Implementing Off-peak Deliveries in the Greater Toronto Area: Costs, Benefits, Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Nestle Canada currently uses 32 routes that serve over 4,500 customers in the Greater Toronto Area (GTA). This study aims to quantify Nestlé's costs and benefits of modifying their ice cream supply chain to incorporate night-time deliveries, while providing a framework for the regulatory, conceptual, and inertial obstacles to implementation. Employing Nestlé's customer data set, we created routing software to determine the proportion of customers who must be willing to accept deliveries outside of normal working hours so that the change would be financially feasible. Based upon a literature review we found that, before proceeding, the following qualitative factors should be considered: safety, sustainability, regulatory concerns, truck noise, traffic, and congestion. Reduction of 3–10 percent in the number of routes may result from switching a suitable proportion of deliveries to night-time, achieving the minimum fleet size when 50–60 percent of locations are served on night routes. The operation of both night-time and daytime deliveries would enable an increase in truck utilization, thus decreasing the number of vehicles required. Recommendations for success of night-time deliveries include preparation of a safety plan, procurement of plate trucks, noise-abatement techniques, and the development of a noise-monitoring program.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it