Demand forecasting for perishable supply chains

Services

Data Science
Forecasting

Technology

Python
ML

Client

Sahyadri Farms

Forecasts that respect seasonality, weather, and farmer variability.

Project

Aqeeq embedded with Sahyadri Farms stakeholders to map workflows, define success metrics, and deliver incrementally with production discipline from sprint one.

Tasks

Discovery & architecture
Core platform build
Data & automation
Launch & enablement

KPIs

1. Production — Grade delivery
2. Measurable — Business outcomes
3. Operated — Post-launch support

Project journey

1. Discovery & architecture
Workflow mapping, technical design, and rollout plan.
2. Core platform build
Engineering of primary user workflows and integrations.
3. Data & automation
Pipelines, rules engines, and AI where appropriate.
4. Launch & enablement
Training, runbooks, and operated handoff.

Challenges

Sahyadri Farms needed systems that could scale with operational complexity — replacing manual coordination and disconnected tools that slowed decision-making.

Manual workflows consumed team capacity and introduced errors at scale.
Legacy tooling could not support modern analytics or automation requirements.
Integration gaps between departments created data silos and delayed responses.

Results

Delivered demand forecasting & planning on schedule with measurable operational improvements
Reduced manual coordination overhead across teams
Integrated with existing systems without big-bang disruption
Documented runbooks and monitoring for sustained operations

Production

Grade delivery

Measurable

Business outcomes

Operated

Post-launch support