Extending the Horizons: Environmental Excellence as Key to Improving Operations
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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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".