Critical challenges in ERP implementation: A qualitative case study in the Canadian oil and gas industry
Bibliographic record
Abstract
This exploratory qualitative single-case study examines critical challenges encountered during ERP implementation based on individual perspectives in four project roles: senior leaders, project managers, project team members, and business users, all specifically in Canadian oil and gas industry. Data was collected by interviewing participants belonging to these categories, and by analyzing project documentation about ERP implementation. The organization for the case study was a leading multinational oil and gas company having a substantial presence in the energy sector in Canada. The study results were aligned with the six management questions regarding critical challenges in ERP: (a) circumstances to implement ERP, (b) benefits and process improvements achieved, (c) best practices implemented, (d) critical challenges encountered, (e) strategies and mitigating actions used, and (f) recommendations to improve future ERP implementations. The study results highlight six key findings. First, the study provided valid circumstances for implementing ERP systems. Second, the study underscored the importance of benefits and process improvements in ERP implementation. Third, the study highlighted that adoption of best practices is crucial for ERP Implementation. Fourth, the study found that critical challenges are encountered in ERP Implementation and are significant during ERP implementation. Fifth, the study found that strategies and mitigating actions can overcome challenges in ERP implementation. Finally, the study provided ten major recommendations on how to improve future ERP implementations.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".