PROPOSED AI APPLICATION

Scrap collection planning assistant

Pickup allocation ignores uncertain volumes

Recycling businessesHistorical data model pilotProposed solution scope

The problem and the proposed outcome

Application: Recycling businesses — Scrap collection planning assistant
Problem to solve: Pickup allocation ignores uncertain volumes
Target buyer: Recyclers, material recovery operators
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether plans improve relevant operational outcomes while all approved hard constraints remain satisfied.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Predictive planning with constraint optimization
Method code: O
AI contribution: Learn uncertain durations, demand, yields or costs from history, then use a constraint optimizer to propose plans under approved rules.
Output artifact: Candidate plan with assumptions, resource conflicts and reviewer controls.
Core modules: History / constraints → learned estimates → constrained scenarios → planner approval
Simple baseline: Fixed-duration schedules, spreadsheet planning and deterministic optimization.
Why test AI: The AI contribution is learned uncertain estimates. Optimization alone is not counted as sufficient evidence of an AI benefit.

Pilot track: Historical data model pilot
Required data: Pickup histories; demand; vehicle constraints
Data readiness: Relevant history, a specified objective, resource data and expert-approved hard constraints are needed.
First development step: Document objectives and hard constraints, benchmark current planning, and identify which uncertain inputs genuinely require a predictive model.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Recyclers, material recovery operators: show a recent case where pickup allocation ignores uncertain volumes, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Collection cost per unit
Model evaluation: Measure estimate error, hard-constraint violations, schedule performance and operational outcomes against rule-based planning.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: No special flag; domain review still applies
Human review boundary: Authorized planner reviews assumptions and constraints before changing any plan, procurement or resource allocation.
Domain review: Qualified technical teams approve specifications and procedures. The first MVP provides reviewed evidence or planning; it does not directly control machines or safety systems.
Related micro-ERP catalogue IDs: IWA-B-1B7F5DADD910; IWA-B-AB365A4E4EC0; IWA-B-73F1417CEDFC
Integration status: Potential segment link only; no product integration implemented.