Logistics and Operations Strategy

The module will position logistics and operations in business strategy and industrial organization. Strategic modelling and optimization approaches and tools for sourcing strategy, facility location, capacity and flexibility management will be presented and applied to problems of different industries. Topics the module covers include: - Competitive strategy (monopoly, simultaneous/sequential quantity competition, capacity competition, competitive locations) - Operations strategy and Industrial Organization (supply chain configuration/operational flexibility) - Capacity strategy (sizing and investment, timing and expansion) - Distribution network strategy (warehouse location problem/hub- and spoke systems) - Process technology (Make-to-order vs. Make-to-stock, factory physics) - Operations and risk management (hedging/sourcing/inventory strategies)

The participants will acquire knowledge on different views of logistics strategy from a market and a resource perspective and will be enabled to apply decision support tools for an effective design of global manufacturing and logistics networks. Students will be able to assess strategic problems from practice, categorize them according to the decisions involved and identify relevant solution methods to solve them. Furthermore, students are equipped with the ability to apply methodologies and techniques from theory in practical environments. After finishing the module, students will be able to evaluate innovative and complicated operations and logistics settings, such as the integration of additive manufacturing (3D printing), and create subsequent innovative solution approaches for strategic decision makers.

At the end of the module a 90-minutes exam will determine the grading of the students. Students choose 3 out of 4 questions. Within each question two different competence areas are assessed. The first part of each questions covers knowledge about strategic operational and logistics concepts from the lecture. Then, in a second part, multiple quantitative methods have to be applied. They involve calculation and the analysis of results like in the exercise classes. Since calculations are to be done, a pocket calculator and a formula sheet summarizing the most relevant formulas and statistical values may be used by the students.

The module requires basic knowledge in statistics (discrete and continuous probability distributions), MS Excel and the course “Modelling, Optimization, and Simulation”, which is due to the extensive use of Mixed-Integer Linear Programming. Basic knowledge of micro-economics theory helps, but is not a must.

The series of lectures provides students with a fundamental knowledge of concepts and methods for assessing and optimizing given problems. Exemplary problem settings are solved during exercise classes, where the content given in the lecture is applied. Optimizations using MS Excel solver and analytical calculations are the basis for a follow-up interpretation of the results. In the process, students present their work and conduct an interactive discussion with fellow students and the lecturer regarding their approach, solution and interpretation. Extending the theoretical exercises, case studies are used to let students analyse and solve real-world problems, which closes the gap between theory and practice. To give students a further glimpse into practice, guest speakers from various industries present their daily challenges and approaches to solve them. This allows students to make the connection between the theoretical concepts they have learned and the requirements in practice and provides the opportunity to discuss questions with practitioners and find problem settings that might be suitable for their final thesis.