scholarly journals RELIABILITY ENGINEERING OF LARGE JIT PRODUCTION SYSTEMS

2012 ◽  
Vol 19 (03) ◽  
pp. 1250011 ◽  
Author(s):  
JOSE FARIA ◽  
EUSEBIO NUNES
Keyword(s):  
Production System ◽  
Production Systems ◽  
Structural Characteristics ◽  
Evaluation Framework ◽  
System Level ◽  
Reliability Engineering ◽  
Generalized Distributions ◽  
Work In Process ◽  
Failure Propagation ◽  
Jit Production

This paper introduces the rationale and the fundamental elements and algorithms of a reliability engineering methodology combining analytical and simulation tools, and discusses its application to the design of a large, multi-cell and heterogeneous production system with just-in-time (JIT) deliveries. In order to cope with the inherent complexity of such analysis, a two level hierarchical modeling and evaluation framework was developed. Local models are first obtained from the failure and repair processes of the manufacturing equipment. Then, these models are combined with the failure propagation delays introduced by the work-in-process buffers in order to obtain the system level model. The evaluation algorithm is able to deal with reliability models containing stochastic processes with generalized distributions. This fundamental requirement comes from the fact that repair and failure propagation processes typically present hyper-exponential distributions, e.g., lognormal distributions, that cannot be assessed using the conventional reliability techniques. The second part of the paper addresses several design issues of the production system that directly impact the reliability of the deliveries, and explains how the behavioral and structural characteristics of JIT production systems were explored in order to implement effective evaluation algorithms.

Just-In Time Production System Using Fuzzy Logic Approach and a Simulation Application

2012 ◽  
Vol 445 ◽  
pp. 1029-1034 ◽  
Author(s):  
Yavuz Ozdemir ◽  
Pelin Alcan ◽  
Huseyin Basligil ◽  
Cagri Dokuz
Keyword(s):  
Fuzzy Logic ◽  
Production System ◽  
Production Systems ◽  
Low Cost ◽  
Production Method ◽  
Transformation Process ◽  
Just In Time ◽  
Time Production ◽  
Fuzzy Logic Approach ◽  
Jit Production

While firms are operating in a global competitive environment, they are subjected to changes because of the increased competitiveness and developed technologies. Therefore, this transformation process forces to produce with just-in-time production and low cost products or services and leads to customer satisfaction. Until today, competitive conditions, efficiency, productivity and quality of production, forced the firms to put more emphasis on production systems. Therefore, the firms are more interested in scientific analysis, planning and controlling of their production systems. As a result, one of the newest approaches is Just in Time (JIT) production system which emerged after WWII in Japan and aims to decrease the inventory cost and maximize the quality. The philosophy of this approach is to produce the necessary amount of production, when and where needed at the required quality.But JIT production system is weak in unclear species. For this purpose, the general and necessary solution is using fuzzy logic. In this paper we discussed about the simulation of an assembly line with 3 steps; firstly using Kanban production method, secondly non-using Kanban production method, and lastly using Kanban production method with fuzzy times. And also the comparisons of these steps will also be studied in this paper.

scholarly journals A novel analysis model for optimization performance of Bernoulli serial production system considering rework processes

2019 ◽  
Vol 234 (1-2) ◽  
pp. 295-309
Author(s):  
Binghai Zhou ◽  
Song Lin
Keyword(s):  
Quality Management ◽  
Production System ◽  
Manufacturing Systems ◽  
Production Systems ◽  
System Modeling ◽  
System Level ◽  
Recent Analysis ◽  
Analysis Model ◽  
Serial Production ◽  
Traditional Approaches

