Organizational memory and the completeness of process modeling in ERP systems
Bibliographic record
Abstract
Enterprise resource planning (ERP) systems not only have a broad functional scope promising to support many different business processes, they also embed many different aspects of the company’s organizational memory. Disparities can exist between those memory contents in the ERP system and related contents in other memory media, such as individuals’ memories, and the organizational structure and culture. It is our contention that, in general, these disparities or memory mismatches, as we will refer to them, lead to under‐performance of ERP systems. In this paper we focus on potential memory mismatches that may arise with respect to the embedding of process knowledge within ERP packages. Packages such as SAP provide a varied and rich environment for process modeling. However, we suspect that there are still many instances where process knowledge is either lost or represented in different ways in different parts of the organization. As we will discuss, the results of such memory mismatches will often not become evident until the system is in use. The overall thrust of the paper is to identify a variety of concerns, intriguing questions and avenues for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".