Introduction

Supply chain management plays a vital role in the success of businesses by ensuring that products are efficiently produced and delivered to customers. As companies face increasing pressure to optimize their supply chain operations, the need for data-driven decision-making has never been more crucial. Supply chain analytics leverages advanced tools and techniques to analyze data, optimize operations, and reduce costs. This course will provide participants with the knowledge and practical skills needed to analyze key supply chain data, predict trends, and improve decision-making across all areas of supply chain management.

By the end of this course, participants will be able to understand the role of data analytics in supply chain operations, use statistical methods to solve complex problems, and apply predictive analytics and optimization techniques to enhance efficiency and effectiveness in the supply chain.

Objectives

By the end of this course, participants will:

• Understand the fundamentals of supply chain management and analytics.
• Learn how to use data analysis techniques to optimize various aspects of the supply chain.
• Develop skills in predictive modeling and forecasting for supply chain operations.
• Apply optimization techniques to enhance inventory management, distribution, and logistics.
• Understand the role of big data, the Internet of Things (IoT), and machine learning in supply chain analytics.
• Gain hands-on experience with tools such as Excel, R, Python, and Power BI for analyzing and visualizing supply chain data.
• Understand the importance of risk management and how to analyze and mitigate supply chain risks.

Who Should Attend?

This course is ideal for:

• Supply chain managers, operations managers, and logistics professionals who want to use data to improve their supply chain strategies.
• Data analysts and business analysts interested in applying their skills to supply chain operations.
• Professionals involved in procurement, inventory management, and demand forecasting.
• Anyone looking to enhance their ability to use analytics to solve supply chain challenges.

Day 1: Introduction to Supply Chain Analytics

Morning Session: Overview of Supply Chain Management

• The role of supply chain management in business success.
• Key components of supply chain operations: Procurement, production, distribution, and logistics.
• The importance of data and analytics in supply chain optimization.
• Introduction to key supply chain metrics: Lead time, inventory turnover, order fulfillment rate, etc.
• Hands-on: Introduction to data sources and tools for supply chain analytics.

Afternoon Session: Data Collection and Preprocessing for Supply Chain Analytics

• Types of supply chain data: Transactional, inventory, transportation, and demand data.
• Data collection methods: ERP systems, IoT devices, and third-party logistics providers.
• Data preprocessing techniques: Cleaning, transformation, and aggregation of data.
• Hands-on: Import and clean sample supply chain data using Excel or R.

Day 2: Demand Forecasting and Inventory Management

Morning Session: Introduction to Demand Forecasting

• The role of demand forecasting in supply chain management.
• Techniques for forecasting demand: Time series analysis, moving averages, and exponential smoothing.
• Using historical sales data to predict future demand.
• Hands-on: Build a basic time series forecasting model using R or Python.

Afternoon Session: Inventory Optimization

• The concept of inventory management: Just-in-time (JIT), Economic Order Quantity (EOQ), and Safety Stock.
• How to use demand forecasts to optimize inventory levels.
• Techniques for reducing stockouts and overstocking: Reorder points and safety stock analysis.
• Hands-on: Implement inventory optimization using the EOQ model in Excel.

Day 3: Supply Chain Optimization Techniques

Morning Session: Optimization for Production and Distribution

• The role of optimization in supply chain efficiency.
• Production scheduling and capacity planning: Linear programming and optimization models.
• Distribution optimization: Transportation problems, vehicle routing, and last-mile delivery.
• Hands-on: Solve a basic production scheduling problem using linear programming in Python.

Afternoon Session: Supplier and Vendor Management

• How to assess and manage supplier performance using data.
• Vendor relationship management and supplier segmentation.
• Strategies for minimizing lead times and improving supplier collaboration.
• Hands-on: Analyze supplier performance and optimize sourcing strategies using R.

Day 4: Advanced Analytics and Machine Learning in Supply Chains

Morning Session: Predictive Analytics in Supply Chain

• The role of predictive analytics in forecasting demand, inventory, and supply chain disruptions.
• Introduction to machine learning techniques for supply chain analytics: Regression, classification, clustering, and neural networks.
• Case studies: Using predictive models to anticipate supply chain disruptions (e.g., demand spikes, supplier failures).
• Hands-on: Build a predictive model for demand forecasting using machine learning techniques in Python.

Afternoon Session: Risk Management and Resilience in Supply Chains

• Identifying and analyzing supply chain risks: Disruptions, demand fluctuations, and natural disasters.
• Quantitative risk analysis techniques: Monte Carlo simulations, scenario analysis, and sensitivity analysis.
• Building a resilient supply chain: Risk mitigation strategies and contingency planning.
• Hands-on: Perform a risk analysis of a supply chain network and identify mitigation strategies using R or Excel.

Day 5: Data Visualization and Decision Support for Supply Chains

Morning Session: Data Visualization for Supply Chain Analytics

• The importance of visualization in communicating supply chain insights to stakeholders.
• Key performance indicators (KPIs) for supply chain visualization: Inventory levels, order cycle time, and supplier performance.
• Tools for creating interactive dashboards: Power BI, Tableau, and Excel.
• Hands-on: Create a supply chain performance dashboard in Power BI.

Afternoon Session: Implementing and Communicating Analytics Results

• How to integrate supply chain analytics into strategic decision-making processes.
• Communicating analytics results to executives and stakeholders: Data storytelling and visual communication.
• Building a culture of data-driven decision-making within the supply chain.
• Hands-on: Present a case study with actionable insights using a supply chain dashboard.

Materials and Tools:

• Required tools: Excel, R, Python, Power BI, Tableau
• Sample datasets: Transaction data, inventory data, demand forecasts, supplier data, transportation data.
• Recommended resources: Online tutorials for Power BI, Python libraries (e.g., pandas, numpy, scikit-learn), and supply chain case studies.