Production system modeling aims to investigate the principles of production procedures and to reveal the relationship between components and systems. Tremendous efforts have been devoted to production system modeling for the serial production system. However, most of the research focuses on the analysis of the systems at the steady state. Due to the emphasis of the quality management, production systems with rework loops are widely used in today’s manufacturing industrials, which the traditional approaches are not applicable to. Since the recent analysis of transients shows significant value and great potential in manufacturing systems, in this article, a new mechanism for rework is introduced based on the principles of quality management and lean production. A novel “Instant-Checking” method is developed to model Bernoulli serial production system considering rework loops. This method overcomes conventional restrictions and limited assumptions, and it extends the problem to systems with complex structures. Meanwhile, the analysis for transients is conducted to demonstrate relationships between component- and system-level characteristics. Finally, numerical experiments are performed to verify the effectiveness of the model.

scholarly journals A Comparative Study of Unbalanced Production Lines Using Simulation Modeling: A Case Study for Solar Silicon Manufacturing

2022 ◽  
Vol 14 (2) ◽  
pp. 697
Author(s):  
Chen-Yang Cheng ◽  
Shu-Fen Li ◽  
Chia-Leng Lee ◽  
Ranon Jientrakul ◽  
Chumpol Yuangyai
Keyword(s):  
Cycle Time ◽  
Performance Optimization ◽  
Production System ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Optimization Technique ◽  
Production Lines ◽  
Work In Process ◽  
Pull Production ◽  
Solar Silicon

In the solar silicon manufacturing industry, the production time for crystal growth is ten times longer than at other workstations. The pre-processing time at the ingot-cutting station causes work-in-process (WIP) accumulation and an excessively long cycle time. This study aimed to find the most effective production system for reducing WIP accumulation and shortening the cycle time. The proposed approach considered pull production systems, and the response surface methodology was adopted for performance optimization. A simulation-based optimization technique was used for determining the optimal pull production system. The comparison between the results of various simulated pull production systems and those of the existing solar silicon manufacturing system showed that a hybrid production system in which a kanban station was installed before the bottleneck station with a CONWIP system incorporated for the rest of the production line could reduce the WIP volume by 26% and shorten the cycle time by 16% under the same throughput conditions.

scholarly journals Soil Bacterial Community Diversity and Composition as Affected by Tillage Intensity Treatments in Corn-Soybean Production Systems

2021 ◽  
Vol 12 (1) ◽  
pp. 157-172
Author(s):  
Shankar G. Shanmugam ◽  
Normie W. Buehring ◽  
Jon D. Prevost ◽  
William L. Kingery
Keyword(s):  
Microbial Community ◽  
Bacterial Community ◽  
Community Composition ◽  
Production System ◽  
Soil Microbial Community ◽  
Production Systems ◽  
Bacterial Community Composition ◽  
Soybean Production ◽  
Soil Microbial ◽  
Soil Bacterial

Our understanding on the effects of tillage intensity on the soil microbial community structure and composition in crop production systems are limited. This study evaluated the soil microbial community composition and diversity under different tillage management systems in an effort to identify management practices that effectively support sustainable agriculture. We report results from a three-year study to determine the effects on changes in soil microbial diversity and composition from four tillage intensity treatments and two residue management treatments in a corn-soybean production system using Illumina high-throughput sequencing of 16S rRNA genes. Soil samples were collected from tillage treatments at locations in the Southern Coastal Plain (Verona, Mississippi, USA) and Southern Mississippi River Alluvium (Stoneville, Mississippi, USA) for soil analysis and bacterial community characterization. Our results indicated that different tillage intensity treatments differentially changed the relative abundances of bacterial phyla. The Mantel test of correlations indicated that differences among bacterial community composition were significantly influenced by tillage regime (rM = 0.39, p ≤ 0.0001). Simpson’s reciprocal diversity index indicated greater bacterial diversity with reduction in tillage intensity for each year and study location. For both study sites, differences in tillage intensity had significant influence on the abundance of Proteobacteria. The shift in the soil bacterial community composition under different tillage systems was strongly correlated to changes in labile carbon pool in the system and how it affected the microbial metabolism. This study indicates that soil management through tillage intensity regime had a profound influence on diversity and composition of soil bacterial communities in a corn-soybean production system.

