Lighthouse Projects · Industry
AI for production and logistics planning
Orders, stock and capacity in one plan, with scenarios before deciding.
AI suggestion: move PO#2841 from line A to D. Removes 12 h of overload.
Problem
Manual planning across spreadsheets and disconnected systems. Poor end-to-end visibility. Hard to simulate scenarios and react to changes.
Solution
Unified data pipeline (orders, stock, capacity, SLAs, costs). AI engine for production and logistics adjustment suggestions. What-if simulation interface integrated with ERP/WMS/TMS.
KPIs
- Reduction in excess stock and stockouts
- OTIF improvement
- Time spent in manual planning
- Operational predictability
How it is measured
Every KPI with a baseline and a target.
The numbers for this project come from your data. We measure the baseline during framing, agree the target with you before building and, in production, measure the same KPIs the same way.
- 01BaselineMeasured during framing, with the data of the process as it runs today.
- 02TargetAgreed with the case owner before building, starting from the baseline.
- 03In productionThe same KPIs, measured the same way, compared with the baseline.
- Reduction in excess stock and stockouts
- Days of cover above the maximum and stockouts per item, from the ERP and the WMS.
- OTIF improvement
- Orders delivered on the requested date and quantity, divided by the total, from the dispatch notes.
- Time spent in manual planning
- Planning team hours per planning cycle, measured before and after.
- Operational predictability
- Gap between the approved plan and what was executed, per line and per week.
Before starting
Prerequisites and risks.
Prerequisites
- Read access to the ERP, WMS and TMS, with orders, stock, capacities and costs.
- Explicit planning rules (priorities, changeover times, constraints), validated by the planners.
- A planner as case owner, to compare the suggestions with the manual plan during the pilot.
Risks and how we handle them
Inconsistent master data
Outdated cycle times, capacities and bills of materials produce wrong plans. Master data quality is assessed during framing, before building.
Suggestions nobody uses
The planner approves or rejects each suggestion and the reason is recorded. That record shows whether the engine gets it right.
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Want to see this in your operation?
90 minutes with you and your team. We leave with 3 concrete opportunities and estimated payback.