “lead time +14d” and watch which KPI breaks first. That's your fragility.

); } /* ---------- 13. Copilot ---------- */ function Copilot({ rows, pos }) { const [q, setQ] = useState(""); const [ans, setAns] = useState(null); const CHIPS = ["Which parts are most likely to stock out?", "Which inventory is obsolete?", "What should I reorder this week?", "Which suppliers are causing inventory risk?", "How can I reduce inventory by 10%?", "What parts should be transferred between warehouses?", "Which parts need engineering review?"]; const ask = text => { setQ(text); setAns(copilot(text, rows, pos) ?? { title: "I can answer the questions below", rows: [], advice: "This copilot is deterministic — computed from your data, no hallucinations. For open-ended questions, ask the builder chat." }); }; return ( <>

setQ(e.target.value)} onKeyDown={e => e.key === "Enter" && ask(q)} placeholder="Ask about stockouts, excess, suppliers, savings, transfers…" style={{ flex: 1, background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 8, padding: "9px 12px", fontSize: 13 }} />

{CHIPS.map(c => )}

{ans && (

{ans.title}

{ans.rows.map((r, i) => (

{r.map((c, j) => {c})}

))} {ans.advice &&

“{ans.advice}”

}

)}

Deterministic copilot: answers are computed live from the data on this page — recommendations, not just rows.

); } /* ---------- 14. Alerts ---------- */ function Alerts({ rows, pos, alertMeta, persist, decisions }) { const list = buildAlerts(rows, pos); const setMeta = (id, patch) => persist(decisions, { ...alertMeta, [id]: { ...(alertMeta[id] ?? {}), ...patch } }); return ( <> a.severity === "critical").length} critical and ${list.filter(a => a.severity === "high").length} high exceptions open.`} why="Each alert carries its root cause — computed, not guessed." impact="Unowned exceptions are the ones that become incidents." doNow="Assign an owner and due date to every critical/high — the fields persist." ifNot="The same alerts greet you tomorrow, one day closer to their consequence." />

{list.slice(0, 40).map(a => { const m = alertMeta[a.id] ?? {}; return (

{a.kind} {a.part} {a.desc} impact: {a.impact} · cause: {a.cause} · do: {a.action} setMeta(a.id, { owner: e.target.value })} style={{ width: 90, background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 6, fontSize: 11, padding: "3px 6px" }} /> setMeta(a.id, { due: e.target.value })} style={{ background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 6, fontSize: 11, padding: "3px 6px" }} />

); })}

); } /* ---------- 18. Data & quality ---------- */ function DataQ({ rows }) { const checks = [ ["Missing lead time", rows.filter(p => p.leadDays == null), "Planning math falls back to a stated 14d assumption"], ["Missing cost", rows.filter(p => p.cost == null), "Value, excess and savings math excludes these parts"], ["Missing supplier", rows.filter(p => p.supplierId == null), "Replenishment can't route a PO"], ["Stale (no issue 6mo+)", rows.filter(p => p.monthsNoIssue >= 6), "Candidates for the obsolescence screen"], ["Near-expiry", rows.filter(p => p.expiryDate && p.expiryDate < new Date(Date.now() + 30 * 86400_000).toISOString().slice(0, 10)), "Write-off risk inside 30 days"] ]; return ( <> s + c[1].length, 0)} master-data issues across ${checks.length} check types.`} why="Every AI recommendation is only as good as the master data under it — the tower states its assumptions instead of hiding them." impact="Each hole degrades one specific calculation, listed per check below." doNow="Fix lead times and costs first; they feed everything else." ifNot="Planners learn which numbers to distrust — and then distrust all of them." /> {checks.map(([name, list, effect]) => (

{name} {effect}

{list.length > 0 &&

{list.slice(0, 10).map(p => p.id).join(" · ")}{list.length > 10 ? ` · +${list.length - 10} more` : ""}

}

))}

Data integration: this tower runs on sample data until yours is connected. Load part master, inventory balances, demand history, POs, and supplier master through the platform's Data page — drop the Excel exports from your ERP/MRP/WMS and the AI maps the columns. API push is available per data source for live feeds.

