Del ‘dominio de la media’

This section is dedicated to review of previous research work on CONWIP specifically. The review included journals and conference proceedings retrieved from electronic databases such as EI Engineering Village, IEEE Xplore, ScienceDirect, and Web of Science, the search term included “CONWIP”.

Based on a literature review conducted in 2003 [32], different research work of CONWIP has been carried to study one or more of three topics. This classification is used here to group the relevant previous work under the following:

Applicability of CONWIP to a manufacturing environment, either by real implementation or by development of computer simulation models for existing manufacturing systems.

To determine the optimum WIP level as a decision variable that can improve a set of performance measures using different solution techniques such as simulation, optimisation, mathematical techniques… etc.

Comparing the performance of CONWIP to push or other pull systems, in order to evaluate the performance of CONWIP to be better or worse than the compared systems based on given performance measures.

2.4.1 Applicability of CONWIP to a Manufacturing

Environment

Based on literature, CONWIP has been applied to different manufacturing environments and also implemented successfully in different industries. CONWIP has been reported to work best for balanced production lines running in a steady state already [47]. Still, it has been applied to job shop manufacturing [55], multi- product environment [56], and multi-product assembly system [57].

Based on the literature review and to the best of our knowledge, unlike DBR that was implemented in real wafer fabrication facilities [37, 43, 58], CONWIP has not been applied other than application of a simplified CONWIP mechanism in terms of the output feedback mechanism. This simplified CONWIP has been tested in a wafer fabrication facility using simulation. Although results showed that the fab performance can be improved when applying the CONWIP; yet, authors of this work suggested that further research should be conducted to develop a fully functional CONWIP lot release control strategy in wafer fabrication facilities [4].

2.4.2 Determining the Optimum WIP Level as a Decision

Variable

Based on the description of the basic (original) CONWIP lot release policy, it is clear that the only variable that needs to be determined is the WIP level that results in optimum performance of the production line. Different research work has been developed to that end such as the development of a mathematical model and an application of an artificial bee colony (ABC) optimisation algorithm to find the optimum WIP level for a multi-product multi-machine serial production line in order to minimize cycle time [59] and the development of deterministic approaches to define the optimum WIP level that maximises throughput rate using minimum WIP level in a flow production line [60].

Multi-CONWIP lot release control strategy problem, which was discussed earlier as one of the variants of CONWIP, has also attracted the attention of researchers to due to the computational complexity of determining the number of segments of a

production line and the WIP level of each segment. Evolutionary simulation optimisation [51] and an evaluation method combined with a genetic algorithm [52] has been successfully applied in semiconductor assembly and test factories. Although multi-CONWIP strategy outperformed CONWIP and Kanban when applying the evolutionary simulation optimisation [51]; yet, the computational complexity and the efficiency of the proposed methods in solving these problems were addressed as potential future research opportunities.

It should be noted that setting the WIP level in either the CONWIP or any of its variations is considered as the main decision variable; yet, another important issue that has been addressed in literature is whether that level is set statically; meaning that the level is set once, or dynamically; meaning that the WIP level can change depending on the state of the system (for example to meet unexpected demand with higher throughput rates). Setting the WIP level once is referred to as “card setting”; while, setting the WIP level dynamically is referred to as “card controlling”. A card control for CONWIP procedure has been suggested and experiments showed that card control produces competitive results when compared to card setting; however, under a make to order environment [61]. Other work related to dynamic CONWIP level includes optimisation using simulation of simple production lines to optimise the parameters used to control the line [62]. Also, adaptive CONWIP has been modelled as a stochastic queueing network and applied to a hybrid production system with two discrete processes that undertake manufacturing and remanufacturing activities [63].

2.4.3 Comparing the Performance of CONWIP to Push or

Other Pull Systems

Performance of CONWIP has been compared to push systems or other pull systems using various analytical and simulation methods in literature.

CONWIP performance of throughput rate and WIP has been compared to push using simulation and applied to unidirectional flow line, with no re-entrancy or rework. Results showed that when there is high variability in the arrivals CONWIP

is better than Push; however, Push can perform better than CONWIP when lots are released on a constant interval [64].

The performance of CONWIP and Kanban was also compared in a make to order environment and simulation was used to evaluate the impact of applying both strategies on the mean and standard deviation of cycle times and confirmed that CONWIP outperforms Kanban [65]. Furthermore, CONWIP system achieved a less average WIP than Kanban given the same rate of throughput when applied to a tree-shaped multi-stage assembly system; on the other hand, when applied to a simple serial production line Kanban is superior to CONWIP [66].

Simulation was also used to compare the impact of CONWIP and DBR on the performance of a back-end semiconductor manufacturing flow line. Findings of that work is that DBR can outperform CONWIP mainly due to the ability of DBR in distinguishing the bottleneck(s) and placing greater control over the portion of the system that directly influences the bottleneck; however, DBR loses this advantage when bottleneck(s) starts shifting [44]. Also, application of CONWIP to unbalanced lines with distinct bottlenecks has been compared to DBR and showed that performance of the lines mainly depend on the characteristics of the line in terms of the position of the bottleneck [16, 67].

Furthermore, the impact of combining different dispatching rules with Push was compared to combining these dispatching rules to CONWIP lot release strategy in two realistic semiconductor test bed fabs. Simulation results and analysis showed that the cycle times reported from Push outperformed CONWIP in both fabs. Also, it showed that most rules failed to outpace FCFS (First Come First Serve) in CONWIP, thus supporting the FCFS recommendation of Hopp and Spearman [15].

Impact of combining dispatching rules with CONWIP on the performance of wafer fabrication facilities on the average throughput rate and cycle time along with the variability in these measures has been tested on Wein’s model, which is a fictitious wafer fab using data gathered the Hewlett-Packard Technology Research Centre Silicon fab [68] and the Minifab model [69]. Both studies have been carried using simulation and both confirmed that the lot release policy is the dominant factor in

Multi-CONWIP discussed in the previous section was compared to CONWIP by evaluating their impact on the performance of semiconductor assembly lines. Simulation results showed that multi-CONWIP outperforms CONWIP and was capable of achieving same throughput rate (target obtained from simulation of base model) at higher WIP levels (higher by 0.6%) and lower cycle times (lower by 0.4%) [70].

It is noticed that since the application environment is highly complex, the only reasonable approach to demonstrating the effectiveness was to use modelling and simulation. Therefore, it is effective to use simulation to analyse and predict the dynamic behaviour of complex systems.

Moreover, simulation has become a popular technique for developing production schedules in a manufacturing environment. Also, it offers the advantage of developing a feasible and accurate schedule in shorter computation times compared to some of the other techniques [9].