FMCG
Operations & Logistics
FMCG operations sit between factory output and distributor shelves. A single day of dispatch slippage ripples into stock-outs, emergency secondary transfers, and distorted primary-secondary ratios that sales and finance interpret as demand or scheme issues. Most teams still reconcile OTIF and fill rates in spreadsheets that lag the daily morning huddle.
FireAI connects ERP dispatch and invoicing, TMS or transporter feeds, yard and loading timestamps, and warehouse confirmations into one operational intelligence layer. You monitor OTIF and fill rate by plant, SKU class, route, and distributor — and ask why a lane degraded in plain language instead of opening five systems.
Beyond reporting, FireAI helps you tie operational failures to commercial outcomes: delayed loads to specific territories’ secondary dips, repeated short-ships to chronic forecast bias for certain packs, and detention at hubs to fleet utilization.
Primary Dispatch, OTIF & Order Fill Rate
OTIF (on-time in-full) and fill rate are the operational heartbeat of FMCG — but definitions vary (promise date vs request date, line-fill vs order-fill). Without a consistent rule set applied across plants and channels, zones compare incompatible numbers in reviews.
FireAI ingests sales orders, dispatch confirmations, GRN at distributor or hub, and carrier POD where available. It computes OTIF and fill at order, line, and quantity level; breaks variance into stock not available, planning cut-off miss, loading delay, and in-transit delay using timestamps you already capture. Trend views highlight plants or SKU families with rising partial shipments before they become chronic service failures.
What you can ask FireAI:
- "Which plants had OTIF below 85% last week and what was the top reason code?"
- "Show fill rate trend for beverage PET lines vs glass — last 8 weeks"
- "List distributors with three or more consecutive in-full misses on priority SKUs"
FMCG operations — dispatch & service
Warehouse Throughput, Pick Accuracy & Inventory Truth
A warehouse can hit dispatch numbers while silently bleeding accuracy — wrong batch picks, mislabeled pallets, or system quantity drift that shows up later as returns, claims, and reconciliation pain for finance. Cycle count programs are useless if not tied to high-variance SKUs and pick paths.
FireAI joins WMS transactions, pick confirmations, cycle counts, and returns to highlight accuracy by zone, shift, and SKU velocity band. It flags SKUs with rising pick-to-dispatch variance or recurring recounts. For FMCG with batch and expiry, it supports FEFO adherence views when batch data exists in source systems.
What you can ask FireAI:
- "Which SKU families had the highest pick variance vs dispatch in April?"
- "Show inventory accuracy % by warehouse zone — last count wave"
- "List batches with >90 days shelf life remaining stuck in slow-moving bins"
Batch Traceability, Recall Readiness & Compliance
Regulatory and customer expectations require forward and backward traceability from manufacturing batch to distributor shipment and, where captured, retail placement. During a quality incident, speed of affected quantity and location determines recall cost and brand risk.
FireAI maps batch → dispatch → distributor → secondary movement using ERP batch IDs, invoice lines, and DMS stock movement. In drill mode you retrieve “where did batch X ship, how much remains at which distributor, which territories had velocity anomalies post-release” without a manual spreadsheet marathon. This complements — not replaces — your QA workflow; it accelerates analytics on existing transaction data.
What you can ask FireAI:
- "Trace total dispatched quantity for batch BR-24091 by distributor and region"
- "Which territories showed abnormal return rate for packs from a specific mfg week?"
- "Show time-to-first-secondary for new batch launches vs norm for that SKU"
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