); } /* ---------- 19. Reports ---------- */ function Reports({ rows, pos, csv }) { const R = [ ["Inventory health report", () => csv("inventory-health", ["Part", "Desc", "Plant", "Avail", "DOH", "Value", "ABC/XYZ", "Status"], rows.map(p => [p.id, p.desc, plantName(p.plantId), p.avail, p.doh, p.value, p.abc + p.xyz, p.status]))], ["Stockout risk report", () => csv("stockout-risk", ["Part", "Desc", "Risk", "Expected date", "Cover days", "Lead days"], rows.filter(p => p.riskScore >= 35).map(p => [p.id, p.desc, p.riskScore, p.stockoutDate, p.stockoutDays, p.lead]))], ["Excess & obsolete report", () => csv("excess-obsolete", ["Part", "Desc", "Kind", "Qty", "Value", "Months no movement"], rows.filter(p => p.excessQty > 0 || p.dead).map(p => [p.id, p.desc, p.dead ? "obsolete" : "excess", p.dead ? p.avail : p.excessQty, p.dead ? p.value : p.excessValue, p.monthsNoIssue]))], ["Reorder recommendations", () => csv("reorder-recs", ["Part", "Desc", "Qty", "Supplier", "Urgency"], replenishment(rows, pos).map(r => [r.part.id, r.part.desc, r.qty, r.supplier, r.urgency]))], ["Safety stock optimization", () => csv("ss-optimization", ["Part", "Current SS", "Recommended SS", "Service", "Cash impact"], rows.filter(p => Math.abs(p.recSS - p.currentSS) > 5).map(p => [p.id, p.currentSS, p.recSS, Math.round(p.svc * 100) + "%", Math.round((p.currentSS - p.recSS) * (p.cost ?? 0))]))], ["ABC/XYZ classification", () => csv("abc-xyz", ["Part", "Desc", "ABC", "XYZ", "CoV", "Annual value"], rows.map(p => [p.id, p.desc, p.abc, p.xyz, p.cov, Math.round((p.cost ?? 0) * p.meanMonthly * 12)]))], ["Supplier risk report", () => csv("supplier-risk", ["Supplier", "OTD", "Avg delay", "Quality issues"], ACTIVE_SUPPLIERS.map(s => [s.name, s.otd + "%", s.avgDelayDays, s.qualityIssues90d]))], ["Savings opportunities", () => csv("savings", ["Kind", "Part", "Desc", "Value", "Action"], savingsOpportunities(rows).map(o => [o.kind, o.part.id, o.part.desc, Math.round(o.value), o.action]))] ]; return (

