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Water & air filtration

Screening for repeatability, not for one good sample

Sample results were excellent and production lots were not. The problem was the aggregation model, which no sample could have revealed.

Buyer
An activated carbon producer
Region
South Asia
Product
Coconut Shell Charcoal
Basis
Illustrative scenario

Illustrative scenario. Illustrative scenario. This describes how a mandate of this type is run and what it produces. It is not a record of a completed engagement, and no client is identified. Replace with real engagements — with written client permission — before publishing.

The problem

What the buyer was facing

The buyer had received strong fixed-carbon results on samples from three separate suppliers, then variable results on the first production containers from each.

Carbonisation was carried out by many small kilns and aggregated by a collector, so a sample reflected one kiln's output rather than the blend.

Fixed carbon below the floor reduced activation yield, which the buyer only discovered after processing.

Approach

What was done, and in what order

  1. 01

    Screening on aggregation method

    How the collector blends, segregates and rejects kiln output — not just what the aggregate sample shows.

  2. 02

    Sampling from the offered lot

    Representative increments across the prepared lot rather than a submitted sample of unknown provenance.

  3. 03

    Repeat testing across lots

    Three consecutive lots tested before a schedule was agreed, to establish variance rather than a single point.

  4. 04

    Destination controls confirmed early

    Charcoal import requirements checked with the buyer's broker during qualification.

Outcome

What the buyer ended up with

  • Variance between lots became a measured, contractible number instead of an unpleasant surprise.
  • The tolerance band was set from observed variance rather than from the best sample.
  • The buyer could model activation yield with a realistic input range.

What was tracked

3 consecutive
Lots tested pre-contract
Aggregation method
Screening focus
Offered lot
Sampling basis
Observed variance
Tolerance set from

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