scholarly journals A WMS Model Interfacing with Material Warehouse in Real Time in Puting Materials to Manufacturing Processes - in Automobile Parts Manufacturing Industry -

2014 ◽  
Vol 16 (2) ◽  
pp. 147-155
Author(s):  
Myung-Dal Kong
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Manufacturing Processes ◽  
Automobile Parts ◽  
Parts Manufacturing

scholarly journals Implementation of Digital Twin for Engine Block Manufacturing Processes

2020 ◽  
Vol 10 (18) ◽  
pp. 6578
Author(s):  
Roman Bambura ◽  
Marek Šolc ◽  
Miroslav Dado ◽  
Luboš Kotek
Keyword(s):  
Real Time ◽  
Production Line ◽  
Manufacturing Industry ◽  
Real Life ◽  
Engine Block ◽  
Manufacturing Processes ◽  
Production Plant ◽  
Implementation Framework ◽  
Digital Twin ◽  
Programmable Logic Control

The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals An Empirical Study on the Model of Self-Efficacy and Organizational Citizenship Behavior Transmitted through Employee Engagement, Organizational Commitment and Job Satisfaction in the Thai Automobile Parts Manufacturing Industry

2021 ◽  
Vol 7 (3) ◽  
pp. 170
Author(s):  
Khahan Na-Nan ◽  
Suteeluck Kanthong ◽  
Jamnean Joungtrakul
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Employee Engagement ◽  
Organizational Citizenship ◽  
Organizational Citizenship Behavior ◽  
Self Efficacy ◽  
Manufacturing Industry ◽  
Statistical Significance ◽  
Citizenship Behavior ◽  
Parts Manufacturing

This research aims to study the direct and indirect influence of self-efficacy on organizational citizenship behavior transmitted through employee engagement, organizational commitment and job satisfaction, and to examine employee engagement, organizational commitment and job satisfaction as partial or full mediators. The study samples were 400 employees in the automobile parts manufacturing industry. The study instruments used by previous researchers were applied and back translation was conducted on all questionnaire items. Content validity and reliability was then tested prior to using them for data collection. Direct and indirect influences and mediators were analyzed with the Hayes Model 81 using the PROCESS Program. Results revealed that self-efficacy had a direct influence on organizational citizenship behavior with statistical significance, with an indirect influence transmitted through employee engagement, organizational commitment and job satisfaction. Employee engagement, organizational commitment and job satisfaction functioned as partial mediators between self-efficacy and organizational citizenship behavior with statistical significance. The model was based on the theory of self-efficacy to express organizational citizenship behavior. However, the study results showed that employee engagement, organizational commitment and job satisfaction play roles as mediators in transmission of effective organizational citizenship behavior. Therefore, these mediators are important factors that can accurately explain organizational citizenship behavior.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

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