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Record W2132888205 · doi:10.1287/msom.1060.0095

Extending the Horizons: Environmental Excellence as Key to Improving Operations

2006· article· en· W2132888205 on OpenAlexafffund
Charles J. Corbett, Robert D. Klassen

Bibliographic record

VenueManufacturing & Service Operations Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOperational excellenceSupply chainProcess managementQuality (philosophy)BusinessComputer scienceControl (management)Principal (computer security)Supply chain managementBehavioral operations researchProduct (mathematics)Lean manufacturingTheory of constraintsRisk analysis (engineering)Operations managementOperations researchMarketingEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.021
Scholarly communication0.0150.027
Open science0.0020.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.005
GPT teacher head0.192
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations593
Published2006
Admission routes2
Has abstractyes

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