Implementing Enterprise Resource Planning Systems: The Role of Learning from Failure
Bibliographic record
Abstract
Introduction Problems associated with software implementations are not new, nor specific to enterprise resource planning (ERP) systems. Nevertheless, ERP systems have been blamed for the poor performance of several organizations (Osterland, 2000). A number of companies have reported negative impacts on earnings as the changeover to an ERP takes place, among them Hershey (Girard and Farmer, 1999), AeroGroup (Asbrand, 1999), and Snap-On (Hoffman, 1998). Hershey, for example, suffered a third quarter (1999) sales decrease of 12.4% and an earnings decrease of 18.6% compared with the preceding year (Osterland, 2000). Implementing an ERP is a major undertaking and the Standish Group International estimates that 90% of SAP R/3 (the dominant ERP) projects run late (Williamson, 1997). The trade press is now also replete with articles on ERP failures, cancellations, and cost/time overruns. Dell canceled its R/3 system after two years when it determined that it could not support the required processes (King, 1997). AeroGroup (Asbrand, 1999), Unisource (IW, 1998), and a number of garbage disposal companies (Bailey, 1999) also abandoned their ERP projects. The most dramatic example of an organization claiming to be damaged by an ERP implementation is that of FoxMeyer Drug Corporation. FoxMeyer claims that its SAP R/3 system sent the company into bankruptcy (Bulkeley, 1996). Note, however, that, at the same time, many companies have experienced benefits that far exceeded their expectations (Davenport, 1998; Deloitte Consulting, 1998).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".