Investigation of communities of practice in a harmonized supply chain ERP landscape
Bibliographic record
Abstract
This Master thesis is investigating how communities of practice can enhance knowledge sharing in a context of improving a harmonized Enterprise Resource Planning (ERP) system used in supply chain. A student from the faculty of production and materials engineering at Lund university (LTH) collaborated with the Global Trinity organization from Alfa Laval (Lund, Sweden) to answer the research question. The global Trinity Organization is the department in charge of delivering IT solution across the Swedish manufacturing company’s supply chain to improve the organization’s value stream. An investigation of two business units (BU) from Alfa Laval was conducted in order to compare their level of maturity regarding communities of practice, but also their supply chain ERPs. The BUs that were used are the gasketed plate heat exchanger (GPHE) and the decanter (DEC) BU. It has been concluded that each BU confirmed that CoP does contribute to knowledge sharing and improving their ERP more efficiently. However, there are also some differences as the GPHE BU is much more mature in terms of CoP and harmonized ERP. Based on the finding made during interviews with various stakeholders from the Global Trinity organization, GPHE and DEC BU, several recommendations were provided in the discussion section in order to better improve CoPs which will lead to a better harmonized ERP landscape. Furthermore, this project is a qualitative research which means that future work should be conducted as a limited amount of literature reviews regarding organizational concepts were used to build some theory related to communities of practice, and change management.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".