Just-in-time Production System Research Articles

Algorithms for sequencing mixed models on an assembly line in a JIT production system

In many mixed-model assembly lines in just-in-time (JIT) production systems, workers have the power and the responsibility to stop the conveyor whenever they fail to complete their operations within their work zones. Therefore, the conveyor stoppage in a sequencing problem should be taken into consideration. In this paper, two algorithms are proposed for finding an optimal or sub-optimal sequence of mixed models that minimizes the total conveyor stoppage time. The branch-and-bound method is devoted to find an optimal solution for small sized problems, while the simulated annealing method is used to cope with large scale problems to obtain a good sub-optimal solution. A numerical example of a 14 model problem shows that the simulated annealing algorithm is about 100 times faster than the branch-and-bound algorithm to find an optimal solution.

Modifying Mixed-Model Assembly Line Sequencing Methods to Consider Weighted Variations for Just-In-Time Production Systems

Mixed-model assembly lines are widely used in just-in-time (JIT) production systems. One of the commonly used goals in sequencing mixed-model assembly lines in JIT production systems is to maintain...

Order Release in JIT Production Systems: A Simulation Study

As an order release system for multi-stage production inventory systems, this paper deals with the Kanban system of Just-in- Time (JIT) production. This paper develops a queuing network model of the Kanban system with a simulation language for discrete-event simulation, SLAM II. Also, this paper aims to improve the Kanban system by modifying the flow of information. By means of discrete event simulation, the performances of the models are investigated and compared.

Just‐in‐time implementation in the Scottish electronics industry

Central Scotland has been successful in replacing many of its traditional industries with a network of over 300 electronics manufacturers and suppliers creating the area popularly known as “Silicon Glen”. Silicon Glen appears to be a very suitable site for the implementation of just‐in‐time (JIT) production systems with both electronics manufacturers and suppliers concentrated in the same geographical area. Contrasts the theory and practice of JIT implementation in “Silicon Glen” and assesses its success in six companies currently operating JIT production systems. The main conclusion is that JIT has been implemented in a variety of forms, with companies selecting the aspects of JIT that are most suitable for their manufacturing process and competitive strategy. JIT is proving highly successful and is bringing substantial gains in quality and efficiency.

Optimal numbers of two kinds of kanbans in a JIT production system

The production-ordering and withdrawal kanbans are used in a Just-in-Time (JIT) production system. In particular, a withdrawal kanban used for a vendor is called a supplier kanban. This paper deals with the JIT production system with the production-ordering and supplier kanbans under stochastic demand. A necessary and sufficient condition, called a stability condition, is derived under which the JIT production system has a stationary distribution of the backlogged demand. An algorithm is devised for determining optimal numbers of two kinds of kanbans that minimize an expected average cost per period. In other words, it determines optimal values of the safety stocks included in the popular formulas for computing the numbers of kanbans. Numerical results are given to show its efficiency

An enlarged JIT programme: its impact on JIT implementation and performance of the production system

Abstract During the past two decades, Japan has emerged as the second economic power of the world. Japan's success has prompted many scholars and practitioners to turn their attention to Japanese management practices. The just-in-time (JIT) production system has been the primary focus of research. However, one major area of JIT which has not been thoroughly studied is the relationship between the customer and the producer. This study explores the concept and practices of enlarged JIT, which involves customers in the JIT plan. Through a descriptive field study, it investigates the impact of enlarged JIT on JIT implementation and production system performance.

PARTICIPATION IN AMERICAN VERSUS JIT SYSTEMS: IS IT THE SAME?

The just‐in‐time (JIT) production system is notable for its emphasis on employee involvement and participation. However, we suggest that the role of participation that is most typically described in the organizational behavior (OB) literature does not match the type of participation practiced in JIT. We introduce a theoretical framework that accounts for these different perspectives: whereas the OB approach treats participation as an intervention and hence as an independent variable, the JIT approach sees it as a side effect of the JIT production system and hence a dependent variable. Understanding of the differences is essential in JIT implementation in the workplace if we are to avoid miscommunication, stress, and disbelief in the system.

A sequencing problem for a mixed-model assembly line in a JIT production system

In an assembly line of a just-in-time (JIT) production system, workers have the power and the responsibility to stop the line when they fail to complete their operations within their work zones. This paper deals with a sequencing problem for the mixed-model assembly conveyor line in the JIT production system. In some environment, the most important criterion is the line stoppage rather than the variation of production rates. The problem is to find an optimal sequence of units that minimizes the total line stoppage time. Lower and upper bounds of the total line stoppage time are derived and the branch-and-bound method is applied to the problem. A numerical example is given.

On some factors affecting the just-in-time production system output variability: A simulation study using Taguchi technique

The main objectives of this research were to analyze the effect of different maintenance policies, machines unreliability, and some other factors on the Just-In-Time (JIT) production system output variability or stability. Maintenance policies of type I and II which were introduced by Proshan and Hunter, and maintenance policy III, which was introduced by Makabi Hajime et al., were considered in this study and their effect on the JIT production system were analyzed. In this research, mathematical predictive and integrative models were developed to demonstrate the effects of different maintenance policies (A), machine unreliability (B), ratio of preventive maintenance time to processing time (D), production line size (C), processing time variability (E) and ratio of minimal repair time to preventive maintenance time (F) on the production line output variability (PLV) in a JIT production system environment. Functional relationship between the dependent variable PLV and the independent variables A, B, C, D, E and F were developed. Using the Taguchi approach in this experimental design all main factors used in this experiment were tested at three levels each using on L27 orthogonal plan, then all main factors and their first order interactions were tested using an L32 orthogonal plan of experiment. Discrete-event simulation, using the GPSS/H simulation language, was employed in order to collect the needed data. The analysis of the data showed that under different situations, different maintenance policies do not have the same effect on the production line performance. With regard to the PLV, maintenance policy III led to a higher variation than policy II, except at the level where the number of machines is less than or equal to 5. The results of this study also demonstrate that the production line performance under policy II is more sensitive to the change on the ratio of minimal repair time to preventive maintenance time when under policy III the same production time performance is more sensitive to production line size. Therefore, the results of this study should help the user in choosing a maintenance policy as a function of the production process parameters in order to decrease production line output variability and improve productivity and stability.

