[ AI inventory forecasting ]
Know What to Reorder Before the Shelf Runs Empty
We build forecasting models that read your sales history, promotions, lead times and outside signals, then turn them into reorder quantities your buyers can approve in one click.
- Forecasts per item, location and week
- SKU level
- Forecasts per item, location and week
- To a backtest on your own data
- 2 to 3 wks
- To a backtest on your own data
- Models and data stay in your account
- Your cloud
- Models and data stay in your account
Trail runner, size 9
Rain shell, medium
Reorder 240 units, arrives in 5 days
Water bottle, 1 L
Example forecast, day 3 of the week
Why Spreadsheet Forecasts Keep Missing
Most teams still forecast with moving averages and gut feel. That works until seasonality, promotions or a supplier delay hits.
- 01
Stockouts on the items that sell
Fast movers run out between orders, and lost sales never show up in the data you use to plan the next order.
- 02
Cash tied up in slow stock
Safety stock is set once and forgotten, so slow items sit in the warehouse while fast ones run short.
- 03
Promotions break the plan
A campaign doubles demand for two weeks, then the forecast treats that spike as the new normal.
- 04
Buyers do not trust the numbers
When a forecast cannot explain itself, buyers override it, and the model never gets better.
How We Build Your Forecasting System
Every engagement starts with a backtest against your current method, so you see the gain before you commit to production.
- 01
Data audit
We map sales, inventory, purchase orders, promotions and lead times, and check how much history each SKU has.
Output: Data quality report
- 02
Backtest
Candidate models are trained on past periods and compared with your current forecast on the weeks that followed.
Output: Accuracy and stock impact by SKU group
- 03
Replenishment rules
Forecasts are turned into reorder points and quantities using lead times, minimum order sizes and service levels you set.
Output: Reorder recommendations
- 04
Rollout and monitoring
Recommendations land in your ERP or a buyer dashboard, with drift alerts and scheduled retraining.
Output: Live system with monitoring
Data the Models Use
We map each source in the first weeks and only use the ones that improve the result.
- Sales by SKU, store and channel
- Stock on hand and in transit
- Purchase orders and supplier lead times
- Promotions and price changes
- Product attributes and hierarchy
- Holidays, weather and local events
What the System Does
Built around how buyers and planners actually work, not around a model in a notebook.
Multi-horizon forecasts
Daily, weekly and monthly predictions for every SKU and location, including new items with little history.
Outside signals
Weather, holidays, search trends and local events are added where they measurably improve accuracy.
Reorder recommendations
Suggested purchase orders that respect lead times, pack sizes and supplier minimums.
Explainable numbers
Each forecast shows the drivers behind it, so buyers can see why a number moved.
Anomaly alerts
When sales drift away from the forecast, the planner gets an alert before the shelf is empty.
Scenario planning
What-if runs for promotions, price changes and supplier delays before you commit stock.
What We Measure With You
We agree targets during the backtest. These are typical goals for projects like this; your results depend on your data and processes.
- 01
- Forecast error
- Weighted error by SKU group against your current method
- 02
- Stockout rate
- Share of SKU days with zero stock on fast movers
- 03
- Inventory days
- Days of cover held across the catalog
- 04
- Buyer overrides
- How often planners change the recommendation
Forecasting Stack
Proven open tools and managed services, chosen to fit your cloud.
Models
- XGBoost
- LightGBM
- Prophet
- Temporal Fusion Transformer
Data
- PostgreSQL
- Snowflake
- dbt
- Apache Airflow
Serving
- AWS SageMaker
- FastAPI
- Docker
Integrations
- SAP
- NetSuite
- Shopify
- Power BI
Where It Fits
- 01Retail and eCommerceStore and online stock planned together across thousands of SKUs.
- 02ManufacturingRaw material and component stock planned against production schedules.
- 03Food and beverageShort shelf lives where overstock turns straight into waste.
- 04Wholesale distributionMulti-warehouse networks with long supplier lead times.
- 05PharmaceuticalsCritical items where a stockout is not an option.
See the Forecast Beat Your Current Plan First
In two to three weeks we backtest on your own sales history, so you see the stock impact before any production work.
Book a Forecasting CallInventory Forecasting Cost
Indicative starting prices. Cloud compute is billed to your account.
2 to 3 weeks
from$2,500
Feasibility backtest
Data audit and a backtest on one product group
6 to 10 weeks
from$12,000
Production forecasting
Forecasts, reorder rules, dashboard and monitoring
3 to 5 months
from$35,000
Network-wide planning
All locations and channels with ERP integration
What Operations Teams Say
Rated 4.9/5 on average across the platforms clients review us on.
Clutch5.0
GoodFirms5.0- Google5.0
- Upwork4.9
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
Inventory Forecasting Questions
Still deciding? A 30-minute call usually answers the rest.
Ask Us DirectlyA year of weekly history covers most seasonality. Items with less history are grouped with similar products, and new items borrow patterns from comparable launches.
Yes. We read from and write back to SAP, NetSuite, Dynamics, Shopify and custom databases, or deliver recommendations through a dashboard if you prefer.
Yes. The system recommends; buyers approve or adjust. Overrides are tracked so the model learns where it is weak.
Every project starts with a backtest that compares the new forecast with your current method on the same historical weeks, before any production work.
Usually weekly or monthly, triggered automatically, with an alert when accuracy drops below the level we agreed.
In your own cloud account by default. We work under NDA and follow ISO 27001 practices.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request