Bibliographic record
Abstract
The aim of this bachelor’s thesis was to research how project reporting is executed in Company X’s Americas region and how the results of the reporting process end up to the directors responsible for the whole region’s project management. Moreover, the objective was to observe the current challenges in the region’s project reporting and suggest improvements for the future needs. This study especially focused on the project reporting process and the actual contents of reports are outside the scope of this study.\n\nThe theoretical framework introduces management reporting and project management in detail. The purpose is to provide an understanding of what projects are, how they are managed and reported to the project management.\n\nThe study uses a qualitative research method approach and a case study method for research data collection. The study was conducted in autumn 2014 and the empirical data were collected in two parts through four thematic interviews, and by researching the case company’s internal and external material. Three interviews were held in October 2014 in Company X Canada at the Burlington office and one in November 2014 at the company’s headquarters in Espoo, Finland.\n\nThe key findings of the study indicate that Company X’s Americas region’s project reporting is currently in a developmental phase. The findings showed that the interviewees in the Americas region observed challenges in the project report preparing process. Challenges were mainly technical as the actual filling of the project data to the project reports raised concerns as the preparing process was illustrated too manually and time-consuming. The interview in Finland declared that project reports have to be prepared mostly manually because the quality of the added project data in the SAP system is not good enough and all the needed data is not necessary available, which makes the automation process difficult. Due to this, Company X has recently started a delivery excellence program in Espoo, of which one purpose is to globally harmonize and automate the company’s project reporting in the future.\n\nThe researcher sees the approach taken by Company X to be too much focused on fixing the need for manual filling of project reports and more emphasis should be put into the quality of project data. Automating the project report filling process does not remove the underlying cause related to the varying data input practices around the region. Unreliability of the project data and financial figures in project reports diminishes the benefits of analysing the project reports.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".