Course Introduction
Supply chain organisations increasingly recognise that traditional intuition-based decision-making struggles to cope with volatility, data abundance and the need for rapid, evidence-based responses. This AI supply chain training programme equips practitioners with integrated frameworks and practical capabilities to harness data analytics and artificial intelligence for improved decision-making across procurement, planning, operations and logistics. Participants develop skills in assessing data readiness, applying predictive and prescriptive techniques, and embedding AI-driven insights into supply chain processes while addressing governance, risk and ethical considerations. The data analytics procurement course emphasis enables measurable gains in forecasting accuracy, supplier performance, cost efficiency and resilience. This course can be facilitated in London, Dubai, Kuala Lumpur, Nairobi, and other major business centres on client-preferred dates.
Why Choose This Course?
Master practical frameworks for applying data analytics and AI techniques specifically to supply chain decision-making challenges including forecasting, optimisation and risk management
Develop capabilities in assessing organisational data maturity, quality and governance requirements that form the foundation for reliable AI-driven insights
Learn to select, implement and integrate appropriate analytics and AI tools that enhance real-time visibility, predictive accuracy and prescriptive decision support across the supply chain
Strengthening skills in interpreting AI outputs, managing algorithmic risks and embedding ethical, responsible practices into supply chain analytics initiatives
Acquire proven approaches to change leadership, capability building and cross-functional adoption that ensure sustainable integration of data-driven decision-making cultures
Position your organisation to achieve superior forecasting, cost reduction, supplier performance and operational resilience through disciplined, evidence-based AI and analytics application
10 Days
13 Jul – 24 Jul 2026
Kuala Lumpur
£7,035
Choose the date and location that suits you:
Who Should Attend ?
Supply Chain Directors and Heads of Procurement seeking to embed data-driven and AI-enabled decision-making into strategic and operational processes
Demand Planning, Inventory and Analytics Managers responsible for forecasting, optimisation and performance improvement
Procurement Category Managers and Supplier Relationship Leads wishing to apply analytics to sourcing, supplier performance and risk decisions
Logistics, Warehouse and Operations Managers optimising flow, resource allocation and fulfilment through data and AI insights
Data, Digital Transformation and IT Leaders supporting supply chain analytics platforms and AI integration
Risk, Compliance and Sustainability Managers integrating analytics into supply chain governance, resilience and responsible sourcing frameworks
Learning Objectives
By the end of this programme, participants will be able to:
Assess organisational data maturity, quality and governance readiness to determine feasible scope and sequencing for AI and analytics initiatives in supply chain contexts
Apply data analytics techniques to enhance end-to-end visibility, identify performance patterns and support evidence-based decision-making across procurement and supply chain operations
Develop and validate predictive models for demand forecasting, supplier risk assessment and operational performance that improve accuracy and reduce uncertainty
Utilise prescriptive analytics and optimisation techniques to recommend actionable decisions in inventory management, supplier selection and logistics network design
Integrate AI-driven insights into procurement processes, supplier performance management and sourcing decisions to reduce costs, mitigate risks and improve value outcomes
Establish robust data governance, model validation and ethical oversight frameworks that ensure responsible, transparent and auditable use of AI in supply chain decision-making
Lead organisational change and capability-building programmes that foster data-literate cultures and sustain adoption of analytics-driven practices across functions and partners
Design performance measurement frameworks and benefits realisation processes that demonstrate the financial, operational and strategic impact of AI and data analytics investments
Course Delivery Approach
Detailed analysis of real-world supply chain case studies illustrating successful AI and analytics applications, common implementation pitfalls and measurable value outcomes
Interactive workshops focused on data maturity assessment, analytics model development, AI use-case prioritisation and governance framework design using authentic organisational scenarios
Simulations exploring predictive forecasting, prescriptive optimisation and risk scenario decision-making under realistic supply chain constraints and data conditions
Structured group exercises developing analytics roadmaps, KPI dashboards, ethical guidelines and changing plans for representative supply chain challenges
Facilitated peer learning sessions for sharing implementation experiences, data quality challenges and proven approaches to sustaining analytics-driven cultures
Personal and organisational action planning with expert feedback to support immediate application and measurable progress in ongoing AI and analytics initiatives
Course Syllabus
01 Strategic Context and Value of AI and Data Analytics in Supply Chains
