Application of Dynamic Value Stream Mapping in Warehousing Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Uncertainty within supply chains increases the risk of not meeting objectives. Warehouses can absorb some of these uncertainties, by accumulating inventory. This accumulation has led many to consider warehouses as a source of waste in supply chains. Hence, there is limited research that seeks improving intrinsic warehouse efficiency; particularly in the context of Lean concepts and Value Stream Mapping (VSM). Since, warehouses seek to absorb uncertainty in supply chain by holding inventory; this uncertainty absorption may introduce variability to warehousing function itself. Therefore a methodology is required, which can capture the embodied dynamic within warehousing function. This paper reflects Lean concepts and, in particular, VSM to warehousing context and introduces some methods and guidelines to assure the proper application of VSM in what is an uncertain and dynamic system. In this paper, warehousing function is formulated based on some abstract processes which vary on their output status. This formulation facilitates identifying value-adding activities as one of the most substantial steps, yet confusing in application of VSM in warehousing context. The suggested methods enable fundamental statistical/mathematical analysis, which leverage VSM to a more dynamic evaluation tool. Application of the introduced approach will facilitate the decision making process for warehouse systems evaluation and improvement. The resultant methodology is applied to a factual case and this serves to demonstrate its practical application. It is worth mentioning that the findings applications, which can be termed ‘dynamic VSM’, are not limited to warehouses but can also be applied to any dynamic environment with non-deterministic processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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