JIT Production System Research Articles

Possibility of Applying JIT Production System And its Impact on Reducing Production Costs An Applied Study on Iraqi Productive Organizations

Possibility of Applying JIT Production System And its Impact on Reducing Production Costs An Applied Study on Iraqi Productive Organizations

An joint decision of production and maintenance plan (Q, N) for a two-stage deteriorating JIT production system with random breakdowns

An joint decision of production and maintenance plan (Q, N) for a two-stage deteriorating JIT production system with random breakdowns

A Simulation Approach for Transition to JIT Production System

A Simulation Approach for Transition to JIT Production System

Effectiveness of JIT Production System among Employees Working in Electrical Industry during Pre-Pandemic Situation at Chennai

Effectiveness of JIT Production System among Employees Working in Electrical Industry during Pre-Pandemic Situation at Chennai

Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

This paper deals with unrelated parallel machines scheduling problem with sequence dependent setup times under fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties, which belongs to NP-hard class. Due to inherent uncertainty in Processing times, setup times and due dates of jobs, they are considered here with triangular and trapezoidal fuzzy numbers in order to take into account the unpredictability of parameters in practical settings. Although this study is not the first one to study on fuzzy parallel machines scheduling problem, it advances this area of research in three fields: (1) it selects a fuzzy environment to cover the whole area of the considered problem not just part of it, and also, it chooses an appropriate fuzzy method based on an in-depth investigation of the effect of spread of fuzziness on the variables; (2) It introduces a mathematical programming model for the addressed problem as an exact method; and (3) due to NP-hardness of the problem, it develops an existing algorithm in the literature for the considered problem through extensive simulated experiments and statistical tests on the same benchmark problem test by proposing a genetic algorithm (GA) and a modified simulated annealing (SA) methods to solve this hard combinatorial optimization problem. The result shows the superiority of our modified SA.

単一組立工程における異なる組立時間をもつ製品の投入順序決定法 : JIT生産方式における多品種の組立スケジューリング(第一報)

単一組立工程における異なる組立時間をもつ製品の投入順序決定法 : JIT生産方式における多品種の組立スケジューリング(第一報)

Mixed-model sequencing problem under capacity and machine idle time constraints in JIT production systems

Abstract Sequencing problems in mixed-model production systems assume a negligible set-up time that permits unconstrained product interspersing. This assumption offers better production sequences in terms of just-in-time objectives such as minimizing parts usage rate variation and minimizing Production Rate Variation (PRV). However, set-up times turn out to be critical in many realistic situations. The present paper addresses the sequencing problem in mixed-model production systems, where internal and external set-up times are considerable measures. Internal set-up time limits free mixing of products, resulting in batching of products; while external set-up time causes machine idle time. The authors present a mathematical model for the problem to find sequences that minimize PRV, satisfying capacity and zero machine idle time constraints. LINGO 15.0 solver could solve the model for small-size problems in a reasonable computational time. For large-size problems, a modified bit-wise mutation algorithm that uses a single unary genetic operator has been developed. The authors have applied the algorithm for diverse problem instances at different production capacities. The same problems are also analyzed by relaxing the zero machine idle time constraint. Results demonstrate how the constraints on capacity and machine idle time disturb the leveling of sequences in mixed-model production systems. The model provides decision makers with the opportunity to select smooth sequences based on the available production time and acceptable machine idle time.

JIT in the Jordanian Industrial Companies

This study aimed at recognizing the application of JIT in the Jordanian public industrial companies. The study adopted the descriptive-analytical approach. To accomplish the study objectives the researcher designed a questionnaire and distributed to a sample of 55 out of 76 industrial companies that represent the population. All distributed forms were retrieved and analyzed by SPSS. The study found that Jordanian public industrial companies don’t apply JIT production system, besides that there were some obstacles that hinder the applying of JIT production system in these companies represented by lack of experience and lack of awareness by the top management. The study recommended making more efforts to increase the awareness of companies’ top management of JIT importance as well as secure the experiences, training courses and the costs necessary to apply such system.

A note on ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947)

This note discusses three points about the problem of sequencing units on a mixed-model assembly line in ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947). Specifically, the mixed-integer quadratic model, the bit-wise mutation algorithm and the bi-objective problem to optimise both product rate variation and makespan. The conclusion of the discussion is that the three alleged contributions are not valid or are outperformed by those presented in some previously published papers.

A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems

Product sequencing in a mixed-model production system is an operational level problem and is computationally hard. This necessitates the use of non-traditional optimisation methods to solve it in a short time. In the first phase, this study addresses the mixed-model sequencing problem with the objective of minimising production rates variation (PRV) which is a just-in-time objective. We formulated a mixed integer quadratic assignment model for this problem. LINGO 14.0 solver could solve this model for small- and medium-size problems. The study presents a bit-wise mutation algorithm to solve the sequencing problem with the same objective. This algorithm uses a single unary operator which makes it simpler and faster. Computational results show that the algorithm succeeds in solving large-size PRV problems in a reasonable time, for which the algorithms reported in the literature could only get near-optimal solutions. In the second phase, we consider both PRV and one of the system performance measures, makespan as objectives for the mixed-model sequencing problem. A mathematical description of the underlying model is provided. The bit-wise mutation algorithm is modified to generate a set of non-dominated solutions which provide the decision-maker with the opportunity to trade-off between the two objectives.

