Bibliographic record
Abstract
Manufacturing industry is the largest business sector in Canada. It has contributed significantly to the Canadian prosperity in terms of employment and economic growth. However, this industry has faced increased competition from low-price producing regions. Also, appreciation of Canadian dollar and increasing price of energy and other resources lowered the profit margins of the Canadian manufacturing industry. In order to survive and gain higher profit margins, Canadian manufacturers have adapted various strategies one of which is to offer high-value customized products to best meet the changing needs of their customers. To that end, there have been significant investments made in advanced technologies such as information and communication technology (ICT) in Canadian manufacturing companies. Due to expensive cost of acquiring ICT and its long term effect, it is important to use suitable holistic approaches for evaluation of this type of investments. The evaluation should involve inclusion of multiple tangible and intangible criteria. It may also include consideration and aggregation of different decision makers’ viewpoints. Unlike some other sectors in the manufacturing industry, systematic approach for assessing ICT investments have not often been used in the forest products industry. In this research project, the evaluation and selection of a design and manufacturing software package at a Canadian cabinet manufacturing company is addressed. A list of design and manufacturing software selection criteria is presented which could be modified and used by any other goods/service producing companies. The impact of interdependencies among the selection criteria on the results of the decision making process is also investigated. Various sensitivity analyses were performed to investigate the stability of the decision when the decision parameters changed. The results show that the inclusion of intangible criteria would yield to a better decision than that of revealed by just considering tangible factors. In the case study presented in this research, a software package with reasonable cost and good features (Software D) was chosen over the cheapest software which did not offer these features. Furthermore, the results show that the inclusion of interdependencies among the evaluation criteria would impact the decision outcome. In the considered case study, the inclusion of such interdependencies not only changed the weights of the alternatives, but also partially changed the ranking of the alternatives. In our case, the ranking of the top alternative (Software D) did not change. Finally, sensitivity analyses which were performed in this research project revealed that the choice of Software D (top ranked software) was stable upon changes in the influence of decision makers. Also, it was determined that this choice was stable upon changes in the importance of selection criteria for the decision makers.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".