Implementing self-service business analytics in support of lean manufacturing initiatives
Bibliographic record
Abstract
Continuous improvement (CI) programs such as Lean Six Sigma (LSS) are the cornerstones of many high-performance corporate cultures. However, numerous obstacles can arise when comes the time to implement and sustain improvements – high failure rates are reported for CI programs. Leveraging existing information systems (IS) can be an obstacle for lean manufacturing initiatives in environments where data is fragmented across multiple databases of Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES). \n \nSelf-service business analytics (SSBA) provide the flexibility required to unify fragmented data with minimal turnaround, which makes this class of software ideal for managers piloting lean manufacturing initiatives. SSBA can enable the managers themselves to design and redesign suitable metrics throughout the typical three to six months duration of LSS projects. \n \nThe main goal of this study is to propose an implementation framework for SSBA supporting lean manufacturing initiatives. This prescriptive framework is designed to guide managers in maximizing results and minimizing delays – making the project successful. \n \nTo achieve this goal, a Design Science Research (DSR) methodology involving an industrial case study is carried out. First, a systematic literature review is conducted, which establishes a research base and highlights research gaps. Then, an implementation workflow is designed for SSBA. Next, this workflow is applied and evaluated at the case company – the Canadian division of an international steel parts manufacturing company with about 15000 employees worldwide. Lessons learned are then outlined and integrated to yield a generalizable implementation framework backed by empirical evidence in manufacturing. \n \nQuantitative evaluation survey results for the implementation case study were above the threshold set. Qualitative observations reveal positive impacts of SSBA supporting lean manufacturing through improved inter-departmental communication leading to better operational decision making.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".