Number Of Kanbans Research Articles

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.

An overview of design and operational issues of kanban systems

We present a literature review and classification of techniques to determine both the design parameters and kanban sequences for just-in-time manufacturing systems. We summarize the model structures, decision variables, performance measures and assumptions in a tabular format. It is important to state that there is a significant relationship between the design parameters, such as the number of kanbans and kanban sizes, and the scheduling decisions in a multiitem, multi-stage, multi-horizon kanban system. An experimental design is developed to evaluate the impact of operational issues, such as sequencing rules and actual lead times on the design parameters.

Optimization of kanban-based manufacturing systems

We develop and analyze an algorithm to maximize the throughput of a serial kanban-based manufacturing system with arbitrary arrival and service process distributions by adjusting the number of kanban allocated to each production stage while maintaining the total work-in-process inventory at any desired level. The optimality properties of the algorithm are proved under a necessary and sufficient “smoothness condition”. The algorithm is driven by throughput sensitivities which, in general, can only be estimated along an observed sample path of the system. It is shown that the algorithm converges to the optimal allocation in probability and, under additional mild conditions, almost surely as well. Finally, it is shown that Finite Perturbation Analysis (FPA) techniques can be used to obtain the sensitivity estimates in order to reduce the amount of simulation required in either on-line or off-line simulation-based optimization.

Optimization of kanban-based manufacturing systems

We develop and analyze an algorithm to maximize the throughput of a serial kanban-based manufacturing system with arbitrary arrival and service process distributions by adjusting the number of kanban allocated to each production stage while maintaining the total work-in-process inventory at any desired level. The optimality properties of the algorithm are proved under a necessary and sufficient “smoothness condition”. The algorithm is driven by throughput sensitivities which, in general, can only be estimated along an observed sample path of the system. It is shown that the algorithm converges to the optimal allocation in probability and, under additional mild conditions, almost surely as well. Some illustrative simulation examples are included, where Finite Perturbation Analysis (FPA) techniques are used to obtain the sensitivity estimates.

Analyzing closed Kanban-controlled assembly systems by iterative aggregation–disaggregation

In recent years, many companies have begun using WIP-limiting strategies such as CONWIP, Kanban or drum-buffer-rope to control parts flows in complex production systems. This paper analyzes assembly systems with a tree structure, random processing times and a constant WIP control system. We present heuristic version of the exact aggregation–disaggregation theory for finite Markov chains to evaluate the performance of these closed Kanban-controlled assembly systems. Because the approximation is theory based, it provides a framework for further model development, with some possible extensions described in the paper. The approximation has the novel feature of doing simultaneous multiple partitions of the state space, in such a way that the associated aggregate transition rates are mutually consistent. The methodology is a novel approach towards extending aggregation ideas to fork-and-join queueing networks, and it provides several useful operational and analytical insights. Numerical comparisons with simulation show that the proposed approximation computes accurate estimates of the plant throughput. It provides a fast way to assess the performance and economic impact of changes in the total WIP level (or the number of Kanbans), or in the part routes, on the throughput rate of the assembly system. Scope and purpose We develop a new methodology for rapid performance analysis of assembly systems with a tree structure, random processing times, and a constant work in process Kanban control system. Extensive evaluations show that this methodology computes accurate estimates of the plant throughput. It provides a fast way for managers to assess the performance and economic impact of changes in the total WIP level (or the number of Kanbans) and in the part routes, on the overall throughput rate of the assembly system.

Transient behavior of a single-stage kanban system based on the queueing model

We deal with a single-stage kanban system with Poisson demand arrivals and Erlang production times, and we study an optimization of the number of kanbans when a change of load to the system is planned. We formulate the system as an M/E s/1 queueing system under piecewise constant load, and propose a numerical method by transient solutions of the M/E s/1 queue. Our method reveals the transient behavior of the kanban system with respect to expected inventory level and expected waiting time to satisfy demand. We suggest the required number of kanbans, and the time required to change to this required number, to optimize these criteria.

An object-oriented approach to model a just-in-time production system

To produce modular and re-usable simulation models, previous studies have found out that an object oriented approach seems to be more appropriate. Various paradigms have been proposed and their results are promising. This paper illustrates how object oriented programming concept can be applied to implement a simulation model of JIT system. Another objective of this research is to implement the simulation model by visual interactive approach. The interactive interface, built in this research allows the managers to change their decisions and justify the consequence very quickly. Moreover, the program during execution can display its results in an animated manner so that users can observe and understand the process during simulation. Some illustrated operations process will be demonstrated in the paper. Computational results obtained using the JIT simulator to examine effects of different number of Kanbans or arrival time distributions are also reported. Later, this paper uses object-oriented approach to implement an animated simulation model in order to analyse the design and implementation factors of a JIT system. The results clearly show that this approach is feasible and deserves more efforts for further research.

