Comparing reactive Kanban systems

In an effort to achieve agile control in a just-in-time (JIT) environment, reactive JIT ordering systems have been proposed for multistage production systems with unstable changes in demand. In reactive JIT ordering systems, the time series data for the demand are monitored, and unstable changes in demand are detected using statistical control charts. Then, in these systems, the number of Kanbans and the buffer size at each stage are adjusted as a response to the detected unstable changes. Recently, an adaptive Kanban system based on inventory levels was proposed and applied to unstable demand conditions. However, its performance has not yet been analysed and compared with the other reactive system. Therefore, the first purpose of this paper is to analyse and compare the performance of the reactive Kanban systems, both the control chart-based and the inventory-based systems. In addition to comparing their performance, as the second purpose, a new inventory-based system is proposed. In the proposed system, the time series data for the demand is not monitored, but instead the inventory level of a product is monitored to detect unstable changes in demand, and the number of Kanbans is adjusted as a response to these detected unstable changes. The performance of the proposed system is investigated and compared with those of the previous systems by using simulation experiments that model unstable demand conditions.     

Transition to a JIT production system through ERP implementation: a case from the automotive industry

ERP systems provide important benefits to institutions, such as improved processes, improved data, supplier–customer integration, better planning and control, as well as real-time insight and just-in-time (JIT) management that can put a business organisation in front of the competition. In this study, the change and transformation experienced by establishing an ERP system are explained for an automotive supplier that was not using an integrated ERP system for a real-world problem. The pre-implementation situation, the problems of the existing system, the necessary reasons for such an application, the determined deficiencies of the installed system and the post-implementation situation are explained in detail. Thanks to the real-time information and integrated data generated by the reports, along with a more sophisticated business understanding, the risk to employees was lowered. As a result of a changeover to real-time Materials Requirement Planning, the decrease in inventory levels, stabilisation in delivery time and efficiency of control over the production processes were increased in addition to real-time tracking and reporting advantages. As a result of this work, the production processes were transformed into JIT. The inventory level was reduced to zero and a formerly used warehouse was no longer required.     

Just-in-time purchasing: an integrated inventory model involving deterministic variable lead time and quality improvement investment

Nowadays supply chain management is a popular practice in manufacturing systems, and just-in-time (JIT) production plays a crucial role in supply chain environments. Companies are using JIT production to gain and maintain a competitive advantage. The characteristics of JIT systems are consistent high quality, small lot sizes, frequent delivery, short lead time, and close supplier ties. This paper presents an integrated inventory model to minimize the sum of the ordering/setup cost, holding cost, quality improvement investment and crashing cost by simultaneously optimizing the order quantity, lead time, process quality and number of deliveries while the probability distribution of the lead time demand is normal. This integrated inventory model is useful particularly for JIT inventory systems where the vendor and the purchaser form a strategic alliance for profit sharing.     

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.     

Controlled production planning for Just-In-Time short-run suppliers

In the face of growing global competitiveness it is necessary for US manufacturers to manage their limited resources in quite the opposite manner of the way they have been managed in the past. Just-In-Time (JIT) is a practice which, by eliminating waste, improving quality, and increasing productivity, has helped manufacturers meet the competition. The core requirement of JIT in practice, however, is the timely delivery of quality products. This requirement has exerted significant economic pressure on short-run JIT suppliers that provide low-volume, diversified products. This paper presents a means by which JIT methods and Statistical Process Control may be applied jointly to short run production in an environment with demand uncertainties. The purpose of this study is threefold: (1) to examine the cost structure of short-run production, (2) to propose the application of controlled production planning, and (3) to discuss the effect of controlled production planning on the cost structure of JIT suppliers. This study examines the economic impact of JIT demands on the cost structure of short-run production and proposes Controlled Production Planning (CPP) for generating signals for delivery failures or overproduction plans. Establishing CPP minimizes short-term production plan fluctuations, reduces inventory based on the market situation, and reduces the total cost of production. Using a computer simulation we demonstrate that CPP is a viable method for achieving these inventory and production goals. Based on this proposal, three inventory strategies are recommended to short-run suppliers in order to minimize their operating costs under different business conditions. The first strategy uses CPP to determine an inventory cap that requires the maintenance of an average level of stock as a lower control limit to cope with short-run demand fluctuation. This strategy is applicable to a business condition with high product quality and low flexibility to respond to fluctuating demands. The second strategy is zero inventory exercised by sole suppliers offering standard products. The third strategy is a flexible production system combined with zero inventory, which is applicable to suppliers facing strong competition without the market position to absorb their production surplus.     

