Using Big data and analytics in Operations #253009

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

• Identify key sources of big data within operational processes (IoT, ERP, SCADA, GPS, etc.) and understand how to manage them.

• Define common operational problems that can be solved using predictive and prescriptive analytics.

• Apply foundational analytical techniques (descriptive, diagnostic, predictive, prescriptive) to operational data.

• Interpret the output of analytics models (e.g., forecasting, optimization, clustering) to make informed operational decisions.

• Design a roadmap for implementing a data-driven culture and capability within their operations function.

• Communicate data-driven insights effectively to stakeholders to secure buy-in for operational improvements.

This course is designed for operations professionals who are responsible for efficiency, performance, and continuous improvement:

• Operations Managers and Directors

• Supply Chain and Logistics Managers

• Plant Managers and Manufacturing Engineers

• Continuous Improvement / Lean Six Sigma Practitioners

• Data Analysts supporting operations functions

• IT Professionals involved in operational technology (OT)

• Senior Leadership seeking to understand the potential of operational analytics

• 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: Foundations of Big Data in Operations

• Morning Session: The Data-Driven Operations Revolution

  • What is Big Data? The 4 V’s (Volume, Velocity, Variety, Veracity) in an operational context.

  • Key Data Sources in Operations: IoT sensors, ERP systems (SAP, Oracle), MES, SCADA, GPS, and customer data.

  • The Business Case: Linking data analytics to key operational KPIs (OEE, OTIF, Cost per Unit).

• Afternoon Session: The Analytics Hierarchy

  • Descriptive Analytics: What happened? (Dashboards, Reports)

  • Diagnostic Analytics: Why did it happen? (Drill-down, Root Cause Analysis)

  • Workshop: Use a dashboard to diagnose the root cause of a drop in equipment efficiency.

Day 2: Predictive Analytics for Proactive Operations

• Morning Session: Forecasting Demand and Requirements

  • Moving beyond Excel: Introduction to advanced forecasting techniques (time series analysis, regression).

  • Improving forecast accuracy to optimize inventory and production planning.

• Afternoon Session: Predictive Maintenance (PdM)

  • From Reactive to Predictive: Using sensor data to predict equipment failures before they happen.

  • Introduction to PdM models and their ROI.

  • Case Study: Calculate the potential savings from implementing PdM on a critical asset.

Day 3: Prescriptive Analytics and Optimization

• Morning Session: Optimizing Operational Flows

  • Introduction to Optimization Models: Linear programming for scheduling, routing, and resource allocation.

  • Network Optimization: Finding the most efficient supply chain and distribution network design.

• Afternoon Session: Simulation and “What-If” Analysis

  • Using digital twins and simulation software to model operational processes and test scenarios.

  • Practical Exercise: Use a simple simulation tool to optimize a warehouse picking route or production schedule.

Day 4: Real-Time Analytics and Technology Tools

• Morning Session: Real-Time Data and IoT

  • Streaming Analytics: Monitoring operations in real-time to identify and respond to issues instantly.

  • The Role of AI and Machine Learning in automating operational decisions.

• Afternoon Session: The Technology Stack

  • Overview of Key Tools: Cloud platforms (AWS, Azure), data visualization (Tableau, Power BI), and analytics software (Python, R, Alteryx).

  • Tool Demo: Connect an IoT data stream to a visualization dashboard to monitor a real-time process.

Day 5: Implementation, Leadership, and Capstone

• Morning Session: Building a Data-Driven Culture

  • Overcoming Organizational Barriers to Adoption.

  • Developing an Implementation Roadmap: Starting with a pilot project.

  • Data Governance and Quality: Ensuring your data is trustworthy.

• Afternoon Session: Capstone Project

  • Final Assignment: Participants work in groups on a comprehensive case study. They must:

    1. Identify a core operational problem.

    2. Propose the data sources and analytical technique to solve it.

    3. Outline the expected business impact and ROI.

    4. Present their data-driven solution to the class.

  • Course Recap: Key Takeaways and Next Steps.

  • Certification of Completion.