Production management

ME-419 / 5 credits

Teacher: Kaboli Amin

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Summary

Production management deals with producing goods sustainably at the right time, quantity, and quality with the minimum cost. This course equips students with practical skills and tools for effectively managing demand, supply, and implementing advanced analytics in manufacturing companies.

Content

This course is based on the following three modules:

 

Module 1) Introduction to Production Management

  • Production Management: What is Production Management, and why do all manufacturing companies need it?
  • From Customer Needs to Plan, Purchase, Make, and Delivery (/Return): How a manufacturing company captures, creates, and adds value, What challenges it faces, How successful manufacturing companies navigate the journey
  • From Strategy to Execution: How to connect operations strategy to the tactical and operational plan efficiently, How to navigate the journey from products to platforms and ecosystems
  • Simulation: How to map value adding network of a manufacturing compnay

 

Module 2) Demand Management

  • Disruptions: Demand Disruptions, Risks, Bullwhip Effect
  • Demand Forecasting & Analytics: Data Analytics, Identifying Key Demand Drivers, Building, Validating and Improving Forecasting Models/Processes
  • Forecasting Methods: Model selection roadmap, Assumptions, Context, Forecasting Steps
  • Qualitative Methods: Brain and Forecasting Biases, Executive Opinion, Salesforce Opinion, Consumer Survey, Delphi Method
  • Quantitative Methods: Casual models, Time Series, Advanced Analytics
  • Casual models: Correlation & causation, Linear trend, Regression models
  • Time Series - Stationary: Naïve, Moving Average, Weighted Moving Average, Exponential Smoothing
  • Time Series - Trend: Linear Trend Model, Holt Model
  • Time Series - Trend and Seasonality: Autocorrelation, Hotel-Winter Model
  • Advanced Demand Analytics: Demand Sensing & Shaping, Predictive Analytics, Machine Learning Models, Real-time Monitoring & Visualization
  • Effective Demand Management: Key Performance Indicators (KPIs), Demand Visibility & Transparency, Practical Tips, Demand Plan/Sales Forecast
  • Simulation: How to tame bullwhip effect, reduce uncertainty, demand variability

 

Module 3) Supply Management

  • Disruptions: Supply Disruptions, Risks, Reverse Bullwhip Effect
  • Supply analytics: Data Analytics, Identifying Key Supply Drivers, Building, Validating, and Improving Production and Inventory Models/Processes
  • Production Planning: AP, MPS, RCCP, MRP
  • AP: Aggerate production planning strategies (Level Plan, Chase Plan, Hybrid Plan)
  • MPS: Master Production Schedule
  • RCCP: Capacity Planning (Rough-Cut Capacity Planning, Capacity Planning using Overall)
  • MRP: Material Requirement Planning
  • Inventory Management: Inventory Classification, Types, Costs, Decision Variables, Instability and Oscillation, Inventory Dynamics
  • Inventory Planning Models: Economic Order Quantity (EOQ), Economic Production Quantity (EPQ), Quantity Discount Model, Procurement and Negotiation with Suppliers, Safety Stock (SS), Periodic Review Model, Promotion Model/Christmas tree/Single Period Inventory Model
  • Advanced Supply Analytics: Predictive Analytics, Machine Learning Models, Real-time Monitoring & Visualization
  • Effective Supply management: Key Performance Indicators (KPIs), Supply Visibility & Transparency, Practical Tips, Supply Plan/Shipment plan
  • Simulation: How to manage inventory and match demand amidst demand variability

 

Module 4) Advanced Topics in Production Management

  • What are the latest trends in Production Management
  • How to effectively orchestrate supply chain (demand and supply) analytics
  • What are the main tools in use and which key digital technologies are driving the future of manufacturing/production

Keywords

Demand management, Supply management, Forecasting, Inventory management, Production planning, Simulation, Supply Chain Analytics.