Analysis of the bullwhip effect in multi-product,multi-stage supply chain systems–a simulation approach

This research aims to develop a simulation approach based on system dynamics modelling (SDM) and adaptive network based fuzzy inference system (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. The proposed model is comprised of three groups of variables influencing the bullwhip effect, namely the structure of a supply chain network, supply chain contributions (ordering process in regular situation or when a supplier has a promotion or shortage gaming) and supply chain performances (the number of defects and ordering lead time). As a result, a two layer simulation model is developed with three generic models. The flexibility of this proposed approach is its ability to model various types of ordering policies which are basic inventory policies, material requirement planning (MRP) system and just in time (JIT) approach. The supply chain of a beverage company was selected to validate and demonstrate the flexibility of the proposed model. The findings of the proposed simulation model are consistent with the results obtained from the case study. The error magnitude of the bullwhip effect level varied between 0 and 9% resulting in bullwhip effect reductions of up to 92%. Accordingly, the bullwhip effect levels are significantly decreased by using the proposed simulation model.     

Modelling of an automated guided vehicle system (AG VS) in a just-in-time (JIT) environment

Like other production systems, just-in-time (JIT) systems need to address the issue of material transport between workstations such as those served by automated guided vehicles (AGVs). Unlike other production systems, however, the JIT philosophy imposes strict requirements on inventory levels and supply-demand protocols which render conventional AGV delivery strategies ineffective and counter-productive. This paper describes the modelling of an AGV system (AGVS) in a JIT environment in a way that is consistent with JIT principles. The influence of a ‘JIT perspective’ is emphasized throughout the model by introducing threshold values for both input and output queues, performance measures that emphasize lower inventories in addition to transport efficiency, and a new dispatching rule that implements better inventory and transport control. The dispatching rule is shown to perform better in a JIT environment than previously developed AGV dispatching rules in both transport and logistic criteria.     

A study of an integrated inventory with controllable lead time

The Japanese experience of Just-in-Time (JIT) production has shown that there are advantages and benefits associated with the efforts to reduce inventory lead time and the associated inventory cost. The length of lead time directly affects the customer service level, inventory investment in safety stock, and the competitive abilities of a business. In most of the literature dealing with inventory problems, either a deterministic model or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable, and is not subject to control. However, in many practical situations, lead time can be reduced by an additional cost. Moreover, the successful implementation of JIT production in today's supply chain enviromnent requires a new spirit of cooperation between the buyer and the vendor (Goyal and Srinivasan 1992). A desirable condition in long time purchase agreements in such a manufacturing environment is the frequent delivry of small quantities of items so as to minimize inventory holding cost for the buyer. The vendor also needs to minimize his or her total inventory costs. An integrated inventory model that allows the two trading parties to form a strategic alliance for profit sharing may prove helpful in breaking down the traditional barriers. This paper presents an integrated inventory model with controllable lead time. The model is shown to provide a lower total cost and shorter lead time compared with those of Banerjee (1986) and Goyal (1988), and is useful for practical inventory problems.     

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。     

Comparing JIT,MRP and TOC,and embedding TOC into MRP

The traditional material requirements planning (MRP) system for planning and controlling production systems is being replaced more and more by just-in-time (JIT) and the theory of constraints (TOC). Because JIT and TOC share many elements with MRP and because MRP is very flexible, it is not difficult to make MRP behave like JIT or TOC. Consequently manufacturers with MRP systems need not dismantle them to implement JIT or TOC. A five-step technical procedure for embedding TOC into MRP is presented in this paper along with an illustrative example from a microelectronics plant. Next a simple production line is analysed using a Markov chain model to examine the types of improvements each approach makes and the effect of these improvements on the performance of the line. Performance measures are the mean and variance of the output, shortage, inventory level, and cycle time of the production line. Insights are developed into the reasons for the superior performance of JIT and TOC.     

Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation

A new forecast-based dynamic inventory control approach is discussed in this paper. In this approach, forecasts and forecast uncertainties are assumed to be exogenous data known in advance at each period over a fixed horizon. The control parameters are derived by using a sequential procedure. The merits of this approach as compared to the classical one are presented. We focus on a single-stage and single-item inventory system with non-stationary demand and lead-time uncertainty. A dynamic re-order point control policy is analysed based on the new approach and its parameters are determined for a given target cycle service level (CSL). The performance of this policy is assessed by means of empirical experimentation on a large demand data set from the pharmaceutical industry. The empirical results demonstrate the benefits arising from using such a policy and allow insights to be gained into other pertinent managerial issues.     

Flexible kanbans to enhance volume flexibility in a JIT environment: a simulation based comparison via ANNs

Kanbans play an important role in the information and material flows in a JIT production system. The traditional kanban system with a fixed number of cards does not work satisfactorily in an unstable environment. In the flexible kanban-type pull control mechanism the number of kanbans is allowed to change with respect to the inventory and backorder level. Based on the need for the flexible kanban, a method was proposed by (Husseini, S.M.M., O’Brien, C., and Hosseini, S.T., 2006. A method to enhance volume flexibility in JIT production control. International Journal of Production Economics, 104 (2), 653–665), using an integer linear programming technique, to flexibly determine the number of kanbans for each stage of a JIT production system, minimising total inventory cost for a given planning horizon. Here, the effectiveness of the method proposed by Husseini et al. is examined by a case study and compared with the results for the conventional method of fixed kanban determination. This is also confirmed by a simulation study using artificial neural networks (ANNs). The main aim of this paper is to show the cost advantage for Husseini et al.'s method over the conventional method in fluctuating demand situations, and especially to prove that simulation via ANNs ensures a simplified representation for this method and is time saving.     