Examining the evolving supply chain landscape and the strategic imperatives driving adoption of data analytics and AI for competitive advantage
Identifying high-impact decision areas across procurement, planning, operations and logistics where analytics and AI deliver the greatest value
Mapping the contribution of data-driven insights to cost reduction, risk mitigation, service improvement and sustainability outcomes
Recognising common failure patterns in analytics initiatives and the critical success factors that differentiate successful implementations
Understanding the interplay between data quality, technology capabilities, organisational culture and governance in enabling effective AI adoption
Establishing clear strategic objectives, success metrics and value propositions for supply chain analytics initiatives that secure executive sponsorship
02 Data Foundations, Quality, Governance and Readiness Assessment
Applying structured frameworks to assess current data maturity, availability, quality and accessibility across supply chain functions and systems
Identifying data gaps, integration challenges and master data management requirements that must be addressed before advanced analytics deployment
Establishing data governance principles, ownership models, quality standards and stewardship roles specific to supply chain decision-making
Conducting readiness assessments that evaluate technology infrastructure, skills, processes and cultural factors influencing analytics success
Prioritising data improvement initiatives based on strategic alignment, feasibility and potential impact on decision quality
Building baseline measurements that enable tracking of data maturity progression and return on analytics investment over time
03 Descriptive Analytics and Real-Time Supply Chain Visibility
Designing and implementing dashboards, scorecards and reporting systems that provide timely, actionable visibility into supply chain performance
Applying descriptive analytics techniques to identify trends, patterns, outliers and root causes across procurement, inventory and logistics data
Establishing real-time monitoring capabilities that support proactive exception management and rapid response to emerging issues
Integrating data from multiple sources including ERP systems, supplier platforms and operational systems into unified visibility layers
Using visual management and storytelling approaches to communicate insights effectively to diverse stakeholder audiences
Embedding descriptive analytics into daily operational reviews and strategic decision forums to drive evidence-based management
04 Predictive Analytics for Demand, Risk and Performance Forecasting
Applying statistical and machine learning techniques to develop robust demand forecasting models that account for seasonality, trends and external factors
Building predictive models for supplier risk, lead time variability, quality issues and operational disruptions using historical and real-time data
Validating, tuning and monitoring predictive models to maintain accuracy and relevance as conditions change
Integrating predictive insights into planning processes including sales and operations planning, inventory optimisation and procurement scheduling
Quantifying forecast uncertainty and developing scenario-based planning approaches that support resilient decision-making
Establishing processes for continuous model improvement based on actual outcomes and new data availability
05 Prescriptive Analytics and Optimisation for Decision Support
Applying optimisation techniques and algorithms to recommend decisions in inventory positioning, replenishment, supplier allocation and logistics routing
Using simulation and scenario modelling to evaluate trade-offs between cost, service, risk and sustainability under different constraints
Developing decision-support tools that translate complex analytics outputs into clear, actionable recommendations for practitioners
Integrating prescriptive analytics into existing planning and execution systems to enable automated or semi-automated decision triggers
Managing the interface between human judgement and algorithmic recommendations to ensure appropriate oversight and accountability
Measuring the impact of prescriptive recommendations on actual performance outcomes and refining models accordingly
06 AI Applications in Procurement, Sourcing and Supplier Management
Applying AI techniques to enhance supplier identification, risk assessment, performance prediction and relationship prioritisation
Using analytics to support category strategy development, spend analysis and identification of sourcing opportunities and consolidation potential
Developing AI-enabled approaches to contract analysis, clause extraction and obligation monitoring that improve compliance and value capture
Leveraging predictive insights for supplier negotiation preparation, pricing analysis and total cost of ownership modelling
Establishing AI-supported supplier performance monitoring that enables early intervention and collaborative improvement
Managing ethical and bias considerations when applying AI to supplier selection, evaluation and development decisions
07 AI-Enabled Inventory, Warehouse and Logistics Optimisation
Applying AI and advanced analytics to optimise inventory policies, safety stock levels and replenishment strategies under demand and supply uncertainty
Using predictive and prescriptive techniques to improve warehouse slotting, picking routes, labour planning and throughput optimisation
