Planning and implementing POLCA: a card-based control system for high variety or custom engineered products
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".