A Systematic Review of Strategic Supply Chain Challenges and Teaching Strategies
Bibliographic record
Abstract
Background: This study provides a comprehensive overview of current supply chain challenges and how they are taught within university circles or among supply chain professionals to simulate reality. Methods: The study applied a systematic literature review, using bibliometric co-citation and concept-centered content analysis for a comprehensive review of 118 relevant articles, leading to the identification of critical challenges in modern supply chain management. Results: These challenges include supplier selection and quality, supply chain networks, and sustainable supply chains. Supply chain educators are encouraged to use games that mirror real-world scenarios to teach these challenges. Results from this review underscore that existing games covered supply chain concepts such as the bullwhip effect, collaboration, networks, supplier selection, quality management, humanitarian logistics, sustainability, lean supply chain, Supply Chain 4.0, and perishable goods supply. Conclusions: The study’s contribution is to assist in selecting games tailored to the supply chain specific aspects and to guide developers in creating realistic games that address recent challenges in supply chain management. It recommends a holistic approach to enhance new supply chain game development, drawing from methodologies such as problem-based learning and Lego Serious Play. This multifaceted approach imparts practical knowledge and comprehensive skills for addressing supply chain intricacies in modern business settings.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".