On control strategies in a multi-stage production system

A multi-stage production system is composed of a set of stations, each station performing a given task, and a set of vehicles, each vehicle moving between two successive stations. A station can choose a buffer or a kanban mechanism for controlling the work-in-process (WIP) in the station. A vehicle can choose a push or a pull policy for carrying parts from its upstream station to its downstream station. A control strategy is formed by combining the WIP mechanisms adopted in all stations and the carrying policies employed by all vehicles. The production system is modelled as a queuing system. Some structural properties of performance measures are characterized. We develop a decomposition approach for large systems, which performs very well. We determine the optimal numbers of buffers or kanbans at all stations in the design period, and the optimal control strategy during operation. Many numerical computations are given for evaluating the efficiencies of the decomposition approach and optimization methods, and further providing some intuitions and insights.     

Flexible work-in-process production system in supply chain management under quality improvement

For a complex product production, any flexible manufacturing system with a work-in-process inventory is recommended for a supply chain management (SCM) system. Building a flexible manufacturing system increases the total cost of the supply chain; for this reason, a discrete investment is important. For flexible production systems, production rate within a finite specific interval of production rate as work-in-process inventory is calculated. The aim of the supply chain is to reduce the total cost when demand during the lead time is a random variable with a normal distribution. A crashing cost is utilised to reduce the duration of lead time within the supply chain system. A model is proposed to obtain the optimal flexible production rate with the reduced total cost of the supply chain. A classical optimisation technique is employed to obtain the closed-form and quasi-closed-form solutions of the decision variables. An improved algorithm is designed to obtain the global minimum cost of SCM under the framework of a flexible production system. An illustrative numerical example and sensitivity analysis are given to test the model. A numerical study proves that this model obtains the minimum cost with the optimal decision variables.     

基于缩短供应链响应周期的运作模式研究

通过分析总结推动式和拉动式供应链的特点及对用户需求响应时间的变化，阐明基于延迟策略的推-拉结合式供应链的综合优势，提出利用延迟和压缩提前期的方法缩短供应链的响应周期；论述了对于不同生产类型企业为缩短响应时间可以采取不同的供应链运作模式；构建了基于延迟策略的模块化供应链模型，描述了该模型的运作流程以及实施中应注意的问题。     

Revolutionizing supply chain management the theory of constraints way: a case study

This research describes in detail an application of theory of constraints (TOC) and its resulting benefits on the supply chain performance of India's largest lock manufacturing company over a period of seven years. Using TOC's thinking process, the core constraints that had limited the company's performance in the areas of production, distribution, supply group and projects were identified and eliminated. TOC's unique approach helped the company achieve a significant reduction in its finished goods, raw material and work-in-process inventories at various levels across the supply chain. The stock-outs and excess in the distribution system nearly disappeared. The existing lead times saw a drastic reduction while the availability of items increased to nearly 100% despite significant decrease in inventory levels in the supply chain. The inventory turns of the distributors and retailers more than tripled and their profitability increased significantly. The overall sales of the company grew nearly three times during the six years post TOC implementation. TOC's holistic approach helped the company to double its profits and improve its cash position during the Great Recession.     

Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

Modelling and analysis of kanban-controlled (just-in-time) production systems under realistic assumptions presents a number of challenges, including the ability to conduct both qualitative and quantitative analysis, and the ability to model control policies. These challenges are due, in part, to station interdependence, blocking and starvation due to limited buffer spaces, and the necessity of modelling both material and kanban flows. Petri nets (PNs) have recently emerged as a promising approach for modelling manufacturing systems. PNs are a graphical and mathematical technique useful for modelling concurrent, asynchronous, distributed, parallel, non-deterministic and stochastic systems. PN models can be analysed to determine both their qualitative and quantitative properties. In this paper, we develop stochastic, coloured PN (SCPN) models of a JIT system utilizing two different kanban control policies: a traditional kanban system (TKS) policy and a flexible kanban system (FKS) policy. The resulting models can be used to represent JIT systems of arbitrary size, producing single or multiple types of products, with fixed order points 1. The models are shown to be live and bounded, and can be simulated to produce quantitative results. Sample simulation results are presented to illustrate the models' capabilities.     

