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Business Data Analysis

AI & Data Analytics for Supply Chain Decision-Making

Procurement, Contracts & Supply Chain Management

Introduction

Course Introduction

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    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

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
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10 Days

13 Jul – 24 Jul 2026

Kuala Lumpur

£7,035

Choose the date and location that suits you:

Kuala Lumpur

13 Jul – 24 Jul 2026

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£7,035

Manama

03 Aug – 07 Aug 2026

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£3,815

Port Louis

14 Sep – 25 Sep 2026

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£7,325

London

12 Oct – 16 Oct 2026

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£4,175

Manchester

09 Nov – 13 Nov 2026

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£4,175

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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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

    MODULE 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.
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