Manufacturing & export
Sports goods manufacturing
Software for an industry that runs on customisation, seasonal peaks, and a supply chain of home-based and small workshops.
Context
What this sector actually looks like
Sialkot’s sports goods industry handles enormous variation: sizes, materials, colourways, club badges, and player names, often on the same order. That variation is the product, and it is also what makes standard order software a poor fit.
Demand is seasonal and lumpy, tied to tournaments and to buyers in other hemispheres. Planning capacity against that pattern by memory is how factories end up either idle or over-committed.
Much of the work is distributed to small workshops and home-based stitchers. Tracking what is where, and what is due back, is a genuine operational problem rather than a reporting one.
The hard parts
Where it usually breaks
Problems worth naming before proposing anything to fix them.
Order variation
Configurable products where every line can differ, and a specification error is discovered after production.
Seasonal capacity
Peaks driven by events months ahead, planned against by memory rather than by history.
Distributed production
Work with dozens of small units, tracked on paper and by phone.
Sampling cycles
Repeated sample rounds with buyers, where version history lives in an inbox.
What we build for it
Where software earns its cost here
Configurable order capture
Options, sizes, and personalisation validated at entry rather than at production.
Demand forecasting
Capacity planning from your own order history and its seasonal shape.
Subcontractor tracking
What is issued, what is outstanding, and what is overdue, per unit.
Buyer portal
Order status, sample rounds, and approvals in one place instead of an email chain.
FAQ
Questions people actually ask
Can it handle personalisation on every line?
Yes. Product configuration is modelled as data rather than as fixed fields, which is what lets a name, a number, and a badge sit on one line without a schema change.
Our subcontractors have no computers.
Then the system meets them where they are — a phone, a WhatsApp message, or a supervisor recording issue and return centrally. Requiring workshops to adopt software is usually the design mistake.
Can we forecast without years of clean data?
Partly. Even inconsistent order history reveals seasonal shape. We are explicit about how much confidence the available data supports rather than presenting a number that looks more certain than it is.
Start here
Tell us what is slow, manual, or breaking.
Answer a few questions and get a written brief back — scope, proposed architecture, and what it would take to build.