Program Overview

Program Overview

The Production Management course is designed to equip students with practical skills and tools for effectively managing demand, supply, and implementing advanced analytics in manufacturing companies. The course focuses on producing goods sustainably at the right time, quantity, and quality with the minimum cost.

Course Description

The course is based on four modules:

• Module 1: Introduction to Production Management
  • What is Production Management, and why is it essential?
  • Understanding Linear Supply Chains: How do traditional production systems operate from sourcing to delivery? What role does production management play in optimizing this flow?
  • Simulation: How to map value-adding network of a manufacturing company? How does supply chain simulation help us design better supply chains?
• Module 2: Demand Management
  • Demand Disruptions: Risks, Variability, Bullwhip Effect
  • Demand Forecasting: Identifying Demand Drivers, Forecasting Models, Validation
  • Forecasting Methods: Context, Assumptions, Selection Roadmap
  • Qualitative Methods: Biases, Executive Opinion, Salesforce Input, Surveys, Delphi
  • Quantitative Methods: Causal Models, Time Series, Advanced Analytics
  • Forecast Validation: Measuring accuracy, error types, feedback loops
  • Effective Demand Management: Key Performance Indicators (KPIs), Visibility, Forecast Planning Tips, Demand Plan/Sales Forecast
  • Simulation: How to tame bullwhip effect and reduce uncertainty
• Module 3: Supply Management
  • Supply Disruptions: Risks, Variability, Reverse Bullwhip Effect
  • Supply analytics: Key Drivers, Data Readiness, Model Improvement
  • Production Planning: AP, MPS, RCCP, MRP
  • Inventory Management: Classification, Costs, Dynamics, Instability
  • Core Inventory Models: EOQ, EPQ, Safety Stock, Periodic Review
  • Special Models & Tactics: Quantity Discounts, Single-Period, Promotions, Supplier Negotiation
  • Effective Supply management: Key Performance Indicators (KPIs), Visibility, Supply Plan/Shipment plan
  • Simulation: How to manage inventory and align supply with demand variability
• Module 4: Next-Gen Production: Analytics, Sustainability & Circularity
  • What are the latest trends in Production Management
  • How to effectively orchestrate supply chain (demand and supply) analytics
  • From Linear to Circular: How are companies shifting from traditional (take–make–dispose) to circular (take–make–return/repair/reuse) models, and how can analytics support this transition?

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

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 figures

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)

Resources

• 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):
  • Yoo, M. J., & Glardon, R. (2018). Manufacturing Operations Management.
  • 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.
  • Stevenson, W. J. (2020). Operations management. McGraw Hill.
  • Slack, N., Chambers, S., & Johnston, R. (2016). Operations management. Pearson education.
  • Chase, C. W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
  • Chase, C. W. (2016). Next generation demand management: People, process, analytics, and technology. John Wiley & Sons.
  • Sterman, J. (2010). Business dynamics. Irwin/McGraw-Hill c2000.
  • 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.".
  • Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
  • Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.
  • Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.
  • Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don't. Penguin.
  • Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard business review , (11), 64-88.
  • Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard business review , (10), 96-114.
  • Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Harvard Business Press.
  • Gupta, S. (2018). Driving digital strategy: A guide to reimagining your business. Harvard Business Press.Chicago
  • 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.

Programs

The course is part of the following programs:

• Mechanical Engineering, Master semester 1
• Mechanical Engineering, Master semester 3
• Management, Technology and Entrepreneurship, Master semester 1
• Management, Technology and Entrepreneurship, Master semester 3
• Robotics, Master semester 1
• Robotics, Master semester 3
• Mechanical engineering minor, Autumn semester

Reference Week

The course schedule is as follows: | Mo| Tu| We| Th| Fr
---|---|---|---|---|---
8-9| | | | |
9-10| | | | |
10-11| | | | |
11-12| | | | |
12-13| | | | |
13-14| | | | | CM1105
14-15| | | |
15-16| | | | | CM013
CM1100
CM1106
CM1104
CM1105
16-17| | | |
17-18| | | | |
18-19| | | | |
19-20| | | | |
20-21| | | | |
21-22| | | | |

Légendes: Lecture Exercise, TP Project, Lab, other

Friday, 13h - 15h: Lecture CM1105 Friday, 15h - 17h: Project, labs, other CM013
CM1100
CM1106
CM1104
CM1105

Related Courses

Results from graphsearch.epfl.ch.