{R.map(([name, fn]) => (

{name}

))}

); } /* ===== DATA MANAGEMENT SCREENS ===== */ const DATA_OBJECTS = [ { id: "part-master", name: "Part Master", why: "The foundation — tells the tower what parts exist, their costs, suppliers, lead times, and planning rules. Without this, nothing else runs.", required: ["Part Number", "Part Description", "Unit of Measure"], optional: ["Category", "Commodity", "Supplier ID", "Standard Cost", "Lead Time Days", "MOQ", "Lot Size", "Shelf Life Days", "ABC Class", "Criticality", "Status"], unlocks: ["Executive Dashboard", "Safety Stock", "Reorder Points", "Stockout Prediction", "Excess Analysis", "ABC/XYZ"], sample: [["P-1001", "Hex Bolt 12-A", "units", "Fasteners", "SUP-101", "0.45", "14", "100"], ["P-1002", "Steel Coil 22", "kg", "Raw Metal", "SUP-103", "4.20", "28", "500"]] }, { id: "inventory-balance", name: "Inventory Balance", why: "Current stock levels — what's on the shelf right now. This is the heartbeat of the tower; without it the dashboard is blind.", required: ["Part Number", "Quantity On Hand"], optional: ["Plant", "Warehouse", "Storage Location", "Available Qty", "Reserved Qty", "In-Transit Qty", "Last Updated Date"], unlocks: ["Executive Dashboard", "Stockout Prediction", "Excess Analysis"], sample: [["P-1001", "2400", "Detroit Assembly", "WH-DET-1", "A-04-2"], ["P-1002", "890", "Juárez Components", "WH-JUA-1", "B-11-3"]] }, { id: "demand-history", name: "Demand History", why: "What you actually consumed per month — the engine needs this to calculate variability, forecast demand, and right-size safety stock. Without it, recommendations are flat assumptions.", required: ["Part Number", "Date", "Demand Quantity"], optional: ["Plant", "Demand Type", "Customer", "Production Line"], unlocks: ["Demand Forecasting", "Safety Stock (variability)", "ABC/XYZ (variability)", "What-If Simulator"], sample: [["P-1001", "2025-07", "1800"], ["P-1001", "2025-08", "2100"], ["P-1002", "2025-07", "340"]] }, { id: "purchase-orders", name: "Purchase Orders", why: "Open and historical POs — so the tower knows what's already on order, what's late, and which suppliers deliver reliably.", required: ["PO Number", "Supplier ID", "Part Number", "Quantity Ordered", "Due Date"], optional: ["Quantity Received", "Order Date", "Status"], unlocks: ["Supplier Performance", "Late PO Alerts", "Replenishment (pipeline)"], sample: [["PO-70001", "SUP-101", "P-1001", "5000", "2026-07-20", "Open"]] }, { id: "supplier-master", name: "Supplier Master", why: "Your vendors: who they are, where, and how reliable. Without this, supplier risk analysis runs on assumptions instead of data.", required: ["Supplier ID", "Supplier Name"], optional: ["Location", "Contact", "Lead Time Days", "On-Time Delivery %", "Quality Score", "Risk Score"], unlocks: ["Supplier Performance", "Supplier Risk", "Replenishment confidence"], sample: [["SUP-101", "Great Lakes Metals", "Michigan", "92", "4.2"]] }, { id: "forecast", name: "Demand Forecast", why: "If your planning team or system already produces a forecast, load it so the tower can compare its own projection against yours and measure accuracy.", required: ["Part Number", "Forecast Date", "Forecast Quantity"], optional: ["Plant", "Forecast Source", "Confidence Level"], unlocks: ["Forecast Accuracy", "Demand Forecasting comparison"], sample: [["P-1001", "2026-08", "2000", "MRP", "High"]] }, { id: "bom", name: "Bill of Materials", why: "Recipes: what raw materials go into each finished good. Required for production-order planning and dependent-demand calculation.", required: ["Parent Part", "Component Part", "Quantity Per"], optional: ["Effective Date", "End Date", "Status"], unlocks: ["Production Orders", "Dependent Demand"], sample: [["FG-100", "P-1001", "4"], ["FG-100", "P-1002", "1.5"]] }, { id: "customer-orders", name: "Customer Orders", why: "Committed demand — so the tower can distinguish firm orders from forecast and flag service-level risks by customer.", required: ["Order Number", "Part Number", "Quantity", "Due Date"], optional: ["Customer", "Priority", "Status"], unlocks: ["Service Level", "Stockout impact by customer"], sample: [["SO-40100", "FG-100", "200", "2026-07-25", "Acme Corp", "High"]] }, { id: "safety-stock-rules", name: "Safety Stock Overrides", why: "If your team has already set safety stock levels they want to keep, load them — the tower will compare its AI calculation against yours and flag the gaps.", required: ["Part Number", "Current Safety Stock"], optional: ["Plant", "Service Level Target", "Approval Status"], unlocks: ["Safety Stock Optimization"], sample: [["P-1001", "400", "Detroit Assembly", "95%"]] }, { id: "eol", name: "Engineering Change / End-of-Life", why: "Which parts are being phased out, superseded, or changed — so the obsolescence engine catches them before finance does.", required: ["Part Number", "Change Type"], optional: ["Effective Date", "End-of-Life Date", "Replacement Part", "Status"], unlocks: ["Obsolescence Management", "Engineering review copilot"], sample: [["P-1042", "End-of-Life", "2026-09-01", "", "P-1099"]] } ]; const READINESS_FEATURES = [ { name: "Executive Dashboard", needs: ["part-master", "inventory-balance"] }, { name: "Safety Stock Optimization", needs: ["part-master", "inventory-balance", "demand-history"] }, { name: "Reorder Point Calculation", needs: ["part-master", "inventory-balance", "demand-history"] }, { name: "Stockout Prediction", needs: ["part-master", "inventory-balance"] }, { name: "Excess Inventory Analysis", needs: ["part-master", "inventory-balance"] }, { name: "Obsolescence Management", needs: ["part-master", "inventory-balance", "eol"] }, { name: "Supplier Risk Analysis", needs: ["supplier-master", "purchase-orders"] }, { name: "Demand Forecasting", needs: ["part-master", "demand-history"] }, { name: "What-If Simulator", needs: ["part-master", "inventory-balance", "demand-history"] }, { name: "Replenishment Planning", needs: ["part-master", "inventory-balance", "supplier-master"] }, { name: "ABC / XYZ Classification", needs: ["part-master", "demand-history"] }, { name: "Production Orders", needs: ["part-master", "inventory-balance", "bom"] } ]; /* ---------- Setup Wizard ---------- */ function SetupWizard({ setView }) { const [step, setStep] = useState(0); const steps = [ { q: "What plants do you want to manage?", help: "A plant is a physical manufacturing or warehouse site. Most companies start with one plant and add more later.", placeholder: "e.g. Detroit Assembly, Juárez Components", field: "plants" }, { q: "Where does your inventory data live today?", help: "This is usually an export from your ERP (SAP, Oracle, NetSuite, Epicor) or a shared spreadsheet your team already maintains.", options: ["ERP export (Excel/CSV)", "Shared spreadsheet", "WMS system", "Manual entry", "Not sure yet"], field: "invSource" }, { q: "Do you have monthly consumption or demand history?", help: "The tower uses 12+ months of how-much-you-used-per-month to forecast demand and calculate safety stock. Without it, recommendations use flat averages from your current daily usage — good enough to start, but less precise.", options: ["Yes, I can export monthly consumption", "Yes, in my ERP/MRP", "No, I only have current stock levels", "Not sure"], field: "demandSource" }, { q: "Do you track supplier and purchase order data?", help: "Supplier lead times and on-time delivery history let the tower score supplier risk and build realistic replenishment plans. Without it, the tower assumes a flat lead time and labels that assumption.", options: ["Yes, I can export PO and supplier data", "Partially — I have supplier names but not delivery history", "No", "Not sure"], field: "supplierSource" } ]; const s = steps[step] ?? null; return ( <>

