PRODUCTIVITY IMPROVEMENT OF AN AUTOMOTIVE ASSEMBLY LINE USING MODULAR ARRANGEMENT OF PREDETERMINED TIME STANDARDS (MODAPTS)

Author(s):  
KUMAR RAJ ◽  
GAGANDEEP ◽  
CHARAK ABHISHEK ◽  
THAKUR GAURAV ◽  
◽  
...  
Author(s):  
A.N. Mustafizul Karim ◽  
Saravanan Tanjong Tuan ◽  
H.M. Emrul Kays

Purpose The purpose of this paper is to address and solve operational problems of an automotive industry in reaching production target by adopting Maynard Operation Sequence Technique (MOST) as lean and productivity improvement strategies. Design/methodology/approach In the undertaken case of auto-car rear window assembly line, a recurring production shortfall in fulfilling the daily demand is seemingly due to inappropriate work method. Initial observation of the operations led to suspect certain lapses in initiatives to adopt the time standards, to reduce or eliminate non-value added motions, to design suitable aisle and to assign tasks among workstations in a balanced manner. Subsequently an attempt is made to pinpoint the causes of poor performance and the bottlenecks through process flow analysis and time study by applying MOST. The elemental tasks are closely examined for possible reduction of workstation times by choosing efficient work methods with ergonomic features. Thus appropriate hand tools, jigs and fixture with nominal investment are prescribed to incorporate in the assembly works. The operational changes as steered by the MOST application have enhanced the workflow with a shorter cycle time which led to a substantial increase in productivity. Findings The productivity of the assembly line is increased by more than 29 percent from the earlier capacity through the MOST application which is deemed to meet the current level of demand. Originality/value The adopted framework for recognizing the effectiveness of MOST to expose and rectify the flaws in work methods without much investment is expected to be beneficial for a manufacturer in securing the competitiveness.


2014 ◽  
Vol 687-691 ◽  
pp. 4056-4059
Author(s):  
Cheng Li Pang

With the mass production and use of car, the social are also increasing the requirements the automotive industry development. More and more automobile manufacturers are hoping to establish an efficient identification system, so that management would be enhanced to improve the efficiency and reduce the error rate. What’s more, motor-dom has been the important application field of RFID technique. In this paper, the paper is to carry out a detailed analysis of technology and research for RFID anti-collision system to guide manufacturers to improve the efficiency of RFID systems in the automotive assembly line and to solve the problem of collision multi-tag identification.


2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Lijun Liu ◽  
Zuhua Jiang ◽  
Bo Song ◽  
Hongyuan Zhu ◽  
Xinyu Li

The operational knowledge of skilled technicians gained from years of experience is invaluable for an enterprise. Possession of such knowledge will facilitate an enterprise sharing technician’s know-how and training of new employees effectively. However, until now there is rare efficient quantitative method to obtain this kind of tacit knowledge. In this paper we propose a concept of engineering-oriented operational empirical knowledge (OEK) to describe this kind of knowledge and design a framework to acquire OEK from skilled technician’s operations. The framework integrates motion analysis, motion elicitation, and intent analysis. The modular arrangement of predetermined time standards (MODAPTS) is used to divide the technician’s operational process into basic motion elements; and the variable precision rough set (VPRS) algorithm is used to extract the technician’s OEK content, which combined with the technician’s intent elicited via interview; the completed OEK is obtained. At the end of our study, an engineering case is used to validate the feasibility of the proposed method, which shows that satisfactory results have been reached for the study.


2014 ◽  
Vol 2 (2) ◽  
pp. 22-39 ◽  
Author(s):  
Annamalai Pandian ◽  
Ahad Ali

This research paper aims to predict the automotive Body-In-White (BIW) robotic welding assembly line performance. A combinational prediction model based on the Autoregressive Moving Average (ARMA) and Artificial Neural Networks (ANN) is developed. Classical methods are often used to predict the assembly line throughput, but not ideal. A combinational prediction model is applied for comprehensive analysis and prediction of the assembly line throughput. The various case studies presented in this paper indicate that the precision of the model is better than the other models. This research has significant practical value to the assembly plant because, based on the prediction, plant can make commitment to achieve the production to meet the market demand. Unpredictable performance of the assembly line in the plant leads to more overtime, less time for maintenance and eventually hurting the company bottom line.


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