Extending value stream mapping: the synchro-MRP case

Nowadays, value stream mapping (VSM) is recognised as the main tool for implementing lean manufacturing. Unfortunately, it always leads to pure pull systems and discourages the adoption of hybrid push/pull ones, although their superiority has been proven in several industrial settings. Due to these issues, this paper presents an enhancement of the standard VSM, which supports the user in designing the future state map of a synchro-MRP system. This new tool includes new mapping icons, simple mathematical formulas and operating guidelines that make it possible to assess the benefits of a synchro-MRP system, with respect to the usual kanban or CONWIP ones. In order to demonstrate the quality and the practical utility of the proposed approach, an industrial application of relevance is finally presented.     

Synchronized manufacturing at final assembly and feeder shops

Many manufacturing environments consist of a final assembly shop that receives components from various feeder shops. Suppose the final assembly shop assembles a mix of products, each requiring varying amounts of different components. Thus, the sequence in which products are assembled imposes time-dependent demands on the feeder shops. Such problems are often found in the assembly of high-tech telecommunications systems and in the assembly of mixed models of automobiles. We develop a variety of models that link the work-in-process inventory in the feeder shop and the finished component inventory to the component production interval and the final assembly sequence. The manufacturing disciplines considered include push, look-ahead and kanban. Feeder shops are assumed to manufacture components continuously, periodically, or at a fixed lot size. Simple performance characteristics of the feeder shop are derived to provide insight into synchronized manufacturing issues and to help evaluate the effect of different final assembly sequences on feeder shops.     

A stochastic model of an unreliable kanban production system

We analyze a stochastic model of a production line withk stations (machines) in series. There are finitecapacity buffers between the machines and at the end of the line. The movement of the workpieces through the line is demand-driven, i.e. we deal with a pull (kanban) production system. Processing times are assumed to be deterministic and constant. There are two sources of randomness in the model: Demand for workpieces from outside is stochastic, and the machines may break down (and then be repaired) with a given probability. A demand from outside is lost if the final buffer is empty. This system is described by a discrete-time Markov chain. The steadystate distribution is given for k=1. This is the basis of a decomposition algorithm which approximates the throughput of the line and the percentage of satisfied demand for arbitraryk. A comparison with simulation results shows that this algorithm is very accurate.     

Takt Time Grouping: implementing kanban-flow manufacturing in an unbalanced,high variation cycle-time process with moving constraints

One-piece flow and kanban/pull methods have been used to reduce work-in-process (WIP) and flowtime in manufacturing flow processes; however, these methods have limitations. For example, one-piece flow does not work well when there are relatively large set-up times required between different components. One-piece flow also requires operations to be well balanced with a minimum of variability in processing times at each operation. Unfortunately, these conditions often do not exist. The theory of constraints drum-buffer-rope (DBR) method is designed for unbalanced processes, and it has been shown to be effective for products with large operation time variation. However, DBR does not generally optimise flowtime and cannot handle a process with moving constraints (bottlenecks). We have developed a method called Takt Time Grouping (TTG) for implementing kanban-flow manufacturing, when one-piece flow or DBR do not provide good results. TTG combines one-piece flow manufacturing, transfer-batch sizing and DBR concepts through the use of a constraints-based transfer-batch sizing formula. Using a discrete event simulation model, it is shown that TTG increases throughput rate as compared to one-piece flow, CONWIP and DBR approaches, with much lower WIP inventory and faster flowtime than CONWIP and DBR.     

Comparison of push and pull control strategies for supply network management in a make-to-stock environment

The performance differences of ‘push’ and ‘pull’ strategies for operational planning and control of a make-to-stock supply network under different environmental conditions (forecast error and initial levels of inventory) were explored. Results suggest that control strategy, forecast error and levels of inventory buffer all significantly affect each of the performance measures studied. Under all combinations of different conditions of inventory buffer level and forecast error, push outperforms pull in terms of customer service level and throughput, while pull outperforms push in term of total inventory. In terms of throughput and customer service level, push is more sensitive to forecast error but less sensitive to levels of inventory buffer than pull.     