{steps.map((_, i) =>

)}

{step < steps.length ? (

{s.q}

{s.help}

{s.options ? (

{s.options.map(o => ( ))}

) : (

)} {step > 0 && }

) : (

You're ready to load data

Based on your answers, start with these data objects — in this order. Each one has a downloadable template and the AI mapper will match your columns automatically.

{["part-master", "inventory-balance", "demand-history", "supplier-master", "purchase-orders"].map((id, i) => { const obj = DATA_OBJECTS.find(o => o.id === id); return (

{i + 1} {obj.name} {obj.why.split(".")[0]}.

); })}

)}

); } /* ---------- Data Objects & Templates ---------- */ function DataObjects() { const [sel, setSel] = useState(null); const downloadTemplate = obj => { const header = [...obj.required, ...obj.optional]; const rows = obj.sample.map(r => r.join(",")); const text = header.join(",") + "\n" + rows.join("\n"); const a = document.createElement("a"); a.href = "data:text/csv;charset=utf-8," + encodeURIComponent(text); a.download = obj.id + "-template.csv"; a.click(); }; const active = sel ? DATA_OBJECTS.find(o => o.id === sel) : null; return ( <>

{DATA_OBJECTS.map(obj => ( ))}

{active && (

{active.name}

{active.why}

Required fields

{active.required.map(f =>

• {f}

)}

Optional fields (improve analysis)

{active.optional.map(f =>

• {f}

)}

Sample rows (included in template)

{[...active.required, ...active.optional.slice(0, active.sample[0]?.length - active.required.length)].join(" | ")}

{active.sample.map((r, i) =>

{r.join(" | ")}

)}

Unlocks: {active.unlocks.join(", ")}

How to upload: Go to the platform Data page → drop the CSV → the AI reads every column and maps it to the tower's fields automatically. If the column names don't match exactly (e.g. your file says ITEM_NO instead of Part Number), the mapper handles it — you'll see each mapping and can adjust before confirming.

)} ); } /* ---------- Readiness Score ---------- */ function Readiness({ dataMode, setView }) { const hasReal = dataMode === "real"; return ( <>

{READINESS_FEATURES.map(f => { const status = hasReal ? "ready" : "sample"; const color = status === "ready" ? C.green : C.sub; const label = status === "ready" ? "✓ Ready" : "Sample data"; const missing = hasReal ? [] : f.needs; return (

{f.name} Needs: {f.needs.map(n => DATA_OBJECTS.find(o => o.id === n)?.name ?? n).join(", ")} {missing.length > 0 && !hasReal && ( Upload {DATA_OBJECTS.find(o => o.id === f.needs[0])?.name} first )}

); })}

Readiness is binary per feature: either the required data objects are loaded with the required fields, or the feature states its assumptions. No feature silently invents data.

