What it measures
Water used per tonne of product produced
it measures the resource efficiency of production runs that have already completed. The water has been consumed and the cost incurred before the KPI is calculated, typically 30 days in arrears.
Business impact
- Increased 'chaos tax' from lost production time. Inefficient, water-intensive cleaning steals hours from the production plan, forcing overtime, weekend runs, and compromising the plant's ability to meet customer service commitments.
- Degraded inventory position. When a line constraint is created by poor sanitation discipline, the plant cannot produce the full range of planned SKUs, leading to stock-outs on some lines and over-stock on others.
- Breaching the site's water abstraction license triggers regulatory fines and production halts. Failing a major retailer's sustainability audit on water use places the entire commercial account at risk of a formal review.
Formula
m3 / Tonne producedGrain
One row per production order batch — plant × product × confirmation date
Inside the Implementation Pack
A preview of what's actually built for this KPI — extracted from the real Implementation Pack, not a mockup.
This is the primary ratio the pack's other measures build on — trend, rank, and period-comparison variants, all on Fact_Production_Order_Batch.
Fact_Production_Order_Batch— 8 mandatory columns — product key, output weight kg, ...Dim_Plant— key + nameDim_Product— key + nameDim_Production_Line— key + nameDim_Recipe— key + nameDim_Shift— key + nameDim_Date— Auto-generated calendar table — no customer data required
Full field-by-field data contract, in 4 SQL dialects (SQL Server, Snowflake, Databricks, BigQuery), ships in the pack.
| Target | 1.5 — performance considered compliant at or below this line |
| Warning | 4 — amber alert — triggers a review |
Both live in one Excel tab — change them, refresh the report, done. No DAX editing needed.
Everything you need to build Water Consumption per Tonne in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.