Supply Chain Analysis and Optimisation Across Industries

Duration: 2 days

This training programme equips participants with the tools and techniques to analyse and optimise supply chains across manufacturing, distribution, and service sectors. Participants will learn to evaluate supply chain performance, identify bottlenecks, manage risks, and apply data analytics for informed decision-making.

Through real-world examples, the course demonstrates how effective supply chain analysis can reduce costs, improve operational efficiency, and strengthen organisational resilience.

The same analytical foundations also extend into AI-powered ecosystems, where Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO) use structured data and predictive models to improve visibility and responsiveness in AI-driven environments.

By the end of this programme, you will learn to:

Understand the fundamentals of supply chain management and analysis.
Evaluate key performance indicators (KPIs) to measure supply chain efficiency.
Use data analytics to identify bottlenecks and optimise inventory, production, and logistics.
Manage supply chain risks and disruptions through strategic decision-making.
Develop actionable insights to enhance overall supply chain performance.

Programme Outline

Day 1: Fundamentals of Supply Chain Analysis

Module 1

Overview of supply chain management (SCM) and its importance across different industries.
Key components of a supply chain: sourcing, production, warehousing, transportation, and returns.
The role of supply chain analysis in improving business operations and cost efficiency.
Example: How a manufacturing firm reduced lead times by streamlining its inbound logistics.

Module 2

Defining and measuring supply chain KPIs: cycle time, inventory turnover, on-time delivery, and logistics cost per unit.
Using KPIs to track performance and identify areas for improvement (use case: evaluating supplier performance to minimise delays).
Balancing operational efficiency with service level expectations in B2B and B2C environments.

Module 3

Techniques for demand forecasting: historical trends, regression models, and seasonal decomposition.
The impact of accurate demand forecasting on production planning and material procurement.
Optimising stock levels to meet customer demand while minimising holding costs (use case: using sales and usage data to forecast replenishment needs).

Module 4: Challenges and Ethical Considerations in AI

Introduction to supply chain analytics and its value in data-driven decision-making.
Analysing supply chain data using descriptive, predictive, and prescriptive methods.
Tools for analysis: Excel, SQL, and visualisation platforms such as Power BI or Tableau.

Q&A and Wrap-Up

Day 2: Advanced Supply Chain Optimisation and Risk Management

Module 5

Understanding supply chain network design: location planning for factories, warehouses, and suppliers.
Optimising network layout to reduce cost and improve delivery reliability.
Centralised vs. decentralised networks: evaluating trade-offs and industry-specific strategies.
Example: How a logistics provider improved turnaround by regionalising their distribution hubs.

Module 6

Identifying and assessing supply chain risks: supplier disruptions, demand variability, geopolitical risk, and regulatory changes.
Risk mitigation strategies (use case: dual sourcing to improve supply continuity).
Building resilient and adaptive supply chains (example: continuity planning and adaptive manufacturing strategies during supply shocks).

Module 7

The importance of sustainability in contemporary supply chain strategies.
Implementing green logistics, reverse logistics, and sustainable sourcing.
Partnering with eco-conscious suppliers to reduce environmental impact (use case: reducing emissions through route optimisation and packaging redesign).

Module 8

The role of technology in enhancing supply chain performance: IoT, AI, and blockchain.
Automating workflows: inventory tracking, order fulfilment, and predictive maintenance (use case: using IoT sensors for real-time monitoring in a factory).
The impact of digital transformation on end-to-end supply chain visibility and responsiveness. Digital transformation also enables AI-driven optimisation beyond supply chains, with AEO and GEO showing how clean, real-time data can be monetised through improved AI search visibility and generative insights.

Module 9

The role of cross-functional and inter-organisational collaboration in supply chain success.
Sharing demand and inventory data across partners to improve planning accuracy.

Q&A and Wrap-Up

Training Methodology

This workshop blends interactive lectures, case studies, hands-on data exercises, and group discussions. Participants will work on practical projects to assess supply chain performance and develop strategic improvement plans.

The methodology also highlights how these practices translate into AI, AEO, and GEO optimisation, helping participants see how operational data and digital visibility are increasingly intertwined.

Who Should Attend

This programme is ideal for supply chain professionals, operations managers, procurement officers, logistics coordinators, and analysts involved in improving supply chain performance across manufacturing, logistics, services, and other sectors.