Application of Machine Learning in Supply chains#400524

• Analyse the impact of future trends on the development and application of machine learning in supply chain management.
• Identify key challenges and opportunities within current supply chain operations, considering how ML can address issues like demand forecasting, inventory management, and risk mitigation.
• Develop and implement a strategic vision for integrating machine learning into their supply chain strategy to improve efficiency, reduce costs, and enhance customer service.
• Explore various machine learning algorithms and techniques applicable to different supply chain functions, such as demand forecasting, anomaly detection, and route optimization.
• Gain practical experience using industry-standard tools and platforms to build and deploy simple machine learning models for supply chain tasks (basic coding skills recommended but not mandatory).
• Communicate effectively about the potential and limitations of machine learning to stakeholders within the organization.
• Develop a future-oriented perspective on ML in supply chains and identify potential disruptions and opportunities related to emerging technologies.
• Create a personalized action plan outlining steps to implement ML solutions within their specific supply chain function.

• Supply chain managers, directors, and planners seeking to optimize logistics processes with machine learning.
• Procurement specialists interested in using ML for smarter sourcing and supplier management.
• Inventory management professionals looking to leverage ML for demand forecasting and optimization.
• Logistics and transportation professionals interested in exploring ML applications for route optimization and fleet management.
• Anyone in a supply chain role wanting to understand the future potential of ML and its impact on their work.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Future of Supply Chains & Machine Learning
• Welcome and program overview.
• The Evolving Landscape of Supply Chains: Exploring how technological advancements, globalization, and changing customer expectations are impacting the future of supply chain management.
• Introduction to Machine Learning: Learning the core concepts of machine learning, different algorithms, and how they can be applied to solve problems in supply chains.
• Future Studies for Supply Chain Professionals: Discovering how to incorporate future studies methodologies like scenario planning and horizon scanning to anticipate potential disruptions and opportunities related to ML in supply chains.
• Case Study: analysing a real-world example of a company that successfully leveraged machine learning to address a specific challenge within their supply chain.

Day 2: Machine Learning Applications in Supply Chain Functions
• Demand Forecasting: Exploring how machine learning algorithms can be used to analyse historical data, identify trends, and predict future demand for products, leading to improved inventory management and production planning.
• Inventory Optimization: Learning how ML can optimize inventory levels to minimize costs associated with holding excess stock or stockouts, considering factors like lead times and demand fluctuations.
• Risk Management and Anomaly Detection: Discussing how machine learning can be used to identify potential disruptions and anomalies in the supply chain, such as supplier delays or transportation issues, allowing for proactive mitigation strategies.
• Hands-on Workshop: Demand Forecasting with Machine Learning: Participants will gain practical experience using a user-friendly platform or introductory coding environment to build a simple machine learning model for demand forecasting on a pre-prepared supply chain dataset.
Day 3: Machine Learning for Procurement & Logistics
• Supplier Management & Sourcing Optimization: Exploring how machine learning can be used to analyse supplier performance data, identify the most reliable partners, and optimize sourcing strategies.
• Route Optimization & Transportation Management: Learning how ML algorithms can be applied to optimize delivery routes, considering factors like traffic patterns, fuel efficiency, and real-time data to improve efficiency and reduce transportation costs.
• Warehouse Management & Automation: Discussing the role of machine learning in warehouse automation, including tasks like demand-driven product placement and predictive maintenance for equipment.
• Guest Speaker: A supply chain leader who successfully implemented ML solutions in their organization can be invited to share their insights and answer participant questions.
Day 4: The Future of ML in Supply Chains
• Emerging Technologies & Disruptions: Exploring potential future trends like autonomous vehicles, blockchain technology, and the Internet of Things (IoT), and how these might further impact the application of ML in supply chains.
• The Human-Machine Collaboration: Discussing the future of work in supply chain management with the integration of ML, focusing on the evolving role of human expertise and decision-making alongside automation.
• Ethical Considerations & Data Governance: Addressing the ethical challenges associated with using machine learning in supply chains, such as bias in algorithms and data privacy concerns.
• Case Study: analysing a real-world example of how a company is preparing for the future of ML in their supply chain by adopting responsible AI practices and building a culture of data literacy.
Day 5: Building Your ML-Powered Supply Chain Strategy
• Developing an ML Implementation Strategy: Creating a roadmap for integrating machine learning into your supply chain operations, considering factors like data infrastructure, talent development, and stakeholder buy-in.
• Identifying ML Use Cases: Participants work in facilitated groups to identify specific opportunities within their own supply chain functions where ML could be applied for optimization and improvement.
• Evaluating & Selecting ML Solutions: Learning strategies for evaluating and selecting appropriate machine learning solutions based on specific needs, functionalities, and costs.
• Building an Action Plan: Participants create personalized action plans outlining steps to explore and implement machine learning solutions within their specific supply chain role or department. This may include:
o Identifying key performance indicators (KPIs) to measure the success of ML initiatives.
o Researching available tools and resources for building or deploying ML models.
o Advocating for responsible and ethical practices in ML implementation within your organization.
• Course Wrap-Up & Ongoing Learning: Reviewing key takeaways from the program, addressing any remaining questions, and discussing ongoing learning resources for staying informed about advancements in machine learning and its application in supply chains.
• Networking & Knowledge Sharing: Participants engage in a facilitated discussion to share their ML action plans, best practices, and potential challenges, fostering collaboration among supply chain professionals.