AI in Intralogistics: Use Cases and Pilot Evaluation
AI in intralogistics can produce forecasts or recommendations about internal material flows. Begin with a specific delay, error or planning problem, rather than an AI label in a product brochure. The value of a model depends on suitable operational data and whether its recommendations can be acted on.
Select a concrete use case
Consumption history and transfer records may support replenishment predictions. Task durations and queues may reveal deliveries at risk of delay. Anomaly detection can flag unusual movements for investigation. These are potential applications, not capabilities guaranteed in every product. Validate the proposed inputs, outputs and operational decisions with the provider.
Distinguish rules, optimisation and machine learning
A replenishment trigger at minimum stock may be a rule. A routing calculation may use optimisation without learning from data. Ask what the model learns, how it is evaluated and whether it suggests or executes actions. Compare it with a simpler baseline; complexity alone does not justify the investment.
Prepare data and an honest baseline
Capture task timestamps, locations, priority changes and reasons for exceptions consistently. Align clocks and transaction identifiers between ERP, WMS and MES. Keep information only known after an event out of predictions made before it. Record existing delivery time and delay rates before measuring the proposed solution.
Run a controlled pilot with a fallback
Start with one flow, such as replenishment for a single line. Observe recommendations without changing execution, then introduce operator approval. Compare similar workloads and record rejected suggestions. Define stop conditions, access rights, model versions and recovery rules. Equipment safety functions must remain with the authorised control layer.
Demo and pilot checklist
- Are the baseline, success criteria and stop conditions agreed before the pilot?
- Are incorrect suggestions and operator corrections recorded?
- Who owns data quality, model changes and fallback operation?
Frequently asked questions
Do we need robots to use AI in internal logistics?
No. Predictions and task recommendations can support manual transport as well. Reliable data and usable decisions matter more than the number of robots.
Does AI guarantee higher productivity?
No. Measure the effect in your own operation, including data preparation, integration, monitoring and training costs. A vendor demonstration is not evidence of your future return.