Operations planning for a multi-stage kanban system

A multi-stage production line which operates under a just-in-time production philosophy with linear demand is considered here. The first workstation processes the raw materials after receiving them from suppliers, a kanban mechanism between the workstations transports the work-in-process to the succeeding workstation, and after processing them, delivers the finished products to a buyer or a warehouse. The problem is to find optimally the number of raw material orders, kanbans circulated between workstations, finished goods shipments to the buyers, and the batch size for each shipment (lot). A cost function is developed based on the costs incurred due to the raw materials, the work-in-process between workstations, and the finished goods. Optimal number of raw material orders that minimizes the total cost is obtained, which is then used to find the optimal number of kanbans, finished goods shipments, and the batch sizes for shipments. Numerical examples are used to demonstrate the computations of optimal parameters, and to configure the kanban mechanism on a timescale. Several avenues for future research are also indicated.

A Reactive Kanban System by Allocation of the Number of Kanbans

A Reactive Kanban System by Allocation of the Number of Kanbans

Determination of the optimal number of kanbans and kanban allocation in a FMS: A simulationbased study

A simulation-based study is presented to compare the performance of a set of kanban allocation rules in a flexible manufacturing system (FMS) operating in a pull mode. This paper also presents a simulation-based direct search approach to determine the optimal number of kanbans and their simultaneous allocation to different part types. This paper presents a realistic cost function that includes a penalty for failing to meet the demand on time, and a penalty for rescheduling delivery of materials from vendors. The model captures limited flexibility in the form of operational flexibility in a FMS. Future extensions to this study are discussed.

SYNTHESIS OF KANBAN CONTROL POLICIES FOR MULTI-STAGE JUST-IN-TIME ASSEMBLY SYSTEMS

This paper studies the problem of determining the number of Kanbans that should be assigned to each production slage of a multi-stage assembly system in a just-in-time production environment. The objective is to minimize the inventory cost of the system. A new algorithm is developed as a means of solving such a problem. The approach is described in some detail and the theoretical results obtained are illustrated by using a numerical example. A set of randomly generated test problems are also included to illustrate the performance of the algorithm. The results show that the algorithm proposed in this paper is indeed superior both in terms of minimizing Ihe required amount of computational effort and improving accuracy.

Using simulation to evaluate the introduction of a Kanban subsystem within an MRP-controlled manufacturing environment

A large assembly plant of parts for the automotive industry uses an MRP system for the mid-term planning and has developed some finite-capacity scheduling software for the scheduling of the final assembly step of its products. This software uses a heuristic which tries to respect due dates and to minimise production costs, which consist mainly of set-ups. One of the problems with this software is that it does not take the availability of subparts into consideration. In fact, the production of subparts must also deal with the problem of set-up minimisation. The difficulty lies in the fact that the technical parameters that determine “a good sequence” differ for each production step. At the moment, human planners spend a lot of time and effort to coordinate and fine tune the production sequences in the various production and assembly steps. There are plans to produce some subparts of the product in a Kanban-controlled manner. A simulation model is described which allows to study the feasibility of these plans and to determine the operational parameters (such as number of Kanbans and container size). This feasibility study was carried out for two scenarios: 1. (1) all subpart types are produced in a Kanban controlled manner and 2. (2) only the production of fast-movers on two (out of three) machines is Kanban controlled. The sensitivy of the results vis-à-vis a number of assumptions (set-up time reduction, machine breakdowns, tool availability, etc.) is also presented.

A rule induction approach for determining the number of kanbans in A just-in-time production system

A procedure based on rule induction is presented which can be used to determine the number of kanbans while simultaneously determining the critical factors in a just-in-time (JIT) production system. In particular, the classification and regression tree (CART) technique developed by Brieman et al. [Breiman, L., Friedman, J., Olshen, R. and Stone, C. J., Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.] is used to automatically generate rules from dynamic shop floor data. An example application of the methodology is presented and the advantages of a rule induction approach are explained. The paper concludes with a discussion of future research directions.

A fuzzy rule-based approach for dynamic control of kanbans in a generic kanban system

This paper studies the dynamic control of kanbans in a generic kanban system. A generic kanban system originating from the Toyota kanban system is a production control scheme in dynamic environments with variable demands and processing times. A dynamic kanban control approach is proposed to determine the number of kanbans used in the system. This approach includes two major functions: example extraction and fuzzy system generation. Examples are generated from simulation and desired examples are collected. Fuzzy rule-based systems are generated from desired examples to dynamically adjust the number of kanbans. Experiments are conducted to evaluate the proposed approach. The generated fuzzy systems are examined on the training process and its feasibility in general situations. This approach is also compared with the simulated annealing approach under the same system status. Both approaches yield similar system performance. The fuzzy system approach, however, is preferred because it does not require the prior demand information and it is a real-time control that can respond to new system status immediately.

