Demand Forecasting & Product Performance Analytics
Movement and sales signals turned into reorder timing, out-of-stock warnings and honest style performance.
The problem it solves
Forecasting fails in this market for an unglamorous reason: the stock numbers feeding it are wrong. No model survives an inventory record that is twenty percent out.
Which is why this module sits deliberately downstream of inventory accuracy, and why we will say plainly that it should not be bought first. Once counts are trustworthy, the same RFID event stream that produced them carries more signal than POS alone ever did — not just what sold, but what was on the floor, what was in the stockroom, what moved to the fitting room and came back.
The build order matters. Rules and seasonality first, because they are explicable and a merchandiser will act on them. Machine learning after there is a baseline to beat and enough clean history to train on. Selling a model before the data supports it is how analytics projects lose credibility permanently.
The workflow
Four steps, in this order. A step out of sequence is a broken process, not a variation.
- 01
Ingest
RFID movement events, POS transactions and stock positions land in one place with a shared item master.
- 02
Baseline
Rules and seasonality models first — explicable, auditable, and something a merchandiser will actually trust.
- 03
Alert
Out-of-stock risk, slow movers and reorder timing surface as actions, not as a dashboard nobody opens.
- 04
Learn
Once there is clean history and a baseline to beat, ML models are introduced against a measured benchmark.
What it is
made of
Four layers, in the order data moves: a tag is read, the read becomes an event, a rule decides what it means, and a business system acts on it.
- 01
Tags
What carries the identity
- No additional tagging — consumes the existing item-level estate
- 02
Readers & edge
What turns presence into an event
- No additional readers — consumes existing reads
- 03
Platform
What decides what the event means
- Analytics module and metric store
- Rules and seasonality baseline models
- Alerting on OOS risk and reorder points
- Optional ML service on top of the baseline
- 04
Integrations
What acts on the decision
- ERP
- POS
- BI export
What we supply
- 01
Analytics module
Software only. This module adds no hardware — it earns its keep by making the reads you already pay for answer commercial questions.
- Shared item master across sources
- Rules and seasonality baseline
- Scheduled reports and alerting
- CSV and BI tool export
- 02
Optional ML service
Introduced only after a rules baseline exists and there is enough clean history to train against. Measured against that baseline, not against a hope.
- Demand forecasting per SKU and store
- Anomaly detection on movement
- Benchmarked against the rules baseline
- Added as a phase two, never a phase one
What to expect
Ranges, not single flattering figures. Baselines are captured for four to eight weeks before a pilot starts, so the delta is measured rather than claimed.
- Out-of-stock rate
- Alerted in advance
- Working capital
- Lower via stock turn
- Reorder timing
- Driven by real counts
- Style performance
- Floor time vs sales
What to plan for
Every module has a failure mode. These are the ones that actually bite on this one, and what we do about them — because finding out later costs more than knowing now.
- Data science maturity is genuinely required, and this module fails loudly without accurate inventory beneath it.
- Sequenced after inventory accuracy is proven. Rules and seasonality ship first because they are explicable; ML only once there is a baseline to beat.
Your existing systems
- ERP
- POS
- BI tools
Where it earns its keep
- Retailers who already have inventory accuracy and want to use it
- Merchandising teams making allocation calls on instinct
- Groups carrying working capital in stock that does not move
Before you
buy
The things operations directors actually ask on the first call.
Can we buy this on its own?
You can, but we would advise against it. Forecasting inherits the accuracy of the stock data underneath it, so without item-level counting you are modelling on a number that is already wrong.
Is there AI in this?
Optionally, and deliberately later. The first release is rules and seasonality, because a merchandiser will act on a recommendation they can explain. ML is added once there is a baseline to measure it against.
Do we need a data team?
Not for the baseline — reports and alerts are configured, not coded. If you want custom models on top, that is where your own analyst or ours becomes relevant.
The rest of retail
- 01
Return Fraud Prevention
Serialised tags linked to the sale, so a return can be checked against the receipt that created it.
- 02
Smart Fitting Rooms
Overhead readers and a wall display that turn the try-on into a conversation instead of a dead end.
- 03
Real-Time Inventory & Automated Replenishment
Item-level reads that keep store and DC stock accurate, and reorder rules that fire on their own.
- 04
EAS & High-Value Item Protection
Gates, tags and detachers you already know — with the incident data they have never given you.
- 05
Fast Checkout & Product Locator
Read a whole basket at once instead of item by item, and find the one unit that is somewhere in the store.
- 06
Supplier Performance & Automated Receiving
Validate a delivery as it comes through the door, and score the supplier on what actually arrived.
- 08
Textile Tracking & Jewellery Security
Apparel tracked from backroom to rail, and high-value pieces counted continuously in the case.
Let's design theright system foryour floor
- Call+880 1713 819528 · 01713 222558
- Emailservice@ratsbd.com
- VisitGround Floor, House-7, Road-13, Sector 14, Uttara, Dhaka-1230
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