Case Article—Budding with ERP: Information and Operations Management Challenges in a Nascent Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The role of analytics in operations and supply chain management (OSCM) has gained significant importance due to the decision-making complexities in the current business environment. The effectiveness of most analytical approaches, in turn, relies on access to timely and accurate data and information. Hence, it is essential for OSCM students to understand the underlying processes and dynamics of information management, for which enterprise resource planning (ERP) systems have become a standard. This case study can be a useful resource for introducing the critical interface between OSCM and information systems. The case study aims to facilitate learning on (1) the limitations of a rudimentary and disconnected information system, (2) the benefits and challenges of ERP implementation, and (3) the important steps to ensure a successful implementation of an ERP system. It provides an interesting context of a fast-growing agribusiness producing regulated products in Canada. The case study has been used in OSCM and management information systems (MIS) courses in two Canadian public Universities. Funding: This work was supported by Mitacs [Grants IT28747 and IT32722]. Supplemental Material: The Teaching Note and data files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it