Proposing a New Framework and an Innovative Approach to Teaching Reengineering and ERP Implementation Concepts
Bibliographic record
Abstract
Recognizing the need to teach ERP implementation and business process reengineering (BPR) concepts simultaneously, as well as the pedagogical limitations of the case teaching method and simulation tools, the objective of this study is to propose a new framework and an innovative teaching approach to improve the ERP training experience for IS students. The proposed framework, derived from Kettinger et al.'s (1997) BPR framework and the AcceleratedSAP® ERP implementation methodology, was developed to center the organization transformation process on business process redesign and not on the ERP solution. The new framework was then tested with post-graduate students completing their MIS program in partnership with a multinational firm that manufactures residential furniture. Analyzing and reengineering one of our partner's processes and configuring SAP R/3 in our laboratory to support one of the firm's newly reengineered sub-processes demonstrated that the framework offers a very structured, rigorous methodology for teaching BPR and ERP implementation concepts. More generally, the proposed framework and teaching approach helped students to learn "more" by doing and to understand the various skills required when conducting ERP and process improvement projects.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".