A hybrid GA-simulation approach to improve JIT systems

This study presents a hybrid approach involving genetic algorithms (GAs) as an optimisation search technique and a simulation model, representing the dynamic behaviour of a system and its limitations, to improve an existing just-in-time (JIT) manufacturing system. To achieve the objective, first, the existing system is modelled and simulated (by considering the system's limitations and its dynamic behaviour). Second, the integrated simulation model is tested and validated by analysis of variance. Third, the hybrid GA-simulation approach is used in an interactive manner to determine the optimal/near-optimal number of kanban cards in different stations of the existing JIT system. The presented hybrid approach is tested and applied to an auto industry production line. Furthermore, it is compared with the practical JIT through analysis of variance (ANOVA) and the results show improvements in the average daily production rate, the average resource utilisation and the average cycle time but some deterioration in the average queue length and in-process inventory is inevitable.     

Implementing just-in-time philosophy to reverse logistics systems: a review

Just-in-time (JIT) and reverse logistics are two important philosophies. Coincidentally, both are related to reducing the impact on the environment. However, they are sometimes in conflict with each other. The former focuses on moving the materials smoothly which require a stable demand and supply, but the latter is weak in terms of predicting how many returned products will be processed. Therefore, there is a need to investigate the impact of JIT to reverse logistics systems. After an extensive literature review based on a model developed for this purpose, different functions, namely reverse logistics structure, process model, product life cycle, information system model, and JIT performance, have been identified that have great impact on integrating JIT with reverse logistics. In short, a process model with JIT can have a better control on cost and efficiency of reverse logistics activities, and product life cycle management with JIT helps to design the product that consider the reverse activities in various process models. An information system can support both process model and product life cycle management for JIT reverse logistics. Details are presented in this paper.     

A matheuristic for workforce planning with employee learning and stochastic demand

This paper focuses on the opportunity to direct the development of responsive capacity by recognising that individuals learn through experience when designing workforce plans. We focus on the operations of a product manufacturer that seeks to maximise profit by selling multiple products, while recognising that demands for each product is uncertain. As such, we study a stochastic integer program wherein an organisation can hedge against uncertainty in demand both by holding inventory (at a cost) and building a more responsive production process. Solving this stochastic program presents many computational difficulties, including the fact that quantitative models of human learning are non-linear and the explosion of instance size that result from modelling uncertainty with scenarios. As a result, we propose a matheuristic for this problem and with an extensive computational study demonstrate its ability to produce high-quality solutions in little time.     

基于JIT的无B/C类成品库存方法研究

针对实施JIT的现有库存控制方法进行了探讨,认为完全地追求“零库存”不适合我国制造业现状,并从供应链管理的角度引入提出了与JIT相匹配的无B／C类成品库存供应链管理策略方法,指出了应用时的实施要点。     

A comparison of inventory cost reduction strategies in a JIT manufacturing system

Although many companies have found JIT manufacturing to be a very effective manufacturing strategy, few models have been developed which can help direct JIT efforts. This article discusses some of the problems with modelling JIT manufacturing, it describes some simple models of inventory costs in a JIT system and it uses these models to evaluate alternative inventory-cost reduction strategies in a printed circuit board (PCB) assembly system.     

A comparison of JIT and TOC buffering philosophies on system performance with unplanned machine downtime

The impact of buffering under just in time (JIT) and theory of constraints (TOC) is studied to determine whether a difference in performance exists in systems faced with unplanned machine downtime. Comparisons are based on results obtained from simulation of a five-station cell utilised in computer substrate manufacturing. Analysis of the simulation output suggests that the TOC technique, drum–buffer–rope (DBR), achieves higher levels of performance as measured by total output and lead time while reducing inventory requirements relative to the tested JIT technique, Kanban. Improved system performance stems from the strategic placement of buffers in DBR, which maximises protection of the constraint from variation rather than attempting to protect each individual station. Further, analysis suggests that DBR systems are more robust than JIT systems in that they are able to maintain higher levels of system performance across a range of inventory levels.     

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.     

Integrated inventory models with fuzzy annual demand and fuzzy production rate in a supply chain

The successful implementation of Just-in-time (JIT) production in today's supply chain environment requires a new spirit of cooperation between the buyer and the vendor. An integrated inventory model with such a consideration is based on the total cost optimization under a common stock policy and business formula. However, the supposition of known annual demand in most related literature may not be realistic. This paper proposes the inclusion of fuzzy annual demand and/or the production rate, and then employs the signed distance, a ranking method for fuzzy numbers, to find the estimate of the common total cost in the fuzzy sense, and subsequently derives the corresponding optimal buyer's quantity and the integer number of lots in which the items are delivered from the vendor to the purchaser. Numerical examples are provided to illustrate the results of proposed models.