CILT Certificate in AI for Supply Chain Professionals (AISCP)
- Explain the main AI techniques used in supply chains and the type of problem each solves.
- Identify and prioritise AI use cases in their own supply chain based on value and data availability.
- Assess data quality and readiness for an AI forecasting or optimisation project.
- Evaluate AI model outputs using accuracy metrics and business impact rather than vendor claims.
- Define human oversight, governance and responsible AI controls for supply chain AI tools.
- Prepare a pilot proposal for an AI use case with scope, data, success criteria and risks.
- AI Fundamentals for Supply Chains
- Machine learning, optimisation and generative AI
- What AI can and cannot do
- AI adoption trends in GCC logistics
- AI in Planning and Inventory
- Machine learning demand forecasting
- Demand sensing and external signals
- Inventory and replenishment optimisation
- AI in Logistics and Warehousing
- Route and load optimisation
- Predictive maintenance for fleets and equipment
- Computer vision and warehouse automation
- AI in Procurement and Supplier Risk
- Spend analytics and contract intelligence
- Supplier risk monitoring
- Generative AI assistants for buyers
- Data Readiness and Model Evaluation
- Data quality and preparation
- Accuracy metrics and backtesting
- Model drift and performance monitoring
- Responsible Adoption
- Human oversight and decision rights
- Bias, privacy and data protection
- Building and scaling an AI pilot
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Program Professional Framework
CILT Certificate in AI for Supply Chain Professionals (AISCP)
Program Overview
Artificial intelligence is changing how supply chains forecast, plan, buy, store and move goods. Designed for supply chain professionals rather than data scientists, this course explains how machine learning, optimisation and generative AI work in practice and where they add value: demand forecasting, inventory optimisation, route planning, supplier risk monitoring, warehouse automation and procurement analytics. Participants learn to assess data readiness, evaluate AI vendors and model results, manage risks such as bias and model drift, and apply responsible AI principles such as those published by SDAIA in Saudi Arabia.
Learning Outcomes
- ✓Explain the main AI techniques used in supply chains and the type of problem each solves.
- ✓Identify and prioritise AI use cases in their own supply chain based on value and data availability.
- ✓Assess data quality and readiness for an AI forecasting or optimisation project.
- ✓Evaluate AI model outputs using accuracy metrics and business impact rather than vendor claims.
- ✓Define human oversight, governance and responsible AI controls for supply chain AI tools.
- ✓Prepare a pilot proposal for an AI use case with scope, data, success criteria and risks.
Target Audience
- Supply chain, planning and logistics managers
- Procurement and inventory specialists
- Supply chain analysts and digital champions
- Operations leaders evaluating AI investments
Detailed Program Modules
01AI Fundamentals for Supply Chains
- Machine learning, optimisation and generative AI
- What AI can and cannot do
- AI adoption trends in GCC logistics
02AI in Planning and Inventory
- Machine learning demand forecasting
- Demand sensing and external signals
- Inventory and replenishment optimisation
03AI in Logistics and Warehousing
- Route and load optimisation
- Predictive maintenance for fleets and equipment
- Computer vision and warehouse automation
04AI in Procurement and Supplier Risk
- Spend analytics and contract intelligence
- Supplier risk monitoring
- Generative AI assistants for buyers
05Data Readiness and Model Evaluation
- Data quality and preparation
- Accuracy metrics and backtesting
- Model drift and performance monitoring
06Responsible Adoption
- Human oversight and decision rights
- Bias, privacy and data protection
- Building and scaling an AI pilot
Training Methodology
Applied Project & Assessment
Applied Project
Participants develop an AI pilot proposal for a supply chain problem in their organisation: use case definition, data inventory and readiness assessment, success metrics, governance controls and an implementation plan.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.


