Investigating the Financial Implications of Converting Manufacturing from existing Portfolio to PPE Products During Covid-19 for Canadian Manufacturing Companies
Bibliographic record
Abstract
For the past three years, we have witnessed the effects that a disruptive event like COVID-19 can have on the industrial sector as well as the effects that have so far been felt throughout the world's supply chain. When the pandemic first began, sales of PPE products increased, which led to a shortage of PPE products in Canada due to supply chain disruptions and limited PPE product production in Canada. So, the Canadian government began importing PPE goods. This incident highlights the need for local production in Canada as many people were unable to afford PPE in time. Due to this, some manufacturing units started manufacturing PPE products to tackle the PPE demand crisis. Numerous studies have been conducted on the economic effects of COVID-19, but little research has been done on the evaluation of industrial firms that changed their product lines or added PPE during COVID-19. The goal of the thesis is to evaluate the economic effects of COVID-19 on manufacturing businesses in Ontario by using a cost-benefit analysis and regression to see whether switching or adding PPE items in addition to their current portfolio would be beneficial. These analyses have been implemented to 111 companies in Ontario. This research assumes that manufacturers' decisions will be based on the net present value of the total benefits of material replacement. The results indicated that adding PPE during the time of the crisis wasn’t beneficial to the companies as adding PPE to the company increased the company’s volatility though its possible, but it is unlikely that adopting PPE increased their revenue volatility as companies did so to reduce the risk of revenue decline. The suggested model's novelty comes in its economic analysis of COVID-19's effects on manufacturers, which considers a variety of personal protective equipment and individual cost-benefit analyses of Ontario companies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".