The Applicability of Statistical Process Control to Systems Involving People Processes and Business Rhythms
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The operation and maintenance (O&M) activities of systems can account for 75% of total lifecycle cost. To effectively manage cost, optimize system “on” time, and mitigate defects/failures during the O&M phase of a system's lifecycle, the application of systems monitoring and control is encouraged. Statistical process control ( SPC ) in general, the control chart specifically, is the most common monitoring approach. The control chart provides alerts with respect to the behavior of systems and processes, as well as changes in process variability. Data applied to control charts is assumed to adhere to a normal distribution, a constraint often satisfied in manufacturing and similar industries where the natural variation in the process or system follows the Gaussian distribution. Systems involving people processes and business rhythms can compromise the normality assumption, reducing the reliability of SPC . Through the application of SPC , this paper proposes a novel approach to monitoring operational systems in the systems engineering O&M phase for the express purpose of reducing high costs by mitigating system discrepancies and uncovering inefficiencies. This paper focuses on processes that require 100% system data sampling due to the operational nature of the system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it