Master of Management Studies (M.M.S) Operations Analytics Syllabus - Mumbai University 2026
This is the M.M.S. syllabus under NEP 2020, in force from the academic year 2025-26: Semesters I and II from the revised circular and Semesters III and IV from the new one, both approved by the Academic Council on 20 May 2025. The earlier choice-based syllabus is still set alongside it for repeating candidates, so check which scheme your exam form names before you revise.
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Syllabus for Operations Analytics
Course objectives
- 1 To introduce analytical techniques and tools applied in operational decision- making.
- 2 To equip students with practical skills in predictive modeling, optimization, and forecasting within operations.
- 3 To develop capabilities for analyzing operational data to enhance process efficiency and performance.
- 4 To foster understanding of analytical software and technology solutions widely used in operations management.
- 5 To cultivate strategic insight into data-driven operational improvements and innovation.
Course outcomes
- CO1: Recall fundamental concepts of data-driven decision-making in operations
- CO2: Understand key analytical techniques used in operations
- CO3: Implement data analytics in real-world operational scenario
- CO4: Analyse & assess the impact of operational efficiency using quantitative models and optimization techniques
- CO5: Evaluate the impact of operational efficiency using appropriate data and optimization techniques for decision making
- CO6: Design innovative data-driven solutions for operations challenges
Unit 1 CO1, CO2 · 3 hours
Introduction to Analytics Gaining data insights and Predictive Analytics Demand analytics-Qualitative forecasting
Unit 2 CO2, CO3 · 3 hours
Demand Analytics Forecasting and time series analysis. Regression Analysis for Operations
Unit 3 CO2, CO3 · 3 hours
Demand Analytics Regression Analysis for Operations
Unit 4 CO3, CO4 · 3 hours
Quality Control Statistical Quality Control Various types of Control Charts (Mean Chart, Variation Charts …)
Unit 5 CO3, CO4 · 3 hours
Machine Learning Block chain in operations. Predictive Maintenance & Failure Analysis
Unit 6 CO4, CO6 · 3 hours
Performance Metrics Inventory, Fulfillment, Alerts, and Flagging etc. Dashboard Designing, Balanced Scorecard Kaplan and Norton Framework, Strategy Map
Unit 7 CO4, CO5 · 3 hours
Introduction to Probabilistic Inventory Control Models. Instantaneous and Continuous demand.
Unit 8 CO4, CO5 · 3 hours
Introduction to Probabilistic Inventory Control Models. Inventory Control Models with and without set-up cost
Unit 9 CO4, CO5 · 3 hours
Introduction to Non-Linear Programming. Lagrange Multiplier
Unit 10 CO4, CO5 · 3 hours
Introduction to Non-Linear Programming. Graphical Method
Textbooks
- 1 “Operations Research: Theory and Applications” by J.K Sharma
- 2 “Machine Learning for Business Analytics” by Shmueli
Reference Books
- 1 “Business Analytics: Practitioner’s Guide” by Rahul Saxena & Anand Srinivasan
- 2 “Manufacturing Planning and Control” by Volmann, Berry, Whybark
- 3 “Quantitative Techniques in Management” by N.D Vohra
Reproduced from the University of Mumbai syllabus for Master of Management Studies (Two Year), Semester III and IV, AC 20/05/2025, Item No. 7.11 (N), in force from the academic year 2025-26. Wording and unit numbering are as printed there. The PDF above is the syllabus's own page, unaltered.
The complete syllabus
This subject is cut from the University circular for its semester. Open a document here if you want the whole thing rather than a single subject.