Material Flow Control in High‐Variety Make‐to‐Order Shops: Combining COBACABANA and POLCA

Abstract

Material flow control mechanisms determine: (i) whether an order should be released onto the shop floor; and (ii) whether a station should be authorized to produce. Well‐known approaches include Kanban, Drum‐Buffer‐Rope (DBR), Constant Work‐in‐Process (ConWIP), Paired‐cell Overlapping Loops of Cards with Authorization (POLCA), Workload Control (WLC), and Control of Balance by Card Based Navigation (COBACABANA). The literature typically treats these approaches as competing, meaning studies argue for the superiority of one over another. However, a closer look reveals that existing mechanisms either focus on order release (ConWIP, DBR, WLC, and COBACABANA) or on production authorization (Kanban and POLCA). This study therefore calls for a paradigm shift and argues that the different mechanisms may play complementary rather than competing roles. Using simulation, we assess the performance of COBACABANA and POLCA in a high‐variety make‐to‐order shop, a type of shop arguably in most need of material flow control given the importance of throughput times and delivery time adherence. Results demonstrate that COBACABANA outperforms POLCA, but the simultaneous adoption of both control mechanisms outperforms the use of either one in isolation. More specifically, adding POLCA production authorization to COBACABANA order release enables the superfluous direct load to be further reduced, resulting in shop floor throughput time reductions of between 15% and 26% while further reducing the percentage tardy and mean tardiness by up to 14%. Compared to no material flow control, the new combined mechanism realizes a reduction of almost 50% in the percentage tardy and more than 30% in mean tardiness.

Self-Organizing Material Flow Control using Smart Products: An Assessment by Simulation

“…In contrast, ConWIP or Workload Control focus on order release control. Thürer et al (2020) recently highlighted that combining both MFC decisions, i.e. order release and production authorization, leads to complementary performance effects.…”

On the integration of card-allocation and dispatching decisions in POLCA systems: an assessment by simulation

“…We cannot therefore predict in advance which setting of POLCA cards will lead to the best performance. In alignment with previous simulation studies on POLCA (e.g., Lödding et al, 2003;Fernandes & Carmo-Silva, 2006;Germs & Riezebos, 2010;Farnoush & Wiktorsson, 2013;Thürer et al, 2017;and Thürer et al, 2021), we therefore treat the level of POLCA cards as an experimental variable, i.e., five levels for the number of cards are considered: 8, 10, 12, 14, and 16 cards per loop. This spectrum of settings was chosen based on preliminary simulation experiments such that we capture the best performance across the performance measures considered in this study.…”

“…Rather, its control is restricted to production authorization at individual station queues. This is different from release methods such as Constant Work-in-Process and Workload Control, which only control the release of orders to the shop floor (Thürer et al, 2021).…”

“…Since its inception, POLCA has received considerable research attention (e.g., Lödding et al, 2003;Fernandes & Carmo-Silva, 2006;Germs & Riezebos, 2010;Farnoush & Wiktorsson, 2013;Harrod & Kanet, 2013;Braglia et al, 2014;Thürer et al, 2017;Severino & Godinho Filho, 2019;and Thürer et al, 2021). A common assumption in this literature is that the POLCA card is attached to the most urgent job in an upstream queue as soon as it returns to this upstream station in its loop (e.g., Fernandes & Carmo-Silva, 2006;Riezebos, 2010;Harrod & Kanet, 2013;Thürer et al, 2017).…”

Improving superfluous load avoidance release (SLAR): A new load-based SLAR mechanism

“…shown to outperform Paired-cell Overlapping Loops of Cards with Authorization (Thürer et al, 2020). Meanwhile, Kanban is arguably not applicable in high-variety make-to-order shops where orders may not be repeated (Suri, 1998).…”