/ Forecasting and procurement · PUBLISHED CASE
Mayur Farms
Four years of operating data turned into planning signals
A poultry business digitised four years of operational data, then introduced AI-supported forecasting, inventory planning and supplier comparison.
/ THE SHORT VERSION
A poultry business digitised four years of operational data, then introduced AI-supported forecasting, inventory planning and supplier comparison.
01 / BUSINESS CONTEXT
The work before the tool.
Mayur Farms coordinates demand, stock, farming operations and procurement. Historical information existed, but it was not structured for consistent forecasting or rapid supplier decisions.
02 / THE INTERVENTION
What changed in the workflow.
The business digitised four years of operating data, introduced an ERP layer, and used Claude for demand forecasts, inventory planning, request-for-proposal preparation and supplier scoring.
The case describes working forecasting and procurement practices. Further automation and broader data integration remain part of the future plan.
03 / REPORTED OUTCOMES
The numbers, with their labels attached.
| Workflow area | Before | After |
|---|---|---|
| Forecasting | Planning relied on judgement and disconnected history | Four years of digitised data supports structured forecasts |
| Procurement | Supplier requests and comparisons prepared manually | AI assists RFP drafting and supplier scoring |
| Decision control | Managers interpreted incomplete signals | Managers validate a clearer planning view and make the final call |
Capability matters when people stay in control.
People validated forecasts, selected suppliers and remained accountable for purchasing decisions. The AI layer supported planning rather than making autonomous commitments.
05 / WHAT A SIMILAR BUSINESS SHOULD TEST
Transfer the pattern, not the conclusion.
- 01
Digitisation may be the first AI task when historical records are fragmented.
- 02
Forecast quality depends on clean definitions, not just the amount of history available.
- 03
Savings estimates should be reconciled against payroll, task volume and actual adoption.
06 / SOURCE AND LIMITS
What this page can and cannot establish.
This analysis summarises outcomes published in MCCIA’s AI: Compilation of case studies (2026), printed pages 24–25. The work was conducted through the MCCIA Applied AI Studio. The report credits the Studio team managed by Neeraj Thakur, with Gauri Kale and Ismail Patel. The featured organisation is not presented as a dotSuper client.
- 01The annual saving is an organisational estimate and the calculation method is not published.
- 02The report does not provide a comparison group or enough detail to isolate AI from ERP and process changes.
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