Using Big data and analytics in Operations #253009

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This 5-day intensive course equips participants with the knowledge and skills to leverage the power of big data and analytics to optimize operational performance across various industries. Participants will learn how to collect, analyze, and interpret large datasets to identify trends, predict outcomes, and make data-driven decisions that improve efficiency, reduce costs, and enhance customer satisfaction.

Upon successful completion of this course, participants will be able to:
• Understand the fundamentals of big data and its applications in operations management.
• Collect, clean, and prepare data for analysis using various tools and techniques.
• Apply data mining and machine learning algorithms to extract valuable insights from operational data.
• Develop and implement data-driven strategies for optimizing operational processes.
• Utilize predictive analytics for forecasting, demand planning, and risk mitigation.
• Build and deploy operational dashboards and reports to track key performance indicators (KPIs).
• Communicate data-driven insights effectively to stakeholders.
• Develop a data-driven culture within their organizations.

This course is designed for a wide range of professionals, including:
• Operations Managers
• Supply Chain Managers
• Logistics Managers
• Production Managers
• Data Analysts
• Business Analysts
• IT Professionals
• Anyone involved in operational decision-making

• 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

• Introduction to Big Data: Characteristics, Sources, and Challenges
• Data Collection and Integration Techniques
• Data Cleaning and Preparation: Handling Missing Data, Outliers, and Inaccuracies
• Introduction to Data Visualization and Storytelling

• Supervised Learning: Regression, Classification
• Unsupervised Learning: Clustering, Dimensionality Reduction
github.com
• Predictive Modeling for Operations: Demand Forecasting, Predictive Maintenance
• Case Studies: Applying Data Mining in Operations

• Supply Chain Optimization: Inventory Management, Logistics, and Transportation
• Production Planning and Scheduling
• Quality Control and Process Improvement
• Customer Relationship Management (CRM) Analytics

• Developing and Implementing Operational Dashboards and Reports
• Data-Driven Decision Making and Action Planning
• Building a Data-Driven Culture within the Organization
• Ethical Considerations and Data Privacy in Operations

• Artificial Intelligence (AI) and Machine Learning in Operations
• Internet of Things (IoT) and Operational Data
• Cloud Computing and Big Data Analytics
• The Future of Operations in the Age of Data
• Case Studies and Real-World Applications
• Q&A and Wrap-up Session

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