All case studies
AI & ML
Consumer Goods
₹115 Cr working capital freed

Lifting forecast accuracy 21 points at global scale

How Meridian moved from spreadsheet forecasting to a hierarchical ML pipeline for 8,400 SKUs across 26 countries.

Client
Meridian CPG
Industry
Consumer Goods
Timeline
5 months to global rollout
Business impact

Results at a glance

+0 pts
Accuracy lift
0
SKUs planned
0%
Cycle time
₹0 Cr
Capital freed
Situation

Where they started

  • Accuracy stuck at 61% weighted MAPE.
  • Plans took 9 days and were stale on arrival.
  • Stockouts and write-offs both trending up.
Approach

How we did the work

  1. 1

    Hierarchical models

    Statistical baselines plus gradient boosting, reconciled across SKU, region and channel.

  2. 2

    S&OP integration

    Wired forecasts directly into planning and replenishment workflows.

  3. 3

    Confidence-first UX

    Backtesting studio built for planner trust before every release.

Results

What changed

Accuracy improved from 61% to 82% weighted MAPE.

Cycle time from 9 days to 6 hours.

₹115 Cr working capital freed in year one.

Technology

Tools of the trade

Python
Prophet
LightGBM
Snowflake
Airflow
Streamlit

"For the first time our planners trust the numbers coming out of the system — because we can see how they were made."

Henrik LarsenGlobal Head of Planning, Meridian CPG
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