Combining business process management and lean manufacturing to improve information and documentation flows: a case study
Bibliographic record
Abstract
Purpose The objective of this work is to demonstrate how the use of a business process management (BPM) methodology reinforced with the use of lean manufacturing (LM) tools and practices enhances information and documentation flows. Design/methodology/approach For this purpose, a case study on a large wind blade manufacturing company is described, in which BPM and LM were combined to improve information and documentation flows associated to the process of quality inspections and quality controls. Findings The joint use of BPM and LM strongly contributed to the improvement of information and documentation flows. The BPM lifecycle can be used to guide the entire improvement process, while LM tools can be used to act at specific points with an emphasis in the process analysis and implementation phases. Thus, LM complements a BPM approach leading to significant process improvements. Practical implications The results show that LM can be used to support some phases of the BPM lifecycle. Furthermore, LM can contribute to identify lean waste in information and documentation flows associated to quality management processes and help in the selection of methods and tools to support process improvements. Originality/value This study is one of the first reporting the use of LM tools and practices as complementary to the BPM methodology to support the improvement of information and documentation flows associated to quality management in a large manufacturing company. This research enriches the literature by presenting empirical evidence that these two continuous improvement approaches are not incompatible in their objectives and visions and can complement each other.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".