How do logistics, supply chain and mobility companies anchor an effective AI strategy?

Prioritization starts with the value proposition: which use cases immediately reduce costs, increase revenue or mitigate risk? In logistics projects typical levers are route optimization (reducing empty miles), demand forecasting (cutting inventory costs) and Contract AI (faster contract reviews). We quantify this value in early workshops and evaluate each use case by impact, feasibility and time‑to‑value.

Technical feasibility is the second decision factor: do you have the necessary telemetry and master data? Are interfaces to TMS/WMS and telematics available? An AI Readiness Assessment reveals technical gaps and provides an effort estimate. Without this foundation, big promises are rarely kept.

Organizational maturity is also considered: who is the owner? What budget exists? We identify stakeholders and build governance structures so prioritized use cases are operationally supported and measurable. Use cases with a clear owner and measurable KPIs always take precedence.

Finally, we create a portfolio: quick wins (PoC‑feasible in weeks), mid‑term initiatives (3–6 months) and strategic programs (>6 months). This portfolio is regularly reprioritized based on data and business performance to dynamically steer resources.

Reliable demand forecasting requires a combination of historical transaction data, real‑time telemetry, seasonal factors, marketing signals and external influences such as weather or traffic data. The more granular the data (SKU, location, time window), the higher the forecast quality — but this also increases storage and model complexity requirements.

Data quality is critical: missing time series, inconsistent SKU mappings or mismatched granularities between TMS and ERP quickly lead to errors. A Data Foundations Assessment identifies such issues and prioritizes cleaning steps, master data management and harmonization of event schemas.

For short‑term (intra‑day) forecasting, telemetry feeds and real‑time inventory data are decisive. For mid‑term planning external data sources and marketing insights help. We recommend a hybrid approach: classical time series models for baselines combined with ML models that process contextual variables.

Finally, data must be delivered in a production‑grade pipeline: automated feature engineering jobs, monitoring for data drift and backtests. Only then does forecast quality remain stable and reproducible as market conditions change.

Route optimization for heterogeneous fleets combines classical operations research methods with ML‑based predictions. The key lies in modeling constraints: vehicle capacities, loading/unloading times, driver working hours, charging infrastructure for e‑vehicles and real‑time traffic data. Our architecture separates planning (tactical) from execution (operational): a planner generates optimized tours, an execution layer adapts in real time to deviations.

For e‑mobility, battery models and charging windows are additionally required. The route AI must integrate state of charge, available charging points and expected charging times. Digital twins of the fleet and simulation runs provide valuable insights before production rollout.

Integration is central: telematics, driver apps, TMS and WMS must be cleanly connected. We implement lean APIs and fallback mechanisms so operational decisions remain possible in case of data loss. We also define clear KPIs: total kilometers, empty miles, punctuality and CO2 emissions per shipment.

A pilot should start in a defined depot or fleet category to control boundary conditions. After successful A/B tests, the solution is scaled stepwise while monitoring, retraining pipelines and governance are continuously operated.

An effective AI governance framework for logistics covers four layers: data governance, model governance, operational governance and compliance. Data governance ensures data quality, model governance defines validation and retraining processes, operational governance regulates model use in live operations, and compliance addresses regulatory and legal requirements.

Practical elements include: model versioning, test suites for drift detection, explainability requirements for safety‑critical decisions and a change log for all model modifications. Additionally, SLAs for model latency and availability are relevant, especially for real‑time planning systems.

In contracts with third parties — telematics providers, data suppliers, cloud providers — data usage rights and security requirements must be clearly defined. Contract AI can help automatically review these contracts and surface risks before signing.

Governance is not a one‑off artifact: we establish a governance board with business, legal and technical representatives that conducts regular reviews and evaluates production models using standardized metrics. This keeps AI usage transparent and controllable.

A well‑defined proof of concept (PoC) in logistics can deliver first actionable results within weeks — typically 4–8 weeks for forecasting or Contract AI PoCs. Success, however, depends on clear, measurable hypotheses and availability of relevant data. Our AI PoC offering (€9,900) is designed exactly for this objective: fast validation without large upfront effort.

The key prerequisites are defined inputs/outputs, success criteria (e.g. MAPE improvement, reduction in processing time) and minimal data access. We deliver a working prototype, performance metrics, a technical summary and a pragmatic production roadmap.

A PoC clarifies technical feasibility, estimates cost per run and identifies operational risks. From this we derive concrete steps for piloting and scaling. If the data situation is unclear, we start with a data readiness sprint to create the necessary data artifacts quickly.

Important: a PoC is not a final product. It is a controlled proof that a use case generates real value. The handover to a pilot phase with production grading criteria then determines scalability.

AI rollouts change processes, roles and decision paths. Operationally this means: new communication channels between dispatch, IT and data science; introduction of decision‑support systems instead of manual approvals; and establishment of responsibilities for model performance and data quality. An explicit change program is therefore essential.

We recommend a staged model: awareness & training for decision‑makers, hands‑on workshops for power users (e.g. dispatchers), pilot introduction with feedback cycles and stepwise scaling with accompanying coaching. Champions within operational teams ensure new tools are adopted and used effectively.

The cultural aspect is central: transparent KPIs, visible quick wins and clear roles prevent AI from being perceived as a threat. At the same time escalation and rollback processes must be defined so that wrong decisions can be reacted to quickly.

Technically, we support the rollout with monitoring dashboards, performance alerts and an incident‑response playbook. This ensures the organization not only uses the technology but sustainably realizes its benefits.