); } /* ---------- Integrations Roadmap ---------- */ function Integrations() { const LIVE = [ { name: "Excel / CSV Upload", status: "live", desc: "Drop a workbook on the Data page — the AI reads every sheet, maps columns, and loads your plant in one click. This is the primary path today." }, { name: "AI Column Mapping", status: "live", desc: "The platform reads your column headers and auto-maps them to the tower's fields (e.g. ITEM_NO → Part Number). You review and confirm before import." }, { name: "Manual Entry", status: "live", desc: "For small corrections: the Plant page lets you add/edit plant objects, and the tower's own screens (Safety Stock, Excess) persist decisions directly." }, { name: "Downloadable Templates", status: "live", desc: "Every data object in the tower has a downloadable CSV template with headers and sample rows — fill it from your ERP export." } ]; const ROADMAP = [ { name: "REST API Push", desc: "Send inventory updates, POs, and demand via API so your ERP pushes changes automatically. Coming next." }, { name: "Scheduled Refresh (daily/hourly)", desc: "Set a file-drop location or API endpoint and the tower refreshes on a schedule." }, { name: "ERP Connectors (SAP, Oracle, NetSuite, Epicor)", desc: "Pre-built adapters that read your ERP's inventory, PO, and demand tables directly." }, { name: "WMS / MES Feed", desc: "Real-time inventory movements from warehouse and shop-floor systems." }, { name: "Database Connection (SQL Server, PostgreSQL, Snowflake)", desc: "Point the tower at a read-only replica or data warehouse for automatic sync." }, { name: "Webhook / Event Updates", desc: "Push individual transactions (receipts, issues, PO changes) as they happen." }, { name: "SFTP / Cloud Storage File Drop", desc: "Drop CSV/Excel files into a watched folder for automatic ingestion." } ]; return ( <>

Live now

{LIVE.map(i => (

{i.name}

{i.desc}

))}

Roadmap — coming, honestly

{ROADMAP.map(i => (

{i.name}

{i.desc}

))}

); } /* ===== ADMIN & SETTINGS SCREENS ===== */ const DEFAULT_CONFIG = { shiftHoursPerDay: 8, workingDaysPerWeek: 5, planningHorizonDays: 90, defaultServiceLevel: 0.95, serviceByCriticality: { 5: 0.99, 4: 0.975, 3: 0.95, 2: 0.90, 1: 0.90 }, currencySymbol: "$", reviewCycleDays: 30, slowMovingDays: 90, obsoleteDays: 180, excessMultiplier: 2.0, defaultLeadTimeDays: 14, safetyFactor: 1.25, stockoutRiskThreshold: 70, approvalRequired: { safetyStock: true, reorderPoint: true, disposition: true, writeOff: true } }; function Settings({ decisions, persist, alertMeta }) { const config = { ...DEFAULT_CONFIG, ...(decisions.__config ?? {}) }; const set = (key, val) => persist({ ...decisions, __config: { ...config, [key]: val } }, alertMeta); const Field = ({ label, help, value, onChange, type, suffix }) => (

{label}

{help}

onChange(type === "number" || !type ? Number(e.target.value) : e.target.value)} style={{ width: 100, background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 8, padding: "8px 12px", fontSize: 14, fontWeight: 600, textAlign: "right" }} /> {suffix && {suffix}}

); return ( <>

set("shiftHoursPerDay", v)} suffix="hours" /> set("workingDaysPerWeek", v)} suffix="days" /> set("planningHorizonDays", v)} suffix="days" /> set("defaultServiceLevel", v / 100)} suffix="%" /> set("defaultLeadTimeDays", v)} suffix="days" /> set("safetyFactor", v)} suffix="×" /> set("reviewCycleDays", v)} suffix="days" /> set("currencySymbol", v)} type="text" />

All changes persist to your organization and take effect on the next screen load. Service levels per criticality tier are configured on the Thresholds screen.

); } function Thresholds({ decisions, persist, alertMeta }) { const config = { ...DEFAULT_CONFIG, ...(decisions.__config ?? {}) }; const set = (key, val) => persist({ ...decisions, __config: { ...config, [key]: val } }, alertMeta); const svc = config.serviceByCriticality ?? DEFAULT_CONFIG.serviceByCriticality; const setSvc = (tier, val) => set("serviceByCriticality", { ...svc, [tier]: val / 100 }); return ( <>

Service level by part criticality

Higher criticality = higher service level = more safety stock. This is the single most impactful setting in the tower.

