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[]).map((x: any) => x.node.title),\n ageDays: daysBetween(n.createdAt, now),\n });\n }\n pages += 1;\n if (!page.pageInfo.hasNextPage) {\n cursor = null;\n break;\n }\n cursor = page.pageInfo.endCursor;\n }\n if (cursor) truncated = true;\n return { orders: out, truncated };\n}\n\nconst BUCKETS: [string, (d: number) => boolean][] = [\n [\"14+ days\", (d) => d >= 14],\n [\"7-13 days\", (d) => d >= 7 && d < 14],\n [\"4-6 days\", (d) => d >= 4 && d < 7],\n [\"2-3 days\", (d) => d >= 2 && d < 4],\n [\"0-1 days\", (d) => d < 2],\n];\n\nfunction analyse(orders: Order[]) {\n const buckets = BUCKETS.map(([label, test]) => {\n const hits = orders.filter((o) => test(o.ageDays));\n return {\n label,\n orders: hits.length,\n value: Number(hits.reduce((s, o) => s + o.totalAmount, 0).toFixed(2)),\n };\n });\n\n // Same-day clusters: 5+ orders created on one date and still unshipped.\n const byDate = new Map();\n for (const o of orders) {\n const d = o.createdAt.slice(0, 10);\n byDate.set(d, [...(byDate.get(d) ?? []), o]);\n }\n const clusters = [...byDate.entries()]\n .filter(([, v]) => v.length >= 5)\n .map(([date, v]) => ({\n date,\n orders: v.length,\n value: Number(v.reduce((s, o) => s + o.totalAmount, 0).toFixed(2)),\n }))\n .sort((a, b) => a.date.localeCompare(b.date));\n\n const paid = orders.filter((o) => o.totalAmount > 0);\n const zeroValue = orders.filter((o) => o.totalAmount === 0);\n\n // Backlog shape: healthy queues are fat at the young end.\n const old = orders.filter((o) => o.ageDays >= 7).length;\n const fresh = orders.filter((o) => o.ageDays < 2).length;\n const inverted = old > fresh * 2 && old >= 5;\n\n return {\n total: orders.length,\n totalValue: Number(\n orders.reduce((s, o) => s + o.totalAmount, 0).toFixed(2),\n ),\n revenueBearing: paid.length,\n zeroValue: zeroValue.length,\n oldestDays: orders.length ? Math.max(...orders.map((o) => o.ageDays)) : 0,\n buckets,\n clusters,\n inverted,\n oldest: [...orders]\n .sort((a, b) => b.ageDays - a.ageDays)\n .slice(0, 15)\n .map((o) => ({\n name: o.name,\n ageDays: o.ageDays,\n value: o.totalAmount,\n items: o.titles.slice(0, 3),\n })),\n };\n}\n\nfunction esc(s: string) {\n return String(s).replace(\n /[&<>\"]/g,\n (c) => ({ \"&\": \"&\", \"<\": \"<\", \">\": \">\", '\"': \""\" }[c]!),\n );\n}\n\nfunction render(\n a: ReturnType,\n store: string,\n generatedAt: string,\n truncated: boolean,\n) {\n const admin = `https://${\n store.replace(\".myshopify.com\", \"\")\n }.myshopify.com/admin`;\n const money = (n: number) =>\n \"$\" +\n n.toLocaleString(\"en-US\", {\n minimumFractionDigits: 2,\n maximumFractionDigits: 2,\n });\n\n return `\n\nFiggy Fulfillment Backlog\n

Fulfillment backlog

Open, unfulfilled orders only · cancelled and closed excluded · generated ${\n esc(generatedAt)\n }

\n\n

${a.total} orders · ${money(a.totalValue)}

\n Oldest ${a.oldestDays} days · ${a.revenueBearing} revenue-bearing · ${a.zeroValue} at $0\n

\n\n${\n a.inverted\n ? `

Backlog shape is inverted.\n More orders are older than a week than were placed in the last two days. A queue that is\n being worked normally is fat at the young end; this one is not.

`\n : \"\"\n }\n\n${\n a.clusters.length\n ? `

Same-day clusters.\n ${\n a.clusters.map((c) =>\n `${c.orders} orders from ${c.date} (${money(c.value)})`\n ).join(\"; \")\n }.\n A single date going untouched usually means a stockout or a skipped batch rather than slow shipping.

`\n : \"\"\n }\n\n

\n\n${\n a.buckets.map((b) =>\n ``\n ).join(\"\")\n }\n

Age	Orders	Value
${b.label}	${b.orders}	${\n money(b.value)\n }

\n\n

\n\n${\n a.oldest.map((o) =>\n `\n \n \n \n `\n ).join(\"\")\n }\n

Oldest orders	Age	Value	Items
${esc(o.name)}	= 7 ? \" old\" : \"\"}\">${o.ageDays}d	${money(o.value)}	${esc(o.items.join(\", \")).slice(0, 70)}

\n\n${\n truncated\n ? `

Result truncated at ${\n MAX_PAGES * PAGE_SIZE\n } orders.

`\n : \"\"\n }\n

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