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When to use this

Use /supply-performance when you want to:
  • Monitor closing stock and prevent stockouts or overstock
  • Track service levels across distribution centers
  • Identify which DCs are underperforming on fill rate
  • Build an inventory health dashboard or alert system

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Key metrics

FieldDescription
closingStockMonth-end inventory in units
closingStockGSVClosing stock valued at gross sales value
serviceLevel% of customer demand met on time (0–1)
dispatchVolumeUnits dispatched out of the DC in the period
storageAndHandlingCostCost of warehousing in the period (plan currency)

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groupBy options

ValueGroups by
dcDistribution center
skuIndividual product SKU
regionGeographic region (aggregates across DCs)

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Example: Flag DCs with service level below 95%

resp = client.get("/supply-performance", params={
    "planId": "plan_9xKz2",
    "scenarioIds": "scen_committed",
    "interval": "M",
    "groupBy": "dc",
    "dateFrom": "2025-01-01",
    "dateTo": "2025-03-31",
})

scenario = resp.json()["scenarios"][0]

at_risk = [
    row for row in scenario["series"]
    if row["serviceLevel"] < 0.95
]

for row in at_risk:
    print(f"⚠️  {row['groupKey']} | {row['period']} | SL={row['serviceLevel']:.1%}")
Output: 
⚠️  DC-PARIS   | 2025-01 | SL=91.2%
⚠️  DC-NAIROBI | 2025-02 | SL=88.7%

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Combining with demand data

A common pattern is to pair supply performance with demand forecast data to compute a coverage ratio (weeks of stock): 
demand = client.get("/demand-forecast", params={
    "planId": "plan_9xKz2",
    "scenarioIds": "scen_committed",
    "interval": "M",
    "groupBy": "region",
}).json()

supply = client.get("/supply-performance", params={
    "planId": "plan_9xKz2",
    "scenarioIds": "scen_committed",
    "interval": "M",
    "groupBy": "region",
}).json()

# Build a coverage map: closing stock / monthly demand
for d_row, s_row in zip(demand["scenarios"][0]["series"], supply["scenarios"][0]["series"]):
    if d_row["demandVolume"] > 0:
        weeks_coverage = (s_row["closingStock"] / d_row["demandVolume"]) * 4.33
        print(f"{s_row['groupKey']} {s_row['period']}: {weeks_coverage:.1f} weeks of stock")