AI System for Reviewing Production Energy Variances in Context

The problem

When meter, production-volume, product and shift data are held in separate sources, their fields must be matched by hand before a contextualised energy variance record can be prepared.

The energy manager must compare every suggestion in the contextualised energy variance record with its source record; missing or conflicting evidence must not replace the final decision.

If changes in consumption caused by product mix or meter calibration are not set aside for review, a normal process difference could be treated as a loss; the record must not proceed without a human decision.

What we set out to improve

• Time taken for a contextualised energy variance record draft and its supporting evidence to reach the energy manager for review
• How records set aside for human review because product mix or meter calibration may have changed consumption are resolved
• Number of suggestions corrected after identifying a risk that a normal process difference could be treated as a loss

How the system works

1. 01 Retrieve meter, production-volume, product and shift data from permitted sources using the record identifier
2. 02 Link source, date and version information to the shared work record
3. 03 Validate fields and units
4. 04 Prepare the contextualised energy variance record with its supporting evidence
5. 05 Check for an exception: product mix or meter calibration may have changed consumption
6. 06 Have the energy manager accept, revise or reject the suggestion
7. 07 Record the approved decision and its source in the processing log

Methods we used

• A source-identified common data schema for meter, production-volume, product and shift data
• A decision structure that separates deterministic rules from AI suggestions
• A review screen showing the contextualised energy variance record alongside the underlying source evidence
• A mandatory human review queue when product mix or meter calibration may have changed consumption
• An event identifier that prevents duplicate processing

Where people stay involved

Data and security

Only the fields required for this decision should be processed from meter, production-volume, product and shift data. Access to the source systems must be authorised separately from the energy manager role, and personal data, commercial information and technical secrets must not be passed into the model context unnecessarily. Reading, suggestions and human decisions must be logged separately.

Who this suits

Frequently asked questions