Artificial Intelligence for Manufacturing and Supply Chain

Artificial Intelligence for Manufacturing and Supply Chain

Course schedule

Classroom Training:
DateVenueDurationPrice
20 - 24 Apr 2026London5 days£4,995
6 - 10 Jul 2026London5 days£4,995
21 - 25 Sep 2026London5 days£4,995
18 - 22 May 2026Riyadh5 days£4,995
15 - 19 Jun 2026Dubai5 days£4,995
13 - 17 Jul 2026Cape Town5 days£4,995
10 - 14 Aug 2026Istanbul5 days£4,995
14 - 18 Sep 2026Riyadh5 days£4,995
12 - 16 Oct 2026Dubai5 days£4,995

Please note: prices shown above are exclusive of VAT (20%).

If you don’t see your preferred course date, please contact us.

Course Overview

This course examines AI’s role in reshaping the financial services landscape. Delegates study applications in risk assessment, fraud detection, and customer analytics. Participants learn how AI enhances compliance and operational efficiency. The programme equips finance professionals to adopt intelligent technologies responsibly and strategically.

Who Should Attend

Operations professionals using AI to optimise manufacturing, logistics, and supply chain processes. Past delegates have included:

  • Supply Chain Managers
  • Operations Analysts
  • Manufacturing Engineers
  • Process Improvement Specialists
  • Data Scientists

Course Outcomes

  • Deploy predictive analytics and AI-driven automation to improve production planning.
  • Use machine learning for inventory optimisation and demand forecasting.
  • Integrate robotics and IoT for smart manufacturing systems.
  • Enhance supply chain resilience through real-time AI insights and simulation models.
  • Adopt AI-based quality control and risk mitigation tools in production environments.

Course Topics

Introduction to Artificial Intelligence

  • Understand AI principles and types across industries.
  • Identify opportunities and challenges in AI adoption.
  • Evaluate emerging AI technologies and their impact.
  • Discuss the ethical, legal, and social implications of AI.

AI in Decision-Making

  • Apply AI insights for operational and strategic decisions.
  • Integrate predictive analytics to improve decision outcomes.
  • Use AI for scenario modelling and data interpretation.
  • Manage biases and transparency issues in algorithmic decisions.

Ethical AI and Governance

  • Implement frameworks for responsible and transparent AI use.
  • Identify ethical risks associated with automated decision systems.
  • Design governance models ensuring fairness and accountability.
  • Promote stakeholder confidence in ethical AI implementation.

AI in Manufacturing

  • Apply AI to predictive maintenance, production planning, and quality control.
  • Optimise manufacturing workflows through digital twin technology.
  • Use AI for demand forecasting and resource efficiency.
  • Explore the intersection of robotics and AI in smart factories.

AI in Supply Chain Management

  • Enhance supply chain visibility using AI-powered analytics and sensors.
  • Apply machine learning to improve logistics and procurement forecasting.
  • Design data-driven risk mitigation frameworks for supply chains.
  • Evaluate sustainability gains from AI-driven supply chain optimisation.

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