Process for achieving digital sustainability in smart manufacturing transformation: a case study of a Chinese steel manufacturer
Bibliographic record
Abstract
Purpose This study aims to explore how firms can achieve digital sustainability (DS) in smart manufacturing transformation. Design/methodology/approach This study uses techniques drawn from grounded theory to analyze onsite interview data and secondary data collected from a representative Chinese steel manufacturer with a focus on smart manufacturing and constructs a theoretical foundation for this topic. Consequently, this work presents a typology of DS capabilities and a process model for their development. Findings To achieve DS, manufacturing firms should develop three types of DS capabilities (i.e. DS production capability, DS management capability and DS environmental governance capability). The following three key challenges must be overcome in developing DS: the efficiency-oriented legacy infrastructure, the lack of metrics for incorporating sustainability goals into data-driven decision-making and the lack of standardization and corresponding approaches to navigating the regulatory landscape. Manufacturers must implement three processes (i.e. structuring, optimizing and scaling) to address these challenges and develop these three types of DS capabilities. The key subprocesses associated with each process are also identified. Originality/value This study responds to the recent call for DS research by enriching the existing conceptualization of this notion as a singular theoretical concept. It provides a typology of DS capabilities and a process model that can support their development. It thus contributes to the literature on digital transformation by identifying key challenges and relevant solutions in smart manufacturing transformation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".