Extending the Horizons: Environmental Excellence as Key to Improving Operations
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
The view that adopting an environmental perspective on operations can lead to improved operations is in itself not novel; phrases such as “lean is green” are increasingly commonplace. The implication is that any operational system that has minimized inefficiencies is also more environmentally sustainable. However, in this paper we argue that the underlying mechanism is one of extending the horizons of analysis and that this applies to both theory and practice of operations management. We illustrate this through two principal areas of lean operations, where we identify how successive extensions of the prevailing research horizon in each area have led to major advances in theory and practice. First, in quality management, the initial emphasis on statistical quality control of individual operations was extended through total quality management to include a broader process encompassing customer requirements and suppliers’ operations. More recently, the environmental perspective extended the definition of customers to stakeholders and defects to any form of waste. Second, in supply chain management, the horizon first expanded from the initial focus on optimizing inventory control with a single planner to including multiple organizations with conflicting objectives and private information. The environmental perspective draws attention to aspects such as reverse flows and end-of-life product disposal, again potentially improving the performance of the overall supply chain. In both cases, these developments were initially driven by practice, where many of the benefits of adopting an environmental perspective were unexpected. Given that these unexpected side benefits seem to recur so frequently, we refer to this phenomenon as the “law of the expected unexpected side benefits.” We conclude by extrapolating from the developmental paths of total quality management and supply chain management to speculate about the future of environmental research in operations management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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