Evaluation of Strategic Software Investments for the Canadian Cabinet Industry
Bibliographic record
Abstract
ABSTRACT Software investments are increasingly important to remain competitive in modern manufacturing. However, wood product industries generally make minimal information technology (IT) investments and are slow adopters. This study determines the types of software that could contribute the most to the future competitiveness of the Canadian cabinet industry using industry and IT expert input into an Analytic Network Process model. Findings include the following. The Quality strategy is the most crucial for the industry's future competitiveness, with a normalized weight of 0.332, and the Delivery strategy is the least important (0.111). For software, Operations & Engineering and Enterprise Resource Management applications are the most important, having final priorities of 0.227 and 0.222, respectively. Content applications are relatively unimportant (0.087). The sensitivity analysis indicates that the results are robust for varying weights of all strategies except Customer Service. A higher emphasis on the Customer Service strategy increases the priority of the Customer Relationship Management and Collaboration applications to the first and the second-highest priority, respectively.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".