Developing AI-supported logistics network design, route optimisation and dynamic scheduling that reduces costs and improve service reliability
Integrating real-time data from IoT devices, telematics and operational systems into analytics models for responsive decision-making
Addressing data quality, integration and latency challenges that affect the effectiveness of AI in physical supply chain operations
Measuring and demonstrating the operational and financial benefits of AI-enabled optimisation in inventory and logistics environments
08 Change Leadership, Capability Building and Ethical AI Adoption
Diagnosing organisational readiness and cultural barriers to analytics and AI adoption across supply chain functions
Designing comprehensive change management and communication strategies that build understanding, trust and commitment to data-driven ways of working
Developing targeted capability-building programmes, training and coaching to close analytics and AI literacy gaps at all organisational levels
Establishing ethical guidelines, bias detection processes and governance mechanisms for responsible AI use in supply chain decisions
Identifying and empowering analytics champions and change agents who can drive adoption and sustain momentum
Measuring adoption rates, behavioural shifts and cultural change to inform ongoing reinforcement and adjustment of transformation efforts
09 Implementation Roadmaps, Integration and Value Realisation
Developing prioritised analytics and AI implementation roadmaps aligned with strategic objectives, data readiness and organisational capacity
Planning and managing the integration of analytics platforms and AI solutions with existing ERP, planning and operational systems
Establishing project governance, risk management and benefits tracking processes for analytics initiatives
Conducting pilot programmes, proof-of-concept evaluations and scaled rollouts that demonstrate value before full deployment
Capturing lessons learned and codifying successful practices to accelerate future analytics initiatives
Communicating implementation progress and realised benefits to stakeholders in ways that sustain support and justify continued investment
10 Future Trends, Continuous Improvement and Sustaining Analytics Excellence
Anticipating emerging technologies, data sources, analytical techniques and regulatory expectations that will shape future supply chain analytics
Building organisational capacity for continuous improvement, experimentation and innovation in the application of AI and data analytics
Developing strategies for scaling successful analytics use cases and preventing regression to intuition-based decision-making
Establishing knowledge management and communities of practice that capture, share and evolve supply chain analytics expertise
Aligning ongoing analytics initiatives with broader organisational digital transformation and data strategy programmes
Positioning the supply chain function as a leader in data-driven maturity that contributes to organisational competitiveness and resilience
Organisational Impact
Improved forecasting accuracy, inventory optimisation and supplier performance that reduce costs, stockouts and working capital requirements
Stronger risk visibility, predictive capability and proactive decision-making that enhance supply chain resilience and reduce disruption impacts
Enhanced data quality, governance and integration that improve the reliability and usability of analytics across the organisation
Sustainable development of internal analytics and AI capability that reduces reliance on external expertise and improves consistency of results
Greater cross-functional alignment and confidence in data-driven recommendations that accelerate decision cycles and improve outcomes
Clear demonstration of analytics value that strengthens the case for continued investment in digital supply chain capabilities
Personal Impact
Elevated professional credibility as a supply chain leader capable of leveraging AI and data analytics for strategic and operational impact
Practical frameworks, tools and confidence to assess, prioritise and implement analytics initiatives with measurable results
Enhanced ability to influence stakeholders, secure sponsorship and lead cultural change required for successful analytics adoption
Stronger skills in predictive modelling, prescriptive optimisation, data interpretation and ethical AI governance directly applicable to complex supply chains
Expanded strategic perspective connecting data analytics and AI to broader organisational performance, risk, resilience and sustainability objectives
Clear personal development pathway toward senior roles in supply chain analytics leadership, digital transformation and data-driven decision-making
General Notes
Sector customisation available on request
Training material provided
Elevoris Certificate of Training issued to all participants
Optional post-programme advisory coaching available
AI and data analytics are transforming supply chain decision-making from reactive and intuition-led to proactive, precise and evidence-based. Organisations that master the strategic and practical application of these capabilities will secure decisive advantages in cost, resilience and responsiveness.
Enrol now in the AI & Data Analytics for Supply Chain Decision-Making programme and develop the expertise to harness data and AI for superior supply chain performance, risk management and strategic value creation.