面向大规模定制的供应链驱动模型的研究与应用

大规模定制是在高效率的大规模生产的基础上,通过产品结构和制造过程的重组,运用现代信息技术,新材料技术、柔性技术等一系列高新技术,以大规模生产的成本和速度,为单个顾客或小批量多品种市场定制任意数量的产品的一种生产模式,本文提出了面向大规模定制的推拉型供应链驱动模式,构建了以装配为分离点的供应链模型,并进行了案例研究。     

Just-in-time manufacturing system: a periodic pull system

The just-in-time manufacturing system, as developed in the Toyota Motor Co., may be stated as producing or stocking ‘only the necessary items in necessary quantities at the necessary time’. When the just-in-time concept is applied to an industrial material management system, it is called a pull system, which means that the amount and time of material flow are determined by the rate and time of the actual stock consumption. Actually, in Toyota, this pull system is operated manually by means of a kanban, a sort of card or tag.

As an alternative to a kanban method, a periodic pull system (PPS) is developed as an operating policy of practising a Pull system. Due to the fact that, in a PPS, the manual information processing time of a kanban method is replaced with an instant on-line computerized processing, the material lead time is much shorter than that of a kanban system. This results in better system performance such as less lead time inventory and faster system response.

A PPS is formulated mathematically and a solution approach is provided for target stock levels, as well as the analysis of the fluctuations of in-process material flow, on-hand stock levels, target availability, etc.     

Analysis of production control systems kanban and CONWIP

In this paper we describe and classify different pull production systems. The production control systems kanban and CONWIP are then analysed with respect to production rate and average W1P. We examine single product flow lines with exponential service time distributions and unlimited demand at the final buffer (saturated lines). We show that the distribution of cards (kanbans) has a significant effect on the performance of kanban systems. Different types of kanban control mechanisms show equivalent performance data, if the distribution pattern is adapted accordingly. Our research shows that the kanban system is more flexible with respect to a given objective than the CONWIP system, because in addition to the total card number the card distribution is a control parameter. Moreover for a given production rate the average WIP is lower in a kanban system than in a CONWIP system. We identify the average WIP in the interstage buffers as important parameter and describe the ‘WIP bowl phenomenon’ as result of optimum card distributions. Concluding remarks and directions for future research conclude the paper.     

Push and pull strategies for controlling multistage production systems

This study considers push and pull strategies to control multistage production systems with random processing times. Such systems are important as they mirror the level of complexity often encountered in practice. We start with definitions of push and pull systems, and develop a framework to compare multistage production systems based upon work-in-process (WIP) and throughput (TP) tradeoff. Surprisingly, we find that often push out performs pull, i.e. push systems accumulate less WIP than pull systems, while maintaining higher PT Concerning pull systems we find that WIP linearly increases in the number of stages and that WIP is not affected by variation in processing time. Concerning push systems we find that the release of material into the system in deterministic time intervals greatly improves performance.     

Simulation-based comparison of push and pull systems in a job-shop environment considering the context of JIT implementation

An investigation of the effects of adopting a pull system in a job-shop environment in contrast to a push system is presented considering the implementation of Just-in-Time production. Implementation of pull systems in job-shop environments should be coordinated with a set of core supporting Just-in-Time concepts, i.e. cellular manufacturing, one-piece production and conveyance, and adaptive material-handling performance. A simulation experiment was carried out to compare the effects of applying these core Just-in-Time concepts on the performances of push and pull systems. The results show that to avoid adverse effects, it is essential that a suitable shop layout and part flow type are adopted for either a push or a pull system based on the extent of set-up time reduction effected by cellular manufacturing. Suitable material-handling equipment can then be employed accordingly to achieve better production performance. In addition, although a push system was always superior to a pull system in the simulated job-shop environment, the performance difference between the two systems was small. This finding implies that pull systems can be expected to bring about greater improvement in production performance than push systems as long as the potential of pull systems in shaping production environments is effectively realized.     

An optimal cart moving policy for a flexible manufacturing system

A flexible manufacturing system is composed of many stations such as a load/unload station, a set of workstations, and a common buffer, that are linked together with a material handling system. Each workstation consists of a limited input buffer, a single machine and a limited output buffer. The material handling system consists of a single cart moving parts in the system according to the process paths required by the parts. A part is blocked when it is moved to a workstation but cannot enter the workstation. The function of the common buffer is to temporarily store blocked parts. A blocked part is treated in accordance with a flexible manufacturing system blocking mechanism. We model the flexible manufacturing system by a closed queueing network with the flexible manufacturing system blocking mechanism and a block-dependent static Markov part routing. An optimal cart moving policy that maximizes the expected system throughput is formulated as an undiscounted semi-Markov decision process. Several properties of the optimization problem are characterized. A loop approach is developed for finding an optimal policy. An example is given to illustrate the methodology, and investigate its convergence.     

