Enabling the strategic development of SMEs through advanced manufacturing systems
Bibliographic record
Abstract
Purpose In order to deepen knowledge and further build theory on the use of advanced manufacturing systems (AMS) in SMEs, the present research seeks to explore the following questions: are the AMS used by SMEs aligned with their network, product and market development strategies? And does the alignment of AMS contribute to the successful outcome of these strategies, that is, to the business performance of manufacturing SMEs? Design/methodology/approach A survey of 248 Canadian manufacturers was used to collect data that were analyzed through cluster analysis and analysis of variance. Findings Three alignment patterns of strategic development were identified and named local SMEs, transition SMEs, and world‐class SMEs. World‐class firms were found to clearly outperform local firms in terms of growth and profitability whereas transition SMEs did not perform significantly better or worse than the other two groups. Research limitations/implications The nature of the sample imposes care in generalizing the results of the study. Co‐alignment constitutes a valid theoretical foundation on which to further investigate the fundamental technology management problem for manufacturing SMEs, namely how these firms can achieve value from ever‐increasing investments in AMS. Practical implications When shifts in the business environment require strategic choices or provide strategic opportunities for development in terms of product innovation, market expansion or network extension, the resulting changes must be inter‐linked and assessed systemically with the SMEs' assimilation and integration of AMS. Originality/value Viewing AMS from a configurational perspective has provided a deeper understanding of the extent to which SMEs co‐align their use of manufacturing technology with their development strategies in order to achieve greater business performance.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".