看板管理运行过程中调控方法的研究

通过对唐山爱信齿轮有限责任公司看板管理系统在运行过程中废品的出现造成收容数不足、新产品与常规产品共线生产、外购部件看板张数设定等方面的分析，分别提出了运行过程中相对应的调控方法，实现了看板管理系统有效的运转；在外购部件看板发行张数调控方法中，根据现场运行的经验总结出了进货循环与安全系数对应关系；调控方法在应用过程中取得了显著的经济效益和管理效应。     

名词浅释

看板管理所谓“看板管理”，就是利用看板进行生产现场管理和作业控制的方式。主要有两种方式，即“单看板方式”和“双看板方式”。单看板方式其特点是仅仅使用“生产看板”来发出作业指令并控制生产过程。双看板方式其特点是同时使用“生产看板”和“取料看板”来控制生产过程。这是丰田公司普遍采用的一种生产管理方法。着板（Kanban）看板是实现难时化生产的一个重要手段。具体而言，看板是一种纸卡片。看板上，一般都标有工厂名称、零部件名称、零部件编码代号、零部件数量、容器种类、上下道工序名称、存放处等信息。经常使用的看板…     

及时生产制(JIT)应用的探讨

实施JIT生产制的目的是降低库存和消除浪费。实现这一目的的手段是看板管理。看板管理需要一定的基础条件,看板管理的运用必须理解看板的概念、看板的运作程序以及应该注意的问题。实施JIT生产制是一个持续改进过程。     

看板管理系统中的搬运形式

通过唐山爱信齿轮有限责任公司实施看板管理的实例,阐述了搬运在．看板管理系统中的作用;结合实践提出了适合小批多品种生产看板管理模式下的搬运形式,重点论述了吊桶搬运、定量成组搬运、吊桶一成组搬运三种搬运形式所适应的场所及在看板生产现场中的具体运作方法,并在实际应用中取得了较好的效果;文末给出的实例阐述了搬运次数的增减与最小在库的关系。     

考虑双目标的JIT生产系统看板数量设定

针对现有JIT系统看板数量决策问题研究多以单目标为主的不足,提出了一种基于实验设计的双目标JIT生产系统看板数量设定方法。该方法同时考虑了高订单满足率和低系统平均在制品水平的双目标优化,以B公司CR油嘴JIT生产系统为例,建立了该JIT生产线的Witness仿真模型以实现数据的收集,以各看板循环回路的看板数量和看板容量进行水平设定,并进行正交实验设计及数据的直观分析处理,然后采用全因子实验的方法,基于帕累托最优的思想获得生产系统看板数量帕累托最优解,形成近似最优看板数量组合的帕累托最优前沿。生产管理人员可根据不同的生产计划和绩效目标从组合中选择合适的看板数量。最后的研究结果验证了该方法的可行性和有效性。     

神龙公司直送看板供应分析与应用

看板管理是准时制生产（ＪＩＴ）的主要管理工具，直送看板供应是看板管理在供应链上的具体应用。阐述了直送看板供应的原理，对神龙公司直送看板供应的一个成功案例进行了量化分析，并预测了这种供应模式在轿车整车厂的未来前景及其带来的管理效益；探索如何将ＪＩＴ管理技术转化为生产力。     

多产品串并联生产系统动态看板的设计和计算

针对机电产品企业普遍采用的多品种、串并联生产系统,提出了批量轮番生产方式下采用动态看板作为控制手段时看板内容的设置和看板数量的计算方法,使得看板的内容和数量能随“需求”而动态变化,实现了“多品种均衡”生产和现场在制品数量的有效控制。     

一种实现小批量准时化生产的可视化电子看板系统

针对传统看板方式不适用于多品种、小批量订单生产的诸多问题,提出了一种新的、基于可视化电子看板技术的解决方案,使得准时化生产方式能成功运用于多品种、小批量订单的市场环境。以电子产品的生产场景为例,采用过程分析的方法,进行了新看板运行流程的设计,提出了可视化电子看板系统的结构、关键技术和适用环境条件。     

看板管理在企业中的应用研究

借鉴看板管理思想,结合企业的实际情况,论述了使用看板的8个条件、6个规则和4个作用,详细介绍了各种看板的形式、内容、计算公式和使用规则,同时引入了看板箱和物料超市的配套概念,从整体上阐述了看板管理的操作模式.     

