Published at : 25 Jan 2021
Volume : IJtech
Vol 12, No 1 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i1.4068
Arief Suwandi | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Teuku Yuri Zagloel | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Akhmad Hidayatno | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
The condition
of risk control failure
causes many consumer complaints because many defective products are found with
purchase orders for iron pipes. Storage management is very important for
companies in maintaining quality and delivery accuracy for customer
satisfaction. At
this stage, there is a definite risk of failure from finished product control,
such as material handling errors and product damage due to storage. The purpose
of this research is to develop a failure risk control model in the finished
goods inventory system. Iron Pipe defects are caused by poor material handling
and product storage in the company. Exogenous variables from this simulation are
the reliability of product handling, percentage of successful rework, and
percentage of deteriorated product. The simulation results show that the
optimistic scenario has the smallest defect of 0% and is followed by a most
likely scenario of 1% and a pessimistic scenario of 4%. The resulting model can
minimize the risk of failure of iron pipe products in finished goods
warehouses, and the model can be applied in more complex real-world cases.
Deteriorated percentage of success rework; Optimistic scenario; Product handling; Warehouse
The rapid development
of science and technology requires every company to have good product quality
to compete with their business competition. A quality product is something that
can meet consumer expectations. Companies that produce iron pipes use steel plate
raw materials which are generally used for construction, such as Pipes, Casing
and Tubing, Subsea Pipes, Steel Water Pipes, Steel Pipes for Piles, and Steel
Pipes for general structures. The risk control failure condition resulted in
many complaints from consumers because defective products were often found in
pipe purchase orders. This is a serious problem for the management of the
pipeline company, and they need to immediately take corrective action to
overcome the problem of defective products being manufactured (Suwandi
et al., 2020).
The risk of failure to store finished products determines how the company proceeds in maintaining product quality and company sustainability. If the defect to a product is high, the company will experience losses and a lack of customer trust, resulting in serious disruption to the company (Suwartha et al., 2015). The risk of failure needs to be identified and then a model developed to reduce the failure to store the finished product (Kilibarda, 2013). Factors causing failure in storage include oxidation, aging, mildew, sealing failure and other slow chemical or physical processes (Liu and Liu, 2018).
The selection of the most suitable
selective inspection, partial flow control, and defect correction policy is
based on an analysis of the impact of actions on the overall system and the
quality performance of the entire process chain, so that quality and
productivity can be maintained at the system level (Grösser,
Reyes-Lecuona, & Granholm, 2017).
This study aims to design a model to control
the risk of failure of iron pipe products in the finished product warehouse by
using a dynamic system that can help reduce the number of damaged products
produced by the company.
This
study focuses on the manufacture of metal pipes, where product damage occurs
due to poor storage and material mishandling.
The dynamic system model developed
describes the risk conditions of the failure of the production process. The
model designed validated the actual results, which did not differ significantly
from the simulation results. The risk of failure in the warehouse of finished
products is based on the field and historical data, which are used to make the
following models: the optimistic model, which is obtained from the minimum
defect; the most likely model, which is obtained from the average defect; and
the pessimistic model, which is obtained from the maximum defect. Each process
in the warehouse is based on monthly historical data and then represented as a
quantified dynamic system.
Several policy scenarios related to the
risk of failure of the production process are tested to obtain a percentage of
product defects each month. Exogenous variables from this simulation are the
reliability of the product handling, the percentage of successful rework, and
the percentage of products that deteriorate.
The simulation results show that the
optimistic scenario has the smallest product defects of 0%, and that the most
likely condition is 1%, while the pessimistic one is 4%. The optimistic
situation has a difference of 115 tons from the actual condition, so the
company can make savings of IDR 1,995,000,000 per month.
The largest benefit comes from an optimistic scenario
with IDR 226,480,000,000/month; the most likely scenarios give a benefit of IDR
224,632,000,000/month; and, finally, the pessimistic scenario gives one of IDR
217,413,000,000/month.
Filename | Description |
---|---|
R1-IE-4068-20200819220955.docx | Figure & Table |
Grösser, S.N., Reyes-Lecuona, A., Granholm, G., 2017. Dynamics of Long-Life Assets: From Technology Adaptation to Upgrading the Business Model. Switzerland: Springer
Hidayatno, A., Rahman, R., Muliadi, R., 2015. Policy Analysis of the Jakarta Carbon Mitigation Plan using System Dynamics to Support Decision Making in Urban Development – Options for Policymakers. International Journal of Technology, Volume 6(5), pp. 886–893
Kilibarda, M., 2013. Logistics Failures in Distribution Process. Logistics International Conference, Volume 114(1), pp. 247–252
Liu, C., Xie, Z., Sun, F., Chen, L., 2015. System Dynamics Analysis on Characteristics of Iron-Flow in Sintering Process. Applied Thermal Engineering, Volume 82, pp. 206–211
Liu, Z., Liu, X., 2018. Storage Reliability Assessment for the Stored Equipment Under Periodical Inspection. Advances in Mechanical Engineering, Volume 10(6), pp. 1–7
Moeis, A.O., Desriani, F., Destyanto, A.R., Zagloel, T.Y., Hidayatno, A., Sutrisno, A., 2020. Sustainability Assessment of the Tanjung Priok Port Cluster. International Journal of Technology, Volume 11(2), pp. 353–363
Poles, R., 2013. System Dynamics Modelling of a Production and Inventory System for Remanufacturing to Evaluate System Improvement Strategies. International Journal of Production Economics, Volume 144(1), pp. 189–199
Qiao-Lun, G., Tie-Gang, G., 2011. System Dynamics Analysis of RFID-EPC’s Impact on Reverse Supply Chain. In: International Conference on Management Science and Engineering - Annual Conference Proceedings, Rome, Italy
Sterman, J.D., 2000. Systems Thinking and Modeling for a Complex World. Management. Volume 6(1), pp. 7–17
Suwandi, A., Zagloel, T.Y., Hidayatno, A., 2018. Conceptual Model of Failure Risk Control on Raw Materials Inventory System. In: IOP Conference Series: Materials Science and Engineering, Jakarta, Indonesia
Suwandi, A., Zagloel, T.Y., Hidayatno, A., 2020. Minimization of Pipe Production Defects using the FMEA method and Dynamic System. International Journal of Engineering Research and Technology , Volume 13(5), pp. 953–961
Suwartha, N., Berawi, M.A., Zagloel, T.Y.M., Surjandari, I., 2015. Enhancing the Quality of Products and Projects through Better Designs and Modeling. International Journal of Technology, Volume 6(5), pp. 718–721