The ConWip Production Control System: a Literature Review

International audienceA growing body of literature dealing with ConWip has been observedduring the past decade. Considering the current industrial challengescharacterized by adaptability, product customization, shortened lead times andcustomer satisfaction, ConWip appears to be an effective and adaptedproduction control system for manufacturers. Given this context, this paper aimsto update the previous literature review about ConWip that was made in 2003and to provide an understanding key through an original classification method.This method allows the reader to distinguish papers that concentrate on ConWipsizing, ConWip performance, ConWip environment or on the comparison ofConWip with other PCS. It also provides a reading key about the researchapproach. Taking these criteria into account, this paper helps to answer thefollowing questions: how can ConWip be implemented? How can ConWip beoptimized? Why and when should ConWip be used? The paper then concludeswith some research avenues

Robustness analysis of pull strategies in multi-product systems

Purpose: This paper examines the behaviour of shared and dedicated Kanban allocation policies of Hybrid Kanban-CONWIP and Basestock-Kanban-CONWIP control strategies in multi-product systems; with considerations to robustness of optimal solutions to environmental and system variabilities. Design/methodology/approach: Discrete event simulation and evolutionary multi-objective optimisation approach were utilised to develop Pareto-frontier or sets of non-dominated optimal solutions and for selection of an appropriate decision set for the control parameters in the shared Kanban allocation policy (S-KAP) and dedicated Kanban allocation policy (D-KAP). Simulation experiments were carried out via ExtendSim simulation application software. The outcomes of PCS+KAP performances were compared via all pairwise comparison and Nelson’s screening and selection procedure for superior PCS+KAP under negligible environmental and system stability. To determine superior PCS+KAP under systems’ and environmental variability, the optimal solutions were tested for robustness using Latin hypercube sampling technique and stochastic dominance test. Findings: The outcome of this study shows that under uncontrollable environmental variability, dedicated Kanban allocation policy outperformed shared Kanban allocation policy in serial manufacturing system with negligible and in complex assembly line with setup times. Moreover, the BK-CONWIP is shown as superior strategy to HK-CONWIP. Research limitations/implications: Future research should be conducted to verify the level of flexibility of BK-CONWIP with respect to product mix and product demand volume variations in a complex multi-product system Practical implications: The outcomes of this work are applicable to multi-product manufacturing industries with significant setup times and systems with negligible setup times. The multi-objective optimisation provides decision support for selection of control-parameters such that operations personnel could easily change parameter settings to achieve a new service level without additional optimisations of the system parameters. Originality/value: The examination of the behaviour of the two Kanban allocation policies in HK-CONWIP and BK-CONWIP in a complex multi-product assembly line with setup-times and environmental variabilities, under erratic demand profiles.Peer Reviewe

Robust production & inventory control systems for multi-product manufacturing flow lines

The production line of modern multi-product manufacturing with erratic demand profiles shows that the selection and implementation of appropriate production control strategy are an important challenge. Organisations that adopt pull-type production control strategies, such as Kanban control strategy, for multi-product production lines find that is necessary to plan high Kanban card allocations in order to maintain volume flexibility to manage demand variability. This can result in line congestion, long lead times and low throughput rate. A recently proposed shared Kanban allocation policy has the benefit of minimising inventories in the line by allocating Kanbans accordingly and therefore maintains volume flexibility. However, many pull production control strategies that have been shown to be successful in single product manufacturing environments, for instance Kanban, CONWIP and Basestock cannot operate the shared Kanban allocation policy naturally. This Thesis presents a practically applicable modification approach to enable pull production control strategies that are naturally unable to operate in a shared Kanban allocation policy mode to operate it. Furthermore, the approach enables the development of a new pull production control strategy referred to as Basestock Kanban CONWIP control strategy that has the capability to operate the shared Kanban allocation policy, minimising inventory and backlog while maintaining volume flexibility. To investigate the performance of the pull production control strategies and policies, discrete event simulation and evolutionary multi-objective optimisation approach were adopted to develop sets of non-dominated optimal solutions for the experiments. Nelson’s screening and selection procedure were used to select the best pull control strategy and Kanban allocation policy when robustness are not considered. Additionally, Latin hypercube sampling technique and stochastic dominance test were employed for selection of a superior policy and strategy under environmental and system variability. Under non-robust conditions (anticipated environmental and system variability), pull control strategies combined with the shared Kanban allocation policy outperforms pull control strategies combined with dedicated Kanban allocation policy. Conversely, pull control strategies combined with the dedicated Kanban allocation policy outperforms pull control strategies combined with shared Kanban allocation policy when the system is prone to environmental and system variabilities. Furthermore Basestock Kanban CONWIP control strategy outperforms the alternatives in both robust and non-robust conditions

Designing a robust production system for erratic demand environments.

Production systems must have the right type of material in the right quantities when required for production. They must minimize the work in progress while ensuring no stock-outstock-out occurs. While these twin opposing goals are achievable when demand is stable, they are difficult to realize under an erratic demand pattern. This dissertation aims to develop a production system that can meet erratic demands with minimal costs or errors. After a detailed introduction to the problem considered, we review the relevant literature. We then conduct a numerical analysis of current production systems, identify their deficiencies, and then present our solution to address these deficiencies via the ARK (Automated Replenishment System) technique. This technique is applied to a real-world problem at Methode Engineering ©. We conclude by detailing the scientific benefit of our technique and proposing ideas for future research

A framework for creating production and inventory control strategies

In multiproduct manufacturing systems, it is difficult to assure that an optimised setting of a pull production control strategy will be able to maintain its service level and inventory control performances. This is because the competition for resources among products is liable to make them affect the service levels of one another. By comparing different pull strategies, this research has observed that tightly coupled strategies are able to maintain lower amount of inventory than decoupled strategies, but they do so at the detriment of service level robustness. As a result, tightly coupled strategies are better suited to manufacturing environments with low variability, while decoupled strategies are more robust in high variability environments. Here, robustness is a measure of how well a strategy is able to minimise the drop below its original optimised service level when the initial system conditions change. Furthermore, the Kanban allocation policy applied under a strategy plays a major role in its ability to manage the performances of multiple products. Experimental results show that the Shared Kanban Allocation Policy (SKAP) keeps a lower amount of inventory than the Dedicated Kanban Allocation Policy (DKAP), but it is more susceptible to the variability in the demand or processing times of one product impacting the service level of another. Therefore, a Hybrid Kanban allocation policy (HKAP) that combines both the DKAP and the SKAP has been implemented. This approach considers products’ demand and processing time attributes before categorising them into the same Kanban sharing group. The results of the implementation of the HKAP show that it can keep as low inventory as the SKAP and avoid products impacting the service levels of one another. Additionally, it offers a better approach to managing large multiproduct systems, as the performances of product groups can be differentially managed through the combination of Kanban sharing and dedication policies. Lastly, the observations on the performances of strategies and policies under different system conditions can be used as a framework through which line designers select strategies and policies to suit their manufacturing system