Logistics Engineering Curriculum Integrated Through Student Projects
Bibliographic record
Abstract
This paper describes an innovative curriculum developed for new Logistics Engineering degree programs at the Faculty of Engineering Management, Poznan University of Technology. The core of the program is based on a sequence of four major courses, which focus on the Product Development, Process Analysis and Optimization, Logistic Processes and Service Engineering. Each course is built around a practical team project. With the project effort as the background, the courses introduce students to key issues in global engineering competence, including collaboration and teamwork, work organization and management, engineering ethics, cross-cultural communication, critical thinking and problem solving, and integration strategies for design, manufacturing and marketing. Projects also introduce entrepreneurial components, as the teams have to develop their concepts in the context of a start-up company. The first course in the series, introduces 2nd year students to basic concepts of consumer product development. It covers the principles of design and innovation process, and also explains essential design tools, such as Quality Function Deployment and Pugh Matrices. It also reviews key manufacturing methods and systems. Students work in small teams to develop their own product ideas from initial concepts to a business plan for a start-up. The course is offered in English. The second course, offered to 3rd year students, introduces fundamental concepts related to industrial process analysis and improvement. Students learn necessary data collection and analysis techniques (such as, for example, Value Stream Mapping) and also the basics of process simulation using a commercial software package. Student teams work with industrial sponsors and develop competing innovative ideas for process transformation and improvement. While the first two courses have already been offered for the first time in the past year in the engineering program (level 1), the third course is still in the planning phase. It will be offered to 4th year students in the first year of their master's program. It will focus on Supply Chain processes, assessment of their performance, lifecycle analysis and management. The student group project will be carried out in an industrial setting, dealing with real-life assignments. The final course, integrating knowledge acquired by the students in the preceding sequence, is focused on the issues of service engineering. It covers such topics as, for example, organizational design, global issues and design of service operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".