Archeological benchmarking: Fred Harvey and the service profit chain, <i>Circa</i> 1876
Bibliographic record
Abstract
Abstract This article illustrates the potential for studying best practices from the past, engaging in what we term archeological benchmarking . Our focus for this study was the Fred Harvey Company, which operated a highly successful string of restaurants and hotels along the Atchison, Topeka and Santa Fe Railroad line starting in 1876, reaching its peak around 1912, and continuing until the early 1950s. Fred Harvey was a visionary businessman who understood many of the key concepts guiding the most successful service operations today. This article describes the operating system Harvey used for delivering 15 million meals per year in 65 restaurants extending over a span reaching from Chicago to San Francisco. The underpinnings of Harvey's system foretold concepts considered new today, particularly the service profit chain [Heskett, J., Jones, T., Loveman, G., Sasser Jr., W.E., Schlesinger, L., 1994] and its reliance on a clear operations strategy supported by well‐trained, loyal employees and a congruent system of measurement. It is significant that Harvey achieved his success without the advantages of modern information systems by relying, instead, on his iconic leadership, dogged attention to mundane details, and the service culture he was able to embed throughout the far‐flung enterprise. The Harvey story is an example of ahead‐of‐its‐time operations thinking, but it also asks us to attend more broadly to the history of the field – as does this entire special issue – as a source of inspiration and grounding.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".