AKTU Operation Management for Business Analytics (BMBAOM02) Syllabus PDF – MBA (Business Analytics) 3rd Semester

Course Objectives

1. Introduce the fundamental concepts, processes, and strategies of operations management.
2. Develop an analytical approach toward solving real-world operational problems.
3. Apply quantitative techniques and tools for improving operational efficiency.
4. Integrate business analytics tools with operations decision-making in areas like supply chain, forecasting, and quality management.
5. Prepare students to use data and dashboards to support operations strategy and decision-making.

Unit 1: Fundamentals of Operations Management & Process Analysis (8Hours)

Introduction to operations management, Role of operations in business functions, Product vs. service operations, Process design and analysis, Process flowcharting, Capacity planning and utilization, Bottleneck analysis and throughput time, Productivity measurement and improvement, Operations strategy alignment with business goals

Unit 2: Operations Forecasting and Decision Models (8 Hours)

Importance of forecasting in operations, Qualitative vs. quantitative forecasting techniques, Time series forecasting: moving averages, exponential smoothing, trend analysis, Regression-based forecasting models, Forecast error measurement: MAD, MAPE, MSE, Decision trees and payoff matrices, Linear programming and optimization in operations, Case study: Forecasting demand using historical data in Excel / Power BI

Unit 3: Inventory Management and Supply Chain Analytics (8 Hours)

Types of inventory and inventory costs, EOQ and Reorder Point models, ABC and VED classification, Safety stock and service level, Supply chain network design and KPIs, Bullwhip effect and risk pooling, Data-driven supplier performance analysis, Inventory dashboards in Excel / Power BI, Case study: Inventory optimization using analytics

Unit 4: Quality Management and Lean Operations (8 Hours)

Total Quality Management (TQM) principles, Six Sigma methodology (DMAIC), Statistical Quality Control (SQC), Control charts and process capability, Lean thinking and waste elimination (7 types of waste), Kaizen and continuous improvement, Value stream mapping, Root cause analysis using Fishbone diagrams, Dashboards for quality analytics.

Unit 5: Project Management and Emerging Trends in Operations Analytics (8 Hours)

Introduction to project scheduling: Gantt chart, CPM, PERT, Resource allocation and crashing, Operational risk management and contingency planning, Integration of business analytics in operations, IoT and automation in operations, Operations KPIs and dashboards using Power BI/Tableau, Industry 4.0 and Smart operations, Capstone project: Analytics-based solution to an operational problem

Course Outcomes

Suggested Readings

Text Books

1. Operations Management by William J. Stevenson, Publisher: McGraw-Hill Education
2. Operations Management: Sustainability and Supply Chain Management by Jay Heizer, Barry Render, Chuck Munson, Publisher: Pearson Education
3. Operations Management for MBAs by Jack R. Meredith, Scott M. Shafer, Publisher: Wiley
4. Business Analytics: Data Analysis & Decision Making by S. Christian Albright & Wayne L. Winston, Publisher: Cengage Learning Supplementary Tools and Resources
5. Excel Solver, Power BI, Tableau (for modeling and dashboards)
6. Kaggle Datasets (for case-based data application)
7. Harvard Business Review Articles on Operations Strategy
8. YouTube Channels: MIT OpenCourseWare – Operations Management