{[5, 4, 3, 2, 1].map(tier => (

= 4 ? C.red : tier === 3 ? C.amber : C.sub }}>Tier {tier}

{tier === 5 ? "Line-stop" : tier === 4 ? "Critical" : tier === 3 ? "Important" : tier === 2 ? "Standard" : "Low"}

setSvc(tier, Number(e.target.value))} style={{ width: "100%", background: C.card, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 8, padding: "6px 8px", fontSize: 16, fontWeight: 700, textAlign: "center" }} />

))}

Inventory classification thresholds

{[ ["Slow-moving threshold", "slowMovingDays", "days", "Parts with no consumption in this many days are flagged as slow-moving."], ["Obsolete threshold", "obsoleteDays", "days", "Parts with no consumption in this many days are flagged as obsolete candidates."], ["Excess multiplier", "excessMultiplier", "× max stock", "Stock above this multiple of calculated max stock is flagged as excess inventory."], ["Stockout risk threshold", "stockoutRiskThreshold", "risk score", "Parts scoring above this on the 0–100 risk scale trigger critical alerts."] ].map(([label, key, suffix, help]) => (

{label}

{help}

set(key, Number(e.target.value))} style={{ width: 80, background: C.well, color: C.text, border: `1px solid ${C.lineD}`, borderRadius: 8, padding: "6px 10px", fontSize: 14, fontWeight: 600, textAlign: "right" }} /> {suffix}

))}

); } function ApprovalMatrix({ decisions, persist, alertMeta }) { const config = { ...DEFAULT_CONFIG, ...(decisions.__config ?? {}) }; const approvals = config.approvalRequired ?? DEFAULT_CONFIG.approvalRequired; const toggle = key => persist({ ...decisions, __config: { ...config, approvalRequired: { ...approvals, [key]: !approvals[key] } } }, alertMeta); const WORKFLOWS = [ { key: "safetyStock", label: "Safety stock changes", desc: "Require accept/reject before AI-recommended safety stock values take effect.", screen: "Safety Stock & ROP" }, { key: "reorderPoint", label: "Reorder point changes", desc: "Require approval before recommended reorder points are applied.", screen: "Safety Stock & ROP" }, { key: "disposition", label: "Excess/obsolete disposition", desc: "Require sign-off on dispose, scrap, or return-to-vendor decisions.", screen: "Excess & Obsolescence" }, { key: "writeOff", label: "Inventory write-offs", desc: "Require approval when adjustments would write off inventory value.", screen: "Alerts & Exceptions" }, { key: "poApproval", label: "Purchase order recommendations", desc: "Require buyer approval before replenishment recommendations are exported.", screen: "Replenishment" }, { key: "supplierEscalation", label: "Supplier escalations", desc: "Require supply chain manager review before flagging a supplier for escalation.", screen: "Suppliers" }, { key: "transferApproval", label: "Inter-plant transfers", desc: "Require both sending and receiving plant manager approval for inventory transfers.", screen: "AI Copilot (transfers)" } ]; return ( <>

{WORKFLOWS.map(w => (

{w.label}

{w.desc}

))}

); } function AuditLog({ decisions, alertMeta }) { // Aggregate all persisted decisions into a readable timeline const events = []; for (const [partId, d] of Object.entries(decisions)) { if (partId === "__config") { events.push({ time: "config", kind: "⚙️ Settings", desc: "Planning parameters updated", part: "—" }); continue; } if (d.ssStatus) events.push({ time: d.ssStatus, kind: d.ssStatus === "approved" ? "✅ SS approved" : "❌ SS rejected", desc: d.ssOverride ? `Override to ${d.ssOverride}` : "AI recommendation accepted", part: partId }); if (d.poStatus) events.push({ time: d.poStatus, kind: d.poStatus === "approved" ? "✅ PO approved" : "❌ PO rejected", desc: d.poQty ? `Qty: ${d.poQty}` : "", part: partId }); if (d.disposition) events.push({ time: "set", kind: "📋 Disposition", desc: d.disposition, part: partId }); } for (const [alertId, m] of Object.entries(alertMeta)) { if (m.owner || m.status) events.push({ time: m.status ?? "assigned", kind: "🔔 Alert", desc: `${m.owner ? "Owner: " + m.owner : ""}${m.due ? " · Due: " + m.due : ""}${m.status ? " · " + m.status : ""}`, part: alertId.split("-").slice(1).join("-") }); } return ( <> {events.length === 0 ? (

No decisions recorded yet. Approve a safety stock recommendation, assign an alert owner, or configure a setting — it will appear here.

) : (

{events.map((e, i) => (

{e.kind.split(" ")[0]} {e.kind.split(" ").slice(1).join(" ")} {e.desc} {e.part}

))}

)}

This log is built from persisted decisions in the tower's state. For platform-level audit (logins, data loads, member changes), see the Team → Audit trail in the main platform nav.

); }

Questions about the AI Inventory Control Tower