Operational and Supply Chain Analytics

Data Analytics and Business Intelligence October 25, 2025
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Introduction

This course focuses on applying data analytics to optimize complex operational processes, reduce costs, and improve efficiency across the supply chain. Participants will learn how to analyze data from warehousing, logistics, manufacturing, and inventory systems to identify bottlenecks and forecast demand. We cover techniques such as predictive maintenance, route optimization, and simulation modeling essential for lean operations. This program provides the analytical framework needed to transform a cost center into a source of competitive advantage by increasing throughput and minimizing waste.

Objectives

Upon completion of this course, participants will be able to:

  • Analyze inventory data to determine optimal reorder points and minimize stockouts and overstocking.
  • Apply statistical process control (SPC) charts to monitor manufacturing quality and detect anomalies.
  • Implement predictive maintenance models to forecast equipment failures and schedule preventive action.
  • Utilize network optimization algorithms for logistics, transport route planning, and warehouse layout.
  • Conduct process mining and simulation to identify and quantify bottlenecks in operational workflows.
  • Design dashboards to monitor key operational KPIs, including OEE, lead time, and fulfillment accuracy.
  • Forecast demand using advanced time-series models to improve planning accuracy.
  • Identify and quantify cost reduction opportunities across the entire supply chain lifecycle.

Target Audience

  • Supply Chain and Logistics Managers
  • Operations and Plant Managers
  • Inventory and Warehouse Specialists
  • Process Improvement and Continuous Improvement Analysts
  • Manufacturing and Production Engineers
  • Procurement and Sourcing Specialists

Methodology

The methodology focuses on leveraging real-world operational datasets for analysis. **Case studies** involve analyzing logistics shipment data to re-engineer distribution routes for cost savings and analyzing historical machine sensor data to build a predictive maintenance model. **Group activities** focus on designing an inventory optimization strategy for a multi-warehouse network. **Individual exercises** require participants to apply SPC control charts to a simulated manufacturing quality dataset and use basic simulation tools for process analysis. **Scenarios** involve unexpected factory downtime, requiring rapid data analysis and **syndicate discussions** to identify the root cause and long-term mitigation strategies.

Personal Impact

  • Gain proficiency in analytical tools for solving complex, real-world operational problems.
  • Enhance ability to quantify the financial impact of process improvements and efficiencies.
  • Acquire skills in high-demand areas like predictive maintenance and demand forecasting.
  • Improve career value by contributing directly to organizational cost reduction and performance.
  • Increase the accuracy and reliability of personal supply chain planning and decisions.

Organizational Impact

  • Significantly reduce operational expenses by optimizing inventory, logistics, and resource utilization.
  • Increase asset uptime and reduce maintenance costs through accurate predictive maintenance scheduling.
  • Improve customer satisfaction and loyalty by increasing fulfillment accuracy and reducing lead times.
  • Enhance manufacturing quality and reduce waste through proactive process control monitoring.
  • Create a resilient and agile supply chain capable of reacting quickly to market volatility.

Course Outline

UNIT 1: Foundations of Operations Data

Metrics and Data Sources
  • Defining Key Operational KPIs (OEE, Lead Time, Cycle Time, Throughput)
  • Sourcing and Structuring Data from ERP, WMS, and IoT Systems
  • Data Preprocessing and Cleansing for Time-Series Operational Data
  • Fundamentals of Statistical Process Control (SPC) and Control Charts
  • Understanding Lean Manufacturing and Six Sigma Metrics

UNIT 2: Inventory and Demand Planning Analytics

Optimizing Stock Levels
  • Forecasting Demand using Exponential Smoothing and ARIMA models
  • Calculating Optimal Safety Stock and Reorder Points
  • Analyzing Inventory Carrying Costs and Stockout Costs
  • Classification and Management of Inventory (ABC Analysis)
  • Identifying and managing demand volatility and seasonality

UNIT 3: Logistics and Network Optimization

Efficiency and Cost Reduction
  • Introduction to Vehicle Routing Problem (VRP) and Heuristics
  • Analyzing Transportation Costs and Route Efficiency
  • Network Design Analytics for Warehouse and Distribution Center Placement
  • Modeling Logistics Risk and Resilience
  • Using GIS tools for spatial analysis and delivery optimization

UNIT 4: Manufacturing and Maintenance Analytics

Asset Reliability and Uptime
  • Predictive Maintenance: Time-to-Failure Modeling using Sensor/IoT Data
  • Calculating Overall Equipment Effectiveness (OEE) and its drivers
  • Anomaly Detection for Quality Control and Defect Reduction
  • Root Cause Analysis (RCA) using data-driven techniques
  • Analyzing Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR)

UNIT 5: Process Mining and Visualization

Identifying Workflow Bottlenecks
  • Fundamentals of Process Mining and Event Log Data Preparation
  • Visualizing Operational Workflows and Identifying Bottlenecks
  • Simulation Modeling (Discrete Event Simulation) for Process Testing
  • Designing Real-Time Operational Monitoring Dashboards
  • Translating Analytical Findings into Actionable Process Improvements

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