Planning and implementing POLCA: a card-based control system for high variety or custom engineered products
Why this work is in the frame
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Bibliographic record
Abstract
Many companies with high variety or custom engineered products are struggling to implement effective material control strategies on the shop floor, and finding that pull/Kanban systems are not meeting their needs in such environments. Paired-cell Overlapping Loops of Cards with Authorisation (POLCA) is a quick response manufacturing strategy designed with these situations in mind. POLCA is a hybrid push-pull strategy that combines the best features of card-based pull systems and push systems. At the same time POLCA gets around the limitations of pull systems in high variety or custom product environments. In partnership with its member companies, the Center for Quick Response Manufacturing (QRM) has recently implemented POLCA at several factories in the US and Canada. In this article, we first give an overview of the POLCA system, explain how it works and provide qualitative comparisons with pull/Kanban systems. Then, we present a step-wise procedure for implementing POLCA in a factory. Using examples from the implementation of POLCA at several factories, we address several practical issues such as computing the number of POLCA cards, determining the quantum of work a POLCA card represents, and addressing part shortages. We also discuss the different performance improvements that have resulted from these implementations including reductions in lead-time, increase in percentage of on-time deliveries and employee satisfaction.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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