AI TRAINING
AI for Supply Chain and Logistics
Apply AI to forecasting, routing, inventory, and supplier risk to build a resilient, data-driven supply chain.
What it covers
This practitioner-level programme equips supply chain, logistics, and procurement professionals with the skills to deploy AI across the full supply chain lifecycle. Participants work through hands-on modules covering demand forecasting with machine learning, route optimisation algorithms, autonomous inventory management, supplier risk scoring, and logistics control tower architectures. The format combines instructor-led sessions with real dataset exercises, enabling participants to move from concept to working prototype within the programme. By the end, teams leave with a prioritised AI implementation roadmap tailored to their operations.
What you'll be able to do
- Build and evaluate a demand forecasting model using real supply chain data and select the right algorithm for your context
- Design a route optimisation workflow using constraint-based or ML-driven solvers and measure cost reduction
- Construct a supplier risk scoring dashboard that ingests external signals such as news, financials, and lead-time variance
- Map an AI-ready logistics control tower architecture that integrates IoT, ERP, and TMS data streams
- Produce a prioritised AI implementation roadmap with business case estimates for at least three supply chain use cases
Topics covered
- Demand forecasting with ML (ARIMA, XGBoost, Prophet)
- Route and network optimisation algorithms
- AI-driven inventory replenishment and safety stock modelling
- Supplier risk scoring and early warning systems
- Logistics control tower design and real-time visibility
- Predictive maintenance for fleet and warehouse assets
- Digital twin concepts for supply chain simulation
- AI ethics, data quality, and change management in operations
Delivery
Delivered as a blended programme over 4–6 weeks: two live virtual workshops per week (3 hours each) plus async exercises on real or synthetic supply chain datasets. In-person cohort format is available for groups of 12+, typically run over 3 consecutive days. Hands-on work accounts for approximately 60% of total learning time. Participants need access to Python (Google Colab provided) or a BI tool such as Power BI. A shared Slack or Teams channel is maintained for peer support throughout.
What makes it work
- Securing a named data engineering resource alongside the supply chain team to ensure data pipelines are production-ready
- Piloting on a single lane, SKU cluster, or supplier segment before scaling AI across the full network
- Embedding model performance reviews into existing S&OP or weekly ops cadences
- Co-designing explainability outputs with planners so AI outputs complement — not replace — human judgement
Common mistakes
- Starting with a control tower platform purchase before establishing clean, unified data from ERP and WMS systems
- Using AI forecasting models trained on pre-pandemic data without accounting for structural demand shifts
- Treating route optimisation as a one-time batch process rather than a continuous, event-driven loop
- Underestimating change management: frontline planners distrust AI recommendations without explainability and training
When NOT to take this
This programme is not the right fit for organisations that do not yet have an ERP or TMS in place and lack historical transactional data — they need foundational data infrastructure work before AI training will generate actionable value.
Providers to consider
Sources
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