A New Procedure of Production Orders Sequencing in Mixed-Model Production Systems

2014 ◽  
Vol 1036 ◽  
pp. 864-868 ◽  
Author(s):  
Marcin Zemczak ◽  
Damian Krenczyk
Keyword(s):  
Task Scheduling ◽  
Production System ◽  
Mixed Model ◽  
Production Systems ◽  
Production Capacity ◽  
Approximate Solutions ◽  
Task Sequence ◽  
Optimal Method ◽  
Sequencing Problem ◽  
Car Sequencing

The paper presents the task scheduling issue, which main aim is to establish a proper sequence of tasks, that would maximize the utilization of companys production capacity. According to the literature sources, the presented sequencing problem, denoted as CSP (Car Sequencing Problem) belongs to the NP-hard class, as has been proven by simple reduction from Hamiltonians Path problem. Optimal method of solution has not yet been found, only approximate solutions have been offered, especially from the range of evolutionary algorithms. Regardless of specific production system, while considering reception of new tasks into the system, current review of the state of the system is required in order to decide whether and when a new order can be accepted for execution. In this paper, the problem of task scheduling is limited to the specific existing mixed-model production system. The main goal is to determine the effective method of creation of task sequence. Through the use of computational algorithms, and automatic analysis of the resulting sequence, rates of production are able to be checked in a real time, and so improvements can be proposed and implemented.

scholarly journals Using Computer Simulation Method to Improve Throughput of Production Systems by Buffers and Workers Allocation

2015 ◽  
Vol 6 (4) ◽  
pp. 60-69 ◽  
Author(s):  
Sławomir Kłos ◽  
Peter Trebuna
Keyword(s):  
Computer Simulation ◽  
Production System ◽  
Production Systems ◽  
Simulation Method ◽  
System Throughput ◽  
Buffer Allocation ◽  
Simulation Experiments ◽  
Buffer Capacities ◽  
The Relationship ◽  
Support Decision Making

Abstract This paper proposes the application of computer simulation methods to support decision making regarding intermediate buffer allocations in a series-parallel production line. The simulation model of the production system is based on a real example of a manufacturing company working in the automotive industry. Simulation experiments were conducted for different allocations of buffer capacities and different numbers of employees. The production system consists of three technological operations with intermediate buffers between each operation. The technological operations are carried out using machines and every machine can be operated by one worker. Multi-work in the production system is available (one operator operates several machines). On the basis of the simulation experiments, the relationship between system throughput, buffer allocation and the number of employees is analyzed. Increasing the buffer capacity results in an increase in the average product lifespan. Therefore, in the article a new index is proposed that includes the throughput of the manufacturing system and product life span. Simulation experiments were performed for different configurations of technological operations.

Responsive Production Systems and Roles that Robots can Play

2009 ◽  
Vol 62-64 ◽  
pp. 275-292
Author(s):  
R.H. Weston
Keyword(s):  
Building Block ◽  
Production System ◽  
World Wide ◽  
Production Systems ◽  
Industrial Robots ◽  
Robotic Systems ◽  
Different Types ◽  
Useful Lifetime

With increased product dynamics world-wide, the average economic lifetime of production systems is falling. Industrial robots are widely assumed to be inherently flexible and therefore that they can function as a programmable building block of response production systems. This paper reviews common capabilities of contemporary industrial robotic systems and investigates their capability to extend the useful lifetime of production system by coping with different types of product dynamic. Also considered are relative capabilities of conventional programmable robots and an emerging generation of programmable and configurable component-based machines.

System dynamics modelling of fixed and dynamic Kanban controlled production systems: a supply chain perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jagan Mohan Reddy K. ◽  
Neelakanteswara Rao A. ◽  
Krishnanand Lanka ◽  
PRC Gopal
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Cycle Time ◽  
Production System ◽  
Demand Uncertainty ◽  
Production Systems ◽  
Decision Variable ◽  
Content Type ◽  
Pull Production ◽  
Kanban System

Purpose Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty. Design/methodology/approach In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios. Findings From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible. Research limitations/implications In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size. Originality/value This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

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