Strategies for Developing an Environmentally Sustainable Supply Chain: Differences Between Manufacturing and Service Sectors
Bibliographic record
Abstract
ABSTRACT This research illuminates the debate on whether there are differences between the manufacturing and service sectors in the matter of developing a sustainable environmental supply chain. Over the past 5 years a survey has been conducted with 800 large European companies, of which half are in the manufacturing sector and half in the service sector. The hypotheses within the survey are related to strategies for developing an environmental supply chain. They were derived from a literature review and were tested by means of a chi‐square test. The survey questionnaire enabled the respondents to give some viewpoints about the hypotheses. In this way, strategies for developing the supply chain such as ISO 14001, the Eco‐Management and Audit Scheme (EMAS), Life Cycle Assessment (LCA), auditing, waste management systems, reverse logistics, environmental indicators, remanufacturing and reuse have been investigated. Results show interesting and unexpected differences between manufacturing and service sectors that can lead to further research, practical implications and even suggestions for the surveyed companies. For instance, the viewpoints of manufacturing and service industries differ over ISO 14001 and EMAS implementation in the supply chain. In addition, service industries approach the implementation of auditing, reverse logistics, reuse and remanufacturing in a way different from that of manufacturing. Other strategies are considered essential by both sectors. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".