A multi-criterion approach for Kanban allocations

In this paper, a single-item, multi-stage, serial production system is considered. Materials in the system are controlled by Kanban discipline. A fixed total number of Kanbans over a given number of serial workstations is allocated. Three conflicting objectives, the average throughput rate (to be maximized), the average work-in-process (to be minimized), and the average flow time (to be minimized) are considered. Stochastic system simulation is used to generate the efficient set of Kanban allocations. Then the analytic hierarchy process (AHP) is utilized to identify the most-preferred allocation. An illustrative example is given.

The effect of sudden material handling system breakdown on the performance of a JIT system

In this paper we explore the impact of sudden breakdown of the material handling system on the performance of a traditional kanban system (TKS). TKS, which is an element of the just-in-time system, is designed to operate in an ideal environment such as constant processing times and uninterrupted processing. However, in a real life environment, the TKS could be subjected to various unpredictable factors including stochastic processing times and process interruption due to equipment failure. These factors would seriously strain the performance of TKS. We consider a TKS in which some stations are dependent on a material handling system to move parts between them. We study the effect of a sudden breakdown of such a material handling system on the performance of the TKS. In addition, we also study a newly developed kanban system (which dynamically and systematically manipulates the number of kanbans in order to offset the blocking and starvation caused by these factors during a production cycle) under the same conditions. We refer to the new system as the 'flexible kanban system' (FKS). We compare the overall performances of the TKS and FKS by considering a variety of cases. We present the solution procedure, results and discussion for these cases.

JIT scheduling rules: a simulation evaluation

Just-In-Time (JIT) production systems capitalize on simplicity and the ability of workers to make decisions in a decentralized manner. In a multiproduct line operating under kanban control, the production worker at a given station must schedule the different jobs awaiting processing using information available locally. In practice, the first-come-first-served (FCFS) rule is commonly used. Recent results reported in the literature indicated that the shortest-processing-time (SPT) rule performed better than the FCFS rule. In this paper, we provide a simulation evaluation of the performance of a number of scheduling rules operating under different JIT production scenarios. Our hypothesis is that there are differences in the relative performance of the scheduling rules under different production scenarios. We differentiate among the JIT scenarios by the extent of setup time reduction already carried out (as indicated by the ratio of setup to processing times), the amount of slack in the system (as measured by the number of kanbans circulating), the extent to which uncertainty has been eliminated (as determined by the stochasticity of processing times), and the complexity of production requirements (as specified by the product-mix in mixed-model assembly). In this way, this paper provides further insights into the performance of scheduling rules operating under different JIT production environments, thereby adding to the scope and depth of research in this particular aspect of JIT production systems.

Evaluation of performance measures for multi-part, single-product kanban controlled assembly systems with stochastic acquisition and production lead times

We consider the following kanban-controlled, multi-stage production assembly system. A number of raw parts are acquired from various suppliers and assembled into a single product. The raw-part acquisition lead times, the production lead time and demand arrival are all random variables. The raw-part acquisition order is made when the inventory level of the common part buffer, consisting of a number of sets of raw parts where a set of raw parts forms a single product, depletes to a reorder point. A production stage consists of the input queue, the output buffer, and the kanban board. The finished product can be backordered with a given allowable quantity. The problem is to evaluate the various system performance measures for a given set of design parameters: the raw-part batch order size, the common buffer size, the reorder point, the number of kanbans circulating in each production stage. A system is decomposed into a number of semi-autonomous Markov processes. A mathematical model is formulated and an iterative algorithm is proposed to evaluate system performance measures. Extensive numerical experiments with simulation analyses show that the computational time is very short and the model proposed is reasonably accurate. Thus, resorting to a genetic algorithm we can find an optimal set of system design parameters.

Operations planning for a single-stage kanban system operating under linear demand

A production line with a single-stage kanban system which works between two adjacent workstations is considered here. Raw materials are replenished at the first workstation and processed through the workstations resulting in the final product for dispatching to the next demanding point which could be either an external buyer or a feeding point to the same or another production line. The kanban which transports the work-in-process from the first station to the second works as a circulating mechanism between the stations. When the demand and the production rates remain constant, the batch sizes of work-in-process remain constant, but if the demand of the products vary over time, the batch size of the kanban system remains no longer unaffected. A study is pursued in this research to determine the number of kanbans between two workstations for a varying (linear) demand pattern. This decision eventually determines batch sizes of the kanbans, the dispatching time intervals, and the schedule for production. In order to achieve this goal, this problem is formulated as an inventory model dealing with raw materials, work-in-process and finished goods inventory. The problem is solved optimally, and the number of kanbans and the batch sizes are scheduled based on the demand pattern of products. Results of the model are demonstrated to show the operational mechanism of the kanbans between the workstations.

Optimum number of Kanbans between two adjacent stations

A model and solution strategies are developed for determining the optimum number of Kanbans between two adjacent workstations. The total cost is expressed as a function of the number of Kanbans, and the parameters, shortage cost of materials and holding cost of containers. Since the number of Kanbans obtained has to be an integer, an incremental search procedure is used, which leads to the construction of a strong bound on the optimal number of Kanbans required.