A “genomic” classification scheme for supply chain management information systems
Bibliographic record
Abstract
Purpose This paper has the objective of demonstrating a more structured and useful method for evaluating functionality of enterprise software packages such as supply chain management information systems (SCM IS). Existing taxonomies have limited utility for software selection and analysis due to the variation and overlap in functionality found in modern enterprise systems. Design/methodology/approach A qualitative analysis of over 1,800 pages of SCM IS documentation and independent analyst reports is used to identify relevant SCM IS functional attributes in the seven most widespread SCM IS packages. Pattern matching and coding of constructs is used to iteratively build a hierarchical taxonomy of SCM IS functionality. Findings The taxonomy developed describes 83 major functional attributes that form five top‐level categories: primary supply chain processes, data management, decision support, relationship management, and performance improvement. The codes representing supply chain processes agree with the widely used Supply Chain Operations Reference (SCOR) process model, although the terminology was not used consistently in vendor and analyst documents. Research limitations/implications The approach described enables richer classification schemes to be built that will better distinguish between the wide‐ranging functionality found in modern enterprise information systems. Practical implications Selection and analysis of SCM IS is difficult due to the functional overlaps in different systems. The approach described enables a more structured, detailed, and useful analysis of an organization's current or proposed information systems. Originality/value This paper contributes a novel approach for conceptualizing and analyzing complex information systems using hierarchical rather than traditional flat taxonomies.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".