# Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
## 2 Literature Review
While job release strategies contribute to POLCA's efficiency, its robustness under conditions of uncertainty is another critical factor. Variability in processing times, demand fluctuations, and inventory stockouts can all challenge POLCA's ability to maintain smooth production flow. Research has examined how different levels of variability influence POLCA's performance, assessing whether alternative workload control mechanisms might provide greater resilience. In particular, studies have explored how stockouts affect WIP levels, tardiness, and overall system stability, offering valuable insights into POLCA's adaptability in dynamic environments. Ref. [15] investigates the effects of inventory stockouts on POLCA, LB-POLCA, and Workload Control (WLC) in an Assemble-to-Order (ATO) system. Using SIMIO-based simulations, results show that stockouts negatively impact performance but do not alter the ranking of control systems: WLC consistently outperforms LB-POLCA, which in turn surpasses POLCA. Key performance metrics such as throughput, tardiness, and the percentage of late jobs indicate that WLC remains the most resilient system under stockout conditions. Ref. [12] (previously mentioned) also examines the impact of processing time variability on POLCA, concluding that high uncertainty weakens POLCA's advantages over alternative control mechanisms. The study highlights that increasing the number of POLCA cards raises WIP and throughput times, with diminishing benefits as variability increases. A systematic review by [16] provides further context for these comparative findings. By analyzing 44 simulation-based studies on production control in make-to-order environments, the authors identify the configuration of the control loop as the main factor driving differences in system performance. They also emphasize the significant impact of order release mechanisms, workload aggregation, and estimation methods. Importantly, their framework links these factors to specific shop-floor characteristics--such as routing variability and bottleneck dominance--offering practical guidance for selecting the most appropriate control strategy. In particular, their analysis highlights that card-based systems like POLCA tend to be more suitable in environments with high routing variability or shifting constraints, supporting the ongoing interest in hybrid and adaptive production control policies. As research continues to refine POLCA's mechanisms and applications, new approaches are emerging that leverage Industry 4.0 technologies to enhance production control. The advent of digitalized, non-hierarchical systems introduces new possibilities for improving flexibility and responsiveness in job shop environments. By integrating real-time decision-making into release, authorization, and dispatching processes, these advancements aim to overcome some of POLCA's inherent limitations. The latest studies explore whether such innovations can offer superior WIP control, particularly in highly constrained or unpredictable manufacturing settings. Ref. [17] introduces DRACO, a novel, non-hierarchical WIP control system designed for Make-to-Order (MTO) production. Leveraging Industry 4.0 technologies, DRACO integrates release, authorization, and dispatching decisions simultaneously. Simulations in a six-workstation job shop demonstrate that DRACO significantly reduces WIP and lead times, particularly under tight production constraints, outperforming traditional hierarchical systems such as POLCA and CONWIP. The existing literature provides valuable insights into POLCA's strengths and limitations. Studies confirm that while POLCA effectively controls WIP and stabilizes lead times, its authorization mechanism may restrict overall performance in highly variable environments. Moreover, centralized job release strategies improve synchronization, but their benefits depend on shop floor conditions. Emerging research suggests that Industry 4.0 technologies could offer superior WIP control by integrating real-time decision-making into production scheduling.