Prompt Details
Model
Chat - (gpt-5.6)
Token size
660
Example input
[BUSINESS_CONTEXT]: Skincare ecommerce store, USD, 8-week planning horizon.
[SKU_DATA]: Vitamin C serum: 140 on hand, 38/week sales, $18 cost. Night cream: 260 on hand, 14/week, $22 cost, expires in 5 months. Gift set: 45 on hand, 24/week in November, $31 cost.
[SUPPLIER_AND_BUDGET]: Purchase cap $12,000. Vitamin C lead time 21 days, MOQ 120. Night cream lead time 30 days, MOQ 100. Gift set lead time 28 days, MOQ 60. Supplier reliability high.
[SEASONAL_AND_OPERATIONAL_CONSTRAINTS]: Current date early November. Demand variability and service target not supplied.
[OUTPUT_PREFERENCES]: Concise, cash-conservative action table with purchase plan and dead-stock recovery test.
Example output
DATA STATUS - PARTIAL: three SKUs are usable; demand variability and service-level target are missing, so safety stock is a scenario.
Gift set: 1.9 weeks cover, 96 units lead-time demand, REORDER NOW, buy MOQ 60 for $1,860. Vitamin C serum: 3.7 weeks cover, 114 lead-time demand, REORDER NOW, buy MOQ 120 for $2,160. Night cream: 18.6 weeks cover, HOLD / MARKDOWN TEST, buy 0.
PURCHASE PLAN: commit $4,020 to the two urgent lines. Reserve the remaining $7,980. Do not buy night cream; expiry risk is rising.
DEAD-STOCK TEST: bundle night cream with a faster moisturizer for 14 days. Stop if sell-through does not improve after one normal replenishment cycle.
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GPT-5.6
Convert SKU sales, stock and supplier data into reorder-now, wait, markdown, bundle or discontinue decisions. It calculates stock cover, lead-time demand and budget-aware order quantities; identifies cash trapped in slow inventory; and produces supplier actions, scenarios and a four-week operating cadence. Missing data stays visible, so the result is useful without false precision.
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Added 2 weeks ago
