Optimizing the Assembly Process in the Hang on Part Station by Adding Supporting Tools at Automotive Company PT. XYZ Indonesia

Felix Siswanto Lie, Anton Royanto Ahmad, Setijo Awibowo


Human is the most important aspect in every manual process. When it comes to the manufacturing process, the power of human will affect a huge part of the result. It does not matter how good the raw material is, if the worker cannot do the good job to the material, the product will not be good as well. The same case happens in an automotive company. In their factory plant, most of all activities are done by human. The automatic process can still be counted. These manual processes affect directly to the cycle time in the stations. In this research, the focus of the observation is in a Hang on Part station which is the first station in the assembly line of this automotive factory plant. Since it is the first place to start the assembly process in whole plant, an improvement is important in order to increase the productivity in the assembly line. The improvement is conducted by designing the supporting tools to help the worker in the station optimizing the activity they do, analyzing the product with software to see if there is any mistake by the design, and count the new cycle time by using the supporting tools to see if there is any improvement. It is expected to reduce the cycle time and reduce the worker in the station.


Autodesk Inventor; Manual Process; Material Handling;Stress Analysis; Supporting Tool(s)

Full Text:


Article Metrics

Abstract views : 138| PDF views : 0


Amin, M. A., & M.A, K. (2013). A time-based quantitative approach for selecting lean strategies for manufacturing organizations. International Journal of Production Research, 1146-1167.

Bharath, R., & Prakash, D. G. (2014). Lead time Reduction Using Lean Manufacturing Principles. Global Journal of Finance and Management, 6(1), 6.

Cerniglia-Lowensen, J. (2015). Learning From Mistakes and Near Mistakes: Using Root Cause Analysis as a Risk Management Tool. Jornal of Radiology Nursing, Volume 34, Issue 1, 4-7.

Creutznacher, T., Berger, U., Lepratti, R., & Lamparter, S. (2016). The Transformable Factory: Adapting Automotive Production Capacities. Procedia CIRP, Volume 41, 171-176.

Diban, D., & Gontijo, L. (2015). The Complexity of Ergonomic in Product Design Requirements. Procedia Manufacturing, Volume 3, 6169-6174.

Doddannavar, R., & Barnard, A. (2005). Hydraulic System. Elsevier Science & Technology Books.

Flott, L. W. (2011). Quality Control Root Cause Analysis. Metalfinishing.

Hsieh, P. (2016, February 2). Programming Optimization. Retrieved from Azillion Monkeys: http://www.azillionmonkeys.com/qed/optimize.html

Power, D. (2016). DSS Types. Retrieved from Global Development Research Center: https://www.gdrc.org/

Rajaniemi, J. (2012). Lead Time Terminology in Manufacturing.

Russo, M., Ceccarelli, M., Corves, B., Hüsing, M., Lorenz, M., Cafolla, D., & Carbone, G. (2017). Design and Test of a Gripper Prototype for Horticulture Products. Robotics and Computer-Integrated Manufacturing Vol. 44, 266-275.

Sokovic, M., Pavletic, D., & Pipan, K. K. (2010). Quality Improvement Methodologies. Journal of Achievements in Materials and Manufacturing Engineering, 476-482.

DOI: http://dx.doi.org/10.25105/jti.v9i2.4921


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.