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Quality and performance

Consistency is a question of analysis, not of talent.

Facilities that deliver reliably rarely differ from the rest in equipment. They differ in knowing their own data and drawing conclusions from it.

Most facilities already hold data; it is simply never analysed. Environmental logs sit in the controller, irrigation records in a notebook, harvest weights in a spreadsheet, laboratory results in an inbox. What is missing is the connection between those sources and a question they are meant to answer.

We set up the KPIs, connect the sources and analyse them across batches and cycles. The aim is to trace a deviation back to its cause rather than to explain it afterwards.

Improvement cycle

Schematic illustration
  1. 01Define the KPIs
  2. 02Measure and record
  3. 03Analyse deviations
  4. 04Narrow the cause
  5. 05Change one thing
  6. 06Verify in the next cycle
Schematic illustration of the approach across successive cultivation cycles.

Cultivation KPIs

A small set of indicators that is actually maintained is worth more than a comprehensive system nobody fills in after two cycles.

  • Yield per area and per cycle
  • Energy consumption per gram produced
  • Recovery after drying and processing
  • Loss rate and its causes
  • Cycle length per cultivar
  • Consistency between batches

Environmental data analysis

Environmental logs are laid against the crop timeline. It is rarely the averages that stand out, but the deviations and when they occurred.

  • Environmental data prepared per room and stage
  • Deviation analysis rather than averages
  • Behaviour during the dark period
  • Relationship between environment and outcome
  • Plausibility checks on the instrumentation

Batch comparison and laboratory results

Interpretation of existing analytical results in the context of cultivar, environment and post-harvest handling. We do not carry out laboratory analysis ourselves.

  • Structured comparison between batches
  • Contextualising cannabinoid and terpene results
  • Relationship to harvest timing and drying
  • Assessment of consistency across cycles
  • Deriving concrete adjustments

Root-cause analysis

A structured way to get from a symptom to a cause, instead of changing several parameters at once and then not knowing which one worked.

  • Structured narrowing of possible causes
  • Separating cause from accompanying symptom
  • Changing one parameter, with a control group
  • Recording the findings
  • Carrying results into the written procedures

Plenty of data, no conclusions?

We review your existing records and tell you what can already be answered from them and what would need to be captured additionally.

Request a technical consultation