Teenva AI & Digital Ventures builds demand forecasting AI for 3PLs, ecommerce fulfillment, FMCG distributors, and manufacturers from Bangalore, India. Teenva predicts shipment volumes, SKU movement, and lane-level load by day/week—so you staff hubs, book fleet capacity, and position inventory before spikes hit, integrated with your WMS/ERP and downstream route optimization.
Spreadsheet seasonality guesses fail during festivals and flash sales. Teenva trains time-series ML on your order history, promotions, and external signals (scoped)—with confidence intervals planners trust.
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Demand forecasting AI—shipment and SKU predictions for capacity, inventory, and staffing.
Hub: AI solutions · Domain: Logistics
What we forecast
| Granularity | Horizon | Typical use |
|---|---|---|
| SKU × location | 1–12 weeks | Replenishment, warehouse slotting |
| Hub / DC inbound | Daily–weekly | Inbound dock labor, cross-dock planning |
| Outbound parcels | Daily | Last-mile fleet sizing, route planning |
| Lane / pin code (scoped) | Weekly | Line-haul truck booking |
| Category rollup | Monthly | Network capacity, supply chain analytics |
| New SKU (scoped) | Launch curve | Analogous product bootstrap |
Forecasts export as API JSON, CSV, or ERP feed—not chat paragraphs.
Signals & models
| Signal | Why it matters |
|---|---|
| Historical orders/shipments | Base seasonality and trend |
| Promotions / markdowns | Lift vs baseline |
| India festivals (scoped) | Diwali, Eid, regional holidays |
| Price changes (scoped) | Elasticity features |
| Weather (scoped) | Category-specific (beverages, apparel) |
| Marketing calendar (scoped) | Campaign-driven spikes |
| Stockouts (scoped) | Censor correction so history isn't understated |
Models: Prophet, ARIMA, gradient boosting, deep learning (scoped)—selected per series volume and stability.
Features we implement
- Forecast API — batch scores for thousands of SKUs/lanes
- Planner UI — override, annotate, compare model vs human
- Confidence intervals — P10/P50/P90 bands for safety stock
- Hierarchy reconciliation — SKU sums match category totals
- Promo planner hook (scoped) — what-if promo lift
- Accuracy monitoring — WAPE, MAPE, bias by segment; drift alerts
- Automated retrain — weekly/monthly on fresh data
- Exception flags — anomaly when actuals diverge from band
- Integration — WMS, TMS, ERP via API integration
Architecture
Historical orders, shipments, inventory (ERP/WMS/data warehouse)
Feature pipeline (calendar, promo, holidays, exogenous)
Model zoo per series class (fast movers vs long tail)
Forecast store (SKU × location × date)
Planner UI + API → replenishment / capacity / routing inputs
Actuals feedback → accuracy dashboard → retrain
- Long-tail SKUs — pooling / category-level fallback to avoid noise
- Deployment — scheduled jobs in your cloud; no data to public LLM APIs
Use cases by business type
| Business | Forecast drives |
|---|---|
| Ecommerce fulfillment | DC labor, packaging stock, carrier manifests |
| Quick commerce (scoped) | Dark store replenishment cadence |
| FMCG distributor | Van load per beat, depot transfers |
| 3PL multi-client (scoped) | Per-client volume commitments |
| Manufacturing spare parts (scoped) | Slow-mover safety stock |
| Retail chain (scoped) | Store replenishment from RC |
Integrations
Core systems
Forecast error costs real money—empty trucks or stockouts. We measure WAPE on holdout and show lift vs your spreadsheet baseline before production.
Demand forecasting vs other logistics AI
| Product | Focus |
|---|---|
| Demand forecasting AI | How much volume/will ship, when? |
| Route optimization AI | Given stops today, best vehicle routes |
| Warehouse intelligence AI | Inside DC: pick paths, slotting |
| Logistics tracking chatbot | Customer/dispatcher status FAQ |
| Supply chain analytics AI | Network KPIs, scenario dashboards |
Together: forecast volume → plan labor and fleet → optimize routes → track shipments.
Why Teenva AI
Forecast + logistics stack
Models wired to TMS/WMS you already runIndia seasonality
Festival calendars and regional promo patterns in featuresPlanner-friendly
Overrides and explainable drivers, not black-box onlyLong-tail aware
Sensible defaults when SKU history is thinOps
Managed IT support when ERP feeds break
Delivery process
Discovery
SKUs, hubs, horizons, KPI (WAPE target), data sources
Data audit
history length, gaps, promo flags, stockout censoring
Baseline
naive seasonal or spreadsheet benchmark
Feature engineering
calendar, holidays, hierarchy
Model train
segment by velocity class; backtest on rolling windows
Planner UI / API
export format agreed with ops
Pilot
one hub or category; planners compare to manual
Production schedule
nightly/weekly batch refresh
Monitor
accuracy by segment; retrain triggers
Explore
Frequently asked questions
12–24 months ideal for seasonality; shorter books use category pooling or external analogs.
Yes—tiered models: top SKUs individual, long tail pooled.
No—production uses time-series ML on structured history, not LLM guesses.
Yes (scoped)—India holiday calendars as features; custom regional events configurable.
Scoped—promo flags in history + what-if UI for planned campaigns.
Forecast outbound parcel count by hub/day feeds fleet and route planning.
UI records override; optional feedback to improve model (scoped).
SAP, custom, Zoho—via API integration services.
Analog SKU or category curve (scoped) until history accumulates.
10–14 weeks MVP (one hub + top SKUs weekly forecast) with clean historical extract.
Related logistics AI solutions
Build your demand forecasting models
SKU, hub, and lane forecasts—daily to monthly horizons with seasonality and promo lift.
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Ready to take your business to the next level?