基于Witness的看板生产系统仿真与参数优化研究

以看板生产系统基本理论为基础,某发动机总装车间为实证案例,应用Witness物流仿真软件对该车间看板生产系统进行仿真与优化,实现在短时间内预见实施效果,对系统绩效如设备利用率、订单处理效率等做出评价。结果表明,实施看板系统后,订单得到及时响应,提高了客户满意度,设备平均利用率和生产效率显著提高,产品产量达到目标产量。最后对看板生产系统的隐性参数进行优化,为不同企业根据实际生产情况实施看板生产系统参数选取提供依据     

Multiproduct Kanban-like Control Systems

Kanban Control Systems are widely used to control the release of parts in multistage manufacturing systems operating under a pull mechanism, i.e. production of new parts is triggered based on the actual demands arriving to the output of the system. Most of the work on Kanban Control Systems deals with single-product manufacturing systems, although Kanban Control Systems have also been used in multiproduct environments. The aim is to study in detail the behaviour of Kanban Control Systems in the context of multiproduct manufacturing systems. In addition to the classical Kanban Control System, we also consider two more general Kanban-Like Control Systems known as Generalized Kanban Control System and Extended Kanban Control System. When extending these pull control mechanisms to multi-product systems, we distinguish two cases, whether kanbans are dedicated to a single part type or shared among the different part types. We provide an in-depth analysis of each of the resulting control mechanism and discuss some important issues arising from the study.     

The optimal operation planning of Kanban to minimize the total operation cost†

This paper derives two formulae to calculate the average inventory yielded by fixed interval withdrawal Kanban and supplier Kanban, respectively, in a just-in-time production system. By using the formulae, another two formulae to determine the minimum number of Kanbans to be required for the fixed interval withdrawal Kanban and the supplier Kanban are also shown. An algorithm to obtain the optimal order interval is proposed to minimize the total operation cost composed of the inventory cost and the withdrawal cost for the Kanban system     

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

This paper evaluates several artificial intelligence heuristics for a simultaneous Kanban controlling and scheduling on a flexible Kanban system. The objective of the problem is to minimise a total production cost that includes due date penalty, inventory, and machining costs. We show that the simultaneous Kanban controlling and scheduling is critical in minimising the total production cost (approximately 30% cost reduction over scheduling without a Kanban controlling). To identify the most effective search method for the simultaneous Kanban controlling and scheduling, we evaluated widely known artificial intelligence heuristics: genetic algorithm, simulated annealing, tabu search, and neighbourhood search. Computational results show that the tabu search performs the best in terms of solution quality. The tabu search also requires a much less computational time than the genetic algorithm and the simulated annealing. To further improve the solution quality and computational time for a simultaneous Kanban controlling and scheduling on a flexible Kanban system, we developed a two-stage tabu search. At the beginning of the tabu search process, an initial solution is constructed by utilising the customer due date information given by a decision support system. The two-stage tabu search performs better than the tabu search with a randomly generated initial solution in both the solution quality and computational time across all problem sizes. The difference in the solution quality is more pronounced at the early stages of the search.     

Parametric appraisal of the JIT system

Much has been written on the essential features and potential benefits of the Justin-Time production system. Using a simulation model, some salient parameters such as (a)scheduling rules, (6) the effects of pull demands level, (c) the effects of the production Kanban size and the minimum Kan ban level, and (d) the significance of the job mix are evaluated. The study shows that a common prevailing practice of assigning greater priorities to jobs with higher pull frequencies is not as efficient as a shortest process time based rule. Unlike the traditional ‘push’ method, raising the pull demand in a JIT system does not ensure a high process utilization level. Results also show that an increase in the production Kanban size and minimum output Kanban level improve the mean job tardiness but causes an escalation in the output Kanban inventory level. Contrary to common conjecture, an enlarged product mix would actually produce better process utilization and mean job tardiness.     

Extended kanban control system: combining kanban and base stock

This paper introduces a new mechanism for the coordination of multi-stage manufacturing systems. This mechanism is called the Extended Kanban Control System (EKCS) and depends on two parameters per stage, the number of kanbans and the base stock of finished parts. The EKCS is a combination of the classical Kanban and Base Stock control systems and includes each system as a special case. The dynamics of the EKCS are described, in particular, in relation to the dynamics of the Generalized Kanban Control System (GKCS), a known control mechanism that also includes the Kanban and Base Stock control systems as special cases. Advantages of the EKCS over the GKCS are discussed. Finally, properties of the dynamics of the EKCS are presented. One important property is that the capacity of the EKCS depends only on the number of kanbans but not on the base stock of finished parts.