A solution approach to a synchronisation problem in a JIT production system

Delivering of orders on time, increasing productivity and reducing costs are all challenges that companies have to cope with on a regular basis. Making production lines compatible solves these problems and means a reduction in line stoppages and cycle time. In continuous production systems in which production is carried out in lots, the main ways to ensure an uninterrupted and smooth flow and have a high production rate, are line balancing and synchronising work stations. In this paper, a line stoppage and productivity problem at an automotive factory (Toyota Turkey plant, Sakarya city) is solved by root-cause analysis. Cycle time and in-process inventory inconsistency causes the problem between paint and assembly lines. Different solutions are researched and the most appropriate one is selected and implemented.

Just-In-Time Manufacturing System: From Introduction to Implement

JIT production system identifies the hidden problems in the value chain and reduces the production waste of the system while increasing the throughout (Sales-Raw Material Cost). Even though the JIT system seems to be interesting and less complicated it requires lot of coordination with supply chain to avoid delays in the production schedule. This article discusses in depth the implementation of JIT manufacturing. The objectives are twofold. The first objective is to acquaint the reader with the overall JIT concept and the factors necessary for its implementation; the concepts presented here represent the ideal principles and methods of implementation.

The Effect of Optimum Number of Kanbans in just in Time Production System to Manufacturing Performance

This paper discusses a method to determine optimum numbers of kanban in Just In Time production system and analyzes effect of optimum numbers of kanban to manufacturing performance. Method to determine kanban numbers was carried out by designing kanban flow based current situation, gathering relevant parameter on floor and company database, and calculating of kanban quantity through basic mathematical model. The basic mathematical model of kanban is derived according to designated kanban flow in JIT production system. The optimum number of kanbans would control and regulate material flow in manufacturing, thus JIT is achieved. As a result, product lead time and inventory stock level are improved. The conclusion is that an appropriate determination numbers of kanban could lead to optimizing manufacturing capability.

Evaluation of Different Factors in Implementation of JIT Production System by Applying Simulation and Entropy Weighting Method

Competitive advantage and more profit and eliminating wastes will cause organizations to use new methods and techniques to enhance their performance and reduce the wastes and inventory in their production process. To achieve these goals, statistical methods such as simulation and mathematically method such as Entropy weighting is used to evaluate the weight and importance of factors in implementation of Just in Time philosophy in manufacturing systems. To certify the results of this research a case study is presented which is a part of production line of one company in IRAN. The result will be discussed about the effects of factors on production line and decision making process for managers.

Sequencing Method for Products with Consideration of Assembly Time and Parts Delivery Cycle Time

To keep the consumption rate of each part is very important for mixed-model assembly line scheduling in JIT production system. The goal chasing method (GC) and a number of papers have been proposed to achieve the parts usage leveling goal. These methods keep the consumption speed of parts constant as much as possible, and aim to decrease the amount of the stock in pre-process indirectly. In this paper, we set objective function based on the stock in pre-process directly. The function is influenced by different assembly time and delivery cycle among each product. And we have proposed the sequencing method. The method is concept of time-based goal chafing method (TBCG) with consideration in parts delivery cycle. By numerical experiments, effect of proposed method is presented.

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.

The Moderate Effect of SPC on the Relations Between Practicing JIT and Production Performance

Our goal of this study is to confirm whether manufacturing companies’ practicing JIT production system have positive effect on production performances and to confirm that manufacturing companies’ practicing SPC shows positive moderate effect on the relation between practicing JIT and their production performances. Based on empirical study to manufacturing companies nationwide, we can conclude that taking JIT practices of sample companies has positive effect on production performances and, moreover, practicing SPC has positive moderate effect on the relation between JIT practices and production performances. These conclusion represents managerial implication that concurrent practicing JIT and SPC could generate more improved production performances.

Performance evaluation of SCM in JIT environment

Under stochastic demand and deterministic processing times, we discussed a single-stage JIT production system with the production-ordering and supplier kanbans and derived a probability generating function (p.g.f.) of the stationary distributions of the backlogged demand. In this paper, we extend the system to supply chain management (SCM) in JIT environment with two kinds of kanbans under stochastic demand, deterministic processing times and withdrawals with lead time. These conditions are more realistic than the previous papers. We develop an algorithm for the exact performance evaluation of the SCM such as the stationary distributions of the inventory level, production quantities and total backlogged demand in each stage, using discrete-time Markov process. Optimal numbers of two kinds of kanbans in the system are determined by minimizing a general total cost function. Numerical examples are given to show the efficiency of the proposed approach.

A method to enhance volume flexibility in JIT production control

The issue of strategic flexibility in manufacturing is reviewed to establish the links between output flexibilities and resource level characteristics. Through these links, and considering a JIT environment, the need for flexible Kanban determination is then recognised with respect to demand fluctuations in the marketplace. This is also confirmed through a review of the related literature. Based on this recognition, a method is proposed, 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. The effectiveness of the proposed method is then examined and compared with the results for the conventional method of fixed Kanban determination. The comparison proved a cost advantage for the proposed method over the conventional method in fluctuating demand situations while the cost advantage increases with increase in demand fluctuations. The proposed method incorporates practical limitations for changing the number of Kanbans in any stage of production. This aspect of the method is also discussed in the paper.

Time-based goal chasing method for mixed-model assembly line problem with multiple work stations

In sequencing problems for mixed-model assembly line in JIT production system, the Goal Chasing method (GC) is widely used for parts used leveling goal. The difference in assembly time of each product is not taken into consideration in the Goal Chasing method. Assembly time usually varies with product types. In recent years, the Time-Based Goal Chasing method (TBGC) has been proposed. The advantage of TBGC is to consider the influence of different assembly time of each product and idle time in production period. TBGC, however, has been only applied to single work station problems. In this paper, TBGC is applied to an assembly line problem with multiple work stations. Furthermore, the sequencing method and use of Simulated Annealing (SA) or Local Search (LS) for this problem are proposed.