Determining economic inventory policies in a multi-stage just-in-time production system

New manufacturing concepts, such as just-in-time (JIT) production and quality at source tremendously impact on productivity and quality in many manufacturing systems. In order to implement the JIT manufacturing concept, one has to analyze its consequence on lot-sizing and work-in-process inventory. Realising the significance of modelling such a situation, a mathematical model is proposed to establish the relationship between the quality at source, work-in-process inventory and lot-sizes in a multi-stage JIT production system. The model along with a search method is used to determine the economic production quantities (EPQs) by minimizing the total system cost.

Equipment Selection Problems in Just-in-Time Manufacturing Systems

This paper is concerned with the development of a mathematical model for solving the problem of equipment selection in just-in-time (JIT) manufacturing systems. Equipment selection issues are important, particularly during the initial stages of implementing JIT. In this paper a mathematical model is proposed for capacity planning and for selecting equipment in a multi-stage JIT production system. An example situation is considered to explain the application and behaviour of the model.

Effect of process unreliability on integrated manufacturing/ production systems

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. The basic idea of JIT production is to produce the right amount of product at the right time. Unfortunately JIT production has been misinterpreted by some as a method that can achieve zero or minimal WIP with a lot size of one. There are no models or theories to achieve the JIT goals 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 what and how much to produce for workstations in the preceding stage. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective to minimize WIP inventory level, one model dealing with two cases of production configuration is developed for deciding the optimum number of kanbans between two adjacent workstations. One case is for the one station to one station configuration (see Section 3 for explanation). The other case is for the multiple station to one station configuration. This model is then solved using a Markov Process approach. The reason for using a Markov process is that we consider the demand of finished products and the production rates of stations as stochastic processes with exponential distribution. In this paper, the model and solution procedure are detailed along with numerical examples.

Making JIT work at NCR Japan

Just-in-time production system (JIT) is a loop line which connects the customer, the producer, and the suppliers. It should be based on long range planning and short range planning. Also the planning is based on good communication between the three partners. The long-term cooperation of the suppliers is essential. Cooperation starts in the early stages of new product development. The frequency of distribution of components can be determined by financial consideration. A visualization of the production control system is useful to enhance the effectiveness of a JIT system. This article also discusses the limitations of the JIT approach

Optimum number of kanbans between two adjacent workstations in a JIT system

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. The basic idea of JIT production is to produce the right amount of product at the right time. Unfortunately JIT production has been misinterpreted by some as a method that can achieve zero or minimal WIP with a lot size of one. There are no models or theories to achieve the JIT goals 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 what and how much to produce for workstations in the preceding stage. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective to minimize WIP inventory level, one model dealing with two cases of production configuration is developed for deciding the optimum number of kanbans between two adjacent workstations. One case is for the one station to one station configuration (see Section 3 for explanation). The other case is for the multiple station to one station configuration. This model is then solved using a Markov Process approach. The reason for using a Markov process is that we consider the demand of finished products and the production rates of stations as stochastic processes with exponential distribution. In this paper, the model and solution procedure are detailed along with numerical examples.

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

SUMMARY 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 usin...

Impact of Computer Technology on the Implementation of Just‐in‐time Production Systems

This article examines the impact of introducing computer technology in just‐in‐time (JIT) systems. Literature review has generally supported the notion that introducing computers within JIT production systems may enhance productivity. Also, the productivity of a computer integrated manufacturing (CIM) system may be enhanced by integrating it with the just‐in‐time (JIT) production system. This integrated production system is called the computer integrated just‐in‐time (CIJIT) production system. This empirical study provides strong evidence that integration of CIM and JIT can significantly improve a firm′s productivity and competitiveness.

Increasing the production rate of a just-in-time production system with variable operation times

Abstract This paper examines a just-in-time (JIT) system with kanbans with three subassembly lines feeding a final assembly station. Variability in operation times exists and variability effects are reduced by increasing work in process levels or by unbalancing the subassembly lines through assignment of work content at each station. Of the several unbalancing methods that were analysed in this study, only the high-medium-low showed a consistent improvement in the output rate of the JIT production system. The output rates with unbalanced stations were always superior to the output rate of the perfectly balanced configurations used as controls. The extent of improvement over the output rate of balanced systems increased directly with the variability in operation times in final assembly and subassembly stations and inversely with the interstage buffer capacity allowed in the system.

Just‐In‐Time Production System: Some Requirements for Implementation

Many consider that Japanese manufacturing management techniques are among several factors which have contributed to Japan's superior quality and high growth rate of productivity. Just‐In‐Time (JIT) is one of the best known Japanese production management systems amongst management in the Western industrialised nations. In this paper the JIT manufacturing system and its key features are discussed. The important prerequisites and factors that are required for implementation of JIT in Western industrialised nations are then elaborated on. It is concluded that proper implementation of the JIT production system requires many changes in the organisation; yet successful implementation results in improved productivity and higher quality.