Case Article—Budding with ERP: Information and Operations Management Challenges in a Nascent Industry
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 .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".