Pull and push contracts in a decentralised assembly system with random component yields

This study investigates how random component yields can influence pricing and production decisions under pull and push contracts. We consider a decentralised assembly system where a manufacturer procures complementary components from two suppliers with random yields. We first characterise the centralised equilibrium decision as a benchmark and then analyse the equilibrium solutions in a decentralised assembly system under each contract. We find that neither contract is always superior to the other in terms of system profit. Under a push contract, suppliers always achieve the first mover advantage with higher payoff. However, the first mover advantage does not hold for the manufacturer under a pull contract. We further conduct sensitivity analysis to study the impact of random component yields and retail price on equilibrium solutions under each contract. Interestingly, the wholesale prices charged by suppliers always increase with supply yield uncertainty under a pull contract, but decrease under a push contract. In contrast with the centralised solution, the equilibrium quantities in the decentralised solution decrease with supply yield uncertainty under both pull and push contracts. We then extend our model to a general case with multiple suppliers. The system payoff decreases with the number of suppliers, and the main results derived in two suppliers setting still hold in the system with multiple suppliers.     

Improving the push–pull strategy in a serial supply chain by a hybrid push–pull control with multiple pulling points

Because of its benefits – from lowered inventory costs to greater flexibility in adapting to shifting market forces – the push–pull strategy is being widely used in today's competitive supply-chain designs. The push–pull strategy also brings potential supply-chain risks related to order fulfilment capability and robustness against external variability. More specifically, the use of this strategy often results in an inability to minimise the impact of lead-time variability. We present a new, hybrid push–pull strategy that incorporates additional stock points after the push–pull boundary as the pulling points in a serial supply chain, which can mitigate the risks and improve the robustness of the push–pull strategy without sacrificing its benefits in inventory cost reduction. For the evaluation and comparison of different supply-chain strategies, a nonlinear, mixed-integer programming model with a cost-minimisation objective function is developed and implemented in the numerical experimentation, with simulated annealing as the search algorithm. Results from the experiments demonstrate the potential improvement by our proposed strategy in terms of the robustness and cost-effectiveness against external variability. The results also verify the risks and limitations of the conventional push–pull strategy and provide some managerial implications regarding the use of push–pull supply chains.     

The extended kanban control system for production coordination of assembly manufacturing systems

In assembly manufacturing systems there are points in the production process where several component parts are put together in areas called assembly cells so as to form more complex parts called subassemblies. In this paper, we present and compare two variants of the Extended Kanban Control System (EKCS) - a recently developed pull production control mechanism that combines base stock and kanban control - for the production coordination of assembly manufacturing systems. In both variants, the production of a new subassembly is authorized only when an assembly kanban is available. Assembly kanbans become available when finished subassemblies are consumed. If an assembly kanban is available, in the first variant, each component part of a subassembly is released into the assembly cell as soon as itis available (independent release). In the second variant, however, it is released only when allother component parts also become available (simultaneous release). In both variants, when a component part is released into the assembly cell, it releases its kanban, thus authorizing the production of a new component part.     

Determining the number of kanbans: a step toward non-stock-production

Just-in-time (JIT) production is a philosophy that calls for reducing work-in-process (WIP) inventory to aid process improvement and reduce process variability. In some cases, JIT production has been misinterpreted as a method that would lead to zero or minimal WIP with a lot size of one. There are no models or theories to achieve the JIT goal, i.e. non-stock-production (NSP), and, in particular, to help determine when and where to maintain this minimal inventory. A kanban system acts as the nerve of a JIT production system whose functions are to direct materials just-in-time to workstations in stages of manufacturing, and to pass information as to what and how much to produce. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective of minimizing WIP inventory level, one model dealing with three cases of production configuration is developed for deciding the optimum number of kanbans. The model is then solved using a Markov process approach which considers the demand of finished products as the departure rate and the production rates of stations as arrival rate. In this paper, the model and solution procedure are illustrated with a numerical example.