Learning Prerequisites

Required courses

  • Probability and Statistics

Recommended courses

  • Continuous Improvement of Manufacturing Systems
  • Supply Chain Management
  • Data Science for Business

Important concepts to start the course

  • Data analysis using Excel
  • Active engagement and teamwork
  • Advanced level of probability and statistics

Learning Outcomes

By the end of the course, the student must be able to:

  • Choose production tools and methods based on performance and cost requirements and needs, taking into consideration applicability limits and associated hypotheses, CP8
  • Model , analyse and optimize the internal logistics of a production and distribution system and the dynamic behaviour of a network of companies, CP9
  • Design a system based on engineering specifications utilizing suitable numerical and analytical tools for optimizing the design parameters, CP10
  • a system based on engineering specifications utilizing suitable numerical and analytical tools for optimizing the design parameters, CP10
  • , analyse and optimize the internal logistics of a production and distribution system and the dynamic behaviour of a network of companies, CP9
  • production tools and methods based on performance and cost requirements and needs, taking into consideration applicability limits and associated hypotheses, CP8

Transversal skills

  • Assess progress against the plan, and adapt the plan as appropriate.
  • Communicate effectively, being understood, including across different languages and cultures.
  • Manage priorities.
  • Negotiate effectively within the group.
  • Evaluate one's own performance in the team, receive and respond appropriately to feedback.
  • Demonstrate the capacity for critical thinking
  • Write a scientific or technical report.
  • Take feedback (critique) and respond in an appropriate manner.
  • Take account of the social and human dimensions of the engineering profession.
  • Take responsibility for environmental impacts of her/ his actions and decisions.
  • Resolve conflicts in ways that are productive for the task and the people concerned.
  • Use both general and domain specific IT resources and tools

Teaching methods

  • Formal lectures
  • Group activities
  • Class discussions
  • Hands-on exercises
  • Project-based learning
  • Games and simulations
  • Guest lectures by leading academic and industry figuers

Expected student activities

  • Individual: Self-study, Active class discussions, case evaluations, Q&A
  • In-group: Teamwork (respect, brainstorming, involvement and constructive feedback)
  • Presentation: Share your findings weekly in class/group coaching sessions

Assessment methods

Continuous evaluation of case reports, projects, individual and group presentations, class discussions, during the semester. More precisely:

  • 25% Participation, and class engagement,
  • 45% Class assignments, presentations, projects, and case reports,
  • 30% Final (Final report and presentation and understanding of the case)

Supervision

Forum Yes
Others

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

    Series of book chapters, hand-outs, and notes will be shared in the class. The following books are recommended for further reading (and not mandatory);

    1. Yoo, M. J., & Glardon, R. (2018). Manufacturing Operations Management.
    2. Jacobs, F. R., Berry, W. L., Whybark, D. C., & Vollmann, T. E. (2011). Manufacturing planning and control for supply chain management: APICS/CPIM Certification Edition. McGraw-Hill Education.
    3. Stevenson, W. J. (2020). Operations management. McGraw Hill.
    4. Slack, N., Chambers, S., & Johnston, R. (2016). Operations management. Pearson education.
    5. Chase, C. W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
    6. Chase, C. W. (2016). Next generation demand management: People, process, analytics, and technology. John Wiley & Sons.
    7. Sterman, J. (2010). Business dynamics. Irwin/McGraw-Hill c2000.
    8. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
    9. Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
    10. Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.
    11. Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.
    12. Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don't. Penguin.
    13. Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard business review, 92(11), 64-88.
    14. Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard business review, 93(10), 96-114.
    15. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Harvard Business Press.
    16. Gupta, S. (2018). Driving digital strategy: A guide to reimagining your business. Harvard Business Press.Chicago
    17. Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: strategy and leadership when algorithms and networks run the world. Harvard Business Press.

      Ressources en bibliothèque

      Notes/Handbook

      • Course slides (main material)
      • Videos
      • Hand-outs during the semester

      Moodle Link

      In the programs

      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional
      • Semester: Fall
      • Exam form: During the semester (winter session)
      • Subject examined: Production management
      • Lecture: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Project: 2 Hour(s) per week x 14 weeks
      • Type: optional

      Reference week

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